Journal of Animal Science

Duthie, C.;Simm, G.;Doeschl-Wilson, A.;Kalm, E.;Knap, P. W.;Roehe, R.

doi: 10.2527/jas.2009-2266pmid: 20228239

ABSTRACT The present study focused on the identification of epistatic QTL pairs for body composition traits (carcass cut, lean tissue, and fat tissue weights) measured at slaughter weight (140 kg of BW) in a 3-generation full-sib population developed by crossing Pietrain sires with a crossbred dam line. Depending on the trait, phenotypic observations were available for 306 to 315 F2 animals. For the QTL analysis, 386 animals were genotyped for 88 molecular markers covering chromosomes SSC1, SSC2, SSC4, SSC6, SSC7, SSC8, SSC9, SSC10, SSC13, and SSC14. In total, 23 significant epistatic QTL pairs were identified, with the additive × additive genetic interaction being the most prevalent. Epistatic QTL were identified across all chromosomes except for SSC13, and epistatic QTL pairs accounted for between 5.8 and 10.2% of the phenotypic variance. Seven epistatic QTL pairs were between QTL that resided on the same chromosome, and 16 were between QTL that resided on different chromosomes. Sus scrofa chromosome 1, SSC2, SSC4, SSC6, SSC8, and SSC9 harbored the greatest number of epistatic QTL. The epistatic QTL pair with the greatest effect was for the entire loin weight between 2 locations on SSC7, explaining 10.2% of the phenotypic variance. Epistatic associations were identified between regions of the genome that contain the IGF-2 or melanocortin-4 receptor genes, with QTL residing in other genomic locations. Quantitative trait loci in the region of the melanocortin-4 receptor gene and on SSC7 showed significant positive dominance effects for entire belly weight, which were offset by negative dominance × dominance interactions between these QTL. In contrast, the QTL in the region of the IGF-2 gene showed significant negative dominance effects for entire ham weight, which were largely overcompensated for by positive additive × dominance genetic effects with a QTL on SSC9. The study shows that epistasis is of great importance for the genomic regulation of body composition in pigs and contributes substantially to the variation in complex traits. INTRODUCTION Numerous QTL have been reported for carcass characteristics in pigs (e.g., Geldermann et al., 2003; Karlskov-Mortensen et al., 2006; Liu et al., 2007). These studies have focused on identifying the individual QTL effects (additive, dominance, and imprinting), without considering interactions between loci (epistasis). When epistasis is ignored, some QTL may remain undetected, and the effects of the identified QTL can be severely biased (Carlborg, 2006). Furthermore, the inclusion of epistasis provides a better understanding of the genomic control of economically important traits. Evidence exists for epistatic QTL in pigs for reproductive traits (Bidanel, 1993; Rodríguez et al., 2005; Noguera et al., 2006), coat color (Hirooka et al., 2002), meat quality (Ovilo et al., 2002; Szyda et al., 2006), and muscle fiber traits (Estellé et al., 2008). Studies in chickens have shown that epistasis is involved in the genomic regulation of growth traits (Carlborg et al., 2003, 2004). Moreover, studies in mice have identified epistatic QTL for growth and obesity (e.g., Brockmann et al., 2000; Yi et al., 2004a,b, 2006). Generally, these studies suggest that different networks of interactions are involved in the genomic regulation of different groups of traits. Body composition of pigs may be controlled by a complex set of interactions; however, there is currently a lack of knowledge of epistatic QTL involved in the genomic regulation of the lean and fat tissue of pigs. This is most likely because of the computational demand associated with this type of analysis, rather than epistasis not being important for the genomic regulation of these traits. In the present study, we investigated epistatic QTL pairs for several carcass cuts as well as lean and fat tissue traits in a commercial pig population, developed by crossing Pietrain sires with a crossbred dam line. MATERIALS AND METHODS All animal care and handling procedures in the federal testing station were reviewed and approved by the Landwirtschaftskammer Schleswig-Holstein, Rendsburg, Germany. Design and Data The QTL mapping experiment in this study was based on data from a resource family of a 3-generation full-sib design. The resource family was created by mating 7 Pietrain grandsires, which were unrelated, to 16 grandams of a crossbred dam line [Leicoma × (Landrace × Large White)]. The Pietrain sires were all heterozygous at the ryanodine receptor 1 (RYR1) locus. Eight boars and 40 sows of the F1 generation were mated to produce 2 litters of the F2 generation, comprising 315 pigs from 49 families. Animals of the F2 generation were housed either individually or in groups of up to 15 pigs of mixed sex in straw-bedded pens. Individually housed pigs (48 gilts and 46 barrows) were fed manually, and feed consumption was recorded for these animals weekly. Group-housed animals (117 gilts and 104 barrows) were supplied food by an electronic feeding station (ACEMA 48, ACEMO, Pontivy, France), which recorded feed consumption at every visit. All animals were provided with 1 of 3 pelleted diets containing 13.8 MJ of ME/kg and 1.2% lysine, 13.8 MJ of ME/kg and 1.1% lysine, or 13.4 MJ of ME/kg and 1.0% lysine for BW ranges 30 to 60, 60 to 90, and 90 to 140 kg of BW, respectively. All animals were provided with ad libitum access to diets, which were formulated above requirements to reach maximal protein deposition. For a more detailed description of data, see Landgraf et al. (2006a,b) and Mohrmann et al. (2006a,b). Carcass Composition Phenotypic data on body composition were collected from pigs slaughtered in a commercial abattoir at 140 kg of BW. Measurements of valuable carcass cuts were obtained using an AutoFOM device (SFK Technology, Søborg, Denmark). This device uses an automatic ultrasound scanning technique to produce a 3-dimensional image of the carcass (Brondum et al., 1998). With the AutoFOM device, measurements were obtained for average fat thickness, belly weight, lean content, lean content of the belly, and weights of the entire and trimmed shoulder, loin, and ham without bones. Thereafter, the right carcass side of each pig was dissected into the primal carcass cuts neck, shoulder, loin, ham, and belly. Neck, shoulder, loin, and ham cuts were further dissected into lean and fat tissue. Moreover, weights of the jowl, thick rib, flank, front and hind hock, tail, and claw were recorded. From the cold left carcass side, further measurements were obtained, including carcass length, sidefat thickness; at the 13th/14th-rib interface, loin eye area, fat area, and thinnest fat measures were obtained; and fat content and area of the belly were obtained. Protein content of the loin and intramuscular fat content were measured in the musculus longissimus thoracis et lumborum using near-infrared reflectance spectroscopy. Additional information about the dissection of carcasses is presented in the study by Landgraf et al. (2006a). Table 1 outlines mean values and SD of traits analyzed in the present study. Table 1. Means and SD of carcass characteristics measured on pigs of the F2 generation Trait  Mean  SD  No. of records  AutoFOM1 (AF) trait         AF average fat thickness, mm  22.295  4.989  313   AF entire shoulder wt, kg  6.176  0.406  313   AF shoulder lean meat wt, kg  4.577  0.408  313   AF entire loin wt, kg  6.265  0.396  313   AF loin lean meat wt, kg  3.764  0.352  313   AF entire ham wt, kg  13.573  0.814  313   AF ham lean meat wt, kg  9.511  1.052  313   AF entire belly wt, kg  9.168  0.548  313   AF lean content, %  50.509  6.403  313   AF lean content of belly, %  43.741  7.891  313  Carcass characteristic—dissected carcass cuts         Entire neck wt, kg  5.316  0.505  306   Neck wt without external fat, kg  4.160  0.430  306   External neck fat wt, kg  1.156  0.285  306   Entire shoulder wt, kg  8.452  0.564  307   Shoulder wt without external fat, kg  5.910  0.584  307   External shoulder fat wt, kg  1.403  0.261  307   Entire loin wt, kg  9.163  0.730  308   Loin wt without external fat, kg  6.650  0.624  308   External loin fat wt, kg  2.513  0.645  308   Entire ham wt, kg  16.908  0.997  310   Ham wt without external fat, kg  11.568  1.087  310   External ham fat wt, kg  2.566  0.493  310   Belly wt, kg  6.461  0.655  308   Jowl wt, kg  1.914  0.284  306   Thick rib, kg  1.441  0.217  307   Flank wt, kg  1.789  0.407  308   Front hock wt, kg  1.139  0.189  307   Hind hock wt, kg  1.430  0.141  310   Tail wt, kg  0.429  0.134  310   Hind claw, kg  0.914  0.122  310  Carcass characteristic—standard performance test         Carcass length, cm  105.192  3.024  310   Sidefat thickness,2 cm  3.847  0.866  315   Thinnest fat measure,2 cm  1.725  0.552  314   Loin eye area M.l.t.l.,2,3 cm2  54.160  6.767  314   Fat area M.l.t.l.,2,3 cm2  24.514  5.884  314   Fat content of belly, %  53.508  8.272  306   Fat area of belly, cm2  23.789  6.782  306   Intramuscular fat content, %  1.343  0.542  313   Protein content of loin, %  24.215  2.066  313  Trait  Mean  SD  No. of records  AutoFOM1 (AF) trait         AF average fat thickness, mm  22.295  4.989  313   AF entire shoulder wt, kg  6.176  0.406  313   AF shoulder lean meat wt, kg  4.577  0.408  313   AF entire loin wt, kg  6.265  0.396  313   AF loin lean meat wt, kg  3.764  0.352  313   AF entire ham wt, kg  13.573  0.814  313   AF ham lean meat wt, kg  9.511  1.052  313   AF entire belly wt, kg  9.168  0.548  313   AF lean content, %  50.509  6.403  313   AF lean content of belly, %  43.741  7.891  313  Carcass characteristic—dissected carcass cuts         Entire neck wt, kg  5.316  0.505  306   Neck wt without external fat, kg  4.160  0.430  306   External neck fat wt, kg  1.156  0.285  306   Entire shoulder wt, kg  8.452  0.564  307   Shoulder wt without external fat, kg  5.910  0.584  307   External shoulder fat wt, kg  1.403  0.261  307   Entire loin wt, kg  9.163  0.730  308   Loin wt without external fat, kg  6.650  0.624  308   External loin fat wt, kg  2.513  0.645  308   Entire ham wt, kg  16.908  0.997  310   Ham wt without external fat, kg  11.568  1.087  310   External ham fat wt, kg  2.566  0.493  310   Belly wt, kg  6.461  0.655  308   Jowl wt, kg  1.914  0.284  306   Thick rib, kg  1.441  0.217  307   Flank wt, kg  1.789  0.407  308   Front hock wt, kg  1.139  0.189  307   Hind hock wt, kg  1.430  0.141  310   Tail wt, kg  0.429  0.134  310   Hind claw, kg  0.914  0.122  310  Carcass characteristic—standard performance test         Carcass length, cm  105.192  3.024  310   Sidefat thickness,2 cm  3.847  0.866  315   Thinnest fat measure,2 cm  1.725  0.552  314   Loin eye area M.l.t.l.,2,3 cm2  54.160  6.767  314   Fat area M.l.t.l.,2,3 cm2  24.514  5.884  314   Fat content of belly, %  53.508  8.272  306   Fat area of belly, cm2  23.789  6.782  306   Intramuscular fat content, %  1.343  0.542  313   Protein content of loin, %  24.215  2.066  313  1AutoFOM device (SFK Technology, Søborg, Denmark). 2Collected at the 13th/14th-rib interface. 3Measured on musculus longissimus thoracis et lumborum. View Large Table 1. Means and SD of carcass characteristics measured on pigs of the F2 generation Trait  Mean  SD  No. of records  AutoFOM1 (AF) trait         AF average fat thickness, mm  22.295  4.989  313   AF entire shoulder wt, kg  6.176  0.406  313   AF shoulder lean meat wt, kg  4.577  0.408  313   AF entire loin wt, kg  6.265  0.396  313   AF loin lean meat wt, kg  3.764  0.352  313   AF entire ham wt, kg  13.573  0.814  313   AF ham lean meat wt, kg  9.511  1.052  313   AF entire belly wt, kg  9.168  0.548  313   AF lean content, %  50.509  6.403  313   AF lean content of belly, %  43.741  7.891  313  Carcass characteristic—dissected carcass cuts         Entire neck wt, kg  5.316  0.505  306   Neck wt without external fat, kg  4.160  0.430  306   External neck fat wt, kg  1.156  0.285  306   Entire shoulder wt, kg  8.452  0.564  307   Shoulder wt without external fat, kg  5.910  0.584  307   External shoulder fat wt, kg  1.403  0.261  307   Entire loin wt, kg  9.163  0.730  308   Loin wt without external fat, kg  6.650  0.624  308   External loin fat wt, kg  2.513  0.645  308   Entire ham wt, kg  16.908  0.997  310   Ham wt without external fat, kg  11.568  1.087  310   External ham fat wt, kg  2.566  0.493  310   Belly wt, kg  6.461  0.655  308   Jowl wt, kg  1.914  0.284  306   Thick rib, kg  1.441  0.217  307   Flank wt, kg  1.789  0.407  308   Front hock wt, kg  1.139  0.189  307   Hind hock wt, kg  1.430  0.141  310   Tail wt, kg  0.429  0.134  310   Hind claw, kg  0.914  0.122  310  Carcass characteristic—standard performance test         Carcass length, cm  105.192  3.024  310   Sidefat thickness,2 cm  3.847  0.866  315   Thinnest fat measure,2 cm  1.725  0.552  314   Loin eye area M.l.t.l.,2,3 cm2  54.160  6.767  314   Fat area M.l.t.l.,2,3 cm2  24.514  5.884  314   Fat content of belly, %  53.508  8.272  306   Fat area of belly, cm2  23.789  6.782  306   Intramuscular fat content, %  1.343  0.542  313   Protein content of loin, %  24.215  2.066  313  Trait  Mean  SD  No. of records  AutoFOM1 (AF) trait         AF average fat thickness, mm  22.295  4.989  313   AF entire shoulder wt, kg  6.176  0.406  313   AF shoulder lean meat wt, kg  4.577  0.408  313   AF entire loin wt, kg  6.265  0.396  313   AF loin lean meat wt, kg  3.764  0.352  313   AF entire ham wt, kg  13.573  0.814  313   AF ham lean meat wt, kg  9.511  1.052  313   AF entire belly wt, kg  9.168  0.548  313   AF lean content, %  50.509  6.403  313   AF lean content of belly, %  43.741  7.891  313  Carcass characteristic—dissected carcass cuts         Entire neck wt, kg  5.316  0.505  306   Neck wt without external fat, kg  4.160  0.430  306   External neck fat wt, kg  1.156  0.285  306   Entire shoulder wt, kg  8.452  0.564  307   Shoulder wt without external fat, kg  5.910  0.584  307   External shoulder fat wt, kg  1.403  0.261  307   Entire loin wt, kg  9.163  0.730  308   Loin wt without external fat, kg  6.650  0.624  308   External loin fat wt, kg  2.513  0.645  308   Entire ham wt, kg  16.908  0.997  310   Ham wt without external fat, kg  11.568  1.087  310   External ham fat wt, kg  2.566  0.493  310   Belly wt, kg  6.461  0.655  308   Jowl wt, kg  1.914  0.284  306   Thick rib, kg  1.441  0.217  307   Flank wt, kg  1.789  0.407  308   Front hock wt, kg  1.139  0.189  307   Hind hock wt, kg  1.430  0.141  310   Tail wt, kg  0.429  0.134  310   Hind claw, kg  0.914  0.122  310  Carcass characteristic—standard performance test         Carcass length, cm  105.192  3.024  310   Sidefat thickness,2 cm  3.847  0.866  315   Thinnest fat measure,2 cm  1.725  0.552  314   Loin eye area M.l.t.l.,2,3 cm2  54.160  6.767  314   Fat area M.l.t.l.,2,3 cm2  24.514  5.884  314   Fat content of belly, %  53.508  8.272  306   Fat area of belly, cm2  23.789  6.782  306   Intramuscular fat content, %  1.343  0.542  313   Protein content of loin, %  24.215  2.066  313  1AutoFOM device (SFK Technology, Søborg, Denmark). 2Collected at the 13th/14th-rib interface. 3Measured on musculus longissimus thoracis et lumborum. View Large Genotypic Data From the F0, F1, and F2 animals, blood samples of 9 mL were collected by puncture of the vena jugularis and their genomic DNA was extracted using the silica-gel method following Myakishev et al. (1995). Chromosomes SSC1, SSC2, SSC4, SSC6, SSC7, SSC8, SSC9, SSC10, SSC13, and SSC14 were chosen for genotyping because of their likely associations with carcass cuts as well as lean and fat tissue. All pigs were genotyped for 88 informative microsatellite markers, of which 10, 9, 9, 9, 10, 8, 9, 9, 8, and 7 genomic markers were located on SSC1, SSC2, SSC4, SSC6, SSC7, SSC8, SSC9, SSC10, SSC13, and SSC14, respectively. Based on the published USDA linkage map, markers and their distances were selected (http://www.marc.usda.gov; Rohrer et al., 1996). This linkage map provided all information relating to their position and alleles, as outlined in Table 2. The average distances between markers were 16.0, 16.5, 16.3, 20.6, 17.3 18.4, 17.3, 16.0, 18.0, and 17.4 cM and the largest gaps between markers were 27.7, 25.2, 26.5, 28.7, 26.2, 23.1, 21.7, 20.8, 24.0, and 23.6 cM on SSC1, SSC2, SSC4, SSC6, SSC7, SSC8, SSC9, SSC10, SSC13, and SSC14, respectively. Table 2. Markers used in the present QTL mapping project, their relative map position using the Meat Animal Research Center pig map, number of different alleles, polymorphic information content (PIC) in the F2 generation, and heterozygosity in the F1 generation (H) Marker  SSC  Position, cM  H  No. of alleles  PIC  SW1514  1  0  0.79  8  0.75  SW1515  1  16.4  0.67  8  0.68  SW1332  1  29.2  0.63  4  0.37  SW1851  1  44.6  0.73  4  0.53  SW1430  1  58.5  0.81  6  0.76  SWR982  1  86.2  0.88  7  0.77  SW1311  1  100.8  0.58  6  0.62  SW1828  1  118.5  0.90  7  0.69  SW1301  1  140.5  0.83  5  0.67  SW2512  1  144  0.77  6  0.55  SWR2516  2  0  0.67  5  0.48  SW2623  2  9.8  0.68  5  0.63  SWR783  2  23.7  0.51  3  0.30  SW240  2  42  0.84  7  0.78  SW1026  2  60.6  0.47  6  0.55  SW1370  2  74.8  0.91  8  0.69  SWR2157  2  89.2  0.78  8  0.68  SWR345  2  114.4  0.87  8  0.75  S0036  2  132.1  0.85  7  0.80  SW2404  4  0  0.91  10  0.81  SW489  4  8  0.66  5  0.53  S0301  4  27.1  0.72  6  0.56  S0001  4  41.8  0.66  6  0.65  SW839  4  62.3  0.44  4  0.45  S0214  4  79.3  0.80  6  0.74  SW445  4  105.8  0.91  10  0.77  MP77  4  120  0.87  8  0.74  SW856  4  130.1  0.98  14  0.84  MP35  6  0  0.70  6  0.59  SW2406  6  21.4  0.74  8  0.61  SW1841  6  41.5  0.98  15  0.88  S0087  6  62.8  0.75  5  0.59  SW122  6  83.3  0.85  7  0.69  S0228  6  105.2  0.69  6  0.68  SW1881  6  121.1  0.96  8  0.76  SW322  6  149.8  0.79  8  0.72  SW2052  6  164.6  0.79  9  0.78  SW2564  7  0  0.69  5  0.49  SWR1343  7  12.2  0.83  4  0.53  SW2155  7  32.9  0.67  4  0.48  SW1369  7  48.2  0.77  8  0.68  SW1856  7  61.5  0.69  5  0.48  SWR2036  7  78.2  0.81  9  0.77  SW632  7  104.4  0.77  6  0.67  SWR773  7  117.3  0.56  3  0.46  SW2537  7  139.5  0.69  7  0.63  SW764  7  156  0.76  5  0.65  SW2410  8  −1.3  0.42  4  0.44  SW905  8  20.8  0.71  6  0.71  SWR1101  8  38.3  0.88  12  0.75  SW444  8  52.5  0.85  7  0.76  S0086  8  62.2  0.69  6  0.56  SW374  8  82.8  0.88  5  0.63  SW1551  8  105.9  0.75  6  0.66  S0178  8  127.7  0.54  7  0.68  SW983  9  4  0.81  6  0.61  SW21  9  15.1  0.65  5  0.50  SW911  9  36.8  0.75  7  0.68  SW2401  9  57.1  0.71  6  0.68  SW2571  9  73.3  0.46  6  0.61  S0019  9  86.4  0.75  6  0.62  SW2093  9  103.6  0.90  6  0.77  SW174  9  122.9  0.81  3  0.51  SW1349  9  142.5  0.81  7  0.75  SW830  10  0  0.67  7  0.64  SWR136  10  7.6  0.77  6  0.72  SW1894  10  23.2  0.65  4  0.50  SW2195  10  44  0.48  3  0.42  SW173  10  56.1  0.35  4  0.39  SW1041  10  67.5  0.46  3  0.41  SW2043  10  87.7  0.56  5  0.72  SW1626  10  108  0.79  11  0.68  SW2067  10  128  0.81  7  0.69  S0282  13  0  0.90  8  0.77  SWR1941  13  14.1  0.87  7  0.71  SW1407  13  27.2  0.88  11  0.83  SW864  13  43.1  0.63  5  0.64  S0068  13  62.2  0.78  9  0.72  SW398  13  79.3  0.69  6  0.66  SW2440  13  102.2  0.96  6  0.79  S0291  13  126.2  0.83  8  0.79  SW857  14  7.4  0.87  9  0.74  S0089  14  14  0.67  7  0.71  SW245  14  32  0.77  7  0.71  SW342  14  53.2  0.79  7  0.71  SW1081  14  72.1  0.87  6  0.65  SW1557  14  87.9  0.64  4  0.49  SWC27  14  111.5  0.45  8  0.41  Marker  SSC  Position, cM  H  No. of alleles  PIC  SW1514  1  0  0.79  8  0.75  SW1515  1  16.4  0.67  8  0.68  SW1332  1  29.2  0.63  4  0.37  SW1851  1  44.6  0.73  4  0.53  SW1430  1  58.5  0.81  6  0.76  SWR982  1  86.2  0.88  7  0.77  SW1311  1  100.8  0.58  6  0.62  SW1828  1  118.5  0.90  7  0.69  SW1301  1  140.5  0.83  5  0.67  SW2512  1  144  0.77  6  0.55  SWR2516  2  0  0.67  5  0.48  SW2623  2  9.8  0.68  5  0.63  SWR783  2  23.7  0.51  3  0.30  SW240  2  42  0.84  7  0.78  SW1026  2  60.6  0.47  6  0.55  SW1370  2  74.8  0.91  8  0.69  SWR2157  2  89.2  0.78  8  0.68  SWR345  2  114.4  0.87  8  0.75  S0036  2  132.1  0.85  7  0.80  SW2404  4  0  0.91  10  0.81  SW489  4  8  0.66  5  0.53  S0301  4  27.1  0.72  6  0.56  S0001  4  41.8  0.66  6  0.65  SW839  4  62.3  0.44  4  0.45  S0214  4  79.3  0.80  6  0.74  SW445  4  105.8  0.91  10  0.77  MP77  4  120  0.87  8  0.74  SW856  4  130.1  0.98  14  0.84  MP35  6  0  0.70  6  0.59  SW2406  6  21.4  0.74  8  0.61  SW1841  6  41.5  0.98  15  0.88  S0087  6  62.8  0.75  5  0.59  SW122  6  83.3  0.85  7  0.69  S0228  6  105.2  0.69  6  0.68  SW1881  6  121.1  0.96  8  0.76  SW322  6  149.8  0.79  8  0.72  SW2052  6  164.6  0.79  9  0.78  SW2564  7  0  0.69  5  0.49  SWR1343  7  12.2  0.83  4  0.53  SW2155  7  32.9  0.67  4  0.48  SW1369  7  48.2  0.77  8  0.68  SW1856  7  61.5  0.69  5  0.48  SWR2036  7  78.2  0.81  9  0.77  SW632  7  104.4  0.77  6  0.67  SWR773  7  117.3  0.56  3  0.46  SW2537  7  139.5  0.69  7  0.63  SW764  7  156  0.76  5  0.65  SW2410  8  −1.3  0.42  4  0.44  SW905  8  20.8  0.71  6  0.71  SWR1101  8  38.3  0.88  12  0.75  SW444  8  52.5  0.85  7  0.76  S0086  8  62.2  0.69  6  0.56  SW374  8  82.8  0.88  5  0.63  SW1551  8  105.9  0.75  6  0.66  S0178  8  127.7  0.54  7  0.68  SW983  9  4  0.81  6  0.61  SW21  9  15.1  0.65  5  0.50  SW911  9  36.8  0.75  7  0.68  SW2401  9  57.1  0.71  6  0.68  SW2571  9  73.3  0.46  6  0.61  S0019  9  86.4  0.75  6  0.62  SW2093  9  103.6  0.90  6  0.77  SW174  9  122.9  0.81  3  0.51  SW1349  9  142.5  0.81  7  0.75  SW830  10  0  0.67  7  0.64  SWR136  10  7.6  0.77  6  0.72  SW1894  10  23.2  0.65  4  0.50  SW2195  10  44  0.48  3  0.42  SW173  10  56.1  0.35  4  0.39  SW1041  10  67.5  0.46  3  0.41  SW2043  10  87.7  0.56  5  0.72  SW1626  10  108  0.79  11  0.68  SW2067  10  128  0.81  7  0.69  S0282  13  0  0.90  8  0.77  SWR1941  13  14.1  0.87  7  0.71  SW1407  13  27.2  0.88  11  0.83  SW864  13  43.1  0.63  5  0.64  S0068  13  62.2  0.78  9  0.72  SW398  13  79.3  0.69  6  0.66  SW2440  13  102.2  0.96  6  0.79  S0291  13  126.2  0.83  8  0.79  SW857  14  7.4  0.87  9  0.74  S0089  14  14  0.67  7  0.71  SW245  14  32  0.77  7  0.71  SW342  14  53.2  0.79  7  0.71  SW1081  14  72.1  0.87  6  0.65  SW1557  14  87.9  0.64  4  0.49  SWC27  14  111.5  0.45  8  0.41  View Large Table 2. Markers used in the present QTL mapping project, their relative map position using the Meat Animal Research Center pig map, number of different alleles, polymorphic information content (PIC) in the F2 generation, and heterozygosity in the F1 generation (H) Marker  SSC  Position, cM  H  No. of alleles  PIC  SW1514  1  0  0.79  8  0.75  SW1515  1  16.4  0.67  8  0.68  SW1332  1  29.2  0.63  4  0.37  SW1851  1  44.6  0.73  4  0.53  SW1430  1  58.5  0.81  6  0.76  SWR982  1  86.2  0.88  7  0.77  SW1311  1  100.8  0.58  6  0.62  SW1828  1  118.5  0.90  7  0.69  SW1301  1  140.5  0.83  5  0.67  SW2512  1  144  0.77  6  0.55  SWR2516  2  0  0.67  5  0.48  SW2623  2  9.8  0.68  5  0.63  SWR783  2  23.7  0.51  3  0.30  SW240  2  42  0.84  7  0.78  SW1026  2  60.6  0.47  6  0.55  SW1370  2  74.8  0.91  8  0.69  SWR2157  2  89.2  0.78  8  0.68  SWR345  2  114.4  0.87  8  0.75  S0036  2  132.1  0.85  7  0.80  SW2404  4  0  0.91  10  0.81  SW489  4  8  0.66  5  0.53  S0301  4  27.1  0.72  6  0.56  S0001  4  41.8  0.66  6  0.65  SW839  4  62.3  0.44  4  0.45  S0214  4  79.3  0.80  6  0.74  SW445  4  105.8  0.91  10  0.77  MP77  4  120  0.87  8  0.74  SW856  4  130.1  0.98  14  0.84  MP35  6  0  0.70  6  0.59  SW2406  6  21.4  0.74  8  0.61  SW1841  6  41.5  0.98  15  0.88  S0087  6  62.8  0.75  5  0.59  SW122  6  83.3  0.85  7  0.69  S0228  6  105.2  0.69  6  0.68  SW1881  6  121.1  0.96  8  0.76  SW322  6  149.8  0.79  8  0.72  SW2052  6  164.6  0.79  9  0.78  SW2564  7  0  0.69  5  0.49  SWR1343  7  12.2  0.83  4  0.53  SW2155  7  32.9  0.67  4  0.48  SW1369  7  48.2  0.77  8  0.68  SW1856  7  61.5  0.69  5  0.48  SWR2036  7  78.2  0.81  9  0.77  SW632  7  104.4  0.77  6  0.67  SWR773  7  117.3  0.56  3  0.46  SW2537  7  139.5  0.69  7  0.63  SW764  7  156  0.76  5  0.65  SW2410  8  −1.3  0.42  4  0.44  SW905  8  20.8  0.71  6  0.71  SWR1101  8  38.3  0.88  12  0.75  SW444  8  52.5  0.85  7  0.76  S0086  8  62.2  0.69  6  0.56  SW374  8  82.8  0.88  5  0.63  SW1551  8  105.9  0.75  6  0.66  S0178  8  127.7  0.54  7  0.68  SW983  9  4  0.81  6  0.61  SW21  9  15.1  0.65  5  0.50  SW911  9  36.8  0.75  7  0.68  SW2401  9  57.1  0.71  6  0.68  SW2571  9  73.3  0.46  6  0.61  S0019  9  86.4  0.75  6  0.62  SW2093  9  103.6  0.90  6  0.77  SW174  9  122.9  0.81  3  0.51  SW1349  9  142.5  0.81  7  0.75  SW830  10  0  0.67  7  0.64  SWR136  10  7.6  0.77  6  0.72  SW1894  10  23.2  0.65  4  0.50  SW2195  10  44  0.48  3  0.42  SW173  10  56.1  0.35  4  0.39  SW1041  10  67.5  0.46  3  0.41  SW2043  10  87.7  0.56  5  0.72  SW1626  10  108  0.79  11  0.68  SW2067  10  128  0.81  7  0.69  S0282  13  0  0.90  8  0.77  SWR1941  13  14.1  0.87  7  0.71  SW1407  13  27.2  0.88  11  0.83  SW864  13  43.1  0.63  5  0.64  S0068  13  62.2  0.78  9  0.72  SW398  13  79.3  0.69  6  0.66  SW2440  13  102.2  0.96  6  0.79  S0291  13  126.2  0.83  8  0.79  SW857  14  7.4  0.87  9  0.74  S0089  14  14  0.67  7  0.71  SW245  14  32  0.77  7  0.71  SW342  14  53.2  0.79  7  0.71  SW1081  14  72.1  0.87  6  0.65  SW1557  14  87.9  0.64  4  0.49  SWC27  14  111.5  0.45  8  0.41  Marker  SSC  Position, cM  H  No. of alleles  PIC  SW1514  1  0  0.79  8  0.75  SW1515  1  16.4  0.67  8  0.68  SW1332  1  29.2  0.63  4  0.37  SW1851  1  44.6  0.73  4  0.53  SW1430  1  58.5  0.81  6  0.76  SWR982  1  86.2  0.88  7  0.77  SW1311  1  100.8  0.58  6  0.62  SW1828  1  118.5  0.90  7  0.69  SW1301  1  140.5  0.83  5  0.67  SW2512  1  144  0.77  6  0.55  SWR2516  2  0  0.67  5  0.48  SW2623  2  9.8  0.68  5  0.63  SWR783  2  23.7  0.51  3  0.30  SW240  2  42  0.84  7  0.78  SW1026  2  60.6  0.47  6  0.55  SW1370  2  74.8  0.91  8  0.69  SWR2157  2  89.2  0.78  8  0.68  SWR345  2  114.4  0.87  8  0.75  S0036  2  132.1  0.85  7  0.80  SW2404  4  0  0.91  10  0.81  SW489  4  8  0.66  5  0.53  S0301  4  27.1  0.72  6  0.56  S0001  4  41.8  0.66  6  0.65  SW839  4  62.3  0.44  4  0.45  S0214  4  79.3  0.80  6  0.74  SW445  4  105.8  0.91  10  0.77  MP77  4  120  0.87  8  0.74  SW856  4  130.1  0.98  14  0.84  MP35  6  0  0.70  6  0.59  SW2406  6  21.4  0.74  8  0.61  SW1841  6  41.5  0.98  15  0.88  S0087  6  62.8  0.75  5  0.59  SW122  6  83.3  0.85  7  0.69  S0228  6  105.2  0.69  6  0.68  SW1881  6  121.1  0.96  8  0.76  SW322  6  149.8  0.79  8  0.72  SW2052  6  164.6  0.79  9  0.78  SW2564  7  0  0.69  5  0.49  SWR1343  7  12.2  0.83  4  0.53  SW2155  7  32.9  0.67  4  0.48  SW1369  7  48.2  0.77  8  0.68  SW1856  7  61.5  0.69  5  0.48  SWR2036  7  78.2  0.81  9  0.77  SW632  7  104.4  0.77  6  0.67  SWR773  7  117.3  0.56  3  0.46  SW2537  7  139.5  0.69  7  0.63  SW764  7  156  0.76  5  0.65  SW2410  8  −1.3  0.42  4  0.44  SW905  8  20.8  0.71  6  0.71  SWR1101  8  38.3  0.88  12  0.75  SW444  8  52.5  0.85  7  0.76  S0086  8  62.2  0.69  6  0.56  SW374  8  82.8  0.88  5  0.63  SW1551  8  105.9  0.75  6  0.66  S0178  8  127.7  0.54  7  0.68  SW983  9  4  0.81  6  0.61  SW21  9  15.1  0.65  5  0.50  SW911  9  36.8  0.75  7  0.68  SW2401  9  57.1  0.71  6  0.68  SW2571  9  73.3  0.46  6  0.61  S0019  9  86.4  0.75  6  0.62  SW2093  9  103.6  0.90  6  0.77  SW174  9  122.9  0.81  3  0.51  SW1349  9  142.5  0.81  7  0.75  SW830  10  0  0.67  7  0.64  SWR136  10  7.6  0.77  6  0.72  SW1894  10  23.2  0.65  4  0.50  SW2195  10  44  0.48  3  0.42  SW173  10  56.1  0.35  4  0.39  SW1041  10  67.5  0.46  3  0.41  SW2043  10  87.7  0.56  5  0.72  SW1626  10  108  0.79  11  0.68  SW2067  10  128  0.81  7  0.69  S0282  13  0  0.90  8  0.77  SWR1941  13  14.1  0.87  7  0.71  SW1407  13  27.2  0.88  11  0.83  SW864  13  43.1  0.63  5  0.64  S0068  13  62.2  0.78  9  0.72  SW398  13  79.3  0.69  6  0.66  SW2440  13  102.2  0.96  6  0.79  S0291  13  126.2  0.83  8  0.79  SW857  14  7.4  0.87  9  0.74  S0089  14  14  0.67  7  0.71  SW245  14  32  0.77  7  0.71  SW342  14  53.2  0.79  7  0.71  SW1081  14  72.1  0.87  6  0.65  SW1557  14  87.9  0.64  4  0.49  SWC27  14  111.5  0.45  8  0.41  View Large Statistical Analysis Because of the computational demand of a genomic scan for epistatic QTL, the analysis was performed in 2 stages following the procedure of Estellé et al. (2008). In the first stage, a 5-cM scan was carried out across all genomic positions. Individual additive and dominance effects were excluded from the first stage of analysis because of the substantial computing demand. As a result, all possible pairwise combinations between QTL at only 5-cM intervals were considered, to preselect potential candidate regions with epistatic effects with the model  [1] where yi is the ith individual phenotype. Fixed effects and covariates were fitted in the model, depending on their significance for the trait. For all traits, sex, RYR1 genotype (MHS), and batch (1 to 9; animals beginning the performance test at the same time were grouped together as 1 batch) were included as fixed class variables in the model, and slaughter weight (slwt) was considered covariate β. The effect of housing system was tested in a preliminary analysis and found not to be significant for the analyzed traits, and was therefore not included in the model. Iaa, Iad, Ida, and Idd are the additive × additive (A×A), additive × dominance (A×D), dominance × additive (D×A), and dominance × dominance (D×D) epistatic effects, respectively; and ei is the residual effect. These 4 epistatic effects were estimated, following the Cockerham decomposition (Cockerham, 1954), by regressing on a linear combination of the individual QTL origin probabilities:  where P1 and P2 refer to the probability of QTL at locations 1 and 2, respectively, and P(QQ) is the probability of the grandpaternal sire line (Pietrain) being homozygous, P(qq) is the probability of the grandmaternal dam line being homozygous, and P(Qq) is the probability of being heterozygous (Varona et al., 2002). Model [1] was tested against a null model in which no epistatic effects were estimated; that is,  Interacting QTL pairs with P-values <0.001 were selected for further analyses. In the second stage, a complete epistatic model including the individual QTL effects was applied using a 1-cM scan around the preselected positions obtained in the first stage. This model included, besides all environmental effects, the individual additive and dominance genetic effects as well as epistatic genetic effects:  [2] where a denotes the individual additive genetic effect and Ca represents the difference in probabilities of being homozygous for alleles of the grandpaternal sire line (QQ) and being homozygous for alleles of the grandmaternal dam line (qq). A positive additive genetic value indicates that the allele originating from the grandpaternal sire line (Pietrain) showed a greater effect than the allele from the grandmaternal dam line, and vice versa. The effect d represents the individual dominance genetic effects and Cd gives the probability of being heterozygous. The dominance effect is defined as the deviation of heterozygous animals from the mean of both types of homozygous animals. A positive dominance value indicates an increase in the trait of interest resulting from a heterozygous genotype, and vice versa. Model [2] was tested against a null model that contained only the individual QTL effects; that is,  [3] Epistatic interactions were reported as significant if they had a nominal P-value of <0.001. All analyses were performed with QxPak software (Pérez-Enciso and Misztal, 2004). RESULTS AND DISCUSSION In total, 23 significant epistatic QTL pairs were identified. Of these, 9 epistatic QTL pairs were identified for entire carcass characteristics, 7 were identified for lean tissue characteristics, and 7 were identified for fat tissue characteristics (Table 3). Epistatic interactions were identified between QTL on SSC1, SSC2, SSC4, SSC6, SSC7, SSC8, SSC9, SSC10, and SSC14. No epistatic QTL were identified on or with SSC13. Epistatic QTL pairs explained between 6.2 and 10.2% of the phenotypic variance for entire carcass characteristics (lean + fat), between 5.9 and 9.5% for lean tissue characteristics, and between 5.8 and 6.8% for fat tissue characteristics. Seven of the significant epistatic QTL pairs were between QTL that resided on the same chromosome, on SSC1, SSC2, SSC4, SSC6, SSC7, and SSC8. All types of epistatic effects were identified (A×A, A×D, D×A, and D×D) in this study, with the A×A interaction being the most prevalent. The epistatic QTL pair with the greatest effect was for entire loin weight between 2 locations on SSC7. This QTL explained a large proportion of the phenotypic variance, at 10.2%. However, the locations are close together; therefore, this interaction has to be interpreted with care. Interactions between QTL that lie in close proximity have been reported for 3 additional traits (Table 3). Future fine mapping analyses are necessary to confirm these interactions. Table 3. Evidence of epistatic interactions for carcass characteristics measured after dissection and by the AutoFOM device1 (AF) Trait  LR2  P-value  Q0 chr3(pos4)  Q1 chr (pos)  % var5  Q0_a ± SE6  Q0_d ± SE6  Q1_a ± SE6  Q1_d ± SE6  Q01_aa ± SE7  Q01_ad ± SE7  Q01_da ± SE7  Q01_dd ± SE7  Entire carcass characteristic (lean + fat)                   AF entire belly wt, kg  21.68  2.3E-04  1 (88)  7 (148)  6.7  −0.086 ±  0.309 ±  0.033 ±  0.663 ±  0.061 ±  −0.122 ±  −0.021 ±  −1.040 ±  0.075  0.118a  0.070  0.130a  0.069  0.130  0.108  0.212a   Hind hock wt, kg  22.52  1.6E-04  1 (35)  8 (107)  6.5  −0.035 ±  0.126 ±  0.036 ±  0.066 ±  0.120 ±  0.049 ±  −0.038 ±  −0.190 ±  0.028  0.053  0.028  0.052  0.025a  0.050  0.048  0.091a   Hind claw, kg  22.33  1.7E-04  1 (63)  9 (23)  7.1  0.031 ±  0.073 ±  0.010 ±  0.072 ±  −0.074 ±  −0.061 ±  −0.055 ±  −0.196 ±  0.023  0.038  0.021  0.042  0.021a  0.040  0.035  0.068a   Entire ham wt, kg  23.51  1.0E-04  2 (10)  9 (66)  7.3  −0.213 ±  −0.408 ±  −0.177 ±  −0.369 ±  −0.242 ±  0.881 ±  0.436 ±  0.529 ±  0.124  0.194a  0.119  0.231  0.118a  0.225a  0.187a  0.345   Belly wt, kg  27.93  1.3E-05  4 (130.1)  4 (31)  8.7  0.043 ±  −0.282 ±  −0.140 ±  −0.187 ±  −0.410 ±  −0.170 ±  0.170 ±  0.433 ±  0.085  0.127a  0.084  0.143  0.083a  0.142  0.123  0.209a   Entire neck wt, kg  21.50  2.5E-04  6 (71)  6 (86)  6.8  −0.876 ±  0.015 ±  0.844 ±  −0.191 ±  −0.450 ±  1.067 ±  −1.145 ±  −0.322 ±  0.380a  0.457  0.389a  0.441  0.328  0.473a  0.503a  0.565   Entire loin wt, kg  33.11  1.1E-06  7 (77)  7 (86)  10.2  1.227 ±  2.197 ±  −1.451 ±  2.571 ±  2.954 ±  −0.524 ±  1.282 ±  −1.764 ±  1.537  1.142  1.509  1.353  0.953a  1.990  1.779  1.467   Flank wt, kg  19.90  5.2E-04  7 (88)  10 (23)  6.2  0.056 ±  0.086 ±  0.037 ±  0.121 ±  0.249 ±  −0.167 ±  −0.227 ±  −0.308 ±  0.067  0.124  0.069  0.113  0.066a  0.106  0.118  0.197   AF entire shoulder wt, kg  24.97  5.1E-05  8 (21)  8 (37)  7.7  0.849 ±  −0.491 ±  −0.729 ±  −0.699 ±  −0.684 ±  −0.991 ±  0.665 ±  0.484 ±  0.273a  0.340  0.254a  0.397  0.225a  0.415a  0.343  0.504  Lean tissue characteristic                   Protein content of loin, %  21.64  2.4E-04  2 (93)  2 (117)  6.7  1.160 ±  1.208 ±  −1.225 ±  1.204 ±  1.378 ±  −1.142 ±  1.162 ±  −1.049 ±  0.323a  0.405a  0.323a  0.402a  0.317a  0.406a  0.408a  0.504a   AF lean content of belly, %  21.87  2.1E-04  2 (9)  8 (55)  6.7  1.973 ±  5.305 ±  0.130 ±  4.762 ±  −3.022 ±  −1.582 ±  −3.089 ±  −10.239 ±  1.168  1.773  1.057  2.012a  1.058a  1.990  1.631  2.954a   Loin eye area M.l.t.l.,8,9 cm2  27.71  1.4E-05  2 (22)  9 (136)  8.4  −2.565 ±  −4.516 ±  −1.856 ±  −3.497 ±  4.275 ±  6.658 ±  5.782 ±  11.189 ±  1.318  2.300  1.255  2.459  1.170a  2.314a  2.110a  4.045a   Protein content of loin, %  19.69  5.7E-04  4 (121)  7 (1)  6.1  −0.149 ±  0.067 ±  0.142 ±  0.127 ±  −0.140 ±  0.522 ±  −0.145 ±  −0.036 ±  0.089  0.128  0.090  0.127  0.089  0.127a  0.128  0.186   Loin wt without external fat, kg  22.05  2.0E-04  4 (89)  14 (66)  6.9  0.088 ±  −0.358 ±  −0.226 ±  −0.022 ±  0.328 ±  0.034 ±  0.505 ±  0.508 ±  0.113  0.196  0.105a  0.202  0.102a  0.196  0.178a  0.334   Loin wt without external fat, kg  18.71  9.0E-04  6 (28)  8 (60)  5.9  0.094 ±  0.061 ±  −0.086 ±  0.335 ±  0.409 ±  −0.188 ±  0.271 ±  −0.468 ±  0.116  0.213  0.108  0.223  0.101a  0.207  0.190  0.379   Neck wt without external fat, kg  19.64  5.9E-04  6 (145)  9 (58)  6.3  −0.099 ±  0.323 ±  0.130 ±  0.431 ±  0.158 ±  0.249 ±  −0.047 ±  −0.776 ±  0.070  0.131a  0.071  0.121a  0.065a  0.112a  0.124  0.225a  Fat tissue characteristic                   External ham fat wt, kg  21.79  2.2E-04  1 (48)  1 (118)  6.8  −0.080 ±  0.168 ±  0.107 ±  −0.073 ±  −0.131 ±  −0.092 ±  −0.367 ±  −0.185 ±  0.071  0.108  0.072  0.101  0.072  0.100  0.106a  0.150   Intramuscular fat content, %  20.82  3.4E-04  1 (126)  4 (94)  6.4  0.257 ±  0.087 ±  0.102 ±  0.246 ±  −0.036 ±  −0.721 ±  −0.171 ±  −0.288 ±  0.090a  0.151  0.088  0.154  0.085  0.150a  0.142  0.258   AF average fat thickness, mm  19.56  6.1E-04  1 (142)  6 (119)  6.1  −1.656 ±  −0.419 ±  0.217 ±  1.858 ±  2.293 ±  4.014 ±  −1.053 ±  0.248 ±  0.672a  0.988  0.652  1.159  0.651a  1.153a  0.956  1.700   External neck fat wt, kg  19.61  6.0E-04  4 (1)  4 (120)  6.1  0.015 ±  −0.075 ±  −0.050 ±  −0.056 ±  −0.139 ±  −0.031 ±  0.005 ±  0.130 ±  0.033  0.048  0.033  0.049  0.033a  0.048  0.048  0.070   Fat content of belly, %  19.54  6.2E-04  4 (106)  6 (12)  6.2  1.027 ±  0.734 ±  −0.478 ±  −0.788 ±  −4.737 ±  −2.345 ±  −0.796 ±  −0.003 ±  1.069  1.572  1.007  1.858  1.020a  1.885  1.478  2.669   Thinnest fat measure,8 cm  18.70  9.0E-04  6 (42)  8 (56)  5.8  −0.131 ±  −0.462 ±  −0.121 ±  −0.369 ±  −0.234 ±  0.112 ±  0.166 ±  0.789 ±  0.088  0.145a  0.079  0.155a  0.078a  0.153  0.134  0.248a   External loin fat wt, kg  18.90  8.2E-04  6 (150)  9 (57)  6.0  0.130 ±  −0.115 ±  −0.078 ±  −0.336 ±  −0.235 ±  −0.325 ±  −0.023 ±  0.901 ±  0.099  0.174  0.097  0.165a  0.093a  0.159a  0.165  0.299a  Trait  LR2  P-value  Q0 chr3(pos4)  Q1 chr (pos)  % var5  Q0_a ± SE6  Q0_d ± SE6  Q1_a ± SE6  Q1_d ± SE6  Q01_aa ± SE7  Q01_ad ± SE7  Q01_da ± SE7  Q01_dd ± SE7  Entire carcass characteristic (lean + fat)                   AF entire belly wt, kg  21.68  2.3E-04  1 (88)  7 (148)  6.7  −0.086 ±  0.309 ±  0.033 ±  0.663 ±  0.061 ±  −0.122 ±  −0.021 ±  −1.040 ±  0.075  0.118a  0.070  0.130a  0.069  0.130  0.108  0.212a   Hind hock wt, kg  22.52  1.6E-04  1 (35)  8 (107)  6.5  −0.035 ±  0.126 ±  0.036 ±  0.066 ±  0.120 ±  0.049 ±  −0.038 ±  −0.190 ±  0.028  0.053  0.028  0.052  0.025a  0.050  0.048  0.091a   Hind claw, kg  22.33  1.7E-04  1 (63)  9 (23)  7.1  0.031 ±  0.073 ±  0.010 ±  0.072 ±  −0.074 ±  −0.061 ±  −0.055 ±  −0.196 ±  0.023  0.038  0.021  0.042  0.021a  0.040  0.035  0.068a   Entire ham wt, kg  23.51  1.0E-04  2 (10)  9 (66)  7.3  −0.213 ±  −0.408 ±  −0.177 ±  −0.369 ±  −0.242 ±  0.881 ±  0.436 ±  0.529 ±  0.124  0.194a  0.119  0.231  0.118a  0.225a  0.187a  0.345   Belly wt, kg  27.93  1.3E-05  4 (130.1)  4 (31)  8.7  0.043 ±  −0.282 ±  −0.140 ±  −0.187 ±  −0.410 ±  −0.170 ±  0.170 ±  0.433 ±  0.085  0.127a  0.084  0.143  0.083a  0.142  0.123  0.209a   Entire neck wt, kg  21.50  2.5E-04  6 (71)  6 (86)  6.8  −0.876 ±  0.015 ±  0.844 ±  −0.191 ±  −0.450 ±  1.067 ±  −1.145 ±  −0.322 ±  0.380a  0.457  0.389a  0.441  0.328  0.473a  0.503a  0.565   Entire loin wt, kg  33.11  1.1E-06  7 (77)  7 (86)  10.2  1.227 ±  2.197 ±  −1.451 ±  2.571 ±  2.954 ±  −0.524 ±  1.282 ±  −1.764 ±  1.537  1.142  1.509  1.353  0.953a  1.990  1.779  1.467   Flank wt, kg  19.90  5.2E-04  7 (88)  10 (23)  6.2  0.056 ±  0.086 ±  0.037 ±  0.121 ±  0.249 ±  −0.167 ±  −0.227 ±  −0.308 ±  0.067  0.124  0.069  0.113  0.066a  0.106  0.118  0.197   AF entire shoulder wt, kg  24.97  5.1E-05  8 (21)  8 (37)  7.7  0.849 ±  −0.491 ±  −0.729 ±  −0.699 ±  −0.684 ±  −0.991 ±  0.665 ±  0.484 ±  0.273a  0.340  0.254a  0.397  0.225a  0.415a  0.343  0.504  Lean tissue characteristic                   Protein content of loin, %  21.64  2.4E-04  2 (93)  2 (117)  6.7  1.160 ±  1.208 ±  −1.225 ±  1.204 ±  1.378 ±  −1.142 ±  1.162 ±  −1.049 ±  0.323a  0.405a  0.323a  0.402a  0.317a  0.406a  0.408a  0.504a   AF lean content of belly, %  21.87  2.1E-04  2 (9)  8 (55)  6.7  1.973 ±  5.305 ±  0.130 ±  4.762 ±  −3.022 ±  −1.582 ±  −3.089 ±  −10.239 ±  1.168  1.773  1.057  2.012a  1.058a  1.990  1.631  2.954a   Loin eye area M.l.t.l.,8,9 cm2  27.71  1.4E-05  2 (22)  9 (136)  8.4  −2.565 ±  −4.516 ±  −1.856 ±  −3.497 ±  4.275 ±  6.658 ±  5.782 ±  11.189 ±  1.318  2.300  1.255  2.459  1.170a  2.314a  2.110a  4.045a   Protein content of loin, %  19.69  5.7E-04  4 (121)  7 (1)  6.1  −0.149 ±  0.067 ±  0.142 ±  0.127 ±  −0.140 ±  0.522 ±  −0.145 ±  −0.036 ±  0.089  0.128  0.090  0.127  0.089  0.127a  0.128  0.186   Loin wt without external fat, kg  22.05  2.0E-04  4 (89)  14 (66)  6.9  0.088 ±  −0.358 ±  −0.226 ±  −0.022 ±  0.328 ±  0.034 ±  0.505 ±  0.508 ±  0.113  0.196  0.105a  0.202  0.102a  0.196  0.178a  0.334   Loin wt without external fat, kg  18.71  9.0E-04  6 (28)  8 (60)  5.9  0.094 ±  0.061 ±  −0.086 ±  0.335 ±  0.409 ±  −0.188 ±  0.271 ±  −0.468 ±  0.116  0.213  0.108  0.223  0.101a  0.207  0.190  0.379   Neck wt without external fat, kg  19.64  5.9E-04  6 (145)  9 (58)  6.3  −0.099 ±  0.323 ±  0.130 ±  0.431 ±  0.158 ±  0.249 ±  −0.047 ±  −0.776 ±  0.070  0.131a  0.071  0.121a  0.065a  0.112a  0.124  0.225a  Fat tissue characteristic                   External ham fat wt, kg  21.79  2.2E-04  1 (48)  1 (118)  6.8  −0.080 ±  0.168 ±  0.107 ±  −0.073 ±  −0.131 ±  −0.092 ±  −0.367 ±  −0.185 ±  0.071  0.108  0.072  0.101  0.072  0.100  0.106a  0.150   Intramuscular fat content, %  20.82  3.4E-04  1 (126)  4 (94)  6.4  0.257 ±  0.087 ±  0.102 ±  0.246 ±  −0.036 ±  −0.721 ±  −0.171 ±  −0.288 ±  0.090a  0.151  0.088  0.154  0.085  0.150a  0.142  0.258   AF average fat thickness, mm  19.56  6.1E-04  1 (142)  6 (119)  6.1  −1.656 ±  −0.419 ±  0.217 ±  1.858 ±  2.293 ±  4.014 ±  −1.053 ±  0.248 ±  0.672a  0.988  0.652  1.159  0.651a  1.153a  0.956  1.700   External neck fat wt, kg  19.61  6.0E-04  4 (1)  4 (120)  6.1  0.015 ±  −0.075 ±  −0.050 ±  −0.056 ±  −0.139 ±  −0.031 ±  0.005 ±  0.130 ±  0.033  0.048  0.033  0.049  0.033a  0.048  0.048  0.070   Fat content of belly, %  19.54  6.2E-04  4 (106)  6 (12)  6.2  1.027 ±  0.734 ±  −0.478 ±  −0.788 ±  −4.737 ±  −2.345 ±  −0.796 ±  −0.003 ±  1.069  1.572  1.007  1.858  1.020a  1.885  1.478  2.669   Thinnest fat measure,8 cm  18.70  9.0E-04  6 (42)  8 (56)  5.8  −0.131 ±  −0.462 ±  −0.121 ±  −0.369 ±  −0.234 ±  0.112 ±  0.166 ±  0.789 ±  0.088  0.145a  0.079  0.155a  0.078a  0.153  0.134  0.248a   External loin fat wt, kg  18.90  8.2E-04  6 (150)  9 (57)  6.0  0.130 ±  −0.115 ±  −0.078 ±  −0.336 ±  −0.235 ±  −0.325 ±  −0.023 ±  0.901 ±  0.099  0.174  0.097  0.165a  0.093a  0.159a  0.165  0.299a  aValues represent significant additive, dominance, or epistatic effects (P < 0.001). 1AutoFOM device (SFK Technology, Søborg, Denmark). 2LR = likelihood ratio. 3chr = chromosome. 4Positions of the QTL in cM. 5Percentages of F2 variance explained by the QTL calculated as the proportion of residual variances attributable to the QTL effect on the residual variances excluding the QTL effect. 6Estimated additive (a) and dominance (d) effects and their SE of the individual QTL. 7Estimated additive × additive (aa), additive × dominance (ad), dominance × additive (da), and dominance × dominance (dd) effects and their SE. 8Collected at the 13th/14th-rib interface. 9Measured on musculus longissimus thoracis et lumborum. View Large Table 3. Evidence of epistatic interactions for carcass characteristics measured after dissection and by the AutoFOM device1 (AF) Trait  LR2  P-value  Q0 chr3(pos4)  Q1 chr (pos)  % var5  Q0_a ± SE6  Q0_d ± SE6  Q1_a ± SE6  Q1_d ± SE6  Q01_aa ± SE7  Q01_ad ± SE7  Q01_da ± SE7  Q01_dd ± SE7  Entire carcass characteristic (lean + fat)                   AF entire belly wt, kg  21.68  2.3E-04  1 (88)  7 (148)  6.7  −0.086 ±  0.309 ±  0.033 ±  0.663 ±  0.061 ±  −0.122 ±  −0.021 ±  −1.040 ±  0.075  0.118a  0.070  0.130a  0.069  0.130  0.108  0.212a   Hind hock wt, kg  22.52  1.6E-04  1 (35)  8 (107)  6.5  −0.035 ±  0.126 ±  0.036 ±  0.066 ±  0.120 ±  0.049 ±  −0.038 ±  −0.190 ±  0.028  0.053  0.028  0.052  0.025a  0.050  0.048  0.091a   Hind claw, kg  22.33  1.7E-04  1 (63)  9 (23)  7.1  0.031 ±  0.073 ±  0.010 ±  0.072 ±  −0.074 ±  −0.061 ±  −0.055 ±  −0.196 ±  0.023  0.038  0.021  0.042  0.021a  0.040  0.035  0.068a   Entire ham wt, kg  23.51  1.0E-04  2 (10)  9 (66)  7.3  −0.213 ±  −0.408 ±  −0.177 ±  −0.369 ±  −0.242 ±  0.881 ±  0.436 ±  0.529 ±  0.124  0.194a  0.119  0.231  0.118a  0.225a  0.187a  0.345   Belly wt, kg  27.93  1.3E-05  4 (130.1)  4 (31)  8.7  0.043 ±  −0.282 ±  −0.140 ±  −0.187 ±  −0.410 ±  −0.170 ±  0.170 ±  0.433 ±  0.085  0.127a  0.084  0.143  0.083a  0.142  0.123  0.209a   Entire neck wt, kg  21.50  2.5E-04  6 (71)  6 (86)  6.8  −0.876 ±  0.015 ±  0.844 ±  −0.191 ±  −0.450 ±  1.067 ±  −1.145 ±  −0.322 ±  0.380a  0.457  0.389a  0.441  0.328  0.473a  0.503a  0.565   Entire loin wt, kg  33.11  1.1E-06  7 (77)  7 (86)  10.2  1.227 ±  2.197 ±  −1.451 ±  2.571 ±  2.954 ±  −0.524 ±  1.282 ±  −1.764 ±  1.537  1.142  1.509  1.353  0.953a  1.990  1.779  1.467   Flank wt, kg  19.90  5.2E-04  7 (88)  10 (23)  6.2  0.056 ±  0.086 ±  0.037 ±  0.121 ±  0.249 ±  −0.167 ±  −0.227 ±  −0.308 ±  0.067  0.124  0.069  0.113  0.066a  0.106  0.118  0.197   AF entire shoulder wt, kg  24.97  5.1E-05  8 (21)  8 (37)  7.7  0.849 ±  −0.491 ±  −0.729 ±  −0.699 ±  −0.684 ±  −0.991 ±  0.665 ±  0.484 ±  0.273a  0.340  0.254a  0.397  0.225a  0.415a  0.343  0.504  Lean tissue characteristic                   Protein content of loin, %  21.64  2.4E-04  2 (93)  2 (117)  6.7  1.160 ±  1.208 ±  −1.225 ±  1.204 ±  1.378 ±  −1.142 ±  1.162 ±  −1.049 ±  0.323a  0.405a  0.323a  0.402a  0.317a  0.406a  0.408a  0.504a   AF lean content of belly, %  21.87  2.1E-04  2 (9)  8 (55)  6.7  1.973 ±  5.305 ±  0.130 ±  4.762 ±  −3.022 ±  −1.582 ±  −3.089 ±  −10.239 ±  1.168  1.773  1.057  2.012a  1.058a  1.990  1.631  2.954a   Loin eye area M.l.t.l.,8,9 cm2  27.71  1.4E-05  2 (22)  9 (136)  8.4  −2.565 ±  −4.516 ±  −1.856 ±  −3.497 ±  4.275 ±  6.658 ±  5.782 ±  11.189 ±  1.318  2.300  1.255  2.459  1.170a  2.314a  2.110a  4.045a   Protein content of loin, %  19.69  5.7E-04  4 (121)  7 (1)  6.1  −0.149 ±  0.067 ±  0.142 ±  0.127 ±  −0.140 ±  0.522 ±  −0.145 ±  −0.036 ±  0.089  0.128  0.090  0.127  0.089  0.127a  0.128  0.186   Loin wt without external fat, kg  22.05  2.0E-04  4 (89)  14 (66)  6.9  0.088 ±  −0.358 ±  −0.226 ±  −0.022 ±  0.328 ±  0.034 ±  0.505 ±  0.508 ±  0.113  0.196  0.105a  0.202  0.102a  0.196  0.178a  0.334   Loin wt without external fat, kg  18.71  9.0E-04  6 (28)  8 (60)  5.9  0.094 ±  0.061 ±  −0.086 ±  0.335 ±  0.409 ±  −0.188 ±  0.271 ±  −0.468 ±  0.116  0.213  0.108  0.223  0.101a  0.207  0.190  0.379   Neck wt without external fat, kg  19.64  5.9E-04  6 (145)  9 (58)  6.3  −0.099 ±  0.323 ±  0.130 ±  0.431 ±  0.158 ±  0.249 ±  −0.047 ±  −0.776 ±  0.070  0.131a  0.071  0.121a  0.065a  0.112a  0.124  0.225a  Fat tissue characteristic                   External ham fat wt, kg  21.79  2.2E-04  1 (48)  1 (118)  6.8  −0.080 ±  0.168 ±  0.107 ±  −0.073 ±  −0.131 ±  −0.092 ±  −0.367 ±  −0.185 ±  0.071  0.108  0.072  0.101  0.072  0.100  0.106a  0.150   Intramuscular fat content, %  20.82  3.4E-04  1 (126)  4 (94)  6.4  0.257 ±  0.087 ±  0.102 ±  0.246 ±  −0.036 ±  −0.721 ±  −0.171 ±  −0.288 ±  0.090a  0.151  0.088  0.154  0.085  0.150a  0.142  0.258   AF average fat thickness, mm  19.56  6.1E-04  1 (142)  6 (119)  6.1  −1.656 ±  −0.419 ±  0.217 ±  1.858 ±  2.293 ±  4.014 ±  −1.053 ±  0.248 ±  0.672a  0.988  0.652  1.159  0.651a  1.153a  0.956  1.700   External neck fat wt, kg  19.61  6.0E-04  4 (1)  4 (120)  6.1  0.015 ±  −0.075 ±  −0.050 ±  −0.056 ±  −0.139 ±  −0.031 ±  0.005 ±  0.130 ±  0.033  0.048  0.033  0.049  0.033a  0.048  0.048  0.070   Fat content of belly, %  19.54  6.2E-04  4 (106)  6 (12)  6.2  1.027 ±  0.734 ±  −0.478 ±  −0.788 ±  −4.737 ±  −2.345 ±  −0.796 ±  −0.003 ±  1.069  1.572  1.007  1.858  1.020a  1.885  1.478  2.669   Thinnest fat measure,8 cm  18.70  9.0E-04  6 (42)  8 (56)  5.8  −0.131 ±  −0.462 ±  −0.121 ±  −0.369 ±  −0.234 ±  0.112 ±  0.166 ±  0.789 ±  0.088  0.145a  0.079  0.155a  0.078a  0.153  0.134  0.248a   External loin fat wt, kg  18.90  8.2E-04  6 (150)  9 (57)  6.0  0.130 ±  −0.115 ±  −0.078 ±  −0.336 ±  −0.235 ±  −0.325 ±  −0.023 ±  0.901 ±  0.099  0.174  0.097  0.165a  0.093a  0.159a  0.165  0.299a  Trait  LR2  P-value  Q0 chr3(pos4)  Q1 chr (pos)  % var5  Q0_a ± SE6  Q0_d ± SE6  Q1_a ± SE6  Q1_d ± SE6  Q01_aa ± SE7  Q01_ad ± SE7  Q01_da ± SE7  Q01_dd ± SE7  Entire carcass characteristic (lean + fat)                   AF entire belly wt, kg  21.68  2.3E-04  1 (88)  7 (148)  6.7  −0.086 ±  0.309 ±  0.033 ±  0.663 ±  0.061 ±  −0.122 ±  −0.021 ±  −1.040 ±  0.075  0.118a  0.070  0.130a  0.069  0.130  0.108  0.212a   Hind hock wt, kg  22.52  1.6E-04  1 (35)  8 (107)  6.5  −0.035 ±  0.126 ±  0.036 ±  0.066 ±  0.120 ±  0.049 ±  −0.038 ±  −0.190 ±  0.028  0.053  0.028  0.052  0.025a  0.050  0.048  0.091a   Hind claw, kg  22.33  1.7E-04  1 (63)  9 (23)  7.1  0.031 ±  0.073 ±  0.010 ±  0.072 ±  −0.074 ±  −0.061 ±  −0.055 ±  −0.196 ±  0.023  0.038  0.021  0.042  0.021a  0.040  0.035  0.068a   Entire ham wt, kg  23.51  1.0E-04  2 (10)  9 (66)  7.3  −0.213 ±  −0.408 ±  −0.177 ±  −0.369 ±  −0.242 ±  0.881 ±  0.436 ±  0.529 ±  0.124  0.194a  0.119  0.231  0.118a  0.225a  0.187a  0.345   Belly wt, kg  27.93  1.3E-05  4 (130.1)  4 (31)  8.7  0.043 ±  −0.282 ±  −0.140 ±  −0.187 ±  −0.410 ±  −0.170 ±  0.170 ±  0.433 ±  0.085  0.127a  0.084  0.143  0.083a  0.142  0.123  0.209a   Entire neck wt, kg  21.50  2.5E-04  6 (71)  6 (86)  6.8  −0.876 ±  0.015 ±  0.844 ±  −0.191 ±  −0.450 ±  1.067 ±  −1.145 ±  −0.322 ±  0.380a  0.457  0.389a  0.441  0.328  0.473a  0.503a  0.565   Entire loin wt, kg  33.11  1.1E-06  7 (77)  7 (86)  10.2  1.227 ±  2.197 ±  −1.451 ±  2.571 ±  2.954 ±  −0.524 ±  1.282 ±  −1.764 ±  1.537  1.142  1.509  1.353  0.953a  1.990  1.779  1.467   Flank wt, kg  19.90  5.2E-04  7 (88)  10 (23)  6.2  0.056 ±  0.086 ±  0.037 ±  0.121 ±  0.249 ±  −0.167 ±  −0.227 ±  −0.308 ±  0.067  0.124  0.069  0.113  0.066a  0.106  0.118  0.197   AF entire shoulder wt, kg  24.97  5.1E-05  8 (21)  8 (37)  7.7  0.849 ±  −0.491 ±  −0.729 ±  −0.699 ±  −0.684 ±  −0.991 ±  0.665 ±  0.484 ±  0.273a  0.340  0.254a  0.397  0.225a  0.415a  0.343  0.504  Lean tissue characteristic                   Protein content of loin, %  21.64  2.4E-04  2 (93)  2 (117)  6.7  1.160 ±  1.208 ±  −1.225 ±  1.204 ±  1.378 ±  −1.142 ±  1.162 ±  −1.049 ±  0.323a  0.405a  0.323a  0.402a  0.317a  0.406a  0.408a  0.504a   AF lean content of belly, %  21.87  2.1E-04  2 (9)  8 (55)  6.7  1.973 ±  5.305 ±  0.130 ±  4.762 ±  −3.022 ±  −1.582 ±  −3.089 ±  −10.239 ±  1.168  1.773  1.057  2.012a  1.058a  1.990  1.631  2.954a   Loin eye area M.l.t.l.,8,9 cm2  27.71  1.4E-05  2 (22)  9 (136)  8.4  −2.565 ±  −4.516 ±  −1.856 ±  −3.497 ±  4.275 ±  6.658 ±  5.782 ±  11.189 ±  1.318  2.300  1.255  2.459  1.170a  2.314a  2.110a  4.045a   Protein content of loin, %  19.69  5.7E-04  4 (121)  7 (1)  6.1  −0.149 ±  0.067 ±  0.142 ±  0.127 ±  −0.140 ±  0.522 ±  −0.145 ±  −0.036 ±  0.089  0.128  0.090  0.127  0.089  0.127a  0.128  0.186   Loin wt without external fat, kg  22.05  2.0E-04  4 (89)  14 (66)  6.9  0.088 ±  −0.358 ±  −0.226 ±  −0.022 ±  0.328 ±  0.034 ±  0.505 ±  0.508 ±  0.113  0.196  0.105a  0.202  0.102a  0.196  0.178a  0.334   Loin wt without external fat, kg  18.71  9.0E-04  6 (28)  8 (60)  5.9  0.094 ±  0.061 ±  −0.086 ±  0.335 ±  0.409 ±  −0.188 ±  0.271 ±  −0.468 ±  0.116  0.213  0.108  0.223  0.101a  0.207  0.190  0.379   Neck wt without external fat, kg  19.64  5.9E-04  6 (145)  9 (58)  6.3  −0.099 ±  0.323 ±  0.130 ±  0.431 ±  0.158 ±  0.249 ±  −0.047 ±  −0.776 ±  0.070  0.131a  0.071  0.121a  0.065a  0.112a  0.124  0.225a  Fat tissue characteristic                   External ham fat wt, kg  21.79  2.2E-04  1 (48)  1 (118)  6.8  −0.080 ±  0.168 ±  0.107 ±  −0.073 ±  −0.131 ±  −0.092 ±  −0.367 ±  −0.185 ±  0.071  0.108  0.072  0.101  0.072  0.100  0.106a  0.150   Intramuscular fat content, %  20.82  3.4E-04  1 (126)  4 (94)  6.4  0.257 ±  0.087 ±  0.102 ±  0.246 ±  −0.036 ±  −0.721 ±  −0.171 ±  −0.288 ±  0.090a  0.151  0.088  0.154  0.085  0.150a  0.142  0.258   AF average fat thickness, mm  19.56  6.1E-04  1 (142)  6 (119)  6.1  −1.656 ±  −0.419 ±  0.217 ±  1.858 ±  2.293 ±  4.014 ±  −1.053 ±  0.248 ±  0.672a  0.988  0.652  1.159  0.651a  1.153a  0.956  1.700   External neck fat wt, kg  19.61  6.0E-04  4 (1)  4 (120)  6.1  0.015 ±  −0.075 ±  −0.050 ±  −0.056 ±  −0.139 ±  −0.031 ±  0.005 ±  0.130 ±  0.033  0.048  0.033  0.049  0.033a  0.048  0.048  0.070   Fat content of belly, %  19.54  6.2E-04  4 (106)  6 (12)  6.2  1.027 ±  0.734 ±  −0.478 ±  −0.788 ±  −4.737 ±  −2.345 ±  −0.796 ±  −0.003 ±  1.069  1.572  1.007  1.858  1.020a  1.885  1.478  2.669   Thinnest fat measure,8 cm  18.70  9.0E-04  6 (42)  8 (56)  5.8  −0.131 ±  −0.462 ±  −0.121 ±  −0.369 ±  −0.234 ±  0.112 ±  0.166 ±  0.789 ±  0.088  0.145a  0.079  0.155a  0.078a  0.153  0.134  0.248a   External loin fat wt, kg  18.90  8.2E-04  6 (150)  9 (57)  6.0  0.130 ±  −0.115 ±  −0.078 ±  −0.336 ±  −0.235 ±  −0.325 ±  −0.023 ±  0.901 ±  0.099  0.174  0.097  0.165a  0.093a  0.159a  0.165  0.299a  aValues represent significant additive, dominance, or epistatic effects (P < 0.001). 1AutoFOM device (SFK Technology, Søborg, Denmark). 2LR = likelihood ratio. 3chr = chromosome. 4Positions of the QTL in cM. 5Percentages of F2 variance explained by the QTL calculated as the proportion of residual variances attributable to the QTL effect on the residual variances excluding the QTL effect. 6Estimated additive (a) and dominance (d) effects and their SE of the individual QTL. 7Estimated additive × additive (aa), additive × dominance (ad), dominance × additive (da), and dominance × dominance (dd) effects and their SE. 8Collected at the 13th/14th-rib interface. 9Measured on musculus longissimus thoracis et lumborum. View Large Entire Carcass Characteristics Weights of important carcass cuts are economically important for the market value of the carcass. In the present study, we identified 10 epistatic interactions for entire carcass cuts. A D×D interaction was identified between QTL on SSC1 and SSC7 for entire belly weight measured by the AutoFom device. The QTL on SSC1 was previously identified in individual QTL mapping analyses by Mohrmann et al. (2006a), and in both studies, this QTL showed a significant dominance effect. Around this location of SSC1, numerous QTL have been reported for lean tissue and fat tissue (Nezer et al., 2002; Beeckmann et al., 2003b; Karlskov-Mortensen et al., 2006). No individual QTL were identified in previous analyses of the data on SSC7, which was surprising because there is strong evidence for QTL on SSC7 in the literature (e.g., Milan et al., 2002; Nezer et al., 2002; Yue et al., 2003a; Kim et al., 2005; Sanchez et al., 2006; Table 4). Therefore, the QTL identified on SSC7 has only expressed its effects through D×D interactions with SSC1. Table 4. Reports of QTL in the literature around similar locations as the QTL identified in the present study Trait  SSC (position1)  Marker interval  Other studies confirming the QTL2  Entire carcass characteristic (lean + fat)   Hind hock wt, kg  1 (35)  SW1332–SW1851  de Koning et al. (2001)   Hind claw, kg  1 (63)  SW1430–SWR982  Beeckmann et al. (2003b)   AF3 entire belly wt, kg  1 (88)  SWR982–SW1311  Nezer et al. (2002); Beeckmann et al. (2003b); Karlskov-Mortensen et al. (2006)   Entire ham wt, kg  2 (10)  SW2623–SWR783  de Koning et al. (2001); Milan et al. (2002); Geldermann et al. (2003); Lee et al. (2003)   Belly wt, kg  4 (31)  S0301–S0001  Cepica et al. (2003b); Geldermann et al. (2003); Kim et al. (2006)   Belly wt, kg  4 (130.1)  SW856  Knott et al. (2002)   Entire neck wt, kg  6 (71)  S0087–SW122  Yue et al. (2003b)   Entire neck wt, kg  6 (86)  SW122–S0228  Rohrer (2000); Grindflek et al. (2001); Varona et al. (2002); Yue et al. (2003b); Edwards et al. (2008b)   Entire loin wt, kg  7 (77)  SW1856–SWR2036  Malek et al. (2001b); Milan et al. (2002); Geldermann et al. (2003); Yue et al. (2003a)   Entire loin wt, kg  7 (86)  SWR2036–SW632  Nezer et al. (2002); Kim et al. (2005); Ponsuksili et al. (2005); Edwards et al. (2008a)   Flank wt, kg  7 (88)  SWR2036–SW632  Nezer et al. (2002); Kim et al. (2005); Ponsuksili et al. (2005); Edwards et al. (2008a)   AF entire belly wt, kg  7 (148)  SW2537–SW764  —   AF entire shoulder wt, kg  8 (21)  SW905–SWR1101  Quintanilla et al. (2002); Sato et al. (2003)   AF entire shoulder wt, kg  8 (37)  SW905–SWR1101  Beeckmann et al. (2003a)   Hind hock wt, kg  8 (107)  SW1551–S0178  —   Hind claw, kg  9 (23)  SW21–SW911  —   Entire ham wt, kg  9 (66)  SW2401–SW2571  Cepica et al. (2003a)   Flank wt, kg  10 (23)  SWR136–SW1894  Quintanilla et al. (2002)  Lean tissue characteristic   AF lean content of belly, %  2 (9)  SWR2516–SW2623  de Koning et al. (2001); Milan et al. (2002); Geldermann et al. (2003); Lee et al. (2003)   Loin eye area M.l.t.l.,4,5 cm2  2 (22)  SW2623–SWR783  Lee et al. (2003)   Protein content of loin, %  2 (93)  SWR2157–SWR345  Malek et al. (2001b); Lee et al. (2003)   Protein content of loin, %  2 (117)  SWR345–S0036  —   Loin wt without external fat, kg  4 (89)  S0214–SW445  Pérez-Enciso et al. (2000); Varona et al. (2002); Cepica et al. (2003b); Geldermann et al. (2003)   Protein content of loin, %  4 (121)  MP77–SW856  Malek et al. (2001b); Cepica et al. (2003b)   Loin wt without external fat, kg  6 (28)  SW2406–SW1841  de Koning et al. (2000); Milan et al. (2002)   Neck wt without external fat, kg  6 (145)  SW1881–SW322  Malek et al. (2001b); Edwards et al. (2008a)   Protein content of loin, %  7 (1)  SW2564–SWR1343  —   AF lean content of belly, %  8 (55)  SW444–S0086  —   Loin wt without external fat, kg  8 (60)  SW444–S0086  Casas-Carrillo et al. (1997); Milan et al. (2002); Kim et al. (2005)   Neck wt without external fat, kg  9 (58)  SW2401–SW2571  Rohrer et al. (2005)   Loin eye area M.l.t.l.,4,5 cm2  9 (136)  SW174–SW1349  Cepica et al. (2003a); Kim et al. (2006)   Loin wt without external fat, kg  14 (66)  SW342–SW1081  Dragos-Wendrich et al. (2003); Geldermann et al. (2003); van Wijk et al. (2006)  Fat tissue characteristic   External ham fat wt, kg  1 (48)  SW1851–SW1430  Malek et al. (2001b); Beeckmann et al. (2003b); Geldermann et al. (2003)   External ham fat wt, kg  1 (118)  SW1311–SW1828  Beeckmann et al. (2003b); Geldermann et al. (2003); Kim et al. (2006)   Intramuscular fat content, %  1 (126)  SW1828–SW1301  Rohrer and Keele (1998a,b); Rohrer (2000); Beeckmann et al. (2003b); Edwards et al. (2008a)   AF average fat thickness, mm  1 (142)  SW1301–SW2512  Rohrer and Keele (1998a); Bidanel et al. (2001); Quintanilla et al. (2002); Beeckmann et al. (2003b); Sanchez et al. (2006)   External neck fat wt, kg  4 (1)  SW2404–SW489  Marklund et al. (1999); Milan et al. (2002)   Intramuscular fat content, %  4 (94)  S0214–SW445  Pérez-Enciso et al. (2000); Varona et al. (2002); Cepica et al. (2003b); Geldermann et al. (2003)   Fat content of belly, %  4 (106)  SW445–MP77  Cepica et al. (2003b); Geldermann et al. (2003)   External neck fat wt, kg  4 (120)  MP77  Malek et al. (2001b); Cepica et al. (2003b)   Fat content of belly, %  6 (12)  MP35–SW2406  van Wijk et al. (2006)   Thinnest fat measure,4 cm  6 (42)  SW1841–S0087  —   AF average fat thickness, mm  6 (119)  S0228–SW1881  Varona et al. (2002); Sato et al. (2003); Kim et al. (2006); Edwards et al. (2008a)   External loin fat wt, kg  6 (150)  SW322–SW2052  Kim et al. (2005)   Thinnest fat measure,4 cm  8 (56)  SW444–S0086  —   External loin fat wt, kg  9 (57)  SW911–SW2401  Rohrer et al. (2005)  Trait  SSC (position1)  Marker interval  Other studies confirming the QTL2  Entire carcass characteristic (lean + fat)   Hind hock wt, kg  1 (35)  SW1332–SW1851  de Koning et al. (2001)   Hind claw, kg  1 (63)  SW1430–SWR982  Beeckmann et al. (2003b)   AF3 entire belly wt, kg  1 (88)  SWR982–SW1311  Nezer et al. (2002); Beeckmann et al. (2003b); Karlskov-Mortensen et al. (2006)   Entire ham wt, kg  2 (10)  SW2623–SWR783  de Koning et al. (2001); Milan et al. (2002); Geldermann et al. (2003); Lee et al. (2003)   Belly wt, kg  4 (31)  S0301–S0001  Cepica et al. (2003b); Geldermann et al. (2003); Kim et al. (2006)   Belly wt, kg  4 (130.1)  SW856  Knott et al. (2002)   Entire neck wt, kg  6 (71)  S0087–SW122  Yue et al. (2003b)   Entire neck wt, kg  6 (86)  SW122–S0228  Rohrer (2000); Grindflek et al. (2001); Varona et al. (2002); Yue et al. (2003b); Edwards et al. (2008b)   Entire loin wt, kg  7 (77)  SW1856–SWR2036  Malek et al. (2001b); Milan et al. (2002); Geldermann et al. (2003); Yue et al. (2003a)   Entire loin wt, kg  7 (86)  SWR2036–SW632  Nezer et al. (2002); Kim et al. (2005); Ponsuksili et al. (2005); Edwards et al. (2008a)   Flank wt, kg  7 (88)  SWR2036–SW632  Nezer et al. (2002); Kim et al. (2005); Ponsuksili et al. (2005); Edwards et al. (2008a)   AF entire belly wt, kg  7 (148)  SW2537–SW764  —   AF entire shoulder wt, kg  8 (21)  SW905–SWR1101  Quintanilla et al. (2002); Sato et al. (2003)   AF entire shoulder wt, kg  8 (37)  SW905–SWR1101  Beeckmann et al. (2003a)   Hind hock wt, kg  8 (107)  SW1551–S0178  —   Hind claw, kg  9 (23)  SW21–SW911  —   Entire ham wt, kg  9 (66)  SW2401–SW2571  Cepica et al. (2003a)   Flank wt, kg  10 (23)  SWR136–SW1894  Quintanilla et al. (2002)  Lean tissue characteristic   AF lean content of belly, %  2 (9)  SWR2516–SW2623  de Koning et al. (2001); Milan et al. (2002); Geldermann et al. (2003); Lee et al. (2003)   Loin eye area M.l.t.l.,4,5 cm2  2 (22)  SW2623–SWR783  Lee et al. (2003)   Protein content of loin, %  2 (93)  SWR2157–SWR345  Malek et al. (2001b); Lee et al. (2003)   Protein content of loin, %  2 (117)  SWR345–S0036  —   Loin wt without external fat, kg  4 (89)  S0214–SW445  Pérez-Enciso et al. (2000); Varona et al. (2002); Cepica et al. (2003b); Geldermann et al. (2003)   Protein content of loin, %  4 (121)  MP77–SW856  Malek et al. (2001b); Cepica et al. (2003b)   Loin wt without external fat, kg  6 (28)  SW2406–SW1841  de Koning et al. (2000); Milan et al. (2002)   Neck wt without external fat, kg  6 (145)  SW1881–SW322  Malek et al. (2001b); Edwards et al. (2008a)   Protein content of loin, %  7 (1)  SW2564–SWR1343  —   AF lean content of belly, %  8 (55)  SW444–S0086  —   Loin wt without external fat, kg  8 (60)  SW444–S0086  Casas-Carrillo et al. (1997); Milan et al. (2002); Kim et al. (2005)   Neck wt without external fat, kg  9 (58)  SW2401–SW2571  Rohrer et al. (2005)   Loin eye area M.l.t.l.,4,5 cm2  9 (136)  SW174–SW1349  Cepica et al. (2003a); Kim et al. (2006)   Loin wt without external fat, kg  14 (66)  SW342–SW1081  Dragos-Wendrich et al. (2003); Geldermann et al. (2003); van Wijk et al. (2006)  Fat tissue characteristic   External ham fat wt, kg  1 (48)  SW1851–SW1430  Malek et al. (2001b); Beeckmann et al. (2003b); Geldermann et al. (2003)   External ham fat wt, kg  1 (118)  SW1311–SW1828  Beeckmann et al. (2003b); Geldermann et al. (2003); Kim et al. (2006)   Intramuscular fat content, %  1 (126)  SW1828–SW1301  Rohrer and Keele (1998a,b); Rohrer (2000); Beeckmann et al. (2003b); Edwards et al. (2008a)   AF average fat thickness, mm  1 (142)  SW1301–SW2512  Rohrer and Keele (1998a); Bidanel et al. (2001); Quintanilla et al. (2002); Beeckmann et al. (2003b); Sanchez et al. (2006)   External neck fat wt, kg  4 (1)  SW2404–SW489  Marklund et al. (1999); Milan et al. (2002)   Intramuscular fat content, %  4 (94)  S0214–SW445  Pérez-Enciso et al. (2000); Varona et al. (2002); Cepica et al. (2003b); Geldermann et al. (2003)   Fat content of belly, %  4 (106)  SW445–MP77  Cepica et al. (2003b); Geldermann et al. (2003)   External neck fat wt, kg  4 (120)  MP77  Malek et al. (2001b); Cepica et al. (2003b)   Fat content of belly, %  6 (12)  MP35–SW2406  van Wijk et al. (2006)   Thinnest fat measure,4 cm  6 (42)  SW1841–S0087  —   AF average fat thickness, mm  6 (119)  S0228–SW1881  Varona et al. (2002); Sato et al. (2003); Kim et al. (2006); Edwards et al. (2008a)   External loin fat wt, kg  6 (150)  SW322–SW2052  Kim et al. (2005)   Thinnest fat measure,4 cm  8 (56)  SW444–S0086  —   External loin fat wt, kg  9 (57)  SW911–SW2401  Rohrer et al. (2005)  1Positions of the QTL in cM. 2References of other studies reporting QTL for similar traits in similar regions of the genome. 3AF = AutoFOM device (SFK Technology, Søborg, Denmark). 4Collected at the 13th/14th-rib interface. 5Measured on musculus longissimus thoracis et lumborum. View Large Table 4. Reports of QTL in the literature around similar locations as the QTL identified in the present study Trait  SSC (position1)  Marker interval  Other studies confirming the QTL2  Entire carcass characteristic (lean + fat)   Hind hock wt, kg  1 (35)  SW1332–SW1851  de Koning et al. (2001)   Hind claw, kg  1 (63)  SW1430–SWR982  Beeckmann et al. (2003b)   AF3 entire belly wt, kg  1 (88)  SWR982–SW1311  Nezer et al. (2002); Beeckmann et al. (2003b); Karlskov-Mortensen et al. (2006)   Entire ham wt, kg  2 (10)  SW2623–SWR783  de Koning et al. (2001); Milan et al. (2002); Geldermann et al. (2003); Lee et al. (2003)   Belly wt, kg  4 (31)  S0301–S0001  Cepica et al. (2003b); Geldermann et al. (2003); Kim et al. (2006)   Belly wt, kg  4 (130.1)  SW856  Knott et al. (2002)   Entire neck wt, kg  6 (71)  S0087–SW122  Yue et al. (2003b)   Entire neck wt, kg  6 (86)  SW122–S0228  Rohrer (2000); Grindflek et al. (2001); Varona et al. (2002); Yue et al. (2003b); Edwards et al. (2008b)   Entire loin wt, kg  7 (77)  SW1856–SWR2036  Malek et al. (2001b); Milan et al. (2002); Geldermann et al. (2003); Yue et al. (2003a)   Entire loin wt, kg  7 (86)  SWR2036–SW632  Nezer et al. (2002); Kim et al. (2005); Ponsuksili et al. (2005); Edwards et al. (2008a)   Flank wt, kg  7 (88)  SWR2036–SW632  Nezer et al. (2002); Kim et al. (2005); Ponsuksili et al. (2005); Edwards et al. (2008a)   AF entire belly wt, kg  7 (148)  SW2537–SW764  —   AF entire shoulder wt, kg  8 (21)  SW905–SWR1101  Quintanilla et al. (2002); Sato et al. (2003)   AF entire shoulder wt, kg  8 (37)  SW905–SWR1101  Beeckmann et al. (2003a)   Hind hock wt, kg  8 (107)  SW1551–S0178  —   Hind claw, kg  9 (23)  SW21–SW911  —   Entire ham wt, kg  9 (66)  SW2401–SW2571  Cepica et al. (2003a)   Flank wt, kg  10 (23)  SWR136–SW1894  Quintanilla et al. (2002)  Lean tissue characteristic   AF lean content of belly, %  2 (9)  SWR2516–SW2623  de Koning et al. (2001); Milan et al. (2002); Geldermann et al. (2003); Lee et al. (2003)   Loin eye area M.l.t.l.,4,5 cm2  2 (22)  SW2623–SWR783  Lee et al. (2003)   Protein content of loin, %  2 (93)  SWR2157–SWR345  Malek et al. (2001b); Lee et al. (2003)   Protein content of loin, %  2 (117)  SWR345–S0036  —   Loin wt without external fat, kg  4 (89)  S0214–SW445  Pérez-Enciso et al. (2000); Varona et al. (2002); Cepica et al. (2003b); Geldermann et al. (2003)   Protein content of loin, %  4 (121)  MP77–SW856  Malek et al. (2001b); Cepica et al. (2003b)   Loin wt without external fat, kg  6 (28)  SW2406–SW1841  de Koning et al. (2000); Milan et al. (2002)   Neck wt without external fat, kg  6 (145)  SW1881–SW322  Malek et al. (2001b); Edwards et al. (2008a)   Protein content of loin, %  7 (1)  SW2564–SWR1343  —   AF lean content of belly, %  8 (55)  SW444–S0086  —   Loin wt without external fat, kg  8 (60)  SW444–S0086  Casas-Carrillo et al. (1997); Milan et al. (2002); Kim et al. (2005)   Neck wt without external fat, kg  9 (58)  SW2401–SW2571  Rohrer et al. (2005)   Loin eye area M.l.t.l.,4,5 cm2  9 (136)  SW174–SW1349  Cepica et al. (2003a); Kim et al. (2006)   Loin wt without external fat, kg  14 (66)  SW342–SW1081  Dragos-Wendrich et al. (2003); Geldermann et al. (2003); van Wijk et al. (2006)  Fat tissue characteristic   External ham fat wt, kg  1 (48)  SW1851–SW1430  Malek et al. (2001b); Beeckmann et al. (2003b); Geldermann et al. (2003)   External ham fat wt, kg  1 (118)  SW1311–SW1828  Beeckmann et al. (2003b); Geldermann et al. (2003); Kim et al. (2006)   Intramuscular fat content, %  1 (126)  SW1828–SW1301  Rohrer and Keele (1998a,b); Rohrer (2000); Beeckmann et al. (2003b); Edwards et al. (2008a)   AF average fat thickness, mm  1 (142)  SW1301–SW2512  Rohrer and Keele (1998a); Bidanel et al. (2001); Quintanilla et al. (2002); Beeckmann et al. (2003b); Sanchez et al. (2006)   External neck fat wt, kg  4 (1)  SW2404–SW489  Marklund et al. (1999); Milan et al. (2002)   Intramuscular fat content, %  4 (94)  S0214–SW445  Pérez-Enciso et al. (2000); Varona et al. (2002); Cepica et al. (2003b); Geldermann et al. (2003)   Fat content of belly, %  4 (106)  SW445–MP77  Cepica et al. (2003b); Geldermann et al. (2003)   External neck fat wt, kg  4 (120)  MP77  Malek et al. (2001b); Cepica et al. (2003b)   Fat content of belly, %  6 (12)  MP35–SW2406  van Wijk et al. (2006)   Thinnest fat measure,4 cm  6 (42)  SW1841–S0087  —   AF average fat thickness, mm  6 (119)  S0228–SW1881  Varona et al. (2002); Sato et al. (2003); Kim et al. (2006); Edwards et al. (2008a)   External loin fat wt, kg  6 (150)  SW322–SW2052  Kim et al. (2005)   Thinnest fat measure,4 cm  8 (56)  SW444–S0086  —   External loin fat wt, kg  9 (57)  SW911–SW2401  Rohrer et al. (2005)  Trait  SSC (position1)  Marker interval  Other studies confirming the QTL2  Entire carcass characteristic (lean + fat)   Hind hock wt, kg  1 (35)  SW1332–SW1851  de Koning et al. (2001)   Hind claw, kg  1 (63)  SW1430–SWR982  Beeckmann et al. (2003b)   AF3 entire belly wt, kg  1 (88)  SWR982–SW1311  Nezer et al. (2002); Beeckmann et al. (2003b); Karlskov-Mortensen et al. (2006)   Entire ham wt, kg  2 (10)  SW2623–SWR783  de Koning et al. (2001); Milan et al. (2002); Geldermann et al. (2003); Lee et al. (2003)   Belly wt, kg  4 (31)  S0301–S0001  Cepica et al. (2003b); Geldermann et al. (2003); Kim et al. (2006)   Belly wt, kg  4 (130.1)  SW856  Knott et al. (2002)   Entire neck wt, kg  6 (71)  S0087–SW122  Yue et al. (2003b)   Entire neck wt, kg  6 (86)  SW122–S0228  Rohrer (2000); Grindflek et al. (2001); Varona et al. (2002); Yue et al. (2003b); Edwards et al. (2008b)   Entire loin wt, kg  7 (77)  SW1856–SWR2036  Malek et al. (2001b); Milan et al. (2002); Geldermann et al. (2003); Yue et al. (2003a)   Entire loin wt, kg  7 (86)  SWR2036–SW632  Nezer et al. (2002); Kim et al. (2005); Ponsuksili et al. (2005); Edwards et al. (2008a)   Flank wt, kg  7 (88)  SWR2036–SW632  Nezer et al. (2002); Kim et al. (2005); Ponsuksili et al. (2005); Edwards et al. (2008a)   AF entire belly wt, kg  7 (148)  SW2537–SW764  —   AF entire shoulder wt, kg  8 (21)  SW905–SWR1101  Quintanilla et al. (2002); Sato et al. (2003)   AF entire shoulder wt, kg  8 (37)  SW905–SWR1101  Beeckmann et al. (2003a)   Hind hock wt, kg  8 (107)  SW1551–S0178  —   Hind claw, kg  9 (23)  SW21–SW911  —   Entire ham wt, kg  9 (66)  SW2401–SW2571  Cepica et al. (2003a)   Flank wt, kg  10 (23)  SWR136–SW1894  Quintanilla et al. (2002)  Lean tissue characteristic   AF lean content of belly, %  2 (9)  SWR2516–SW2623  de Koning et al. (2001); Milan et al. (2002); Geldermann et al. (2003); Lee et al. (2003)   Loin eye area M.l.t.l.,4,5 cm2  2 (22)  SW2623–SWR783  Lee et al. (2003)   Protein content of loin, %  2 (93)  SWR2157–SWR345  Malek et al. (2001b); Lee et al. (2003)   Protein content of loin, %  2 (117)  SWR345–S0036  —   Loin wt without external fat, kg  4 (89)  S0214–SW445  Pérez-Enciso et al. (2000); Varona et al. (2002); Cepica et al. (2003b); Geldermann et al. (2003)   Protein content of loin, %  4 (121)  MP77–SW856  Malek et al. (2001b); Cepica et al. (2003b)   Loin wt without external fat, kg  6 (28)  SW2406–SW1841  de Koning et al. (2000); Milan et al. (2002)   Neck wt without external fat, kg  6 (145)  SW1881–SW322  Malek et al. (2001b); Edwards et al. (2008a)   Protein content of loin, %  7 (1)  SW2564–SWR1343  —   AF lean content of belly, %  8 (55)  SW444–S0086  —   Loin wt without external fat, kg  8 (60)  SW444–S0086  Casas-Carrillo et al. (1997); Milan et al. (2002); Kim et al. (2005)   Neck wt without external fat, kg  9 (58)  SW2401–SW2571  Rohrer et al. (2005)   Loin eye area M.l.t.l.,4,5 cm2  9 (136)  SW174–SW1349  Cepica et al. (2003a); Kim et al. (2006)   Loin wt without external fat, kg  14 (66)  SW342–SW1081  Dragos-Wendrich et al. (2003); Geldermann et al. (2003); van Wijk et al. (2006)  Fat tissue characteristic   External ham fat wt, kg  1 (48)  SW1851–SW1430  Malek et al. (2001b); Beeckmann et al. (2003b); Geldermann et al. (2003)   External ham fat wt, kg  1 (118)  SW1311–SW1828  Beeckmann et al. (2003b); Geldermann et al. (2003); Kim et al. (2006)   Intramuscular fat content, %  1 (126)  SW1828–SW1301  Rohrer and Keele (1998a,b); Rohrer (2000); Beeckmann et al. (2003b); Edwards et al. (2008a)   AF average fat thickness, mm  1 (142)  SW1301–SW2512  Rohrer and Keele (1998a); Bidanel et al. (2001); Quintanilla et al. (2002); Beeckmann et al. (2003b); Sanchez et al. (2006)   External neck fat wt, kg  4 (1)  SW2404–SW489  Marklund et al. (1999); Milan et al. (2002)   Intramuscular fat content, %  4 (94)  S0214–SW445  Pérez-Enciso et al. (2000); Varona et al. (2002); Cepica et al. (2003b); Geldermann et al. (2003)   Fat content of belly, %  4 (106)  SW445–MP77  Cepica et al. (2003b); Geldermann et al. (2003)   External neck fat wt, kg  4 (120)  MP77  Malek et al. (2001b); Cepica et al. (2003b)   Fat content of belly, %  6 (12)  MP35–SW2406  van Wijk et al. (2006)   Thinnest fat measure,4 cm  6 (42)  SW1841–S0087  —   AF average fat thickness, mm  6 (119)  S0228–SW1881  Varona et al. (2002); Sato et al. (2003); Kim et al. (2006); Edwards et al. (2008a)   External loin fat wt, kg  6 (150)  SW322–SW2052  Kim et al. (2005)   Thinnest fat measure,4 cm  8 (56)  SW444–S0086  —   External loin fat wt, kg  9 (57)  SW911–SW2401  Rohrer et al. (2005)  1Positions of the QTL in cM. 2References of other studies reporting QTL for similar traits in similar regions of the genome. 3AF = AutoFOM device (SFK Technology, Søborg, Denmark). 4Collected at the 13th/14th-rib interface. 5Measured on musculus longissimus thoracis et lumborum. View Large A QTL on SSC1 showed an interaction with a QTL on SSC8 for hind hock weight. Neither of these QTL were identified in previous analyses of the present data, which may be expected because the negative interaction effect is almost as large as the sum of the individual QTL effects. Around the same location of SSC1, a QTL has been reported for growth rate (de Koning et al., 2001). However, there are no reports in the literature confirming the QTL on SSC8. A further location of SSC1 showed an interaction with SSC9 for weight of the hind claw. These QTL were not identified in previous analyses. These QTL showed no significant additive or dominance effects and expressed their effects only through novel interactions between additive as well as dominance effects. The QTL on SSC1 for the hind claw was close to SW1430. Many QTL for carcass traits have been identified around this location (Beeckmann et al., 2003b). Several epistatic effects were identified between the telomeric end of the p-arm of SSC2 and 66 cM of SSC9 for entire ham weight. In the present study, the QTL on SSC2 and SSC9 showed substantial interactions between additive and dominance effects, which more than offset the negative effects associated with dominance and A×A genetic effects. From previous analysis of these data, numerous QTL were identified around this location of SSC2, where Pietrain alleles were associated with increased lean tissue and reduced fatness (Duthie et al., 2008). Whereas the QTL on SSC2 was affected by individual dominance effects, the QTL on SSC9 showed no significant (P > 0.001) individual QTL effects in the present study. However, in previous analyses, QTL were identified in this genomic location for entire shoulder weight and shoulder weight without external fat (Duthie et al., 2008). There are reports of QTL around this location of SSC2 for carcass traits, lean tissue, and fat tissue and around the region of SSC9 for BW (de Koning et al., 2001; Milan et al., 2002; Geldermann et al., 2003; Lee et al., 2003). In the region of the QTL on SSC2, a paternally expressed QTL that affects growth and fat deposition has been mapped to the IGF-2 locus (Jeon et al., 1999; Nezer et al., 1999). In the present analysis, imprinting effects have not been included in the model because of the substantial increase in complexity of the epistatic QTL analysis. This exclusion may reduce the power of detecting epistatic effects between QTL expressing imprinting at one or more loci. Therefore, including the IGF-2 genotypes and considering their imprinting in an epistatic QTL analysis would be of interest for identifying IGF-2 genomic interactions in further analyses. Interactions between additive or dominance effects were identified between 2 locations of SSC4, on the p-arm (31 cM) and the telomeric end of the q-arm (130.1 cM), for belly weight. The QTL on the p-arm was not identified in previous individual QTL analyses of the data, whereas a QTL was identified for lean content at 33 cM, for which Pietrain alleles were associated with decreased lean tissue (Duthie et al., 2008). Quantitative trait loci have been reported around 31 cM for numerous carcass traits as well as lean and fat tissue; however, only a single QTL has been reported at the telomeric end of the q-arm for daily BW gain (Knott et al., 2002). For entire neck weight, A×D and D×A interactions were identified between 2 close genomic locations of SSC6 (71 and 86 cM). There are numerous reports in the literature for QTL associated with carcass traits, lean tissue, and fat tissue in these locations (Rohrer et al., 2000; Grindflek et al., 2001; Varona et al., 2002; Yue et al., 2003b; Edwards et al., 2008b). No QTL were detected near 71 cM from previous individual QTL analyses of these data, but Mohrmann et al. (2006a) reported a large number of QTL around the QTL at 86 cM for several carcass cuts (lean + fat), fat tissue, lean tissue characteristics, and chemical body composition, at which Pietrain alleles were associated with decreased fat tissue and increased lean tissue. The significant additive effect identified at the QTL (86 cM) in the present study indicated that Pietrain alleles were associated with increased neck weight. This QTL is in the same genomic location as the RYR1 locus (Rohrer et al., 1996); however, it is independent from the RYR1 locus because its effect has been adjusted for as a fixed effect in the model. A novel epistatic A×A QTL pair was identified on 2 locations of SSC7 for entire loin weight. No individual QTL effects were identified at either of these QTL, outlining why they were not identified from previous individual QTL analyses. There are reports of QTL around these locations for numerous carcass characteristics (Malek et al., 2001; Milan et al., 2002; Nezer et al., 2002; Geldermann et al., 2003; Yue et al., 2003a; Kim et al., 2005; Ponsuksili et al., 2005; Edwards et al., 2008a). An A×A interaction was identified between SSC7 and SSC10 for flank weight. Again, at these QTL no individual QTL effects were identified. The QTL on SSC7 was located around the same region as for entire loin weight in the present study. Around this location of SSC7, there are reports of QTL for leanness, fatness, and growth (Nezer et al., 2002; Kim et al., 2005; Ponsuksili et al., 2005; Edwards et al., 2008a), whereas around this location of SSC10, a QTL has been reported for backfat (Quintanilla et al., 2002). For entire shoulder weight, A×A and A×D interactions were identified between 2 close genomic locations of SSC8 (21 and 37 cM). Duthie et al. (2008) identified QTL at 37 cM for protein content of the loin, at which Pietrain alleles were associated with less protein content. In this study, Pietrain alleles were associated with less shoulder weight at this QTL. The QTL at 21 cM was not identified previously; therefore, it exhibits effects only through the interactions. Quantitative trait loci have been reported around these locations for numerous carcass traits, daily BW gain, and lean tissue (Quintanilla et al., 2002; Beeckmann et al., 2003a; Sato et al., 2003). Lean Tissue Characteristics One of the main goals of commercial pig production has been to increase lean tissue. A large number of studies have investigated QTL for lean tissue (e.g., Rohrer and Keele, 1998b; Malek et al., 2001a; Geldermann et al., 2003) from individual QTL analyses. In the present study, we identified 7 epistatic QTL pairs for lean tissue characteristics. For protein content of the loin tissue, all fitted interactions and all individual QTL effects were significant between 2 genomic locations on SSC2 (93 and 117 cM). A QTL was previously identified at 92 cM for shoulder weight without external fat (Duthie et al., 2008). Around this location (93 cM), QTL have been reported for daily BW gain and backfat (Malek et al., 2001b; Lee et al., 2003). At the telomeric end of the p-arm of SSC2, additive as well as dominance interactions were detected with SSC8 for lean content of the belly. Numerous QTL were previously identified for lean and fat tissue QTL around this location of SSC2 (Duthie et al., 2008). The dominance effects of the QTL on SSC8, however, were not detected in previous individual QTL mapping analyses of the data. A slightly different location of SSC2 (22 cM) showed further interactions of all fitted combinations with SSC9 for loin eye area. No individual QTL effects were identified at these QTL; however, QTL were reported in this resource family for lean tissue at the same location on SSC2 (Duthie et al., 2008). Interestingly, all interactions were positive, and may thus be an explanation for heterosis of these crosses in lean content. Around this location of SSC2, QTL have been reported for lean tissue as well as backfat (Lee et al., 2003), and on SSC9, QTL have been reported for fatness, daily BW gain, and BW (Cepica et al., 2003a; Kim et al., 2006). In previous individual QTL mapping of the present resource family, no QTL were identified on SSC7 and only a few QTL were identified on SSC4. In the present study, we identified A×D interactions between these chromosomes for protein content of the loin. Quantitative trait loci have been reported around this location of SSC4 for carcass weight, BW, and liver weight (Malek et al., 2001b; Cepica et al., 2003b). Moreover, SSC4 showed positive interaction effects with SSC14 for loin weight without external fat. These positive interaction effects were almost 4 times as large as the negative additive genetic effects of the QTL on SSC14. These negative additive genetic effects at the QTL on SSC14 indicated that Pietrain alleles were associated with less lean meat of the loin. The QTL on SSC14 was at the same genomic location of SSC14 as the reported QTL for ham lean meat weight (Duthie et al., 2008), where Pietrain alleles were also associated with decreased lean tissue weight, interpreted as a cryptic allele. Around both of these QTL, there are reports in the literature for QTL associated with numerous carcass characteristics, including lean and fat tissue (Perez-Enciso et al., 2000; Varona et al., 2002; Cepica et al., 2003b; Dragos-Wendrich et al., 2003; Geldermann et al., 2003; van Wijk et al., 2006). A further interaction between additive genetic effects was identified between QTL on SSC6 and SSC8 for loin weight without external fat. No individual QTL effects were identified at these QTL, and these were not identified in previous analyses. Around the location of the QTL on SSC6, QTL have been identified for loin and ham percentage in the carcass and intramuscular fat content (de Koning et al., 2000; Milan et al., 2002), and in the region of SSC8, QTL have been reported for several weights of carcass cuts and daily BW gain (Casas-Carrillo et al., 1997; Milan et al., 2002; Kim et al., 2005). The SSC6 showed further epistatic effects with SSC9 for neck weight without external fat. At both individual QTL, heterozygote animals were associated with increased lean weight. Mohrmann et al. (2006a) reported, for the same resource family, QTL around this location of SSC6 for lean and fat tissue showing dominance effects, whereas the QTL on SSC9 was not identified previously. The negative D×D effects may be the reason for not detecting the QTL on SSC9 in an individual QTL mapping approach. Quantitative trait loci have been reported around this location of SSC6 for carcass length and loin eye area (Malek et al., 2001b; Edwards et al., 2008a), and have been reported on SSC9 for lean weight and loin eye area (Rohrer et al., 2005). Fat Tissue Characteristics Selection for reduced fatness has been an important goal within pig breeding over the last 50 yr. Fat tissue has negative associations with consumer acceptability and the economic value of the carcass, and it has waste and environmental impacts. In the present study, we identified epistatic interactions for 7 traits associated with fatness. Epistatic D×A genetic effects were identified in 2 genomic locations of SSC1 for external ham fat weight (48 and 118 cM). This QTL for external ham fat weight at 48 cM was identified close to SW185 on SSC1. In a previous individual QTL analysis of the data, Mohrmann et al. (2006a) reported QTL at 119 cM of SSC1 for the entire loin weight and the external loin fat weight, attributed to dominance effects. The QTL on SSC1 for external ham fat weight at 118 cM is close to SW1828. At both of these QTL, a large number of QTL have been reported for carcass traits, lean tissue, fat tissue, and daily BW gain (Malek et al., 2001b; Beeckmann et al., 2003b; Geldermann et al., 2003; Kim et al., 2006). Sus scrofa chromosome 1 also showed an A×D interaction with SSC4 for intramuscular fat content. Significant additive effects at the QTL on SSC1 indicated that the alleles from the Pietrain breed were associated with greater intramuscular fat content. However, this positive additive genetic effect was offset by an almost 3 times greater negative interaction effect with SSC4. The QTL on SSC1 and SSC4 were not identified in previous individual QTL mapping of the data. However, numerous QTL have been identified around both of these QTL for carcass characteristics, lean and fat tissue, and BW (Rohrer and Keele,b; Perez-Enciso et al., 2000; Rohrer, 2000; Varona et al., 2002; Beeckmann et al., 2003b; Cepica et al., 2003b; Geldermann et al., 2003; Edwards et al., 2008a). Furthermore, SSC1 showed interactions with SSC6 for average fat thickness measured by the AutoFom device. The QTL on SSC6 was previously reported by Mohrmann et al. (2006a). However, they estimated a significant individual dominance effect, whereas the present study showed that it is more likely due to an interaction between additive and dominance effects. Around this location of SSC1, there are a large number of reports for fat tissue, along with lean tissue and growth (Rohrer and Keele, 1998a; Bidanel et al., 2001; Quintanilla et al., 2002; Beeckmann et al., 2003b; Sanchez et al., 2006), and around the location of SSC6, there are reports for fatness, leanness, and growth (Varona et al., 2002; Sato et al., 2003; Kim et al., 2006; Edwards et al., 2008a). An A×A genetic interaction was identified between the 2 telomeric ends of SSC4 for external neck fat weight. No individual QTL effects were identified at these QTL, and they were not identified in previous analyses of the data. At the telomeric end of the p-arm, there are reports of QTL for fat tissue, as well as BW and belly weight (Marklund et al., 1999; Milan et al., 2002). At the telomeric end of the q-arm, there are no reports of QTL for fatness; however, there are reports for carcass weight, BW, and liver weight (Malek et al., 2001b; Cepica et al., 2003b). A different location of SSC4 showed A×A genetic interactions with SSC6 for fat content of the belly. At these QTL, no individual QTL effects were identified and they were not identified in previous analyses. Around this location of SSC4, no QTL have been reported for fat tissue, but there are reports of QTL for lean tissue (Cepica et al., 2003b; Geldermann et al., 2003). In the region of the QTL on SSC6, there is only 1 report of QTL for ham weight (van Wijk et al., 2006). For the thinnest fat measure, additive and dominance interactions were identified between SSC6 and SSC8. In addition, significant dominance effects were identified at both QTL, indicating that heterozygous animals were associated with thinner fat at both QTL. An interaction was described previously in this study for loin weight without external fat between SSC6 and SSC8. The QTL on SSC8 were both identified between SW444 and S0086. The QTL on SSC6 were not identified in the same marker bracket. At the location of the QTL on SSC6, Mohrmann et al. (2006a) found significant dominance effects influencing chemical body composition (protein and lipid content) measured at 30 kg of BW. At these QTL, heterozygous animals were associated with less lipid and protein content of the empty body and less protein content of the fat-free substance. There are no reports in the literature of QTL for similar traits around either QTL. For external loin fat weight, interactions were identified between genomic locations of SSC6 and SSC9 similar to that of neck weight without external fat. At the QTL on SSC9, heterozygous animals are associated with less fat weight of this carcass cut and increased lean. The QTL on SSC6 has been reported previously by Mohrmann et al. (2006a) for this resource family for many fat tissue characteristics. Furthermore, there are reports in the literature for fatness QTL around both QTL (Kim et al., 2005; Rohrer et al., 2005). There are numerous reports of QTL in the literature for carcass characteristics and lean tissue and fat tissue characteristics in pigs in many genomic locations throughout the genome (e.g., Rohrer and Keele,b; Bidanel et al., 2001; Milan et al., 2002; Geldermann et al., 2003; Sanchez et al., 2006; Liu et al., 2007). There are many potential candidate genes that can be found in locations similar to some of the QTL identified in the present study. These are outlined in Table 5. Previous analyses of the phenotypic data from the commercial population (Pietrain sires × crossbred dam line) of the present study identified numerous QTL for entire carcass characteristics, as well as lean and fat tissue characteristics (Mohrmann et al., 2006a; Duthie et al., 2008). However, in these studies the role of epistasis in the genomic regulation of body composition has not been considered. To date, there is limited evidence for epistatic QTL across all species of livestock. This is most likely because tools and methodologies have not been available for this type of research and because of the computational demand associated with the analysis. Table 5. Potential candidate genes in locations of the epistatic QTL of the present study QTL (SSC, position)  Trait  Candidate gene  Role of candidate gene  Entire carcass characteristic   SSC1, 88 cM  Entire belly wt  Melanocortin-4 receptor  • Important for controlling energy balance and BW; hence, is a candidate gene for traits associated with feed intake and energy homeostasis-related traits (Meidtner et al., 2006).        • Reports of an association with growth and fatness (Kim et al., 2000; Park et al., 2002; Houston et al., 2004; Meidtner et al., 2006).        • Could be a useful marker to increase growth of the slow-growing Pietrain breed by increasing feed intake (Meidtner et al., 2006).   SSC1, 63 cM  Weight of hind claw  IGF-1 receptor  • Role in regeneration, metabolism, and proliferation in a variety of cell types (Schweiger et al., 2005).   SSC1, 59 cM  Carcass length    • Regulates growth and differentiation of a variety of cells and controls BW (Kopečný et al., 2002).   SSC2, 10 cM  Entire ham wt  IGF-2  • Paternally expressed (Jeon et al., 1999; Nezer et al., 1999).        • Caused by a nucleotide substitution in intron 3 (Van Laere et al., 2003).   SSC4, 31 cM  Belly wt  F-BOX protein 32  • Expression increased in myotubules during muscle atrophy, whereas mice deficient in this gene were resistant to atrophy (Bodine et al., 2001, Yu et al., 2005).        • Could be an important gene for muscle mass development (Glass, 2003).      Exostosis (multiple) 1  • Candidate gene for growth-related traits (Cepica et al., 2002).   SSC6, 86 cM  Entire neck wt  Ryanodine receptor 1  • A mutation at this locus is associated with malignant hyperthermia syndrome (Fujii et al., 1991).        • Significantly associated with production traits in pigs (Kadarmideen, 2008).   SSC7, 86 cM  Entire loin wt  Proteasome (prosome, macropain) activator subunit 1 (PA28α) and proteasome (prosome, macropain) activator subunit 2 (PA28β)  • Encodes proteasome activators PA28α and β subunits, 2 subunits of PA28, which is an activator of the proteosome and plays an important role in antigen presentation mediated by the major histocompatibility complex class I (Dubiel et al., 1992).        • Evidence that a polymorphism in this gene is associated with weaning weight (Wang et al., 2004).  Lean tissue characteristic     SSC2, 9 cM  Lean content of belly  IGF-2  •Role outlined above.   SSC4, 121 cM  Protein content of loin  Transforming growth factor, β receptor III  • Mediates the diverse effects of transforming growth factor-β, which is involved in tissue development and repair processes (Johnson et al., 1995).   SSC4, 89 cM  Loin wt without external fat  Myocyte enhancer factor 2D  • Member of the myocyte enhancer binding factor 2 gene family (Wagenknecht et al., 2003).        • Thought to be involved in myogenesis (Breitbart et al., 1993).      Myelin protein zero  • Identified in the same location as QTL for carcass traits (lean and fat mass; Cepica et al., 2003b, Wagenknecht et al., 2005).      Lamin A/C  • Encodes lamins A and C (Wagenknecht et al., 2006).        • Mice lacking lamin A have severely retarded postnatal growth and premature death, and developed cardiac and skeletal myopathy (Sullivan et al., 1999).        • QTL for carcass traits identified around this region (Cepica et al., 2003b); therefore, is a candidate gene for muscle development and growth.      Thioredoxin-interacting protein  • Role in cell proliferation and growth (Yu et al., 2007).        • Significant effects on several important growth traits, including carcass weight as well as daily BW gain in pigs (Yu et al., 2007).   SSC9, 58 cM  Neck wt without external fat  Succinate dehydrogenase complex, subunit D  • One of the subunits of the succinate dehydrogenase complex.        • Candidate for production traits because of its role in this complex in the process of aerobic respiration. Expression of this gene was associated with growth and meat quality traits in pigs (Guimaraes et al., 2007).        • Associated with loin muscle area (Zhu et al., 2005).  QTL (SSC, position)  Trait  Candidate gene  Role of candidate gene  Entire carcass characteristic   SSC1, 88 cM  Entire belly wt  Melanocortin-4 receptor  • Important for controlling energy balance and BW; hence, is a candidate gene for traits associated with feed intake and energy homeostasis-related traits (Meidtner et al., 2006).        • Reports of an association with growth and fatness (Kim et al., 2000; Park et al., 2002; Houston et al., 2004; Meidtner et al., 2006).        • Could be a useful marker to increase growth of the slow-growing Pietrain breed by increasing feed intake (Meidtner et al., 2006).   SSC1, 63 cM  Weight of hind claw  IGF-1 receptor  • Role in regeneration, metabolism, and proliferation in a variety of cell types (Schweiger et al., 2005).   SSC1, 59 cM  Carcass length    • Regulates growth and differentiation of a variety of cells and controls BW (Kopečný et al., 2002).   SSC2, 10 cM  Entire ham wt  IGF-2  • Paternally expressed (Jeon et al., 1999; Nezer et al., 1999).        • Caused by a nucleotide substitution in intron 3 (Van Laere et al., 2003).   SSC4, 31 cM  Belly wt  F-BOX protein 32  • Expression increased in myotubules during muscle atrophy, whereas mice deficient in this gene were resistant to atrophy (Bodine et al., 2001, Yu et al., 2005).        • Could be an important gene for muscle mass development (Glass, 2003).      Exostosis (multiple) 1  • Candidate gene for growth-related traits (Cepica et al., 2002).   SSC6, 86 cM  Entire neck wt  Ryanodine receptor 1  • A mutation at this locus is associated with malignant hyperthermia syndrome (Fujii et al., 1991).        • Significantly associated with production traits in pigs (Kadarmideen, 2008).   SSC7, 86 cM  Entire loin wt  Proteasome (prosome, macropain) activator subunit 1 (PA28α) and proteasome (prosome, macropain) activator subunit 2 (PA28β)  • Encodes proteasome activators PA28α and β subunits, 2 subunits of PA28, which is an activator of the proteosome and plays an important role in antigen presentation mediated by the major histocompatibility complex class I (Dubiel et al., 1992).        • Evidence that a polymorphism in this gene is associated with weaning weight (Wang et al., 2004).  Lean tissue characteristic     SSC2, 9 cM  Lean content of belly  IGF-2  •Role outlined above.   SSC4, 121 cM  Protein content of loin  Transforming growth factor, β receptor III  • Mediates the diverse effects of transforming growth factor-β, which is involved in tissue development and repair processes (Johnson et al., 1995).   SSC4, 89 cM  Loin wt without external fat  Myocyte enhancer factor 2D  • Member of the myocyte enhancer binding factor 2 gene family (Wagenknecht et al., 2003).        • Thought to be involved in myogenesis (Breitbart et al., 1993).      Myelin protein zero  • Identified in the same location as QTL for carcass traits (lean and fat mass; Cepica et al., 2003b, Wagenknecht et al., 2005).      Lamin A/C  • Encodes lamins A and C (Wagenknecht et al., 2006).        • Mice lacking lamin A have severely retarded postnatal growth and premature death, and developed cardiac and skeletal myopathy (Sullivan et al., 1999).        • QTL for carcass traits identified around this region (Cepica et al., 2003b); therefore, is a candidate gene for muscle development and growth.      Thioredoxin-interacting protein  • Role in cell proliferation and growth (Yu et al., 2007).        • Significant effects on several important growth traits, including carcass weight as well as daily BW gain in pigs (Yu et al., 2007).   SSC9, 58 cM  Neck wt without external fat  Succinate dehydrogenase complex, subunit D  • One of the subunits of the succinate dehydrogenase complex.        • Candidate for production traits because of its role in this complex in the process of aerobic respiration. Expression of this gene was associated with growth and meat quality traits in pigs (Guimaraes et al., 2007).        • Associated with loin muscle area (Zhu et al., 2005).  View Large Table 5. Potential candidate genes in locations of the epistatic QTL of the present study QTL (SSC, position)  Trait  Candidate gene  Role of candidate gene  Entire carcass characteristic   SSC1, 88 cM  Entire belly wt  Melanocortin-4 receptor  • Important for controlling energy balance and BW; hence, is a candidate gene for traits associated with feed intake and energy homeostasis-related traits (Meidtner et al., 2006).        • Reports of an association with growth and fatness (Kim et al., 2000; Park et al., 2002; Houston et al., 2004; Meidtner et al., 2006).        • Could be a useful marker to increase growth of the slow-growing Pietrain breed by increasing feed intake (Meidtner et al., 2006).   SSC1, 63 cM  Weight of hind claw  IGF-1 receptor  • Role in regeneration, metabolism, and proliferation in a variety of cell types (Schweiger et al., 2005).   SSC1, 59 cM  Carcass length    • Regulates growth and differentiation of a variety of cells and controls BW (Kopečný et al., 2002).   SSC2, 10 cM  Entire ham wt  IGF-2  • Paternally expressed (Jeon et al., 1999; Nezer et al., 1999).        • Caused by a nucleotide substitution in intron 3 (Van Laere et al., 2003).   SSC4, 31 cM  Belly wt  F-BOX protein 32  • Expression increased in myotubules during muscle atrophy, whereas mice deficient in this gene were resistant to atrophy (Bodine et al., 2001, Yu et al., 2005).        • Could be an important gene for muscle mass development (Glass, 2003).      Exostosis (multiple) 1  • Candidate gene for growth-related traits (Cepica et al., 2002).   SSC6, 86 cM  Entire neck wt  Ryanodine receptor 1  • A mutation at this locus is associated with malignant hyperthermia syndrome (Fujii et al., 1991).        • Significantly associated with production traits in pigs (Kadarmideen, 2008).   SSC7, 86 cM  Entire loin wt  Proteasome (prosome, macropain) activator subunit 1 (PA28α) and proteasome (prosome, macropain) activator subunit 2 (PA28β)  • Encodes proteasome activators PA28α and β subunits, 2 subunits of PA28, which is an activator of the proteosome and plays an important role in antigen presentation mediated by the major histocompatibility complex class I (Dubiel et al., 1992).        • Evidence that a polymorphism in this gene is associated with weaning weight (Wang et al., 2004).  Lean tissue characteristic     SSC2, 9 cM  Lean content of belly  IGF-2  •Role outlined above.   SSC4, 121 cM  Protein content of loin  Transforming growth factor, β receptor III  • Mediates the diverse effects of transforming growth factor-β, which is involved in tissue development and repair processes (Johnson et al., 1995).   SSC4, 89 cM  Loin wt without external fat  Myocyte enhancer factor 2D  • Member of the myocyte enhancer binding factor 2 gene family (Wagenknecht et al., 2003).        • Thought to be involved in myogenesis (Breitbart et al., 1993).      Myelin protein zero  • Identified in the same location as QTL for carcass traits (lean and fat mass; Cepica et al., 2003b, Wagenknecht et al., 2005).      Lamin A/C  • Encodes lamins A and C (Wagenknecht et al., 2006).        • Mice lacking lamin A have severely retarded postnatal growth and premature death, and developed cardiac and skeletal myopathy (Sullivan et al., 1999).        • QTL for carcass traits identified around this region (Cepica et al., 2003b); therefore, is a candidate gene for muscle development and growth.      Thioredoxin-interacting protein  • Role in cell proliferation and growth (Yu et al., 2007).        • Significant effects on several important growth traits, including carcass weight as well as daily BW gain in pigs (Yu et al., 2007).   SSC9, 58 cM  Neck wt without external fat  Succinate dehydrogenase complex, subunit D  • One of the subunits of the succinate dehydrogenase complex.        • Candidate for production traits because of its role in this complex in the process of aerobic respiration. Expression of this gene was associated with growth and meat quality traits in pigs (Guimaraes et al., 2007).        • Associated with loin muscle area (Zhu et al., 2005).  QTL (SSC, position)  Trait  Candidate gene  Role of candidate gene  Entire carcass characteristic   SSC1, 88 cM  Entire belly wt  Melanocortin-4 receptor  • Important for controlling energy balance and BW; hence, is a candidate gene for traits associated with feed intake and energy homeostasis-related traits (Meidtner et al., 2006).        • Reports of an association with growth and fatness (Kim et al., 2000; Park et al., 2002; Houston et al., 2004; Meidtner et al., 2006).        • Could be a useful marker to increase growth of the slow-growing Pietrain breed by increasing feed intake (Meidtner et al., 2006).   SSC1, 63 cM  Weight of hind claw  IGF-1 receptor  • Role in regeneration, metabolism, and proliferation in a variety of cell types (Schweiger et al., 2005).   SSC1, 59 cM  Carcass length    • Regulates growth and differentiation of a variety of cells and controls BW (Kopečný et al., 2002).   SSC2, 10 cM  Entire ham wt  IGF-2  • Paternally expressed (Jeon et al., 1999; Nezer et al., 1999).        • Caused by a nucleotide substitution in intron 3 (Van Laere et al., 2003).   SSC4, 31 cM  Belly wt  F-BOX protein 32  • Expression increased in myotubules during muscle atrophy, whereas mice deficient in this gene were resistant to atrophy (Bodine et al., 2001, Yu et al., 2005).        • Could be an important gene for muscle mass development (Glass, 2003).      Exostosis (multiple) 1  • Candidate gene for growth-related traits (Cepica et al., 2002).   SSC6, 86 cM  Entire neck wt  Ryanodine receptor 1  • A mutation at this locus is associated with malignant hyperthermia syndrome (Fujii et al., 1991).        • Significantly associated with production traits in pigs (Kadarmideen, 2008).   SSC7, 86 cM  Entire loin wt  Proteasome (prosome, macropain) activator subunit 1 (PA28α) and proteasome (prosome, macropain) activator subunit 2 (PA28β)  • Encodes proteasome activators PA28α and β subunits, 2 subunits of PA28, which is an activator of the proteosome and plays an important role in antigen presentation mediated by the major histocompatibility complex class I (Dubiel et al., 1992).        • Evidence that a polymorphism in this gene is associated with weaning weight (Wang et al., 2004).  Lean tissue characteristic     SSC2, 9 cM  Lean content of belly  IGF-2  •Role outlined above.   SSC4, 121 cM  Protein content of loin  Transforming growth factor, β receptor III  • Mediates the diverse effects of transforming growth factor-β, which is involved in tissue development and repair processes (Johnson et al., 1995).   SSC4, 89 cM  Loin wt without external fat  Myocyte enhancer factor 2D  • Member of the myocyte enhancer binding factor 2 gene family (Wagenknecht et al., 2003).        • Thought to be involved in myogenesis (Breitbart et al., 1993).      Myelin protein zero  • Identified in the same location as QTL for carcass traits (lean and fat mass; Cepica et al., 2003b, Wagenknecht et al., 2005).      Lamin A/C  • Encodes lamins A and C (Wagenknecht et al., 2006).        • Mice lacking lamin A have severely retarded postnatal growth and premature death, and developed cardiac and skeletal myopathy (Sullivan et al., 1999).        • QTL for carcass traits identified around this region (Cepica et al., 2003b); therefore, is a candidate gene for muscle development and growth.      Thioredoxin-interacting protein  • Role in cell proliferation and growth (Yu et al., 2007).        • Significant effects on several important growth traits, including carcass weight as well as daily BW gain in pigs (Yu et al., 2007).   SSC9, 58 cM  Neck wt without external fat  Succinate dehydrogenase complex, subunit D  • One of the subunits of the succinate dehydrogenase complex.        • Candidate for production traits because of its role in this complex in the process of aerobic respiration. Expression of this gene was associated with growth and meat quality traits in pigs (Guimaraes et al., 2007).        • Associated with loin muscle area (Zhu et al., 2005).  View Large In pigs, epistatic QTL have been reported so far for reproductive traits (Bidanel, 1993; Rodríguez et al., 2005; Noguera et al., 2006), coat color (Hirooka et al., 2002), meat quality traits (meat color and intramuscular fat content; Ovilo et al., 2002; Szyda et al., 2006), and muscle fiber traits (Estellé et al., 2008). No epistatic QTL have been reported for body composition, such as entire carcass cuts or lean tissue and fat tissue characteristics. The present study is, to our knowledge, the first report to estimate epistatic interactions for carcass characteristics measured at slaughter weight in the pig. Carlborg and Haley (2004) outlined the importance of a relatively large data set for the analysis of epistatic QTL. Small data sets will detect only epistatic QTL pairs with large effects. In the present study, we identified a large number of epistatic QTL pairs; this study is only the first step in understanding the contribution of epistasis to the genetic control of body composition in pigs. We have not covered the whole genome; therefore, many more epistatic interactions are probably involved in the genomic regulation of body composition. Estellé et al. (2008) identified numerous significant epistatic QTL pairs for muscle fiber traits in a pig population of Iberian × Landrace F2 cross, using a methodology similar to that in the present study. They identified all 2-locus epistatic effects (A×A, A×D, D×A, D×D) but did not find that any particular epistatic effect was prevalent in their study. The interactions identified in the present study were at different genomic locations than those of Estellé et al. (2008). This may be because muscle fiber traits are under different genomic control or because of breed differences, because the study by Estellé et al. (2008) was based on an experimental cross between Iberian and Landrace pigs. They found that the epistatic interactions formed a network of connected pairs of epistatic QTL. They also indicated that this may be a common phenomenon because Carlborg et al. (2006) reported similar networks. Estellé et al. (2008) found that SSC10 and SSC11 behaved as hubs for this network. There is no clear evidence of this type of network in our study. However, SSC1, SSC2, SSC4, SSC6, SSC8, and SSC9 seemed particularly active with respect to epistasis. Sus scrofa chromosome 10 did not seem as important in our study, with only 1 interaction being identified on SSC10; SSC11 was not genotyped in the present study. Information about the involvement of epistatic QTL in the genomic regulation of body composition is limited in livestock. There is, however, some evidence of the involvement of epistasis in the genomic regulation of growth in chickens, particularly early growth (Carlborg et al., 2003, 2004). Furthermore, there is considerable evidence indicating an important role for epistatic interactions in the genomic control of growth and obesity in mice. Routman and Cheverud (1997) reported epistatic QTL for adult BW. Brockmann et al. (2000) reported epistatic effects for serum concentrations of leptin, insulin, and IGF-1 and for BW, abdominal fat weight, and muscle weight. They reported co-coordinated regulation of BW and muscle weight by the interaction of 2 pairs of loci, 1 of which also influenced serum concentrations of lipid. They indicated that these interactions may contribute to the strong genetic correlation between BW and muscle weight. Yi et al. (2004b) also found that epistasis played an important role controlling obesity in mice. They reported that different groups of traits were influenced by different interactions, such that a different genetic architecture was identified for obesity traits and total cholesterol. They also found that the total phenotypic variance explained by epistatic interactions was greater than those explained by main effects. The epistatic QTL pairs identified in the present study also contributed to greater proportions of the phenotypic variance than QTL identified from individual QTL analysis. In a further study of mice, Yi et al. (2004a) reported an epistatic effect between mouse chromosomes 7 and 3 for hepatic lipase activity. The QTL on chromosome 7 was detected in a nonepistatic analysis in the same location. The QTL on chromosome 3 had a weak main effect on hepatic lipase activity and was not detected in the nonepistatic analysis; however, chromosome 3 was found to interact strongly with chromosome 7. Further studies in mice reported epistatic QTL pairs for abdominal fat percentage, abdominal fat weight, BW, kidney weight, spleen weight (Carlborg et al., 2005), organ weights, and limb length traits (Wolf et al., 2006). Yi et al. (2006) found that epistasis was more important for BW in mice at older ages than at younger ages, in contradiction to the report of Ishikawa et al. (2005), who found that epistasis was more important for early growth than late stages of growth in mice. Yi et al. (2006) also found that epistasis influenced fatness and organ weights. A concern in epistatic QTL analysis is multiple testing and the risk of false-positive results, based on the large number of tests that are carried out. Therefore, to minimize the risk of false-positive results, in the present study a more stringent threshold was applied to the epistatic analysis compared with the individual QTL analysis of previous work based on these data (Mohrmann et al., 2006a; Duthie et al., 2008). In the present study, we have identified a large number of epistatic QTL pairs involved in the regulation of many carcass traits, including lean and fat tissue weights in pigs. It is obvious from this study and from studies of poultry and mice that epistasis is important for the genomic regulation of growth and body composition. Information about epistatic interactions can add to our understanding of the genomic networks that form the fundamental basis of biological systems. In addition to knowledge about the individual QTL or genes that influence a biological system, information about the effect of interactions between genes will build on the understanding of the genomic networks that influence variation in biological systems (Carlborg and Haley, 2004). Future QTL analyses should therefore focus their attention on uncovering the role of epistasis in the genomic regulation of economically important traits. LITERATURE CITED Beeckmann P. Moser G. Bartenschlager H. Reiner G. Geldermann H. 2003a. Linkage and QTL mapping for Sus scrofa chromosome 8. J. Anim. Breed. Genet.  120( Suppl. 1): 66– 73. Google Scholar CrossRef Search ADS   Beeckmann P. Schröffel J. Moser G. Bartenschlager H. Reiner G. Geldermann H. 2003b. Linkage and QTL mapping for Sus scrofa chromosome 1. J. Anim. Breed. Genet.  120( Suppl. 1): 1– 10. Google Scholar CrossRef Search ADS   Bidanel J. P. 1993. Estimation of crossbreeding parameters between Large White and Meishan porcine breeds. 3. Dominance and epistatic components of heterosis on reproductive traits. Genet. Sel. Evol.  25: 263– 281. Google Scholar CrossRef Search ADS   Bidanel J. P. Milan D. Iannuccelli N. Amigues Y. Boscher M. Y. Bourgeois F. Caritez J. C. Gruand J. Le Roy P. Lagant H. Quintanilla R. Renard C. Gellin J. Ollivier L. Chevalet C. 2001. Detection of quantitative trait loci for growth and fatness in pigs. Genet. Sel. Evol.  33: 289– 309. https://doi.org/11403749 Google Scholar CrossRef Search ADS PubMed  Bodine S. C. Latres E. Baumhueter S. Lai V. K. M. Nunez L. Clarke B. A. Poueymirou W. T. Panaro F. J. Na E. Q. Dharmarajan K. Pan Z. Q. Valenzuela D. M. DeChiara T. M. Stitt T. N. Yancopoulos G. D. Glass D. J. 2001. Identification of ubiquitin ligases required for skeletal muscle atrophy. Science  294: 1704– 1708. https://doi.org/11679633 Google Scholar CrossRef Search ADS PubMed  Breitbart R. E. Liang C. S. Smoot L. B. Laheru D. A. Mahdavi V. Nadalginard B. 1993. A fourth human MEF2 transcription factor, hMEF2D, is an early marker of the myogenic lineage. Development  118: 1095– 1106. https://doi.org/8269842 Google Scholar PubMed  Brockmann G. A. Kratzsch J. Haley C. S. Renne U. Schwerin M. Karle S. 2000. Single QTL effects, epistasis, and pleiotropy account for two-thirds of the phenotypic F2 variance of growth and obesity in DU6i × DBA/2 mice. Genome Res.  10: 1941– 1957. https://doi.org/11116089 Google Scholar CrossRef Search ADS PubMed  Brondum J. Egebo M. Agerskov C. Busk H. 1998. On-line pork carcass grading with the Autofom ultrasound system. J. Anim. Sci.  76: 1859– 1868. Google Scholar CrossRef Search ADS PubMed  Carlborg, O. 2006. Detection of epistatic QTL.  Proc. 8th World Congr. Genet. Appl. Livest. Prod., Belo Horizonte, Brazil. CD-ROM Commun. No. 20-01. Carlborg O. Brockmann G. A. Haley C. S. 2005. Simultaneous mapping of epistatic QTL in DU6i × DBA/2 mice. Mamm. Genome  16: 481– 494. https://doi.org/16151693 Google Scholar CrossRef Search ADS PubMed  Carlborg O. Haley C. S. 2004. Epistasis: Too often neglected in complex trait studies? Nat. Rev. Genet.  5: 618– 625. https://doi.org/15266344 Google Scholar CrossRef Search ADS PubMed  Carlborg O. Hocking P. M. Burt D. W. Haley C. S. 2004. Simultaneous mapping of epistatic QTL in chickens reveals clusters of QTL pairs with similar genetic effects on growth. Genet. Res.  83: 197– 209. https://doi.org/15462413 Google Scholar CrossRef Search ADS PubMed  Carlborg O. Jacobsson L. Ahgren P. Siegel P. Andersson L. 2006. Epistasis and the release of genetic variation during long-term selection. Nat. Genet.  38: 418– 420. https://doi.org/16532011 Google Scholar CrossRef Search ADS PubMed  Carlborg O. Kerje S. Schutz K. Jacobsson L. Jensen P. Andersson L. 2003. A global search reveals epistatic interaction between QTL for early growth in the chicken. Genome Res.  13: 413– 421. https://doi.org/12618372 Google Scholar CrossRef Search ADS PubMed  Casas-Carrillo E. Prill-Adams A. Price S. G. Clutter A. C. Kirkpatrick B. W. 1997. Mapping genomic regions associated with growth rate in pigs. J. Anim. Sci.  75: 2047– 2053. https://doi.org/9263050 Google Scholar CrossRef Search ADS PubMed  Cepica S. Rohrer G. A. Masopust M. 2002. Linkage mapping of a HaeIII PCR-RFLP within the porcine EXT1 gene. Anim. Genet.  33: 81– 82. https://doi.org/11849148 Google Scholar CrossRef Search ADS PubMed  Cepica S. Schröffel J. Stratil A. Hojný J. Pierzchala M. Kuryl J. Brunsch C. Sternstein I. Davoli R. Fontanesi L. Reiner G. Bartenscelager H. Moser G. Geldermann H. 2003a. Linkage and QTL mapping for Sus scrofa chromosome 9. J. Anim. Breed. Genet.  120( Suppl. 1): 74– 81. Google Scholar CrossRef Search ADS   Cepica S. Stratil A. Kopecny M. Blazkova P. Schröffel J. Davoli R. Fontanesi L. Reiner G. Bartenschlager H. Moser G. Geldermann H. 2003b. Linkage and QTL mapping for Sus scrofa chromosome 4. J. Anim. Breed. Genet.  120( Suppl. 1): 28– 37. Google Scholar CrossRef Search ADS   Cockerham C. C. 1954. An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics  39: 859– 882. https://doi.org/17247525 Google Scholar PubMed  de Koning D. J. Rattink A. P. Harlizius B. Groenen M. A. M. Brascamp E. W. van Arendonk J. A. M. 2001. Detection and characterization of quantitative trait loci for growth and reproduction traits in pigs. Livest. Prod. Sci.  72: 185– 198. Google Scholar CrossRef Search ADS   de Koning D. J. Rattink A. P. Harlizius B. van Arendonk J. A. M. Brascamp E. W. Groenen M. A. M. 2000. Genome-wide scan for body composition in pigs reveals important role of imprinting. Proc. Natl. Acad. Sci. USA  97: 7947– 7950. https://doi.org/10859367 Google Scholar CrossRef Search ADS   Dragos-Wendrich M. Sternstein I. Brunsch C. Moser G. Bartenschlager H. Reiner G. Geldermann H. 2003. Linkage and QTL mapping for Sus scrofa chromosome 14. J. Anim. Breed. Genet.  120( Suppl. 1): 111– 118. Google Scholar CrossRef Search ADS   Dubiel W. Pratt G. Ferrell K. Rechsteiner M. 1992. Purification of an 11-S regulator of the multicatalytic protease. J. Biol. Chem.  267: 22369– 22377. https://doi.org/1429590 Google Scholar PubMed  Duthie C. Simm G. Doeschl-Wilson A. Kalm E. Knap P. W. Roehe R. 2008. Quantitative trait loci for chemical body composition traits in pigs and their positional associations with body tissues, growth and feed intake. Anim. Genet.  39: 130– 140. https://doi.org/18307580 Google Scholar CrossRef Search ADS PubMed  Edwards D. B. Ernst C. W. Raney N. E. Doumit M. E. Hoge M. D. Bates R. O. 2008a. Quantitative trait locus mapping in an F2 Duroc × Pietrain resource population: II. Carcass and meat quality traits. J. Anim. Sci.  86: 254– 266. https://doi.org/17965326 Google Scholar CrossRef Search ADS   Edwards D. B. Ernst C. W. Tempelman R. J. Rosa G. J. M. Raney N. E. Hoge M. D. Bates R. O. 2008b. Quantitative trait loci mapping in an F2 Duroc × Pietrain resource population: I. Growth traits. J. Anim. Sci.  86: 241– 253. Google Scholar CrossRef Search ADS   Estellé J. Gil F. Vázquez J. M. Latorre R. Ramírez G. Barragán M. C. Folch J. M. Noguera J. L. Toro M. A. Pérez-Enciso M. 2008. A quantitative trait locus genome scan for porcine muscle fiber traits reveals overdominance and epistasis. J. Anim. Sci.  86: 3290– 3299. https://doi.org/18641172 Google Scholar CrossRef Search ADS PubMed  Fujii J. Otsu K. Zorzato F. Deleon S. Khanna V. K. Weiler J. E. Obrien P. J. Maclennan D. H. 1991. Identification of a mutation in porcine ryanodine receptor associated with malignant hyperthermia. Science  253: 448– 451. https://doi.org/1862346 Google Scholar CrossRef Search ADS PubMed  Geldermann H. Müller E. Moser G. Reiner G. Bartenschlager H. Cepica S. Stratil A. Kuryl J. Moran C. Davoli R. Brunsch C. 2003. Genome-wide linkage and QTL mapping in porcine F2 families generated from Pietrain, Meishan and Wild Boar crosses. J. Anim. Breed. Genet.  120: 363– 393. Google Scholar CrossRef Search ADS   Glass D. J. 2003. Molecular mechanisms modulating muscle mass. Trends Mol. Med.  9: 344– 350. https://doi.org/12928036 Google Scholar CrossRef Search ADS PubMed  Grindflek E. Szyda J. Liu Z. T. Lien S. 2001. Detection of quantitative trait loci for meat quality in a commercial slaughter pig cross. Mamm. Genome  12: 299– 304. https://doi.org/11309662 Google Scholar CrossRef Search ADS PubMed  Guimaraes S. E. F. Rothschild M. F. Ciobanu D. Stahl C. H. Lonergan S. M. 2007. SNP discovery, expression and association analysis for the SDHD gene in pigs. J. Anim. Breed. Genet.  124: 246– 253. https://doi.org/17651329 Google Scholar CrossRef Search ADS PubMed  Hirooka H. de Koning D. J. van Arendonk J. A. M. Harlizius B. de Groot P. N. Bovenhuis H. 2002. Genome scan reveals new coat color loci in exotic pig cross. J. Hered.  93: 1– 8. https://doi.org/12011168 Google Scholar CrossRef Search ADS PubMed  Houston R. D. Cameron N. D. Rance K. A. 2004. A melanocortin-4 receptor (MC4R) polymorphism is associated with performance traits in divergently selected Large White pig populations. Anim. Genet.  35: 386– 390. https://doi.org/15373742 Google Scholar CrossRef Search ADS PubMed  Ishikawa A. Hatada S. Nagamine Y. Namikawa T. 2005. Further mapping of quantitative trait loci for postnatal growth in an intersubspecific backcross of wild Mus musculus castaneus and C57BL/6J mice. Genet. Res.  85: 127– 137. https://doi.org/16174331 Google Scholar CrossRef Search ADS PubMed  Jeon J. T. Carlborg Ö. Törnsten A. Giuffra E. Amarger V. Chardon P. Andersson-Eklund L. Andersson K. Hansson I. Lundström K. Andersson L. 1999. A paternally expressed QTL affecting skeletal and cardiac muscle mass in pigs maps to the IGF2 locus. Nat. Genet.  21: 157– 158. https://doi.org/9988263 Google Scholar CrossRef Search ADS PubMed  Johnson D. W. Qumsiyeh M. Benkhalifa M. Douglas D. A. 1995. Assignment of human transforming growth-factor-β type-I and type-III receptor genes (TGFBR1 and TGFBR3) to 9q33-q34 and 1p32-p33, respectively. Genomics  28: 356– 357. https://doi.org/8530052 Google Scholar CrossRef Search ADS PubMed  Kadarmideen H. N. 2008. Biochemical, ECF18R, and RYR1 gene polymorphisms and their associations with osteochondral diseases and production traits in pigs. Biochem. Genet.  46: 41– 53. https://doi.org/17943437 Google Scholar CrossRef Search ADS PubMed  Karlskov-Mortensen P. Bruun C. S. Braunschweig M. H. Sawera M. Markljung E. Enfält A. C. Hedebro-Velander I. Josell Å. Lindahl G. Lundström K. von Seth G. Jørgensen C. B. Andersson L. Fredholm M. 2006. Genome-wide identification of quantitative trait loci in a cross between Hampshire and Landrace I: Carcass traits. Anim. Genet.  37: 156– 162. https://doi.org/16573530 Google Scholar CrossRef Search ADS PubMed  Kim C. W. Hong Y. H. Yun S. I. Lee S. R. Kim Y. H. Kim M. S. Chung K. H. Jung W. Y. Kwon E. J. Hwang S. S. Park D. H. Cho K. K. Lee J. G. Kim B. W. Kim J. W. Kang Y. S. Yeo J. S. Chang K. T. 2006. Use of microsatellite markers to detect quantitative trait loci in Yorkshire pigs. J. Reprod. Dev.  52: 229– 237. https://doi.org/16415521 Google Scholar CrossRef Search ADS PubMed  Kim J. J. Zhao H. H. Thomsen H. Rothschild M. F. Dekkers J. C. M. 2005. Combined line-cross and half-sib QTL analysis of crosses between outbred lines. Genet. Res.  85: 235– 248. https://doi.org/16174342 Google Scholar CrossRef Search ADS PubMed  Kim K. S. Larsen N. Short T. Plastow G. Rothschild M. F. 2000. A missense variant of the porcine melanocortin-4 receptor (MC4R) gene is associated with fatness, growth, and feed intake traits. Mamm. Genome  11: 131– 135. https://doi.org/10656927 Google Scholar CrossRef Search ADS PubMed  Knott S. A. Nyström P. E. Andersson-Eklund L. Stern S. Marklund L. Andersson L. Haley C. S. 2002. Approaches to interval mapping of QTL in a multigeneration pedigree: The example of porcine chromosome 4. Anim. Genet.  33: 26– 32. https://doi.org/11849134 Google Scholar CrossRef Search ADS PubMed  Kopečný M. Stratil A. Bartenschlager H. Peelman L. J. Van Poucke M. Geldermann H. 2002. Linkage and radiation hybrid mapping of the porcine IGF1R and TPM2 genes to chromosome 1. Anim. Genet.  33: 398– 400. https://doi.org/12354161 Google Scholar CrossRef Search ADS PubMed  Landgraf S. Susenbeth A. Knap P. W. Looft H. Plastow G. Kalm E. Roehe R. 2006a. Allometric association between in vivo estimation of body composition during growth using deuterium dilution technique and chemical analysis of serial slaughtered pigs. Anim. Sci.  82: 223– 231. Landgraf S. Susenbeth A. Knap P. W. Looft H. Plastow G. Kalm E. Roehe R. 2006b. Developments of carcass cuts, organs, body tissues and chemical body composition during growth of pigs. Anim. Sci.  82: 889– 899. Google Scholar CrossRef Search ADS   Lee S. S. Chen Y. Moran C. Cepica S. Reiner G. Bartenschlager H. Moser G. Geldermann H. 2003. Linkage and QTL mapping for Sus scrofa chromosome 2. J. Anim. Breed. Genet.  120( Suppl. 1): 11– 19. Google Scholar CrossRef Search ADS   Liu G. Jennen D. G. J. Tholen E. Juengst H. Kleinwächter T. Hölker M. Tesfaye D. Ün G. Schreinemachers H. J. Murani E. Ponsuksili S. Kim J. J. Schellander K. Wimmers K. 2007. A genome scan reveals QTL for growth, fatness, leanness and meat quality in a Duroc-Pietrain resource population. Anim. Genet.  38: 241– 252. https://doi.org/17459017 Google Scholar CrossRef Search ADS PubMed  Malek M. Dekkers J. C. M. Lee H. K. Baas T. J. Prusa K. Huff-Lonergan E. Rothschild M. F. 2001a. A molecular genome scan analysis to identify chromosomal regions influencing economic traits in the pig. II. Meat and muscle composition. Mamm. Genome  12: 637– 645. https://doi.org/11471059 Google Scholar CrossRef Search ADS   Malek M. Dekkers J. C. M. Lee H. K. Baas T. J. Rothschild M. F. 2001b. A molecular genome scan analysis to identify chromosomal regions influencing economic traits in the pig. I. Growth and body composition. Mamm. Genome  12: 630– 636. https://doi.org/11471058 Google Scholar CrossRef Search ADS   Marklund L. Nystrom P. E. Stern S. Andersson-Eklund L. Andersson L. 1999. Confirmed quantitative trait loci for fatness and growth on pig chromosome 4. Heredity  82: 134– 141. https://doi.org/10098263 Google Scholar CrossRef Search ADS PubMed  Meidtner K. Wermter A. K. Hinney A. Remschmidt H. Hebebrand J. Fries R. 2006. Association of the melanocortin 4 receptor with feed intake and daily gain in F2 Mangalitsa × Pietrain pigs. Anim. Genet.  37: 245– 247. https://doi.org/16734684 Google Scholar CrossRef Search ADS PubMed  Milan D. Bidanel J. P. Iannuccelli N. Riquet J. Amigues Y. Gruand J. Le Roy P. Renard C. Chevalet C. 2002. Detection of quantitative trait loci for carcass composition traits in pigs. Genet. Sel. Evol.  34: 705– 728. https://doi.org/12486399 Google Scholar CrossRef Search ADS PubMed  Mohrmann M. Roehe R. Knap P. W. Looft H. Plastow G. S. Kalm E. 2006a. Quantitative trait loci associated with AutoFOM grading characteristics, carcass cuts and chemical body composition during growth of Sus scrofa. Anim. Genet.  37: 435– 443. https://doi.org/16978171 Google Scholar CrossRef Search ADS   Mohrmann M. Roehe R. Susenbeth A. Baulain U. Knap P. W. Looft H. Plastow G. S. Kalm E. 2006b. Association between body composition of growing pigs determined by magnetic resonance imaging, deuterium dilution technique and chemical analysis. Meat Sci.  72: 518– 531. Google Scholar CrossRef Search ADS   Myakishev, M. V., G. I. Kapanadze, G. O. Shaikhayev, G. P. Georgiev, and D. R. Beritashvili 1995. Extraction of DNA from the Whole Blood by Silica Gel. Inst.  Gene Biol., Moscow, Russia. Nezer C. Moreau L. Brouwers B. Coppieters W. Detilleux J. Hanset R. Karim L. Kvasz A. Leroy P. Georges M. 1999. An imprinted QTL with major effect on muscle mass and fat deposition maps to the IGF2 locus in pigs. Nat. Genet.  21: 155– 156. https://doi.org/9988262 Google Scholar CrossRef Search ADS PubMed  Nezer C. Moreau L. Wagenaar D. Georges M. 2002. Results of a whole genome scan targeting QTL for growth and carcass traits in a Pietrain × Large White intercross. Genet. Sel. Evol.  34: 371– 387. https://doi.org/12081803 Google Scholar CrossRef Search ADS PubMed  Noguera, J. L., M. C. Rodriguez, L. Varona, A. Tomas, G. Munoz, O. Ramirez, C. Barragan, M. Arque, J. P. Bidanel, M. Amills, C. Ovilo, and A. Sanchez 2006. Epistasis is a fundamental component of the genetic architecture of prolificacy in pigs.  Proc. 8th World Congr. Genet. Appl. Livest. Prod., Belo Horizonte, Brazil. CD-ROM Commun. No. 11-06. Ovilo C. Clop A. Noguera J. L. Oliver M. A. Barragán C. Rodríguez C. Silió L. Toro M. A. Coll A. Folch J. M. Sánchez A. Babot D. Varona L. Pérez-Enciso M. 2002. Quantitative trait locus mapping for meat quality traits in an Iberian × Landrace F2 pig population. J. Anim. Sci.  80: 2801– 2808. https://doi.org/12462246 Google Scholar CrossRef Search ADS PubMed  Park H. B. Carlborg Ö. Marklund S. Andersson L. 2002. Melanocortin-4 receptor (MC4R) genotypes have no major effect on fatness in a Large White × Wild Boar intercross. Anim. Genet.  33: 155– 157. https://doi.org/12047230 Google Scholar CrossRef Search ADS PubMed  Pérez-Enciso M. Clop A. Noguera J. L. Óvilo C. Coll A. Folch J. M. Babot D. Estany J. Oliver M. A. Díaz I. Sánchez A. 2000. A QTL on pig chromosome 4 affects fatty acid metabolism: Evidence from an Iberian by Landrace intercross. J. Anim. Sci.  78: 2525– 2531. https://doi.org/11048916 Google Scholar CrossRef Search ADS PubMed  Pérez-Enciso M. Misztal I. 2004. Qxpak: A versatile mixed model application for genetical genomics and QTL analyses. Bioinformatics  20: 2792– 2798. https://doi.org/15166025 Google Scholar CrossRef Search ADS PubMed  Ponsuksili S. Chomdej S. Murani E. Blaser U. Schreinemachers H. J. Schellander K. Wimmers K. 2005. SNP detection and genetic mapping of porcine genes encoding enzymes in hepatic metabolic pathways and evaluation of linkage with carcass traits. Anim. Genet.  36: 477– 483. https://doi.org/16293120 Google Scholar PubMed  Quintanilla R. Milan D. Bidanel J. P. 2002. A further look at quantitative trait loci affecting growth and fatness in a cross between Meishan and Large White pig populations. Genet. Sel. Evol.  34: 193– 210. https://doi.org/12081807 Google Scholar CrossRef Search ADS PubMed  Rodríguez C. Tomás A. Alves E. Ramirez O. Arqué M. Muñoz G. Barragán C. Varona L. Silió L. Amills M. Noguera J. L. 2005. QTL mapping for teat number in an Iberian-by-Meishan pig intercross. Anim. Genet.  36: 490– 496. https://doi.org/16293122 Google Scholar PubMed  Rohrer G. A. 2000. Identification of quantitative trait loci affecting birth characters and accumulation of backfat and weight in a Meishan-White Composite resource population. J. Anim. Sci.  78: 2547– 2553. https://doi.org/11048919 Google Scholar CrossRef Search ADS PubMed  Rohrer G. A. Alexander L. J. Hu Z. L. Smith T. P. L. Keele J. W. Beattie C. W. 1996. A comprehensive map of the porcine genome. Genome Res.  6: 371– 391. https://doi.org/8743988 Google Scholar CrossRef Search ADS PubMed  Rohrer G. A. Keele J. W. 1998a. Identification of quantitative trait loci affecting carcass composition in swine: I. Fat deposition traits. J. Anim. Sci.  76: 2247– 2254. https://doi.org/9781479 Google Scholar CrossRef Search ADS   Rohrer G. A. Keele J. W. 1998b. Identification of quantitative trait loci affecting carcass composition in swine: II. Muscling and wholesale product yield traits. J. Anim. Sci.  76: 2255– 2262. https://doi.org/9781480 Google Scholar CrossRef Search ADS   Rohrer G. A. Thallman R. M. Shackelford S. Wheeler T. Koohmaraie M. 2005. A genome scan for loci affecting pork quality in a Duroc-Landrace F2 population. Anim. Genet.  37: 17– 27. Google Scholar CrossRef Search ADS   Routman E. J. Cheverud J. M. 1997. Gene effects on a quantitative trait: Two-locus epistatic effects measured at microsatellite markers and at estimated QTL. Evolution  51: 1654– 1662. Google Scholar CrossRef Search ADS PubMed  Sanchez M. P. Riquet J. Iannuccelli N. Gogué J. Billon Y. Demeure O. Caritez J. C. Burgaud G. Fève K. Bonnet M. Péry C. Lagant H. Le Roy P. Bidanel J. P. Milan D. 2006. Effects of quantitative trait loci on chromosomes 1, 2, 4, and 7 on growth, carcass, and meat quality traits in backcross Meishan × Large White pigs. J. Anim. Sci.  84: 526– 537. https://doi.org/16478944 Google Scholar CrossRef Search ADS PubMed  Sato S. Oyamada Y. Atsuji K. Nade T. Sato S. Kobayashi E. Mitsuhashi T. Nirasawa K. Komatsuda A. Saito Y. Terai S. Hayashi T. Sugimoto Y. 2003. Quantitative trait loci analysis for growth and carcass traits in a Meishan × Duroc F2 resource population. J. Anim. Sci.  81: 2938– 2949. https://doi.org/14677848 Google Scholar CrossRef Search ADS PubMed  Schweiger M. Steffl M. Amselgruber W. M. 2005. Cell-type specific expression of IGF-1R in porcine islet cells. Growth Horm. IGF Res.  15: 33– 38. https://doi.org/15701570 Google Scholar CrossRef Search ADS PubMed  Sullivan T. Escalante-Alcalde D. Bhatt H. Anver M. Bhat N. Nagashima K. Stewart C. L. Burke B. 1999. Loss of a-type lamin expression compromises nuclear envelope integrity leading to muscular dystrophy. J. Cell Biol.  147: 913– 920. https://doi.org/10579712 Google Scholar CrossRef Search ADS PubMed  Szyda, J., and E. Grindflek, I. S. Wideroe, and S. Lien 2006. Search for linked QTL and genetic epistasis in swine with application of the false discovery rate.  Proc. 8th World Congr. Genet. Appl. Livest. Prod., Belo Horizonte, Brazil. CD-ROM Commun. No. 20-02. Van Laere A. S. Nguyen M. Braunschweig M. Nezer C. Collette C. Moreau L. Archibald A. L. Haley C. S. Buys N. Tally M. Andersson G. Georges M. Andersson L. 2003. A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature  425: 832– 836. https://doi.org/14574411 Google Scholar CrossRef Search ADS PubMed  van Wijk H. J. Dibbits B. Baron E. E. Brings A. D. Harlizius B. Groenen M. A. M. Knol E. F. Bovenhuis H. 2006. Identification of quantitative trait loci for carcass composition and pork quality traits in a commercial finishing cross. J. Anim. Sci.  84: 789– 799. https://doi.org/16543555 Google Scholar CrossRef Search ADS PubMed  Varona L. Ovilo C. Clop A. Noguera J. L. Pérez-Enciso M. Coll A. Folch J. M. Barragán C. Toro M. A. Babot D. Sánchez A. 2002. QTL mapping for growth and carcass traits in an Iberian by Landrace pig intercross: Additive, dominant and epistatic effects. Genet. Res.  80: 145– 154. https://doi.org/12534217 Google Scholar CrossRef Search ADS PubMed  Wagenknecht D. Bartenschlager H. Van Poucke M. Geldermann H. Peelman L. J. Majzlik I. Stratil A. 2005. Linkage and radiation hybrid mapping of the porcine MPZ gene to chromosome 4q. Anim. Genet.  36: 181– 182. https://doi.org/15771742 Google Scholar CrossRef Search ADS PubMed  Wagenknecht D. Stratil A. Bartenschlager H. Van Poucke M. Peelman L. J. Majzlik I. Geldermann H. 2003. Linkage and radiation hybrid mapping of the porcine MEF2D gene to chromosome 4q. Anim. Genet.  34: 232– 233. https://doi.org/12755827 Google Scholar CrossRef Search ADS PubMed  Wagenknecht D. Stratil A. Bartenschlager H. Van Poucke M. Peelman L. J. Majzlik I. Geldermann H. 2006. SNP identification, linkage and radiation hybrid mapping of the porcine lamin A/C (LMNA) gene to chromosome 4q. J. Anim. Breed. Genet.  123: 280– 283. https://doi.org/16882095 Google Scholar CrossRef Search ADS PubMed  Wang Y. F. Yu M. Te Pas M. F. W. Yerle M. Liu B. Fan B. Xiong T. A. Li K. 2004. Sequence characterization, polymorphism and chromosomal localizations of the porcine PSME1 and PSME2 genes. Anim. Genet.  35: 361– 366. https://doi.org/15373739 Google Scholar CrossRef Search ADS PubMed  Wolf J. B. Pomp D. Eisen E. J. Cheverud J. M. Leamy L. J. 2006. The contribution of epistatic pleiotropy to the genetic architecture of covariation among polygenic traits in mice. Evol. Dev.  8: 468– 476. https://doi.org/16925682 Google Scholar CrossRef Search ADS PubMed  Yi N. J. Chiu S. Allison D. B. Fisler J. S. Warden C. H. 2004a. Epistatic interaction between two nonstructural loci on chromosomes 7 and 3 influences hepatic lipase activity in BSB mice. J. Lipid Res.  45: 2063– 2070. https://doi.org/15314098 Google Scholar CrossRef Search ADS   Yi N. J. Diament A. Chiu S. Kim K. Allison D. B. Fisler J. S. Warden C. H. 2004b. Characterization of epistasis influencing complex spontaneous obesity in the BSB model. Genetics  167: 399– 409. https://doi.org/15166164 Google Scholar CrossRef Search ADS   Yi N. J. Zinniel D. K. Kim K. Eisen E. J. Bartolucci A. Allison D. B. Pomp D. 2006. Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice. Genet. Res.  87: 45– 60. https://doi.org/16545150 Google Scholar CrossRef Search ADS PubMed  Yu J. Liu B. Fan B. Zhu M. J. Xiong T. A. Yu M. Li K. Zhao S. H. 2005. The porcine FBXO32 gene: Map assignment, SNP detection and tissue expression. Anim. Genet.  36: 451– 452. https://doi.org/16167995 Google Scholar CrossRef Search ADS PubMed  Yu M. Geiger B. Deeb N. Rothschild M. F. 2007. Investigation of TXNIP (thioredoxin-interacting protein) and TRX (thioredoxin) genes for growth-related traits in pigs. Mamm. Genome  18: 197– 209. https://doi.org/17406940 Google Scholar CrossRef Search ADS PubMed  Yue G. Stratil A. Cepica S. Schröffel J. Schröffelova D. Fontanesi L. Cagnazzo M. Moser G. Bartenschlager H. Reiner G. Geldermann H. 2003a. Linkage and QTL mapping for Sus scrofa chromosome 7. J. Anim. Breed. Genet.  120( Suppl. 1): 56– 65. Google Scholar CrossRef Search ADS   Yue G. Stratil A. Kopecny M. Schröffelova D. Schröffel J. Hojný J. Cepica S. Davoli R. Zambonelli P. Brunsch C. Sternstein I. Moser G. Bartenschlager H. Reiner G. Geldermann H. 2003b. Linkage and QTL mapping for Sus scrofa chromosome 6. J. Anim. Breed. Genet.  120( Suppl. 1): 45– 55. Google Scholar CrossRef Search ADS   Zhu Z. M. Zhang J. B. Li K. Zhao S. H. 2005. Cloning, mapping and association study with carcass traits of the porcine SDHD gene. Anim. Genet.  36: 191– 195. https://doi.org/15932396 Google Scholar CrossRef Search ADS PubMed  American Society of Animal Science

Fontanesi, L.;Speroni, C.;Buttazzoni, L.;Scotti, E.;Dall'Olio, S.;Costa, L. Nanni;Davoli, R.;Russo, V.

doi: 10.2527/jas.2009-2560pmid: 20382874

ABSTRACT The objective of this study was to evaluate the effects of mutations in 2 genes [IGF2 and cathepsin D (CTSD)] that map on the telomeric end of the p arm of SSC2. In this region, an imprinted QTL affecting muscle mass and fat deposition was reported, and the IGF2 intron3-g.3072G>A substitution was identified as the causative mutation. In the same chromosome region, we assigned, by linkage mapping, the CTSD gene, a lysosomal proteinase, for which we previously identified an SNP in the 3′-untranslated region (AM933484, g.70G>A). We have already shown strong effects of this CTSD mutation on several production traits in Italian Large White pigs, suggesting a possible independent role of this marker in fatness and meat deposition in pigs. To evaluate this hypothesis, after having refined the map position of the CTSD gene by radiation hybrid mapping, we analyzed the IGF2 and the CTSD polymorphisms in 270 Italian Large White and 311 Italian Duroc pigs, for which EBV and random residuals from fixed models were calculated for several traits. Different association analyses were carried out to distinguish the effects of the 2 close markers. In the Italian Large White pigs, the results for IGF2 were highly significant for all traits when using either EBV or random residuals (e.g., using EBV: lean cuts, P = 2.2 × 10−18; ADG, P = 2.6 × 10−16; backfat thickness, P = 2.2 × 10−9; feed:gain ratio, P = 2.3 × 10−9; ham weight, P = 1.5 × 10−6). No effect was observed for meat quality traits. The IGF2 intron3-g.3072G>A mutation did not show any association in the Italian Duroc pigs, probably because of the small variability at this polymorphic site for this breed. However, a significant association was evident for the CTSD marker (P < 0.001) with EBV of all carcass and production traits in Italian Duroc pigs (lean content, ADG, backfat thickness, feed:gain ratio) after excluding possible confounding effects of the IGF2 mutation. The effects of the CTSD g.70G>A mutation were also confirmed in a subset of Italian Large White animals carrying the homozygous genotype IGF2 intron3-g.3072GG, and by haplotype analysis between the markers of the 2 considered genes in the complete data set. Overall, these results indicate that the IGF2 intron3-g.3072G>A mutation is not the only polymorphism affecting fatness and muscle deposition on SSC2p. Therefore, the CTSD g.70G>A polymorphism could be used to increase selection efficiency in marker-assisted selection programs that already use the IGF2 mutation. However, for practical applications, because the CTSD gene should not be imprinted (we obtained this information from expression analysis in adult skeletal muscle), the different modes of inheritance of the 2 genes have to be considered. INTRODUCTION An imprinted QTL with paternal expression affecting muscle mass deposition, carcass fatness, and heart size has been identified in the telomeric end of the p arm of SSC2 (Jeon et al., 1999; Nezer et al., 1999). The presence of QTL in this region was subsequently reported in other studies, confirming imprinted effects in some cases and Mendelian inheritance in other cases (de Koning et al., 2000; Rattink et al., 2000; Evans et al., 2003; Lee et al., 2003; Jungerius et al., 2004; Thomsen et al., 2004; Vidal et al., 2005; Sanchez et al., 2006; van Wijk et al., 2006; Liu et al., 2007, 2008; Tribout et al., 2008). The causative mutation of the paternally expressed QTL was identified in a highly conserved regulatory region of intron 3 of the IGF2 gene (Van Laere et al., 2003). The IGF2 intron3-g.3072G>A substitution disrupts a repressor nuclear factor binding site, causing a 3-fold overexpression of postnatal skeletal muscle IGF2 mRNA in pigs inheriting the mutation from their sires, leading to increased muscle mass and, in turn, reduced backfat deposition (Van Laere et al., 2003). Recently, we assigned, by linkage mapping, the cathepsin D (CTSD) gene on SSC2 close to the IGF2 region and showed that an SNP (g.70G>A, AM933484) in the 3′-untranslated region (UTR) was strongly associated with several carcass and production traits in a sib-tested Italian Large White population (Russo et al., 2008). Because the IGF2 intron3-g.3072G>A substitution was not analyzed in that study, we could not clearly state whether the effects of the CTSD marker were due to linkage disequilibrium with the IGF2 quantitative trait nucleotide. To clarify this issue, we investigated the association between both IGF2 intron3-g.3072G>A and CTSD g.70G>A polymorphisms and meat, carcass, and production traits in Italian Large White and Italian Duroc pigs. MATERIALS AND METHODS All procedures described were in compliance with Italian and European Union regulations for animal care and slaughter. Animals Two groups of pigs were included in the association analysis between DNA markers and EBV or random residuals (RR) from fixed models for production and carcass traits and phenotypic measures for meat quality traits. The first group was made up of 270 sib-tested Italian Large White pigs (179 females and 91 castrated males from 79 different sires) already described by Russo et al. (2008). From this group that originally included 272 pigs, 2 pigs were excluded, 1 because it was heterozygous at the ryanodine receptor 1 (RYR1) g.1843C>T polymorphic site (Fuji et al., 1991) and another because it was not possible to obtain a genotype for the IGF2 intron3-g.3072G>A mutation. The second group of pigs was composed of 297 sib-tested Italian Duroc pigs (187 females and 110 castrated males from 135 different sires) slaughtered from 1995 to 2003. The Italian herdbook routinely conducts on-station tests on triplets of littermates (2 females and 1 castrated male). Siblings are performance tested and slaughtered for the genetic evaluation of a boar from the same litter. In addition, a panel of unrelated pigs of different breeds (Table 1), for which no EBV or phenotypic measures were available, was used for allele frequency evaluation. Table 1. Allele frequencies at the IGF2 intron3-g.3072G>A and cathepsin D (CTSD) g.70G>A polymorphisms in different pig breeds Breed1  IGF2  CTSD  No. of pigs  g.3072G  g.3072A  No. of pigs  g.70G  g.70A  Italian Large White  270  0.419  0.581  270  0.081  0.919  Italian Duroc  297  0.025  0.975  297  0.077  0.923  Italian Landrace  29  0.914  0.086  23  0.065  0.935  Cinta Senese  20  0.975  0.025  20  0.025  0.975  Casertana  17  1.000  0.000  17  0.000  1.000  Nero Siciliano  21  0.881  0.119  18  0.000  1.000  Breed1  IGF2  CTSD  No. of pigs  g.3072G  g.3072A  No. of pigs  g.70G  g.70A  Italian Large White  270  0.419  0.581  270  0.081  0.919  Italian Duroc  297  0.025  0.975  297  0.077  0.923  Italian Landrace  29  0.914  0.086  23  0.065  0.935  Cinta Senese  20  0.975  0.025  20  0.025  0.975  Casertana  17  1.000  0.000  17  0.000  1.000  Nero Siciliano  21  0.881  0.119  18  0.000  1.000  1The Italian Large White and Italian Duroc pigs were the same animals as used in the association analyses. All these pigs had genotype CC at the g.1843C>T polymorphic site of the ryanodine receptor 1 (RYR1) gene. Allele frequencies of CTSD for the Italian Landrace breed have been reported in Russo et al. (2008). View Large Table 1. Allele frequencies at the IGF2 intron3-g.3072G>A and cathepsin D (CTSD) g.70G>A polymorphisms in different pig breeds Breed1  IGF2  CTSD  No. of pigs  g.3072G  g.3072A  No. of pigs  g.70G  g.70A  Italian Large White  270  0.419  0.581  270  0.081  0.919  Italian Duroc  297  0.025  0.975  297  0.077  0.923  Italian Landrace  29  0.914  0.086  23  0.065  0.935  Cinta Senese  20  0.975  0.025  20  0.025  0.975  Casertana  17  1.000  0.000  17  0.000  1.000  Nero Siciliano  21  0.881  0.119  18  0.000  1.000  Breed1  IGF2  CTSD  No. of pigs  g.3072G  g.3072A  No. of pigs  g.70G  g.70A  Italian Large White  270  0.419  0.581  270  0.081  0.919  Italian Duroc  297  0.025  0.975  297  0.077  0.923  Italian Landrace  29  0.914  0.086  23  0.065  0.935  Cinta Senese  20  0.975  0.025  20  0.025  0.975  Casertana  17  1.000  0.000  17  0.000  1.000  Nero Siciliano  21  0.881  0.119  18  0.000  1.000  1The Italian Large White and Italian Duroc pigs were the same animals as used in the association analyses. All these pigs had genotype CC at the g.1843C>T polymorphic site of the ryanodine receptor 1 (RYR1) gene. Allele frequencies of CTSD for the Italian Landrace breed have been reported in Russo et al. (2008). View Large Performance Test, and Carcass and Meat Quality Traits The 2 groups of tested pigs used for the association study were performance tested at the test station of the National Association of Pig Breeders. The test period begins when piglets are 30 to 45 d old and ends when they reach 155 ± 5 kg of BW. The nutritive level was quasi ad libitum, meaning that about 60% of pigs were able to ingest the entire supplied ration. Feed intake was recorded daily and BW was measured every 2 wk, and then daily BW gain and the feed:gain ratio (FGR) were calculated. At the end of test, selected animals from 2 contiguous batches on trial were mixed at loading and transported to a commercial abattoir located at 24.5 km away from the test station. After unloading, pigs were immediately stunned by CO2 (concentration 87%) using a dip lift system (Butina, Holbaek, Denmark) and killed by exsaguination in a lying position. Within 3 h postmortem at the abattoir, backfat thickness (BFT) at the musculus gluteus medius, weight of lean cuts (LC, necks and loins), and ham weights (HW, in Italian Large White pigs only) were measured. Only for Italian Large White pigs, measures of pH at 2 h postmortem; pH at 24 h postmortem; glycolytic potential, including glycogen and lactate content (30 min postmortem); and cathepsin B activity (24 h postmortem) were obtained on musculus semimembranosus, as described previously (Fontanesi et al., 2008; Russo et al., 2008). Analysis of DNA Markers The DNA samples were extracted from blood, lyophilized muscle samples, or hair roots by using standard protocols (Sambrook et al., 1989). The PCR primers designed for analysis of the IGF2 intron3-g.3072G>A substitution were forward, 5′-GACCGAGCCAGGGACGAG-3′; reverse, 5′-CGCGCCCCACGCGCTCCCACGCTG-3′. The underlined base in the IGF2 reverse primer is a mismatched nucleotide inserted to create an artificial restriction site for AdeI (CACNNNGTG). The PCR primers designed for the analysis of the CTSD g.70G>A polymorphism were forward, 5′-GCTGTGCACCCTAGGAACC-3′; reverse, 5′-TCGTCAGGTCCAGGACAAAC-3′ (Russo et al., 2008). Polymerase chain reaction was carried out using a PT-100 thermal cycler (MJ Research, Watertown, MA) in a final volume of 20 μL that included 10 pmol of each primer, 2.5 mM of MgSO4 (IGF2) or 2.5 mM of MgCl2 (CTSD), 2.5 mM each deoxynucleotide 5′-triphosphate, 1 U of EuroTaq DNA polymerase (EuroClone Ltd., Paington, Devon, UK), 0.3× PCRx Enhancer Solution (PCRx Enhancer System, Invitrogen, Carlsbad, CA) only for IGF2 and 1× PCRx AmpBuffer (PCRx Enhancer System, Invitrogen; IGF2) or 1× EuroTaq amplification buffer (CTSD). The PCR profiles were as follows: an initial step of denaturation for 5 min at 95°C; 35 cycles of 30 s at 95°C, 30 s at 62°C (IGF2) or 59°C (CTSD), and 30 s at 72°C; the final extension step was for 5 min at 72°C. Mutations were analyzed by PCR-RFLP. Digestion of the IGF2 amplified fragment of 85 bp with AdeI (Fermentas, Vilnius, Lithuania; 5 units in a 25-μL reaction volume containing 5 μL of PCR product and 1× enzyme buffer at 37°C overnight) resulted in 2 fragments of 65 and 20 bp when the g.3072A was present. No digestion occurred in the g.3072G allele. Digestion of the CTSD amplicon (184 bp) was obtained with MscI (Fermentas) using the same reaction conditions reported above. The CTSD g.70G allele was not cut (184 bp), whereas the g.70A allele resulted in 2 fragments of 117 and 67 bp. The PCR-RFLP products were resolved on 10% polyacrylamide:bis-acrylamide 29:1 gels stained with ethidium bromide. Radiation Hybrid Mapping of CTSD The INRA-Minnesota 7,000-rad radiation hybrid (RH) panel (Yerle et al., 1998), consisting of 118 rodent-porcine hybrid cell lines, was screened by means of PCR using the same CTSD primers and PCR conditions reported above. No PCR fragment was obtained from the control rodent genomic DNA. The PCR reactions were visualized on 10% polyacrylamide:bis-acrylamide 29:1 or 2% agarose gels. The results of RH PCR products were analyzed with the INRA-Minnesota 7,000-rad RH panel mapping tool (accessible through http://imprh.toulouse.inra.fr/; Milan et al., 2000). Expression Analysis of the CTSD g.70G>A Alleles Samples of LM were collected at the abattoir from 270 sib-tested Italian Large White pigs (the same animals used in the association study), immediately frozen in liquid N, and stored at −80°C. Total RNA was extracted from 19 muscle samples of pigs heterozygous at the CTSD polymorphic site (8 with the GG genotype and 11 with the GA genotype at the IGF2 mutated site) by using the SV Total RNA Isolation System kit (Promega Corporation, Madison, WI) according to the instructions of the manufacturer. The RNA was treated with DNase I using a RNase-Free DNase Set (Qiagen, Hilden, Germany) as recommended by the manufacturer. The DNase-treated RNA was checked for genomic DNA contamination amplifying a fragment of another gene whose primers were designed on intronic regions (data not shown). From each sample, approximately 1 μg of total RNA was reverse transcribed using an ImProm-II Reverse Transcription System kit (Promega Corporation), as reported in the manual supplied by the manufacturer. The PCR products of 320 bp obtained with primers designed in the 3′-UTR of CTSD (forward: 5′-CTGTTCTGTTCCGTGGTGTC-3′; reverse: 5′-CCTTCCTCGTCAGGTCCA-3′) with the same PCR conditions reported above were then sequenced using the same PCR primers and the Big Dye v3.1 cycle sequencing kit (Applied Biosystems, Foster City, CA). Sequencing reactions, after EDTA, 100% ethanol, and 70% ethanol precipitations, were loaded on an ABI3100 Avant sequencer (Applied Biosystems). Sequencing electropherograms were read with Sequencing Analysis software v. 3.7 (Applied Biosystems). In addition, CTSD PCR products were obtained from genomic DNA of 6 heterozygous g.70GA pigs and sequenced as indicated above. These sequencing reactions represented the reference samples with a natural 50:50 ratio of the 2 alleles. Evaluation of possible preferential expression of the 2 CTSD alleles in adult skeletal muscle was obtained as a normalized ratio between the sequencing peak height of the 2 bases at the polymorphic site compared with the reference samples, as described in Fontanesi et al. (2009). Statistical Analyses The properties of EBV in association studies with DNA markers have been evaluated in several reports, mainly dealing with QTL mapping and association studies in dairy cattle, suggesting a smaller, or at best equivalent, power in using EBV as compared with phenotypic measurements (Thomsen et al., 2001; Israel and Weller, 2002; Viitala et al., 2003; Daetwyler et al., 2008). The use of EBV is convenient in commercial pig breeding stocks because they are routinely calculated by the company or the herdbook. However, depending on the structure of the pig population under investigation, the use of EBV could lead to underestimation of QTL effects (Tribout et al., 2008). An alternative to the use of EBV is the use of phenotypic data. However, to remove the effects of “noisy” environmental fixed factors, RR from analyses of phenotypic data by fixed GLM could be used instead. Because fixed models are capable of removing only the effects of known environmental sources of variation, RR will include all genetic factors (without any assumption regarding their mode of inheritance), permanent environmental factors, and unknown environmental factors together with measurement errors. The EBV for ADG (expressed in grams), LC (expressed in kilograms), BFT (expressed in millimeters), and FGR were calculated for both groups of sib-tested Italian pigs. The EBV for HW (expressed in kilograms) was calculated for Italian Large White pigs only. The EBV were calculated using a BLUP multiple-trait animal model with different models for each trait. Depending on the trait, models included the fixed effects of sex, batch on trial, inbreeding coefficient of the animal, the interaction of sex × age at slaughter, slaughter date, and the random effects of litter and animal. The RR were calculated for all considered traits in the 2 sib-tested pig populations (ADG, LC, BFT, FGR, and HW in Italian Large White; ADG, LC, BFT, and FGR in Italian Duroc). The RR were obtained by using linear fixed models including the same factors used for each trait in the BLUP multiple-trait animal model. Because DNA of the parent animals was not available, it was not possible to evaluate the origin of the alleles in the pigs of these 2 groups and to design models including imprinting effects. However, other studies that fitted Mendelian models could identify associations between imprinted genes and phenotypes (Nezer et al., 1999; Cheng et al., 2008). Association analyses were carried out independently for the 2 groups of sib-tested Italian pigs. Associations between DNA markers and EBV or RR were assessed by using the GLM procedure (SAS Inst. Inc. Cary, NC). The models included only the fixed effects of individual marker genotypes. All other factors contributing to variability of the investigated traits were already considered in the calculation of EBV or RR. For meat quality traits, the MIXED procedure of SAS was applied to a model that included slaughter date (6 different days at the same abattoir), sex, and the IGF2 intron3-g.3072G>A genotype as fixed effects and the sire as random effect. When these models were used, different levels of analysis were considered to isolate the effects of the IGF2 and CTSD markers: The IGF2 intron3-g.3072G>A polymorphism was analyzed in the Italian Large White pigs (n = 270), considering all 3 genotypes (model 1). To eliminate possible confounding caused by the imprinting effect in the animals with a heterozygous genotype at the IGF2 locus (g.3072GA), the data were also elaborated by excluding this genotypic class and considering only the 2 homozygous genotypes. This was possible only in the Italian Large White group of pigs (n = 162), which presented enough homozygous genotypes (g.3072GG and g.3072AA) for this elaboration (model 2). In the Italian Duroc pigs (n = 297), an association analysis between the IGF2 genotypes and either EBV or RR was carried out, excluding the 2 homozygous g.3072GG animals or including them in the heterozygous class (g.3072GA). In the Italian Large White pigs (n = 270), an association analysis between the CTSD genotypes and either EBV or RR was carried out, excluding the 4 homozygous g.70GG animals or including them in the heterozygous class (g.70GA; results of the association analysis between EBV and the CTSD marker in the Italian Large White pigs have been described by Russo et al., 2008). In the Italian Duroc population tested, only 2 animals with the CTSD genotype g.70GG were detected. An association analysis between either EBV or RR and the CTSD marker genotypes in this breed was carried out using 2 genotypic classes: one composed of animals with the g.70GG + g.70GA (n = 2 + 42 animals) genotypes or only the g.70GA genotype (the 2 g.70GG animals were excluded), and another with the g.70AA genotype. In addition, an association analysis was carried out by including all animals (n = 297) or excluding pigs that were heterozygous at the IGF2 polymorphic site (n = 11), or by excluding the only animal that was heterozygous at both the CTSD and IGF2 markers. An association analysis with the CTSD marker genotypes in the Italian Large White pigs was carried out for the animals having genotype IGF2 intron3-g.3072GG (n = 59). In addition, haplotypes between the IGF2 and CTSD markers were inferred by using the PHASE program v. 2.0 (Stephens et al., 2001). Evaluation of the haplotype substitution effect on EBV and RR was obtained only in the Italian Large White population (n = 270), using the REG procedure of SAS with a model including the number (0, 1, 2) of the 3 haplotypes available. The additive genetic effect for the IGF2 genotypes in the Italian Large White population was estimated as one-half the difference between the EBV or RR of the 2 homozygous groups. The dominance effect at the IGF2 locus was estimated as the difference between the EBV or RR of the heterozygous group and the average of the EBV or RR of the 2 homozygous groups. Differences from zero of the estimates of additive and dominance effects were tested by t-test (Russo et al., 2008). RESULTS AND DISCUSSION Allele Frequencies Allele frequencies of the IGF2 and CTSD markers for the 2 sib-tested pig populations (Italian Large White and Italian Duroc breeds) are shown in Table 1. At the IGF2 locus, the Italian Large White pigs showed balanced allele frequencies (IGF2 intron3-g.3072G = 0.419; IGF2 intron3-g.3072A = 0.581), whereas in the Italian Duroc population, the wild-type g.3072G allele was quite rare (0.025), confirming data already reported for other Duroc populations (Vykoukalová et al., 2006; Yang et al., 2006; Ojeda et al., 2008). Eleven Italian Duroc pigs showed the IGF2 intron3-g.3072GA genotype and 2 had the intron3-g.3072GG genotype. In the Italian Landrace, Cinta Senese, Casertana, and Nero Siciliano breeds, the IGF2 intron3-g.3072G allele was fixed or almost fixed, partially confirming for the local breeds the results reported by Ojeda et al. (2008). A wide range of IGF2 allele frequencies was observed when comparing other Landrace populations (Ojeda et al., 2008). This might reflect different selection strategies and the use of paternal and maternal Landrace lines in crossbreeding schemes. In fact, the IGF2 intron3-g.3072A allele seems to have negative effects on reproductive performance of the sows caused by excessive leanness (Buys et al., 2006). In both the Italian Large White and Italian Duroc populations, allele g.70G of the CTSD gene was less frequent. In the other analyzed breeds, this allele was identified in only 1 Cinta Senese pig and in few Italian Landrace animals. RH Mapping of CTSD Linkage mapping of the porcine CTSD gene positioned this gene in the telomeric end of the p arm of SSC2 (Russo et al., 2008). However, we wanted to confirm and refine its position to better evaluate its closeness with IGF2. The RH mapping indicated that the closest marker obtained by 2-point analysis was Swc9 (distance = 24 centirads; logarithm of odds = 13.71; retention fraction = 27%), which is a microsatellite in the 3′-UTR of the porcine IGF2 gene (Nezer et al., 1999). The closeness between IGF2 and CTSD might pose some difficulties in dissecting the effects of both genes when using experimental populations in which artificial linkage disequilibrium is created when crossing divergent lines or breeds. This might be the origin of speculative conclusions in several studies that reported QTL in the SSC2p end and that were not conclusive in distinguishing the presence of different QTL in this region (de Koning et al., 2000; Rattink et al., 2000; Lee et al., 2003; Jungerius et al., 2004; Thomsen et al., 2004; Estellé et al., 2005; Vidal et al., 2005; Sanchez et al., 2006; Liu et al., 2007, 2008; Tribout et al., 2008). Commercial populations, in which it could be possible to identify recombinants between these 2 markers, might represent a suitable material to distinguish their effects. Assessment of Allele Specific Expression of the CTSD Gene in Skeletal Muscle Because the CTSD gene is located close to the imprinted IGF2 gene in the SSC2p region, where several reports have identified imprinted and nonimprinted QTL (Jeon et al., 1999; Nezer et al., 1999; de Koning et al., 2000; Rattink et al., 2000; Evans et al., 2003; Lee et al., 2003; Jungerius et al., 2004; Thomsen et al., 2004; Vidal et al., 2005; Sanchez et al., 2006; van Wijk et al., 2006; Liu et al., 2007, 2008; Tribout et al., 2008), we questioned whether allele-specific expression would have affected the CTSD gene. In theory, an imprinted gene would exhibit monoallelic expression (hemizygosity) in a parent-of-origin-specific manner, whereas a biallelically expressed gene (not imprinted) would exhibit heterozygosity at a polymorphic site. A significant allelic imbalance from the 50:50 ratio could be the cause of preferential expression (imprinted genes in which both alleles are expressed but where one is expressed more strongly than the other in a parent-of-origin-specific way) or differential allelic expression (allelic expression in which alleles of nonimprinted genes are not expressed equally at the mRNA level; Khatib, 2007). To analyze the expression status of the CTSD gene in adult skeletal muscle, we used 19 heterozygous g.70GA Italian Large White pigs and compared the sequenced reverse transcription-PCR products with the sequenced genomic DNA PCR products. In all analyzed samples, both alleles were detected (data not shown), indicating that CTSD is not an imprinted gene, at least in adult skeletal muscle. Moreover, we did not note any significant deviation of the 2 alleles from the 50:50 ratio (the estimated transcript ratio was on average 47:53 ± 5 for the G and A alleles, respectively). Therefore, no preferential expression or differential allelic expression affected the CTSD gene in the analyzed tissue. Skeletal muscle showed imprinting of the IGF2 gene both in fetuses and in adult pigs (Nezer et al., 1999; Van Laere et al., 2003; Wrzeska et al., 2006; Li et al., 2008). Both IGF2 and the close H19 gene showed imprinting in several other porcine tissues and also at the embryonic level (Han et al., 2008; Li et al., 2008; Park et al., 2009), similar to other species (Morison et al., 2005). However, biallelic expression of the porcine CTSD gene excluded imprinting mechanisms in the skeletal muscle. Even if no report in other species suggests CTSD to be an imprinted gene, because the porcine CTSD gene seems ubiquitously expressed in pigs (Mei et al., 2008), additional studies should be carried out to confirm its biallelic expression in other tissues. Association Analyses We evaluated the effects of the IGF2 intron3-g.3072G>A SNP in the Italian Large White population, for which no previous data have been reported. Results of the association analyses are shown in Table 2. Significant differences between the 3 genotypes were evident for all EBV and RR considered, with the most significant result being for LC EBV (P = 2.2 × 10−18, model 1). The same significant result (P < 1.0 × 10−20) was obtained after excluding heterozygous animals because we did not know the origin (paternal or maternal) of the g.3072A allele. Differences in genotype effects were more significant with EBV than with RR. This seems to have been due to a reduction in error in the EBV models. In general, we obtained far more significant results than other studies concerning the effects of the IGF2 alleles on production traits in commercial pig populations (i.e., Estellé et al., 2005; Vykoukalová et al., 2006; Oczkowicz et al., 2009). This could have been due to the use of EBV or RR instead of raw phenotypic measures in the association analysis and possibly to the high quality of performance data. The direction of the effects was consistent with known genetic correlations among traits, with the IGF2 intron3-g.3072AA genotype showing the greatest LC, ADG, and HW, the least BFT, and the most favorable FGR. The IGF2 intron3-g.3072GG genotype showed opposite effects. The IGF2 intron3-g.3072GA genotype was intermediate between the 2 homozygous genotypes. As a consequence, additive effects for all EBV (Table 3) and RR (data not shown) were highly significant. Considering that only the paternally inherited g.3072A allele should be expressed before birth to increase muscle mass (Nezer et al., 1999; Van Laere et al., 2003), least squares means of the heterozygous animals might suggest that one-half of these pigs could have received the g.3072A allele from their father and one-half from their mother, obtaining the averaged observed intermediate values compared with the 2 homozygous genotypes. However, SE (Table 2) and SD (data not shown) were less than expected in the heterozygous group of animals: should a heterozygote g.3072GA pig receive the A allele from its mother, its effect on the response trait would be similar to that of the g.3072GG animals, whereas if the A allele were received from its father, its effect would be similar to that of the g.3072AA animals. Therefore, heterozygotes are expected to show larger variability than each of the 2 homozygous groups. There are 3 possible explanations. The first is that the calculation of both EBV and RR could reduce variability, but this hypothesis is not plausible because different factors and assumptions underlie the calculation of EBV and RR, whereas the 2 analyses showed almost overlapping results. A second hypothesis is that partial postnatal depression of the maternal allele in skeletal muscle (Van Laere et al., 2003) might occur, which has also been observed in other tissues (Wrzeska et al., 2006). In this way, it could explain the superiority of heterozygous animals inheriting the g.3072A from their mothers over the homozygous g.3072GG pigs, reducing variability in the heterozygous class (Van Laere et al., 2003). On the other hand, the supposed partial activity of the maternally inherited g.3072A allele would be compatible with the inferiority of the g.3072GA pigs to the g.3072AA pigs. Gene expression analyses in different tissues and stages would be needed to clarify the role of the maternally inherited g.3072A allele in affecting performance and production traits. A third hypothesis would consider the presence of other polymorphism(s) affecting the analyzed traits in complete or partial linkage disequilibrium with the IGF2 intron3-g.3072G>A, making the effects of the 2 alleles perfectly additive. Table 2. Association analysis between the IGF2 intron3-g.3072G>A genotypes and EBV and random residuals (RR) for meat production and carcass traits and phenotypic measures for meat quality variables in Italian Large White pigs (least squares means ± SE) Trait1  Genotype2  P-value3  g.3072GG (n = 59)  g.3072GA (n = 108)  g.3072AA (n = 103)  Model 1  Model 2  EBV ADG, g  +13.932 ± 3.035  +30.398 ± 2.243  +47.806 ± 2.298  2.6 × 10−16  2.7 × 10−14  EBV LC, kg  +0.584 ± 0.207  +1.758 ± 0.153  +3.063 ± 0.157  2.2 × 10−18  <1.0 × 10−20  EBV BFT, mm  0.146 ± 0.461  −1.830 ± 0.340  −3.605 ± 0.349  2.2 × 10−9  1.1 × 10−10  EBV HW, kg  +0.268 ± 0.075  +0.583 ± 0.056  +0.771 ± 0.057  1.5 × 10−6  1.6 × 10−7  EBV FGR  −0.057 ± 0.019  −0.144 ± 0.014  −0.209 ± 0.014  2.3 × 10−9  2.2 × 10−9  RR ADG, g  −19.777 ± 10.148  −12.374 ± 7.391  18.094 ± 7.652  0.0028  0.0033  RR LC, kg  −1.547 ± 0.287  −0.458 ± 0.236  1.300 ± 0.216  2.9 × 10−7  5.9 × 10−14  RR BFT, mm  2.764 ± 0.619  −0.215 ± 0.462  −1.481 ± 0.467  0.0002  9.8 × 10−8  RR HW, kg  −0.326 ± 0.156  −0.110 ± 0.122  0.299 ± 0.118  0.0063  0.0017  RR FGR  0.120 ± 0.040  0.038 ± 0.030  −0.055 ± 0.030  0.0028  0.0006  pH1  5.940 ± 0.033  5.920 ± 0.025  5.930 ± 0.025  0.876  0.907  pHu  5.673 ± 0.028  5.642 ± 0.022  5.668 ± 0.022  0.534  0.940  Glycogen, μmol  46.186 ± 3.124  48.855 ± 2.408  49.551 ± 2.460  0.650  0.300  Lactate, μmol  58.080 ± 2.045  57.776 ± 1.534  54.870 ± 1.575  0.301  0.163  GP, μmol  104.470 ± 3.173  106.840 ± 2.440  104.230 ± 2.492  0.665  0.829  Catb, nmol  1.176 ± 0.032  1.156 ± 0.024  1.151 ± 0.025  0.803  0.734  Trait1  Genotype2  P-value3  g.3072GG (n = 59)  g.3072GA (n = 108)  g.3072AA (n = 103)  Model 1  Model 2  EBV ADG, g  +13.932 ± 3.035  +30.398 ± 2.243  +47.806 ± 2.298  2.6 × 10−16  2.7 × 10−14  EBV LC, kg  +0.584 ± 0.207  +1.758 ± 0.153  +3.063 ± 0.157  2.2 × 10−18  <1.0 × 10−20  EBV BFT, mm  0.146 ± 0.461  −1.830 ± 0.340  −3.605 ± 0.349  2.2 × 10−9  1.1 × 10−10  EBV HW, kg  +0.268 ± 0.075  +0.583 ± 0.056  +0.771 ± 0.057  1.5 × 10−6  1.6 × 10−7  EBV FGR  −0.057 ± 0.019  −0.144 ± 0.014  −0.209 ± 0.014  2.3 × 10−9  2.2 × 10−9  RR ADG, g  −19.777 ± 10.148  −12.374 ± 7.391  18.094 ± 7.652  0.0028  0.0033  RR LC, kg  −1.547 ± 0.287  −0.458 ± 0.236  1.300 ± 0.216  2.9 × 10−7  5.9 × 10−14  RR BFT, mm  2.764 ± 0.619  −0.215 ± 0.462  −1.481 ± 0.467  0.0002  9.8 × 10−8  RR HW, kg  −0.326 ± 0.156  −0.110 ± 0.122  0.299 ± 0.118  0.0063  0.0017  RR FGR  0.120 ± 0.040  0.038 ± 0.030  −0.055 ± 0.030  0.0028  0.0006  pH1  5.940 ± 0.033  5.920 ± 0.025  5.930 ± 0.025  0.876  0.907  pHu  5.673 ± 0.028  5.642 ± 0.022  5.668 ± 0.022  0.534  0.940  Glycogen, μmol  46.186 ± 3.124  48.855 ± 2.408  49.551 ± 2.460  0.650  0.300  Lactate, μmol  58.080 ± 2.045  57.776 ± 1.534  54.870 ± 1.575  0.301  0.163  GP, μmol  104.470 ± 3.173  106.840 ± 2.440  104.230 ± 2.492  0.665  0.829  Catb, nmol  1.176 ± 0.032  1.156 ± 0.024  1.151 ± 0.025  0.803  0.734  1LC = lean cuts (kg); BFT = backfat thickness (mm); HW = ham weight (kg); FGR = feed:gain ratio; pH1 = pH at 2 h postmortem; pHu = pH at 24 h postmortem; glycogen and lactate (micromoles of lactic acid equivalent per gram of fresh muscle); GP = glycolytic potential (micromoles of lactic acid equivalent per gram of fresh muscle); Catb = cathepsin B activity (nanomoles of 7-amino-4-methylcoumarin released per minute per gram of muscle). 2The number of animals for each genotype class was as follows: g.3072GG, n = 59; g.3072GA, n = 108; g.3072AA, n = 103. 3Model 1 included all 3 genotypes. Model 2 included only the 2 homozygous genotypes (g.3072GG and g.3072AA). LSM for the 2 genotypes were the same as for model 1. View Large Table 2. Association analysis between the IGF2 intron3-g.3072G>A genotypes and EBV and random residuals (RR) for meat production and carcass traits and phenotypic measures for meat quality variables in Italian Large White pigs (least squares means ± SE) Trait1  Genotype2  P-value3  g.3072GG (n = 59)  g.3072GA (n = 108)  g.3072AA (n = 103)  Model 1  Model 2  EBV ADG, g  +13.932 ± 3.035  +30.398 ± 2.243  +47.806 ± 2.298  2.6 × 10−16  2.7 × 10−14  EBV LC, kg  +0.584 ± 0.207  +1.758 ± 0.153  +3.063 ± 0.157  2.2 × 10−18  <1.0 × 10−20  EBV BFT, mm  0.146 ± 0.461  −1.830 ± 0.340  −3.605 ± 0.349  2.2 × 10−9  1.1 × 10−10  EBV HW, kg  +0.268 ± 0.075  +0.583 ± 0.056  +0.771 ± 0.057  1.5 × 10−6  1.6 × 10−7  EBV FGR  −0.057 ± 0.019  −0.144 ± 0.014  −0.209 ± 0.014  2.3 × 10−9  2.2 × 10−9  RR ADG, g  −19.777 ± 10.148  −12.374 ± 7.391  18.094 ± 7.652  0.0028  0.0033  RR LC, kg  −1.547 ± 0.287  −0.458 ± 0.236  1.300 ± 0.216  2.9 × 10−7  5.9 × 10−14  RR BFT, mm  2.764 ± 0.619  −0.215 ± 0.462  −1.481 ± 0.467  0.0002  9.8 × 10−8  RR HW, kg  −0.326 ± 0.156  −0.110 ± 0.122  0.299 ± 0.118  0.0063  0.0017  RR FGR  0.120 ± 0.040  0.038 ± 0.030  −0.055 ± 0.030  0.0028  0.0006  pH1  5.940 ± 0.033  5.920 ± 0.025  5.930 ± 0.025  0.876  0.907  pHu  5.673 ± 0.028  5.642 ± 0.022  5.668 ± 0.022  0.534  0.940  Glycogen, μmol  46.186 ± 3.124  48.855 ± 2.408  49.551 ± 2.460  0.650  0.300  Lactate, μmol  58.080 ± 2.045  57.776 ± 1.534  54.870 ± 1.575  0.301  0.163  GP, μmol  104.470 ± 3.173  106.840 ± 2.440  104.230 ± 2.492  0.665  0.829  Catb, nmol  1.176 ± 0.032  1.156 ± 0.024  1.151 ± 0.025  0.803  0.734  Trait1  Genotype2  P-value3  g.3072GG (n = 59)  g.3072GA (n = 108)  g.3072AA (n = 103)  Model 1  Model 2  EBV ADG, g  +13.932 ± 3.035  +30.398 ± 2.243  +47.806 ± 2.298  2.6 × 10−16  2.7 × 10−14  EBV LC, kg  +0.584 ± 0.207  +1.758 ± 0.153  +3.063 ± 0.157  2.2 × 10−18  <1.0 × 10−20  EBV BFT, mm  0.146 ± 0.461  −1.830 ± 0.340  −3.605 ± 0.349  2.2 × 10−9  1.1 × 10−10  EBV HW, kg  +0.268 ± 0.075  +0.583 ± 0.056  +0.771 ± 0.057  1.5 × 10−6  1.6 × 10−7  EBV FGR  −0.057 ± 0.019  −0.144 ± 0.014  −0.209 ± 0.014  2.3 × 10−9  2.2 × 10−9  RR ADG, g  −19.777 ± 10.148  −12.374 ± 7.391  18.094 ± 7.652  0.0028  0.0033  RR LC, kg  −1.547 ± 0.287  −0.458 ± 0.236  1.300 ± 0.216  2.9 × 10−7  5.9 × 10−14  RR BFT, mm  2.764 ± 0.619  −0.215 ± 0.462  −1.481 ± 0.467  0.0002  9.8 × 10−8  RR HW, kg  −0.326 ± 0.156  −0.110 ± 0.122  0.299 ± 0.118  0.0063  0.0017  RR FGR  0.120 ± 0.040  0.038 ± 0.030  −0.055 ± 0.030  0.0028  0.0006  pH1  5.940 ± 0.033  5.920 ± 0.025  5.930 ± 0.025  0.876  0.907  pHu  5.673 ± 0.028  5.642 ± 0.022  5.668 ± 0.022  0.534  0.940  Glycogen, μmol  46.186 ± 3.124  48.855 ± 2.408  49.551 ± 2.460  0.650  0.300  Lactate, μmol  58.080 ± 2.045  57.776 ± 1.534  54.870 ± 1.575  0.301  0.163  GP, μmol  104.470 ± 3.173  106.840 ± 2.440  104.230 ± 2.492  0.665  0.829  Catb, nmol  1.176 ± 0.032  1.156 ± 0.024  1.151 ± 0.025  0.803  0.734  1LC = lean cuts (kg); BFT = backfat thickness (mm); HW = ham weight (kg); FGR = feed:gain ratio; pH1 = pH at 2 h postmortem; pHu = pH at 24 h postmortem; glycogen and lactate (micromoles of lactic acid equivalent per gram of fresh muscle); GP = glycolytic potential (micromoles of lactic acid equivalent per gram of fresh muscle); Catb = cathepsin B activity (nanomoles of 7-amino-4-methylcoumarin released per minute per gram of muscle). 2The number of animals for each genotype class was as follows: g.3072GG, n = 59; g.3072GA, n = 108; g.3072AA, n = 103. 3Model 1 included all 3 genotypes. Model 2 included only the 2 homozygous genotypes (g.3072GG and g.3072AA). LSM for the 2 genotypes were the same as for model 1. View Large Table 3. Additive and dominance effects (±SE) obtained for the IGF2 intron3-g.3072G>A marker in Italian Large White pigs Trait1  Additive effect  P-value  Dominance effect  P-value  EBV ADG, g  16.937 ± 1.903  <0.0001  −0.471 ± 2.942  0.873  EBV LC, kg  1.240 ± 0.130  <0.0001  −0.066 ± 0.201  0.743  EBV BFT, mm  −1.876 ± 0.288  <0.0001  −0.100 ± 0.446  0.823  EBV HW, kg  0.251 ± 0.047  <0.0001  0.063 ± 0.073  0.387  EBV FGR  −0.076 ± 0.012  <0.0001  −0.011 ± 0.018  0.540  Trait1  Additive effect  P-value  Dominance effect  P-value  EBV ADG, g  16.937 ± 1.903  <0.0001  −0.471 ± 2.942  0.873  EBV LC, kg  1.240 ± 0.130  <0.0001  −0.066 ± 0.201  0.743  EBV BFT, mm  −1.876 ± 0.288  <0.0001  −0.100 ± 0.446  0.823  EBV HW, kg  0.251 ± 0.047  <0.0001  0.063 ± 0.073  0.387  EBV FGR  −0.076 ± 0.012  <0.0001  −0.011 ± 0.018  0.540  1LC = lean cuts (kg); BFT = backfat thickness (mm); HW = ham weight (kg); FGR = feed:gain ratio. View Large Table 3. Additive and dominance effects (±SE) obtained for the IGF2 intron3-g.3072G>A marker in Italian Large White pigs Trait1  Additive effect  P-value  Dominance effect  P-value  EBV ADG, g  16.937 ± 1.903  <0.0001  −0.471 ± 2.942  0.873  EBV LC, kg  1.240 ± 0.130  <0.0001  −0.066 ± 0.201  0.743  EBV BFT, mm  −1.876 ± 0.288  <0.0001  −0.100 ± 0.446  0.823  EBV HW, kg  0.251 ± 0.047  <0.0001  0.063 ± 0.073  0.387  EBV FGR  −0.076 ± 0.012  <0.0001  −0.011 ± 0.018  0.540  Trait1  Additive effect  P-value  Dominance effect  P-value  EBV ADG, g  16.937 ± 1.903  <0.0001  −0.471 ± 2.942  0.873  EBV LC, kg  1.240 ± 0.130  <0.0001  −0.066 ± 0.201  0.743  EBV BFT, mm  −1.876 ± 0.288  <0.0001  −0.100 ± 0.446  0.823  EBV HW, kg  0.251 ± 0.047  <0.0001  0.063 ± 0.073  0.387  EBV FGR  −0.076 ± 0.012  <0.0001  −0.011 ± 0.018  0.540  1LC = lean cuts (kg); BFT = backfat thickness (mm); HW = ham weight (kg); FGR = feed:gain ratio. View Large Despite the strong association of the IGF2 marker with meat, carcass, and production traits, no significant result was observed for the meat quality traits included in this study (Table 2), confirming, to some extent, the results obtained by others (Estellé et al., 2005; Van den Maagdenberg et al., 2007; Oczkowicz et al., 2009). Association results between the IGF2 polymorphism and the EBV or RR in the Italian Duroc pigs were not significant (data not shown), probably because of the small number of heterozygous (n = 11) and g.3072GG (n = 2) animals. However, the direction of the effects was the same as observed in the Italian Large White pigs. To evaluate the effects of the CTSD SNP, we first considered the Italian Duroc pigs, excluding an animal that was heterozygous at both the CTSD g.70G>A and the IGF2 intron3-g.3072G>A mutations. (All other Italian Duroc pigs that were g.70AG or g.70GG for the CTSD gene carried the IGF2 intron3-g.3072AA genotype.) Results of the association study in this group, carried out by excluding the 2 CTSD g.70GG animals and considering only the CTSD g.70AG and g.70AA genotypic classes, are shown in Table 4. Exclusion of all IGF2 intron3-g.3072GA and GG animals showed almost the same results, as well as separate inclusion of the 2 CTSD g.70GG animals or inclusion of them in the CTSD g.70AG genotypic class (data not shown). The less frequent genotypic class (CTSD g.70AG) showed less ADG, less LC, greater BFT, and less favorable FGR compared with the CTSD g.70AA genotype (P < 0.001 for all EBV and BFT RR; P < 0.10 for all other RR). These results were almost overlapping with the results we obtained in the Italian Large White pigs (Table 4; Russo et al., 2008). However, in the Italian Duroc pigs, no confounding effects could be attributed to the IGF2 intron3-g.3072G>A mutation. Table 4. Association analysis between the cathepsin D (CTSD) g.70G>A genotypes and EBV and random residuals (RR) in Italian Duroc pigs and RR in Italian Large White pigs (least squares means ± SE) Breed  Trait1  Genotype2  P-value  g.70GA (n = 42)  g.70AA (n = 253)  Italian Duroc  EBV ADG, g  +15.419 ± 4.486  +32.996 ± 1.821  0.0008    EBV LC, kg  +0.867 ± 0.308  +2.158 ± 0.127  0.0001    EBV BFT, mm  0.286 ± 0.601  −2.196 ± 0.248  0.0002    EBV FGR  −0.082 ± 0.029  −0.176 ± 0.010  0.0008    RR ADG, g  −13.045 ± 10.634  6.641 ± 4.384  0.0880    RR LC, kg  −0.602 ± 0.373  0.153 ± 0.154  0.0625    RR BFT, mm  2.176 ± 0.769  −0.273 ± 0.317  0.0035    RR FGR  0.0617 ± 0.043  −0.023 ± 0.018  0.0700      Genotype3        g.70GA (n = 36)  g.70AA (n = 230)     Italian Large White  RR ADG, g  −29.315 ± 12.957  3.044 ± 5.126  0.0210    RR LC, kg  −1.242 ± 0.440  0.210 ± 0.174  0.0024    RR BFT, mm  1.729 ± 0.835  −0.424 ± 0.330  0.0172    RR HW, kg  −0.416 ± 0.215  0.073 ± 0.085  0.0354    RR FGR  0.148 ± 0.053  −0.005 ± 0.021  0.0079  Breed  Trait1  Genotype2  P-value  g.70GA (n = 42)  g.70AA (n = 253)  Italian Duroc  EBV ADG, g  +15.419 ± 4.486  +32.996 ± 1.821  0.0008    EBV LC, kg  +0.867 ± 0.308  +2.158 ± 0.127  0.0001    EBV BFT, mm  0.286 ± 0.601  −2.196 ± 0.248  0.0002    EBV FGR  −0.082 ± 0.029  −0.176 ± 0.010  0.0008    RR ADG, g  −13.045 ± 10.634  6.641 ± 4.384  0.0880    RR LC, kg  −0.602 ± 0.373  0.153 ± 0.154  0.0625    RR BFT, mm  2.176 ± 0.769  −0.273 ± 0.317  0.0035    RR FGR  0.0617 ± 0.043  −0.023 ± 0.018  0.0700      Genotype3        g.70GA (n = 36)  g.70AA (n = 230)     Italian Large White  RR ADG, g  −29.315 ± 12.957  3.044 ± 5.126  0.0210    RR LC, kg  −1.242 ± 0.440  0.210 ± 0.174  0.0024    RR BFT, mm  1.729 ± 0.835  −0.424 ± 0.330  0.0172    RR HW, kg  −0.416 ± 0.215  0.073 ± 0.085  0.0354    RR FGR  0.148 ± 0.053  −0.005 ± 0.021  0.0079  1LC = lean cuts (kg); BFT = backfat thickness (mm); FGR = feed:gain ratio. 2The number of animals for each genotype class in the Italian Duroc population was as follows: g.70GA, n = 42; g.70AA, n = 253. Only 2 animals had genotype g.70GG. These pigs were excluded from this analysis. 3The number of animals for each genotype class in the Italian Large White population was as follows: g.70GA, n = 36; g.70AA, n = 230. Only 4 animals had genotype g.70GG. These pigs were excluded from this analysis. View Large Table 4. Association analysis between the cathepsin D (CTSD) g.70G>A genotypes and EBV and random residuals (RR) in Italian Duroc pigs and RR in Italian Large White pigs (least squares means ± SE) Breed  Trait1  Genotype2  P-value  g.70GA (n = 42)  g.70AA (n = 253)  Italian Duroc  EBV ADG, g  +15.419 ± 4.486  +32.996 ± 1.821  0.0008    EBV LC, kg  +0.867 ± 0.308  +2.158 ± 0.127  0.0001    EBV BFT, mm  0.286 ± 0.601  −2.196 ± 0.248  0.0002    EBV FGR  −0.082 ± 0.029  −0.176 ± 0.010  0.0008    RR ADG, g  −13.045 ± 10.634  6.641 ± 4.384  0.0880    RR LC, kg  −0.602 ± 0.373  0.153 ± 0.154  0.0625    RR BFT, mm  2.176 ± 0.769  −0.273 ± 0.317  0.0035    RR FGR  0.0617 ± 0.043  −0.023 ± 0.018  0.0700      Genotype3        g.70GA (n = 36)  g.70AA (n = 230)     Italian Large White  RR ADG, g  −29.315 ± 12.957  3.044 ± 5.126  0.0210    RR LC, kg  −1.242 ± 0.440  0.210 ± 0.174  0.0024    RR BFT, mm  1.729 ± 0.835  −0.424 ± 0.330  0.0172    RR HW, kg  −0.416 ± 0.215  0.073 ± 0.085  0.0354    RR FGR  0.148 ± 0.053  −0.005 ± 0.021  0.0079  Breed  Trait1  Genotype2  P-value  g.70GA (n = 42)  g.70AA (n = 253)  Italian Duroc  EBV ADG, g  +15.419 ± 4.486  +32.996 ± 1.821  0.0008    EBV LC, kg  +0.867 ± 0.308  +2.158 ± 0.127  0.0001    EBV BFT, mm  0.286 ± 0.601  −2.196 ± 0.248  0.0002    EBV FGR  −0.082 ± 0.029  −0.176 ± 0.010  0.0008    RR ADG, g  −13.045 ± 10.634  6.641 ± 4.384  0.0880    RR LC, kg  −0.602 ± 0.373  0.153 ± 0.154  0.0625    RR BFT, mm  2.176 ± 0.769  −0.273 ± 0.317  0.0035    RR FGR  0.0617 ± 0.043  −0.023 ± 0.018  0.0700      Genotype3        g.70GA (n = 36)  g.70AA (n = 230)     Italian Large White  RR ADG, g  −29.315 ± 12.957  3.044 ± 5.126  0.0210    RR LC, kg  −1.242 ± 0.440  0.210 ± 0.174  0.0024    RR BFT, mm  1.729 ± 0.835  −0.424 ± 0.330  0.0172    RR HW, kg  −0.416 ± 0.215  0.073 ± 0.085  0.0354    RR FGR  0.148 ± 0.053  −0.005 ± 0.021  0.0079  1LC = lean cuts (kg); BFT = backfat thickness (mm); FGR = feed:gain ratio. 2The number of animals for each genotype class in the Italian Duroc population was as follows: g.70GA, n = 42; g.70AA, n = 253. Only 2 animals had genotype g.70GG. These pigs were excluded from this analysis. 3The number of animals for each genotype class in the Italian Large White population was as follows: g.70GA, n = 36; g.70AA, n = 230. Only 4 animals had genotype g.70GG. These pigs were excluded from this analysis. View Large A second evaluation of the effects of the CTSD g.70G>A polymorphism was obtained by considering only Italian Large White pigs homozygous for the IGF2 intron3-g.3072G allele (n = 59 animals). Animals with this genotype were the only pigs carrying the CTSD g.70G allele. Differences between the CTSD genotype least squares means of the HW EBV, ADG RR, BFT RR, and LC RR showed P < 0.05, and for LC EBV and HW RR showed P < 0.10, even in this small group of pigs. The other comparisons were not significant, probably because of the small sample size (Table 5). However, the effect on HW and LC, as well as the differences between least squares means of the other traits, were in the expected direction, with animals carrying the CTSD g.70G allele showing less HW, less LC, less ADG, greater BFT, and less favorable FGR as compared with the CTSD g.70AA animals. These results supported an effect of the CTSD g.70G>A polymorphism independent from the genotype at the IGF2 intron3-g.3072G>A SNP (Table 4; Russo et al., 2008). Table 5. Association analysis between cathepsin D (CTSD) g.70G>A genotypes and EBV and random residuals (RR) in Italian Large White animals with the IGF2 intron3-g.3072GG genotype (n = 59 pigs; least squares means ± SE) Trait1  Genotype2  P-value  g.70GG + g.70GA (n = 4 + 17)  g.70AA (n = 38)  EBV ADG, g  +9.619 ± 5.141  +16.316 ± 3.822  0.300  EBV LC, kg  +0.182 ± 0.289  +0.806 ± 0.215  0.088  EBV BFT, mm  +0.771 ± 0.668  −0.200 ± 0.497  0.248  EBV HW, kg  +0.039 ± 0.107  +0.394 ± 0.080  0.010  EBV FGR  −0.034 ± 0.029  −0.069 ± 0.022  0.349  RR ADG, g  −41.139 ± 13.097  −7.449 ± 8.915  0.038  RR LC, kg  −2.320 ± 0.464  −1.098 ± 0.316  0.034  RR BFT, mm  4.364 ± 0.998  1.639 ± 0.679  0.028  RR HW, kg  −0.711 ± 0.262  −0.157 ± 0.178  0.086  RR FGR  0.200 ± 0.065  0.071 ± 0.044  0.105  Trait1  Genotype2  P-value  g.70GG + g.70GA (n = 4 + 17)  g.70AA (n = 38)  EBV ADG, g  +9.619 ± 5.141  +16.316 ± 3.822  0.300  EBV LC, kg  +0.182 ± 0.289  +0.806 ± 0.215  0.088  EBV BFT, mm  +0.771 ± 0.668  −0.200 ± 0.497  0.248  EBV HW, kg  +0.039 ± 0.107  +0.394 ± 0.080  0.010  EBV FGR  −0.034 ± 0.029  −0.069 ± 0.022  0.349  RR ADG, g  −41.139 ± 13.097  −7.449 ± 8.915  0.038  RR LC, kg  −2.320 ± 0.464  −1.098 ± 0.316  0.034  RR BFT, mm  4.364 ± 0.998  1.639 ± 0.679  0.028  RR HW, kg  −0.711 ± 0.262  −0.157 ± 0.178  0.086  RR FGR  0.200 ± 0.065  0.071 ± 0.044  0.105  1LC = lean cuts (kg); BFT = backfat thickness (mm); HW = ham weight (kg); FGR = feed:gain ratio. 2The number of animals for each genotype class was as follows: g.70GG, n = 4; g.70GA, n = 17; g.70AA, n = 38. The 4 animals with genotype g.70GG were included in the genotypic class with the pigs having the g.70GA genotype. View Large Table 5. Association analysis between cathepsin D (CTSD) g.70G>A genotypes and EBV and random residuals (RR) in Italian Large White animals with the IGF2 intron3-g.3072GG genotype (n = 59 pigs; least squares means ± SE) Trait1  Genotype2  P-value  g.70GG + g.70GA (n = 4 + 17)  g.70AA (n = 38)  EBV ADG, g  +9.619 ± 5.141  +16.316 ± 3.822  0.300  EBV LC, kg  +0.182 ± 0.289  +0.806 ± 0.215  0.088  EBV BFT, mm  +0.771 ± 0.668  −0.200 ± 0.497  0.248  EBV HW, kg  +0.039 ± 0.107  +0.394 ± 0.080  0.010  EBV FGR  −0.034 ± 0.029  −0.069 ± 0.022  0.349  RR ADG, g  −41.139 ± 13.097  −7.449 ± 8.915  0.038  RR LC, kg  −2.320 ± 0.464  −1.098 ± 0.316  0.034  RR BFT, mm  4.364 ± 0.998  1.639 ± 0.679  0.028  RR HW, kg  −0.711 ± 0.262  −0.157 ± 0.178  0.086  RR FGR  0.200 ± 0.065  0.071 ± 0.044  0.105  Trait1  Genotype2  P-value  g.70GG + g.70GA (n = 4 + 17)  g.70AA (n = 38)  EBV ADG, g  +9.619 ± 5.141  +16.316 ± 3.822  0.300  EBV LC, kg  +0.182 ± 0.289  +0.806 ± 0.215  0.088  EBV BFT, mm  +0.771 ± 0.668  −0.200 ± 0.497  0.248  EBV HW, kg  +0.039 ± 0.107  +0.394 ± 0.080  0.010  EBV FGR  −0.034 ± 0.029  −0.069 ± 0.022  0.349  RR ADG, g  −41.139 ± 13.097  −7.449 ± 8.915  0.038  RR LC, kg  −2.320 ± 0.464  −1.098 ± 0.316  0.034  RR BFT, mm  4.364 ± 0.998  1.639 ± 0.679  0.028  RR HW, kg  −0.711 ± 0.262  −0.157 ± 0.178  0.086  RR FGR  0.200 ± 0.065  0.071 ± 0.044  0.105  1LC = lean cuts (kg); BFT = backfat thickness (mm); HW = ham weight (kg); FGR = feed:gain ratio. 2The number of animals for each genotype class was as follows: g.70GG, n = 4; g.70GA, n = 17; g.70AA, n = 38. The 4 animals with genotype g.70GG were included in the genotypic class with the pigs having the g.70GA genotype. View Large A third evaluation was obtained using the IGF2 and CTSD haplotypes in the Italian Large White population. Three haplotypes were inferred: haplotype [G:G] = IGF2 intron3-g.3072G and CTSD g.70G; haplotype [G:A] = IGF2 intron3-g.3072G and CTSD g.70A; and haplotype [A:A] = IGF2 intron3-g.3072A and CTSD g.70A. In the Italian Duroc group of pigs, only a few haplotypes were represented (data not shown); therefore, this population was not fully informative for an association analysis including haplotypes of these 2 markers. Table 6 shows the estimated regression coefficient of the haplotype substitution effect for the considered EBV and RR. No significant results were obtained for meat quality traits (data not shown), but highly significant results were generally obtained for all EBV and RR. Haplotype [G:G] was associated with reduced ADG, LC, and HW and greater BFT and FGR (P < 0.001 for EBV; P < 0.01 for most RR). On the contrary, haplotype [A:A] was associated with greater ADG, LC, and HW and less BFT and FGR (P < 0.0001), whereas haplotype [G:A] was intermediate (P < 0.05, considering all measures, but ranging from P < 0.0001 to P = 0.0436). Table 6. Estimated regression coefficients (±SE) of the haplotype substitution effect and the haplotype effects as a proportion of the SD for EBV and random residuals (RR) of 5 production traits in the Italian Large White population Trait1  Haplotype [G:G]2 (n = 48)  Haplotype [G:A]2 (n = 178)  Haplotype [A:A]2 (n = 314)  Regression coefficient  P-value  Proportion  Regression coefficient  P-value  Proportion  Regression coefficient  P-value  Proportion  EBV ADG, g  −17.430 ± 3.829  <0.0001  −0.268  −13.666 ± 2.130  <0.0001  −0.365  16.990 ± 1.870  <0.0001  0.485  EBV LC, kg  −1.214 ± 0.266  <0.0001  −0.268  −1.025 ± 0.146  <0.0001  −0.393  1.247 ± 0.128  <0.0001  0.512  EBV BFT, mm  2.037 ± 0.555  0.0003  0.219  1.458 ± 0.315  <0.0001  0.272  −1.864 ± 0.284  <0.0001  −0.372  EBV HW, kg  −0.348 ± 0.088  0.0001  −0.234  −0.164 ± 0.051  0.0015  −0.192  0.244 ± 0.046  <0.0001  0.305  EBV FGR  0.093 ± 0.022  <0.0001  0.247  0.055 ± 0.013  <0.0001  0.254  −0.075 ± 0.011  <0.0001  −0.370  RR ADG, g  −31.639 ± 11.544  0.0065  −0.165  −14.686 ± 6.720  0.0297  −0.133  22.016 ± 6.179  0.0004  0.213  RR LC, kg  −1.412 ± 0.393  0.0004  −0.215  −1.304 ± 0.219  <0.0001  −0.342  1.547 ± 0.196  <0.0001  0.435  RR BFT, mm  1.970 ± 0.746  0.0088  0.159  1.519 ± 0.428  0.0005  0.212  −1.897 ± 0.392  <0.0001  −0.284  RR HW, kg  −0.479 ± 0.191  0.0129  −0.152  −0.255 ± 0.111  0.0224  −0.139  0.361 ± 0.102  0.0005  0.212  RR FGR  0.148 ± 0.047  0.0019  0.188  0.056 ± 0.028  0.0436  0.123  −0.092 ± 0.025  0.0003  −0.217  Trait1  Haplotype [G:G]2 (n = 48)  Haplotype [G:A]2 (n = 178)  Haplotype [A:A]2 (n = 314)  Regression coefficient  P-value  Proportion  Regression coefficient  P-value  Proportion  Regression coefficient  P-value  Proportion  EBV ADG, g  −17.430 ± 3.829  <0.0001  −0.268  −13.666 ± 2.130  <0.0001  −0.365  16.990 ± 1.870  <0.0001  0.485  EBV LC, kg  −1.214 ± 0.266  <0.0001  −0.268  −1.025 ± 0.146  <0.0001  −0.393  1.247 ± 0.128  <0.0001  0.512  EBV BFT, mm  2.037 ± 0.555  0.0003  0.219  1.458 ± 0.315  <0.0001  0.272  −1.864 ± 0.284  <0.0001  −0.372  EBV HW, kg  −0.348 ± 0.088  0.0001  −0.234  −0.164 ± 0.051  0.0015  −0.192  0.244 ± 0.046  <0.0001  0.305  EBV FGR  0.093 ± 0.022  <0.0001  0.247  0.055 ± 0.013  <0.0001  0.254  −0.075 ± 0.011  <0.0001  −0.370  RR ADG, g  −31.639 ± 11.544  0.0065  −0.165  −14.686 ± 6.720  0.0297  −0.133  22.016 ± 6.179  0.0004  0.213  RR LC, kg  −1.412 ± 0.393  0.0004  −0.215  −1.304 ± 0.219  <0.0001  −0.342  1.547 ± 0.196  <0.0001  0.435  RR BFT, mm  1.970 ± 0.746  0.0088  0.159  1.519 ± 0.428  0.0005  0.212  −1.897 ± 0.392  <0.0001  −0.284  RR HW, kg  −0.479 ± 0.191  0.0129  −0.152  −0.255 ± 0.111  0.0224  −0.139  0.361 ± 0.102  0.0005  0.212  RR FGR  0.148 ± 0.047  0.0019  0.188  0.056 ± 0.028  0.0436  0.123  −0.092 ± 0.025  0.0003  −0.217  1LC = lean cuts (kg); BFT = backfat thickness (mm); HW = ham weight (kg); FGR = feed:gain ratio. 2Haplotypes are indicated as follows: [G:G] = IGF2 intron3-g.3072G and cathepsin D (CTSD) g.70G; [G:A] = IGF2 intron3-g.3072G and CTSD g.70A; [A:A] = IGF2 intron3-g.3072A and CTSD g.70A. View Large Table 6. Estimated regression coefficients (±SE) of the haplotype substitution effect and the haplotype effects as a proportion of the SD for EBV and random residuals (RR) of 5 production traits in the Italian Large White population Trait1  Haplotype [G:G]2 (n = 48)  Haplotype [G:A]2 (n = 178)  Haplotype [A:A]2 (n = 314)  Regression coefficient  P-value  Proportion  Regression coefficient  P-value  Proportion  Regression coefficient  P-value  Proportion  EBV ADG, g  −17.430 ± 3.829  <0.0001  −0.268  −13.666 ± 2.130  <0.0001  −0.365  16.990 ± 1.870  <0.0001  0.485  EBV LC, kg  −1.214 ± 0.266  <0.0001  −0.268  −1.025 ± 0.146  <0.0001  −0.393  1.247 ± 0.128  <0.0001  0.512  EBV BFT, mm  2.037 ± 0.555  0.0003  0.219  1.458 ± 0.315  <0.0001  0.272  −1.864 ± 0.284  <0.0001  −0.372  EBV HW, kg  −0.348 ± 0.088  0.0001  −0.234  −0.164 ± 0.051  0.0015  −0.192  0.244 ± 0.046  <0.0001  0.305  EBV FGR  0.093 ± 0.022  <0.0001  0.247  0.055 ± 0.013  <0.0001  0.254  −0.075 ± 0.011  <0.0001  −0.370  RR ADG, g  −31.639 ± 11.544  0.0065  −0.165  −14.686 ± 6.720  0.0297  −0.133  22.016 ± 6.179  0.0004  0.213  RR LC, kg  −1.412 ± 0.393  0.0004  −0.215  −1.304 ± 0.219  <0.0001  −0.342  1.547 ± 0.196  <0.0001  0.435  RR BFT, mm  1.970 ± 0.746  0.0088  0.159  1.519 ± 0.428  0.0005  0.212  −1.897 ± 0.392  <0.0001  −0.284  RR HW, kg  −0.479 ± 0.191  0.0129  −0.152  −0.255 ± 0.111  0.0224  −0.139  0.361 ± 0.102  0.0005  0.212  RR FGR  0.148 ± 0.047  0.0019  0.188  0.056 ± 0.028  0.0436  0.123  −0.092 ± 0.025  0.0003  −0.217  Trait1  Haplotype [G:G]2 (n = 48)  Haplotype [G:A]2 (n = 178)  Haplotype [A:A]2 (n = 314)  Regression coefficient  P-value  Proportion  Regression coefficient  P-value  Proportion  Regression coefficient  P-value  Proportion  EBV ADG, g  −17.430 ± 3.829  <0.0001  −0.268  −13.666 ± 2.130  <0.0001  −0.365  16.990 ± 1.870  <0.0001  0.485  EBV LC, kg  −1.214 ± 0.266  <0.0001  −0.268  −1.025 ± 0.146  <0.0001  −0.393  1.247 ± 0.128  <0.0001  0.512  EBV BFT, mm  2.037 ± 0.555  0.0003  0.219  1.458 ± 0.315  <0.0001  0.272  −1.864 ± 0.284  <0.0001  −0.372  EBV HW, kg  −0.348 ± 0.088  0.0001  −0.234  −0.164 ± 0.051  0.0015  −0.192  0.244 ± 0.046  <0.0001  0.305  EBV FGR  0.093 ± 0.022  <0.0001  0.247  0.055 ± 0.013  <0.0001  0.254  −0.075 ± 0.011  <0.0001  −0.370  RR ADG, g  −31.639 ± 11.544  0.0065  −0.165  −14.686 ± 6.720  0.0297  −0.133  22.016 ± 6.179  0.0004  0.213  RR LC, kg  −1.412 ± 0.393  0.0004  −0.215  −1.304 ± 0.219  <0.0001  −0.342  1.547 ± 0.196  <0.0001  0.435  RR BFT, mm  1.970 ± 0.746  0.0088  0.159  1.519 ± 0.428  0.0005  0.212  −1.897 ± 0.392  <0.0001  −0.284  RR HW, kg  −0.479 ± 0.191  0.0129  −0.152  −0.255 ± 0.111  0.0224  −0.139  0.361 ± 0.102  0.0005  0.212  RR FGR  0.148 ± 0.047  0.0019  0.188  0.056 ± 0.028  0.0436  0.123  −0.092 ± 0.025  0.0003  −0.217  1LC = lean cuts (kg); BFT = backfat thickness (mm); HW = ham weight (kg); FGR = feed:gain ratio. 2Haplotypes are indicated as follows: [G:G] = IGF2 intron3-g.3072G and cathepsin D (CTSD) g.70G; [G:A] = IGF2 intron3-g.3072G and CTSD g.70A; [A:A] = IGF2 intron3-g.3072A and CTSD g.70A. View Large Overall, these results indicated a separate effect, consistent across Italian Large White and Italian Duroc, of the CTSD g.70G>A marker. This confirms the hypothesis that an additional QTL exists in the IGF2 region of SSC2 (de Koning et al., 2000; Rattink et al., 2000; Lee et al., 2003; Jungerius et al., 2004; Estellé et al., 2005; Sanchez et al., 2006; van Wijk et al., 2006; Liu et al., 2007, 2008; Tribout et al., 2008). Cathepsin D is an aspartic lysosomal proteinase involved in a broad spectrum of functions, such as protein degradation, apoptosis, and autophagy, and it has been associated with certain pathological conditions such as cancer, Alzheimer's disease, and neuronal ceroid lipofuscinosis (Zaidi et al., 2008). However, it remains to be clarified whether the CTSD gene is directly involved in affecting the traits examined in this study or whether mutation(s) in (an)other close gene(s) in this region could possibly be the causative factor(s) determining greater ADG, greater muscle mass, and less backfat deposition. Bioinformatic predictions did not strongly support a putative function of the 3′-UTR CTSD nucleotide transition or by the surrounding nucleotides (data not shown). Functional studies should be carried out to verify this issue. In this study, the porcine CTSD gene did not appear to be imprinted in adult skeletal muscle. Therefore, this gene should follow a Mendelian inheritance model, with putative additive effects (data not shown). This could be one of the reasons why the positive CTSD allele is the most frequent both in Italian Duroc and in Italian Large White breeds. Even if the selection goals for these 2 breeds take into account several meat quality traits, these populations have still undergone intense family selection for almost 20 yr, based on BLUP EBV that were probably very efficient in selecting the g.70A allele. In conclusion, our results indicate that the IGF2 intron3-g.3072G>A mutation is not the only quantitative trait nucleotide affecting production traits in the telomeric end of the p arm of SSC2, as demonstrated by the analysis of the CTSD g.70G>A polymorphism. Marker-assisted selection already applied using the IGF2 mutation (Van Laere et al., 2003; Buys et al., 2006) could increase in efficiency by adding information from the CTSD genotype. To this aim, it would be interesting to analyze the combined effects of these 2 polymorphic sites in other breeds and lines. For all practical applications, however, the different modes of inheritance of the 2 genes have to be taken into account. LITERATURE CITED Buys, N., G. Van Den Abeele, A. Stinckens, J. Deley, and M. Georges 2006. Effect of the IGF2-intron3-G3072A mutation on prolificacy in sows.  Proc. 8th World Congr. Genet. Appl. Livest. Prod., Belo Horizonte, Minas Gerais, Brazil. Cheng H. C. Zhang F. W. Jiang C. D. Li F. E. Xiong Y. Z. Deng C. Y. 2008. Isolation and imprinting analysis of the porcine DLX5 gene and its association with carcass traits. Anim. Genet.  39: 395– 399. https://doi.org/18498429 Google Scholar CrossRef Search ADS PubMed  Daetwyler H. D. Schenkel F. S. Sargolzaei M. Robinson J. A. 2008. A genome scan to detect quantitative trait loci for economically important traits in Holstein cattle using two methods and a dense single nucleotide polymorphism map. J. Dairy Sci.  91: 3225– 3236. https://doi.org/18650300 Google Scholar CrossRef Search ADS PubMed  de Koning D. J. Rattink A. P. Harlizius B. van Arendonk J. A. Brascamp E. W. Groenen M. A. 2000. Genome-wide scan for body composition in pigs reveals important role of imprinting. Proc. Natl. Acad. Sci. USA  97: 7947– 7950. https://doi.org/10859367 Google Scholar CrossRef Search ADS   Estellé J. Mercadé A. Noguera J. L. Pérez-Enciso M. Ovilo C. Sánchez A. Folch J. M. 2005. Effect of the porcine IGF2-intron3-G3072A substitution in an outbred Large White population and in an Iberian × Landrace cross. J. Anim. Sci.  83: 2723– 2728. https://doi.org/16282609 Google Scholar CrossRef Search ADS PubMed  Evans G. J. Giuffra E. Sanchez A. Kerje S. Davalos G. Vidal O. Illán S. Noguera J. L. Varona L. Velander I. Southwood O. I. de Koning D. J. Haley C. S. Plastow G. S. Andersson L. 2003. Identification of quantitative trait loci for production traits in commercial pig populations. Genetics  164: 621– 627. https://doi.org/12807782 Google Scholar PubMed  Fontanesi L. Beretti F. Riggio V. Gómez Gonzáles E. Dall'Olio S. Davoli R. Russo V. Portolano B. 2009. Copy number variation and missense mutations of the agouti signaling protein (ASIP) gene in goat breeds with different coat colours. Cytogenet. Genome Res.  126: 333– 347. https://doi.org/20016133 Google Scholar CrossRef Search ADS PubMed  Fontanesi L. Davoli R. Nanni Costa L. Beretti F. Scotti E. Tazzoli M. Tassone F. Colombo M. Buttazzoni L. Russo V. 2008. Investigation of candidate genes for glycolytic potential of porcine skeletal muscle: Association with meat quality and production traits in Italian Large White pigs. Meat Sci.  80: 780– 787. Google Scholar CrossRef Search ADS PubMed  Fujii J. Otsu K. Zorzato F. de Leon S. Khanna V. K. Weiler J. E. O'Brien P. J. MacLennan D. H. 1991. Identification of a mutation in porcine ryanodine receptor associated with malignant hyperthermia. Science  253: 448– 451. Google Scholar CrossRef Search ADS PubMed  Han D. W. Im Y. B. Do J. T. Gupta M. K. Uhm S. J. Kim J. H. Schöler H. R. Lee H. T. 2008. Methylation status of putative differentially methylated regions of porcine IGF2 and H19. Mol. Reprod. Dev.  75: 777– 784. https://doi.org/18247333 Google Scholar CrossRef Search ADS PubMed  Israel C. Weller J. I. 2002. Estimation of quantitative trait loci effects in dairy cattle populations. J. Dairy Sci.  85: 1285– 1297. https://doi.org/12086066 Google Scholar CrossRef Search ADS PubMed  Jeon J. T. Carlborg O. Törnsten A. Giuffra E. Amarger V. Chardon P. Andersson-Eklund L. Andersson K. Hansson I. Lundström K. Andersson L. 1999. A paternally expressed QTL affecting skeletal and cardiac muscle mass in pigs maps to the IGF2 locus. Nat. Genet.  21: 157– 158. https://doi.org/9988263 Google Scholar CrossRef Search ADS PubMed  Jungerius B. J. van Laere A. S. Te Pas M. F. van Oost B. A. Andersson L. Groenen M. A. 2004. The IGF2-intron3-G3072A substitution explains a major imprinted QTL effect on backfat thickness in a Meishan × European white pig intercross. Genet. Res.  84: 95– 101. https://doi.org/15678747 Google Scholar CrossRef Search ADS PubMed  Khatib H. 2007. Is it genomic imprinting or preferential expression? BioEssays  29: 1022– 1028. Google Scholar CrossRef Search ADS PubMed  Lee S. S. Chen Y. Moran C. Cepica S. Reiner G. Bartenschlager H. Moser G. Geldermann H. 2003. Linkage and QTL mapping for Sus scrofa chromosome 2. J. Anim. Breed. Genet.  120( Suppl. 1): 11– 19. Google Scholar CrossRef Search ADS   Li C. Bin Y. Curchoe C. Yang L. Feng D. Jiang Q. O'Neill M. Tian X. C. Zhang S. 2008. Genetic imprinting of H19 and IGF2 in domestic pigs (Sus scrofa). Anim. Biotechnol.  19: 22– 27. https://doi.org/18228173 Google Scholar CrossRef Search ADS PubMed  Liu G. Jennen D. G. Tholen E. Juengst H. Kleinwächter T. Hölker M. Tesfaye D. Un G. Schreinemachers H. J. Murani E. Ponsuksili S. Kim J. J. Schellander K. Wimmers K. 2007. A genome scan reveals QTL for growth, fatness, leanness and meat quality in a Duroc-Pietrain resource population. Anim. Genet.  38: 241– 252. https://doi.org/17459017 Google Scholar CrossRef Search ADS PubMed  Liu G. Kim J. J. Jonas E. Wimmers K. Ponsuksili S. Murani E. Phatsara C. Tholen E. Juengst H. Tesfaye D. Chen J. L. Schellander K. 2008. Combined line-cross and half-sib QTL analysis in Duroc-Pietrain population. Mamm. Genome  19: 429– 438. https://doi.org/18712441 Google Scholar CrossRef Search ADS PubMed  Mei Y. Chen Y. Li J. Gao P. Wang C. Zhang H. Ling F. Li Y. Xie S. Li S. Zhang G. 2008. Sequence identification, tissue distribution and polymorphism of the porcine cathepsin D (CTSD) gene. Anim. Biotechnol.  19: 144– 158. https://doi.org/18607787 Google Scholar CrossRef Search ADS PubMed  Milan D. Hawken R. Cabau C. Leroux S. Genet C. Lahbib Y. Tosser G. Robic A. Hatey F. Alexander L. Beattie C. Schook L. Yerle M. Gellin J. 2000. IMpRH server: An RH mapping server available on the Web. Bioinformatics  16: 558– 559. https://doi.org/10980153 Google Scholar CrossRef Search ADS PubMed  Morison I. M. Ramsay J. P. Spencer H. G. 2005. A census of mammalian imprinting. Trends Genet.  21: 457– 465. https://doi.org/15990197 Google Scholar CrossRef Search ADS PubMed  Nezer C. Moreau L. Brouwers B. Coppieters W. Detilleux J. Hanset R. Karim L. Kvasz A. Leroy P. Georges M. 1999. An imprinted QTL with major effect on muscle mass and fat deposition maps to the IGF2 locus in pigs. Nat. Genet.  21: 155– 156. https://doi.org/9988262 Google Scholar CrossRef Search ADS PubMed  Oczkowicz M. Tyra M. Walinowicz K. Rozycki M. Rejduch B. 2009. Known mutation (A3072G) in intron 3 of the IGF2 gene is associated with growth and carcass composition in Polish pig breeds. J. Appl. Genet.  50: 257– 259. https://doi.org/19638682 Google Scholar CrossRef Search ADS PubMed  Ojeda A. Huang L. S. Ren J. Angiolillo A. Cho I. C. Soto H. Lemús-Flores C. Makuza S. M. Folch J. M. Pérez-Enciso M. 2008. Selection in the making: A worldwide survey of haplotypic diversity around a causative mutation in porcine IGF2. Genetics  178: 1639– 1652. https://doi.org/18245828 Google Scholar CrossRef Search ADS PubMed  Park C. H. Kim H. S. Lee S. G. Lee C. K. 2009. Methylation status of differentially methylated regions at Igf2/H19 locus in porcine gametes and preimplantation embryos. Genomics  93: 179– 186. https://doi.org/18983907 Google Scholar CrossRef Search ADS PubMed  Rattink A. P. de Koning D. J. Faivre M. Harlizius B. van Arendonk J. A. Groenen M. A. 2000. Fine mapping and imprinting analysis for fatness trait QTLs in pigs. Mamm. Genome  11: 656– 661. https://doi.org/10920235 Google Scholar CrossRef Search ADS PubMed  Russo V. Fontanesi L. Scotti E. Beretti F. Davoli R. Nanni Costa L. Virgili R. Buttazzoni L. 2008. Single nucleotide polymorphisms in several porcine cathepsin genes are associated with growth, carcass, and production traits in Italian Large White pigs. J. Anim. Sci.  86: 3300– 3314. https://doi.org/18708606 Google Scholar CrossRef Search ADS PubMed  Sambrook, J., E. F. Fritsch, and T. Maniatis 1989. Molecular Cloning: A Laboratory Manual.  2nd ed. Cold Spring Harbor Laboratory Press, New York, NY. Sanchez M. P. Riquet J. Iannuccelli N. Gogué J. Billon Y. Demeure O. Caritez J. C. Burgaud G. Fève K. Bonnet M. Péry C. Lagant H. Le Roy P. Bidanel J. P. Milan D. 2006. Effects of quantitative trait loci on chromosomes 1, 2, 4, and 7 on growth, carcass, and meat quality traits in backcross Meishan × Large White pigs. J. Anim. Sci.  84: 526– 537. https://doi.org/16478944 Google Scholar CrossRef Search ADS PubMed  Stephens M. Smith N. J. Donnelly P. 2001. A new statistical method for haplotype reconstruction from population data. Am. J. Hum. Genet.  68: 978– 989. https://doi.org/11254454 Google Scholar CrossRef Search ADS PubMed  Thomsen H. Lee H. K. Rothschild M. F. Malek M. Dekkers J. C. 2004. Characterization of quantitative trait loci for growth and meat quality in a cross between commercial breeds of swine. J. Anim. Sci.  82: 2213– 2228. https://doi.org/15318717 Google Scholar CrossRef Search ADS PubMed  Thomsen H. Reinsch N. Xu N. Looft C. Grupe S. Kühn C. Brockmann G. A. Schwerin M. Leyhe-Horn B. Hiendleder S. Erhardt G. Medjugorac I. Russ I. Förster M. Brenig B. Reinhardt F. Reents R. Blümel J. Averdunk G. Kalm E. 2001. Comparison of estimated breeding values, daughter yield deviations and de-regressed proofs within a whole genome scan for QTL. J. Anim. Breed. Genet.  118: 357– 370. Google Scholar CrossRef Search ADS   Tribout T. Iannuccelli N. Druet T. Gilbert H. Riquet J. Gueblez R. Mercat M. J. Bidanel J. P. Milan D. Le Roy P. 2008. Detection of quantitative trait loci for reproduction and production traits in Large White and French Landrace pig populations. Genet. Sel. Evol.  40: 61– 78. https://doi.org/18096115 Google Scholar PubMed  Van den Maagdenberg K. Claeys E. Stinckens A. Buys N. De Smet S. 2007. Effect of age, muscle type, and insulin-like growth factor-II genotype on muscle proteolytic and lipolytic enzyme activities in boars. J. Anim. Sci.  85: 952– 960. https://doi.org/17202393 Google Scholar CrossRef Search ADS PubMed  Van Laere A.-S. Nguyen M. Braunschweig M. Nezer C. Collette C. Moreau L. Archibald A. L. Haley C. S. Buys N. Tally M. Andersson G. Georges M. Andersson L. 2003. A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature  425: 832– 836. https://doi.org/14574411 Google Scholar CrossRef Search ADS PubMed  van Wijk H. J. Dibbits B. Baron E. E. Brings A. D. Harlizius B. Groenen M. A. Knol E. F. Bovenhuis H. 2006. Identification of quantitative trait loci for carcass composition and pork quality traits in a commercial finishing cross. J. Anim. Sci.  84: 789– 799. https://doi.org/16543555 Google Scholar CrossRef Search ADS PubMed  Vidal O. Noguera J. L. Amills M. Varona L. Gil M. Jiménez N. Dávalos G. Folch J. M. Sánchez A. 2005. Identification of carcass and meat quality quantitative trait loci in a Landrace pig population selected for growth and leanness. J. Anim. Sci.  83: 293– 300. https://doi.org/15644499 Google Scholar CrossRef Search ADS PubMed  Viitala S. M. Schulman N. F. de Koning D. J. Elo K. Kinos R. Virta A. Virta J. Mäki-Tanila A. Vilkki J. H. 2003. Quantitative trait loci affecting milk production traits in Finnish Ayrshire dairy cattle. J. Dairy Sci.  86: 1828– 1836. https://doi.org/12778594 Google Scholar CrossRef Search ADS PubMed  Vykoukalová Z. Knoll A. Dvorák J. Cepica S. 2006. New SNPs in the IGF2 gene and association between this gene and backfat thickness and lean meat content in Large White pigs. J. Anim. Breed. Genet.  123: 204– 207. https://doi.org/16706926 Google Scholar CrossRef Search ADS PubMed  Wrzeska M. Żyga A. Rejduch B. Słota E. 2006. A note on biallelic expression of the IGF2 gene in the liver and brain of adult pigs. J. Anim. Feed Sci.  15: 57– 60. Google Scholar CrossRef Search ADS   Yang G. C. Ren J. Guo Y. M. Ding N. S. Chen C. Y. Huang L. S. 2006. Genetic evidence for the origin of an IGF2 quantitative trait nucleotide in Chinese pigs. Anim. Genet.  37: 179– 180. https://doi.org/16573535 Google Scholar CrossRef Search ADS PubMed  Yerle M. Pinton P. Robic A. Alfonso A. Palvadeau Y. Delcros C. Hawken R. Alexander L. Beattie C. Schook L. Milan D. Gellin J. 1998. Construction of a whole-genome radiation hybrid panel for high-resolution gene mapping in pigs. Cytogenet. Cell Genet.  82: 182– 188. https://doi.org/9858812 Google Scholar CrossRef Search ADS PubMed  Zaidi N. Maurer A. Nieke S. Kalbacher H. 2008. Cathepsin D: A cellular roadmap. Biochem. Biophys. Res. Commun.  376: 5– 9. https://doi.org/18762174 Google Scholar CrossRef Search ADS PubMed  American Society of Animal Science

Casellas, J.;Noguera, J. L.;Reixach, J.;Díaz, I.;Amills, M.;Quintanilla, R.

doi: 10.2527/jas.2009-2205pmid: 20418459

ABSTRACT Concern about pork quality has increased during last decades. Given the influence of fat content and composition on sensorial, nutritional, and technological variables of pork meat, an accurate knowledge about genetic control of pig lipid metabolism is required. This study focused on providing a broad characterization for serum and meat lipid trait heritability estimates in pigs. Analyses were performed on a population of 370 Duroc barrows and measured the additive polygenic background for the serum concentrations of cholesterol, triglyceride, and low- and high-density lipoproteins at 45 and 190 d of age (at slaughter), as well as intramuscular fat, cholesterol content, and C:12 to C:22 fatty acid content in longissimus thoracis et lumborum and gluteus medius muscles at slaughter. These traits were analyzed under Bayesian univariate animal linear models, and the statistical relevance of heritability estimates was evaluated through Bayes factor (BF); the model with polygenic additive effects was favored when BF >1. All serum lipid traits showed relevant genetic determinism, but the BF reached greater values at 190 d of age. Serum lipid traits displayed moderate modal estimates for heritability that ranged from 0.18 to 0.30. On the other hand, the genetic determinism for meat quality traits showed a heterogeneous behavior with large and less-than-1 BF. In general, longissimus thoracis et lumborum and gluteus medius muscles showed a similar pattern, with strong evidence of polygenic additive effects for intramuscular fat and palmitic, stearic, and cis-vaccenic fatty acids content, whereas oleic and muscle cholesterol content showed moderate to weak BF with moderate heritabilities. Similarly, results regarding linoleic, arachidonic, n-3, and n-6 fatty acids suggested a moderate genetic determinism, but only in gluteus medius muscle. For the remaining traits (myristic and palmitoleic fatty acids in both muscles, along with linoleic, arachidonic, n-3, and n-6 fatty acids in the longissimus thoracis et lumborum muscle), no statistical evidence for genetic control was observed in this study. As a whole, these results confirm the complexity of lipid metabolism in pigs. INTRODUCTION Lipid metabolism in pigs is an area of relevance in genetics research because it is directly or indirectly involved in human nutrition and health (Jarratt and Mahaffie, 2002). The intramuscular fat (IMF) content and its fatty acid (FA) and cholesterol (CHOL) composition show an impact on various traits related with pork quality, such as technological attributes (i.e., firmness, storage stability), flavor, nutritive value, and palatability (Cameron and Enser, 1991; Fischer, 2005; Wood et al., 2008). Moreover, these meat quality traits are also linked with serum lipid concentrations (Averette Gatlin et al., 2003), highlighting the complexity of lipid metabolism. From a nutritional aspect, pork is an important source of oleic and essential n-6 and n-3 FA, with their well-known influences on human health (Tribole, 2006). The characteristics of fat associated to meat are key factors for the production of high-quality dry-cured hams, where IMF content and FA composition can affect the drying period, ripening, and flavor of final products (Chizzolini et al., 1998; López-Bote, 1998). In spite of the relevance of pig meat composition for humans, our knowledge about the genetic control of pig serum and muscle lipid traits remains limited. There are few estimates of the genetic parameters for FA in pigs, mainly restricted to the most abundant FA (Fernández et al., 2003; Suzuki et al., 2006). The aim of this research was to characterize the heritability of 2 groups of relevant lipid-related traits in pigs: the blood concentrations of CHOL, lipoproteins, and triglycerides of young pigs at 2 ages, and the lipid characteristics of the longissimus thoracis et lumborum and gluteus medius muscles focused on IMF, CHOL content, and FA profile at slaughter. These analyses were performed on a Duroc population through the simplified Bayes factor (BF) test developed by García-Cortés et al. (2001) and Varona et al. (2001). MATERIALS AND METHODS All experimental procedures were approved by the Animal Care and Use Committee of the Institut de Recerca i Tecnologia Agroalimentàries (http://www.irta.es). Animal Material Source This study was performed on a commercial Duroc line (Selección Batallé SA, Riudarenes, Spain), which is primarily used for producing dry-cured ham. Phenotypic data were collected from 370 castrated males generated between August, 2003 and May, 2005 in 4 contemporary batches (95, 114, 83, and 78 animals per batch), under an experimental design of half-sib families. More specifically, 370 purebred Duroc sows distributed in 3 farms were mated with 5 purebred Duroc boars, and 1 male offspring per litter was taken at random (49, 93, 81, 84, and 63 sons per sire). After weaning, castrated animals born on the 3 farms (150, 153, and 55 animals per farm) were moved to the test station (IRTA Pig Control Center, Monells, Spain), where they were initially (transition period) housed with other animals foreign to the experiment. Barrows from each contemporary batch were moved together to the fattening and control units at 2 to 3 mo of age and randomly distributed over 10 pens (5 pens at each side of a central corridor in the same barn) in groups of 8 to 12 animals. Pigs were all raised under the same standard intensive management conditions and fed ad libitum up to slaughter. During the first period of fattening (up to 90 kg of BW, around 150 d of age) barrows were fed a standard diet with 18% protein, 3.8% fiber, 7.0% fat, 1.0% lysine, and 0.3% methionine (2,450 kcal/kg; DM basis). In the second period of fattening (the last 30 to 40 d before slaughter), animals were fed a standard diet with 15.9% protein, 4.5% fiber, 5.2% fat, 0.7% lysine, and 0.2% methionine (2,375 kcal/kg; DM basis). Pigs were slaughtered at 122.5 kg ± 0.7 kg of BW and 193.7 d ± 0.4 d of age. All relevant data were recorded (e.g., farm of origin, batch and pen of fattening, along with age, backfat thickness, and BW at blood collection and at slaughter). Lipid Profile in Blood and Meat Samples Blood samples (5 mL) were taken from a jugular vein (Framstad et al., 1988) of each barrow at 45.0 ± 0.4 and 190.4 ± 0.4 d of age and appropriately processed to measure total CHOL (CHOL45 and CHOL190), low-density lipoproteins (LDL45 and LDL190), high-density lipoproteins (HDL45 and HDL190), and triglyceride (TG45 and TG190) serum concentrations as described by Gallardo et al. (2008). Venous blood was collected into EDTA-containing TapVal tubes (Aquisel, Barcelona, Spain). Blood samples were kept on ice until centrifugation (10 min at 1,560 × g at 4°C), after which serum was collected and stored at −80°C until analyzed. Serum CHOL was measured by a CHOL oxidase-based method employing CHOL esterase, CHOL oxidase, and peroxidase enzymes (Richmond, 1992). Serum HDL concentrations were determined by immune-inhibition (Rafai and Warnick, 1994), whereas serum triglyceride (TG) concentration was quantified by means of a glycerol kinase reaction with the method reported by Fossati and Prencipe (1982). Finally, serum LDL concentration was calculated according to the equation reported by Friedewald et al. (1972). Two 200-g samples were taken from the longissimus thoracis et lumborum and gluteus medius muscles during carcass operations in the slaughterhouse (approximately 30 min after slaughter). After appropriate processing, IMF content was determined by Near Infrared Transmittance (Infratec 1625, Tecator Hoganas, Sweden), FA (C:12 to C:22 interval) composition was analyzed by gas chromatography of methyl esters as described in Mach et al. (2006), and muscle CHOL content (CHOLmusc) was measured following Cayuela et al. (2003). All these quantifications were performed in the Centre de Tecnologia dels Aliments of IRTA (Monells, Spain). Statistical Analyses Linear mixed model analyses were performed on CHOL, HDL, LDL, and TG serum concentrations (Table 1), as well as CHOLmusc and the percentage of IMF and myristic, palmitic, palmitoleic, stearic, oleic, cis-vaccenic, linoleic, and arachidonic FA in gluteus medius and longissimus thoracis et lumborum (Table 2). Fatty acids with a less-than-1 average percentage in both muscles were not included in the analyses due to their biological relevance and because their small measures were close to the instrumentation error, but the sum of n-3 and n-6 FA were also analyzed given their implications on human health (Table 2). Serum concentrations were log-transformed to correct departures from normality as in Gallardo et al. (2008), whereas the remaining traits did not show relevant departures from normality (Gallardo et al., 2008). Nongenetic sources of variation were preliminarily evaluated with the Generalized Linear Models procedure (SAS Institute Inc., Cary, NC), leading to the following models for Table 1. Phenotypic summary of lipid serum traits recorded in Duroc pigs at 45 and 190 d of age Trait  Abbreviation  n  Mean1  SE  At 45 d           Cholesterol, mg/dL  CHOL45  333  77.30  0.74   HDL2-cholesterol, mg/dL  HDL45  333  30.43  0.38   LDL3-cholesterol, mg/dL  LDL45  333  38.03  0.48   Triglycerides, mg/dL  TG45  333  44.08  1.07  At 190 d           Cholesterol, mg/dL  CHOL190  316  125.15  1.47   HDL-cholesterol, mg/dL  HDL190  316  51.66  0.57   LDL-cholesterol, mg/dL  LDL190  316  63.12  1.15   Triglycerides, mg/dL  TG190  316  51.37  1.31  Trait  Abbreviation  n  Mean1  SE  At 45 d           Cholesterol, mg/dL  CHOL45  333  77.30  0.74   HDL2-cholesterol, mg/dL  HDL45  333  30.43  0.38   LDL3-cholesterol, mg/dL  LDL45  333  38.03  0.48   Triglycerides, mg/dL  TG45  333  44.08  1.07  At 190 d           Cholesterol, mg/dL  CHOL190  316  125.15  1.47   HDL-cholesterol, mg/dL  HDL190  316  51.66  0.57   LDL-cholesterol, mg/dL  LDL190  316  63.12  1.15   Triglycerides, mg/dL  TG190  316  51.37  1.31  1Raw means from phenotypic data. 2HDL = high-density lipoprotein. 3LDL = low-density lipoprotein. View Large Table 1. Phenotypic summary of lipid serum traits recorded in Duroc pigs at 45 and 190 d of age Trait  Abbreviation  n  Mean1  SE  At 45 d           Cholesterol, mg/dL  CHOL45  333  77.30  0.74   HDL2-cholesterol, mg/dL  HDL45  333  30.43  0.38   LDL3-cholesterol, mg/dL  LDL45  333  38.03  0.48   Triglycerides, mg/dL  TG45  333  44.08  1.07  At 190 d           Cholesterol, mg/dL  CHOL190  316  125.15  1.47   HDL-cholesterol, mg/dL  HDL190  316  51.66  0.57   LDL-cholesterol, mg/dL  LDL190  316  63.12  1.15   Triglycerides, mg/dL  TG190  316  51.37  1.31  Trait  Abbreviation  n  Mean1  SE  At 45 d           Cholesterol, mg/dL  CHOL45  333  77.30  0.74   HDL2-cholesterol, mg/dL  HDL45  333  30.43  0.38   LDL3-cholesterol, mg/dL  LDL45  333  38.03  0.48   Triglycerides, mg/dL  TG45  333  44.08  1.07  At 190 d           Cholesterol, mg/dL  CHOL190  316  125.15  1.47   HDL-cholesterol, mg/dL  HDL190  316  51.66  0.57   LDL-cholesterol, mg/dL  LDL190  316  63.12  1.15   Triglycerides, mg/dL  TG190  316  51.37  1.31  1Raw means from phenotypic data. 2HDL = high-density lipoprotein. 3LDL = low-density lipoprotein. View Large Table 2. Phenotypic summary of intramuscular fat and fatty acid (FA) composition in longissimus thoracis et lumborum and gluteus medius muscles of Duroc pigs Trait  Abbreviation  n  Longissimus thoracis et lumborum  Gluteus medius  Mean1  SE  Mean  SE  Intramuscular fat, %  IMF  321  3.84  0.08  5.17  0.11  CHOL2 content, mg/g  CHOLmusc  321  58.60  0.52  64.65  0.61  Myristic FA, %  C14:0  321  1.36  0.02  1.38  0.01  Palmitic FA, %  C16:0  321  23.42  0.09  23.25  0.07  Palmitoleic FA, %  C16:1  321  2.95  0.03  0.29  0.001  Stearic FA, %  C18:0  321  11.68  0.07  11.19  0.06  Oleic FA, %  C18:1n-9  321  34.61  0.29  34.85  0.25  Cis-vaccenic FA, %  C18:1n-7  321  4.30  0.02  4.07  0.02  Linoleic FA, %  C18:2  321  14.44  0.29  15.11  0.23  Arachidonic FA, %  C20:4  321  3.62  0.10  3.25  0.09  n-6 FA, %  n-6  321  19.13  0.40  19.51  0.33  n-3 FA, %  n-3  321  0.92  0.02  1.11  0.02  Trait  Abbreviation  n  Longissimus thoracis et lumborum  Gluteus medius  Mean1  SE  Mean  SE  Intramuscular fat, %  IMF  321  3.84  0.08  5.17  0.11  CHOL2 content, mg/g  CHOLmusc  321  58.60  0.52  64.65  0.61  Myristic FA, %  C14:0  321  1.36  0.02  1.38  0.01  Palmitic FA, %  C16:0  321  23.42  0.09  23.25  0.07  Palmitoleic FA, %  C16:1  321  2.95  0.03  0.29  0.001  Stearic FA, %  C18:0  321  11.68  0.07  11.19  0.06  Oleic FA, %  C18:1n-9  321  34.61  0.29  34.85  0.25  Cis-vaccenic FA, %  C18:1n-7  321  4.30  0.02  4.07  0.02  Linoleic FA, %  C18:2  321  14.44  0.29  15.11  0.23  Arachidonic FA, %  C20:4  321  3.62  0.10  3.25  0.09  n-6 FA, %  n-6  321  19.13  0.40  19.51  0.33  n-3 FA, %  n-3  321  0.92  0.02  1.11  0.02  1Raw means from phenotypic data. 2CHOL = cholesterol. View Large Table 2. Phenotypic summary of intramuscular fat and fatty acid (FA) composition in longissimus thoracis et lumborum and gluteus medius muscles of Duroc pigs Trait  Abbreviation  n  Longissimus thoracis et lumborum  Gluteus medius  Mean1  SE  Mean  SE  Intramuscular fat, %  IMF  321  3.84  0.08  5.17  0.11  CHOL2 content, mg/g  CHOLmusc  321  58.60  0.52  64.65  0.61  Myristic FA, %  C14:0  321  1.36  0.02  1.38  0.01  Palmitic FA, %  C16:0  321  23.42  0.09  23.25  0.07  Palmitoleic FA, %  C16:1  321  2.95  0.03  0.29  0.001  Stearic FA, %  C18:0  321  11.68  0.07  11.19  0.06  Oleic FA, %  C18:1n-9  321  34.61  0.29  34.85  0.25  Cis-vaccenic FA, %  C18:1n-7  321  4.30  0.02  4.07  0.02  Linoleic FA, %  C18:2  321  14.44  0.29  15.11  0.23  Arachidonic FA, %  C20:4  321  3.62  0.10  3.25  0.09  n-6 FA, %  n-6  321  19.13  0.40  19.51  0.33  n-3 FA, %  n-3  321  0.92  0.02  1.11  0.02  Trait  Abbreviation  n  Longissimus thoracis et lumborum  Gluteus medius  Mean1  SE  Mean  SE  Intramuscular fat, %  IMF  321  3.84  0.08  5.17  0.11  CHOL2 content, mg/g  CHOLmusc  321  58.60  0.52  64.65  0.61  Myristic FA, %  C14:0  321  1.36  0.02  1.38  0.01  Palmitic FA, %  C16:0  321  23.42  0.09  23.25  0.07  Palmitoleic FA, %  C16:1  321  2.95  0.03  0.29  0.001  Stearic FA, %  C18:0  321  11.68  0.07  11.19  0.06  Oleic FA, %  C18:1n-9  321  34.61  0.29  34.85  0.25  Cis-vaccenic FA, %  C18:1n-7  321  4.30  0.02  4.07  0.02  Linoleic FA, %  C18:2  321  14.44  0.29  15.11  0.23  Arachidonic FA, %  C20:4  321  3.62  0.10  3.25  0.09  n-6 FA, %  n-6  321  19.13  0.40  19.51  0.33  n-3 FA, %  n-3  321  0.92  0.02  1.11  0.02  1Raw means from phenotypic data. 2CHOL = cholesterol. View Large a) serum lipid concentration traits at 45 d of age,   b) serum lipid concentrations at 190 d of age,   c) FA,   d) IMF,   and e) CHOLmusc,   where Y was the phenotypic record, Bi was the fattening contemporary batch, Fj was the farm of origin, covj was a different covariate depending on the trait (BW for CHOL, HDL, and LDL, and age at blood collection for TG), and IMFj and BTj were continuous covariates with the percentage of IMF and backfat thickness (mm) fat at slaughter, respectively, and eij was the residual term. The statistical relevance of the heritability parameter was evaluated through the BF described by García-Cortés et al. (2001) and Varona et al. (2001; see Appendix for a comprehensive description of this BF approach). Note that BF is the ratio of the posterior probabilities of 2 competing models, taking any positive value between >0 and In this case, a linear mixed model with additive polygenic effects (numerator model) was compared against a model without additive polygenic effects (denominator model), where greater-than-1 BF favored the numerator model and less-than-1 BF favored the denominator model. In this report, the BF results were discussed within the context of the Jeffreys (1984) discrete scale of evidences. This scale classifies the BF according to 6 different levels of evidence for the numerator model, objectively classifying the BF as denominator model supported, not worth more than a bare mention, substantial evidence, strong evidence, very strong evidence, and decisive evidence (see Appendix). From now on, this terminology will be systematically used when referring to the BF. For each analysis, a unique chain with 25,000 iterations was launched, after discarding the first 5,000 elements as burn-in. Convergence was evaluated by visual inspection after plotting the Markov chain Monte Carlo sampled values for all variance components across iterations. Moreover, the Raftery and Lewis (1992) approach was used to objectively evaluate the convergence and length of the burn-in period on the variance component parameters. For all traits and variances, convergence was guaranteed with less than 500 iterations. RESULTS Phenotypic Estimates The average phenotypic values for CHOL, HDL, LDL, and TG in 45- and 190-d-old Duroc barrows are shown in Table 1. Note that TG190 concentrations were approximately 1.2 times greater than TG45 whereas CHOL190, HDL190, and LDL190 serum concentrations were approximately 1.7 times greater that their corresponding estimates at 45 d of age. Longissimus thoracis et lumborum and gluteus medius muscles showed relevant differences (P < 0.05) in terms of IMF and CHOLmusc, with values of 3.84 ± 0.08% vs. 5.17 ± 0.08%, and 58.60 ± 0.52 mg/g vs. 64.65 ± 0.61 mg/g, respectively (Table 2). As a whole, oleic (n-9; ~34%), palmitic (~23%), and linoleic (~15%) acids were the most abundant FA, accounting for almost 75% of the overall FA amount. Genetic Determinism for Serum Lipid and Meat Composition Traits Results based on the BF test for heritability estimation are shown in Tables 3 and 4. Bayes factors between models with and without a genetic component gave values greater than 1 for all analyzed serum lipid concentrations (Table 3), providing evidence that the model was more predictive when polygenic additive effects were included. The greatest BF were reached by serum lipid concentrations at 190 d, where LDL190 and TG190 showed strong evidence (BF >10) and CHOL190 showed very strong evidence (BF >31.62) of genetic determinism according to the Jeffreys (1984) scale. Posterior estimates of heritabilities (mean and mode) reflected medium values (from 0.18 to 0.48) for all traits analyzed (Table 3). However, it should be noted that the large SD associated to the limited sample size of our experimental population led to large highest posterior density regions at 95% (HPD95), which included values near to zero for HDL45, LDL45, and HDL190 heritabilities and lesser BF. Table 3. Bayes factor and heritability estimates for lipid serum traits in Duroc pigs at 45 and 190 d of age Trait1  Bayes factor2  Heritability3  Mean  Mode  PSD  HPD95  45-d-old pigs             CHOL45  3.1  0.38  0.29  0.22  0.06 to 0.68   HDL45  2.2  0.47  0.30  0.26  0.01 to 0.81   LDL45  1.3  0.27  0.18  0.18  0.001 to 0.62   TG45  8.7  0.42  0.31  0.19  0.07 to 0.70  190-d-old pigs             CHOL190  47.9  0.37  0.28  0.20  0.08 to 0.62   HDL190  2.1  0.45  0.22  0.27  0.02 to 0.80   LDL190  16.3  0.36  0.30  0.19  0.07 to 0.60   TG190  16.1  0.34  0.23  0.20  0.05 to 0.58  Trait1  Bayes factor2  Heritability3  Mean  Mode  PSD  HPD95  45-d-old pigs             CHOL45  3.1  0.38  0.29  0.22  0.06 to 0.68   HDL45  2.2  0.47  0.30  0.26  0.01 to 0.81   LDL45  1.3  0.27  0.18  0.18  0.001 to 0.62   TG45  8.7  0.42  0.31  0.19  0.07 to 0.70  190-d-old pigs             CHOL190  47.9  0.37  0.28  0.20  0.08 to 0.62   HDL190  2.1  0.45  0.22  0.27  0.02 to 0.80   LDL190  16.3  0.36  0.30  0.19  0.07 to 0.60   TG190  16.1  0.34  0.23  0.20  0.05 to 0.58  1Total cholesterol (CHOL), low-density lipoproteins (LDL), high-density lipoproteins (HDL), and triglyceride (TG) serum concentrations at 45 d (e.g., CHOL45) and 190 d of age (e.g., CHOL190). 2Bayes factor of the model with additive polygenic effects against the same model without additive polygenic effects following García-Cortés et al. (2001). 3The posterior distribution of heritability was characterized by several basic statistics: mean, mode, posterior SD (PSD), and highest posterior density region at 95% (HPD95). View Large Table 3. Bayes factor and heritability estimates for lipid serum traits in Duroc pigs at 45 and 190 d of age Trait1  Bayes factor2  Heritability3  Mean  Mode  PSD  HPD95  45-d-old pigs             CHOL45  3.1  0.38  0.29  0.22  0.06 to 0.68   HDL45  2.2  0.47  0.30  0.26  0.01 to 0.81   LDL45  1.3  0.27  0.18  0.18  0.001 to 0.62   TG45  8.7  0.42  0.31  0.19  0.07 to 0.70  190-d-old pigs             CHOL190  47.9  0.37  0.28  0.20  0.08 to 0.62   HDL190  2.1  0.45  0.22  0.27  0.02 to 0.80   LDL190  16.3  0.36  0.30  0.19  0.07 to 0.60   TG190  16.1  0.34  0.23  0.20  0.05 to 0.58  Trait1  Bayes factor2  Heritability3  Mean  Mode  PSD  HPD95  45-d-old pigs             CHOL45  3.1  0.38  0.29  0.22  0.06 to 0.68   HDL45  2.2  0.47  0.30  0.26  0.01 to 0.81   LDL45  1.3  0.27  0.18  0.18  0.001 to 0.62   TG45  8.7  0.42  0.31  0.19  0.07 to 0.70  190-d-old pigs             CHOL190  47.9  0.37  0.28  0.20  0.08 to 0.62   HDL190  2.1  0.45  0.22  0.27  0.02 to 0.80   LDL190  16.3  0.36  0.30  0.19  0.07 to 0.60   TG190  16.1  0.34  0.23  0.20  0.05 to 0.58  1Total cholesterol (CHOL), low-density lipoproteins (LDL), high-density lipoproteins (HDL), and triglyceride (TG) serum concentrations at 45 d (e.g., CHOL45) and 190 d of age (e.g., CHOL190). 2Bayes factor of the model with additive polygenic effects against the same model without additive polygenic effects following García-Cortés et al. (2001). 3The posterior distribution of heritability was characterized by several basic statistics: mean, mode, posterior SD (PSD), and highest posterior density region at 95% (HPD95). View Large Table 4. Bayes factor and heritability for the intramuscular fat and fatty acid (FA) composition of 2 muscles in Duroc pigs Trait  Bayes factor1  Heritability2  Mean  Mode  PSD  HPD95  Longissimus thoracis et lumborum muscle             Intramuscular fat, %  1,152.3  0.55  0.65  0.18  0.18 to 0.91   CHOL3 content, mg/g  3.9  0.30  0.20  0.20  0.01 to 0.65   Myristic FA, %  0.2  0.25  0.09  0.21  0.00 to 0.67   Palmitic FA, %  15.6  0.47  0.30  0.26  0.08 to 0.88   Palmitoleic FA, %  0.8  0.30  0.12  0.19  0.00 to 0.60   Stearic FA, %  883.0  0.45  0.33  0.22  0.09 to 0.86   Oleic FA, %  1.3  0.30  0.15  0.21  0.00 to 0.69   Cis-vaccenic FA, %  37.1  0.41  0.32  0.21  0.05 to 0.84   Linoleic FA, %  0.6  0.25  0.14  0.20  0.00 to 0.68   Arachidonic FA, %  0.3  0.26  0.12  0.22  0.00 to 0.74   n-6 FA, %  0.6  0.24  0.14  0.19  0.001 to 0.64   n-3 FA, %  0.4  0.28  0.14  0.21  0.001 to 0.79  Gluteus medius muscle             Intramuscular fat, %  992.9  0.47  0.58  0.17  0.18 to 0.86   CHOL content, mg/g  4.8  0.35  0.22  0.21  0.02 to 0.70   Myristic FA, %  0.1  0.16  0.08  0.16  0.00 to 0.50   Palmitic FA, %  40.4  0.44  0.30  0.24  0.06 to 0.90   Palmitoleic FA, %  0.9  0.27  0.12  0.18  0.01 to 0.62   Stearic FA, %  1,575.0  0.43  0.53  0.22  0.09 to 0.81   Oleic FA, %  1.6  0.32  0.21  0.21  0.00 to 0.73   Cis-vaccenic FA, %  566.6  0.38  0.29  0.20  0.04 to 0.78   Linoleic FA, %  8.9  0.37  0.29  0.21  0.01 to 0.77   Arachidonic FA, %  2.9  0.36  0.31  0.23  0.01 to 0.82   n-6 FA, %  1.1  0.24  0.14  0.13  0.001 to 0.49   n-3 FA, %  2.0  0.22  0.16  0.14  0.002 to 0.50  Trait  Bayes factor1  Heritability2  Mean  Mode  PSD  HPD95  Longissimus thoracis et lumborum muscle             Intramuscular fat, %  1,152.3  0.55  0.65  0.18  0.18 to 0.91   CHOL3 content, mg/g  3.9  0.30  0.20  0.20  0.01 to 0.65   Myristic FA, %  0.2  0.25  0.09  0.21  0.00 to 0.67   Palmitic FA, %  15.6  0.47  0.30  0.26  0.08 to 0.88   Palmitoleic FA, %  0.8  0.30  0.12  0.19  0.00 to 0.60   Stearic FA, %  883.0  0.45  0.33  0.22  0.09 to 0.86   Oleic FA, %  1.3  0.30  0.15  0.21  0.00 to 0.69   Cis-vaccenic FA, %  37.1  0.41  0.32  0.21  0.05 to 0.84   Linoleic FA, %  0.6  0.25  0.14  0.20  0.00 to 0.68   Arachidonic FA, %  0.3  0.26  0.12  0.22  0.00 to 0.74   n-6 FA, %  0.6  0.24  0.14  0.19  0.001 to 0.64   n-3 FA, %  0.4  0.28  0.14  0.21  0.001 to 0.79  Gluteus medius muscle             Intramuscular fat, %  992.9  0.47  0.58  0.17  0.18 to 0.86   CHOL content, mg/g  4.8  0.35  0.22  0.21  0.02 to 0.70   Myristic FA, %  0.1  0.16  0.08  0.16  0.00 to 0.50   Palmitic FA, %  40.4  0.44  0.30  0.24  0.06 to 0.90   Palmitoleic FA, %  0.9  0.27  0.12  0.18  0.01 to 0.62   Stearic FA, %  1,575.0  0.43  0.53  0.22  0.09 to 0.81   Oleic FA, %  1.6  0.32  0.21  0.21  0.00 to 0.73   Cis-vaccenic FA, %  566.6  0.38  0.29  0.20  0.04 to 0.78   Linoleic FA, %  8.9  0.37  0.29  0.21  0.01 to 0.77   Arachidonic FA, %  2.9  0.36  0.31  0.23  0.01 to 0.82   n-6 FA, %  1.1  0.24  0.14  0.13  0.001 to 0.49   n-3 FA, %  2.0  0.22  0.16  0.14  0.002 to 0.50  1Bayes factor of the model with additive polygenic effects against the same model without additive polygenic effects following García-Cortés et al. (2001). 2The posterior distribution of heritability was characterized by several basic statistics: mean, mode, posterior SD (PSD), and highest posterior density region at 95% (HPD95). 3CHOL = cholesterol. View Large Table 4. Bayes factor and heritability for the intramuscular fat and fatty acid (FA) composition of 2 muscles in Duroc pigs Trait  Bayes factor1  Heritability2  Mean  Mode  PSD  HPD95  Longissimus thoracis et lumborum muscle             Intramuscular fat, %  1,152.3  0.55  0.65  0.18  0.18 to 0.91   CHOL3 content, mg/g  3.9  0.30  0.20  0.20  0.01 to 0.65   Myristic FA, %  0.2  0.25  0.09  0.21  0.00 to 0.67   Palmitic FA, %  15.6  0.47  0.30  0.26  0.08 to 0.88   Palmitoleic FA, %  0.8  0.30  0.12  0.19  0.00 to 0.60   Stearic FA, %  883.0  0.45  0.33  0.22  0.09 to 0.86   Oleic FA, %  1.3  0.30  0.15  0.21  0.00 to 0.69   Cis-vaccenic FA, %  37.1  0.41  0.32  0.21  0.05 to 0.84   Linoleic FA, %  0.6  0.25  0.14  0.20  0.00 to 0.68   Arachidonic FA, %  0.3  0.26  0.12  0.22  0.00 to 0.74   n-6 FA, %  0.6  0.24  0.14  0.19  0.001 to 0.64   n-3 FA, %  0.4  0.28  0.14  0.21  0.001 to 0.79  Gluteus medius muscle             Intramuscular fat, %  992.9  0.47  0.58  0.17  0.18 to 0.86   CHOL content, mg/g  4.8  0.35  0.22  0.21  0.02 to 0.70   Myristic FA, %  0.1  0.16  0.08  0.16  0.00 to 0.50   Palmitic FA, %  40.4  0.44  0.30  0.24  0.06 to 0.90   Palmitoleic FA, %  0.9  0.27  0.12  0.18  0.01 to 0.62   Stearic FA, %  1,575.0  0.43  0.53  0.22  0.09 to 0.81   Oleic FA, %  1.6  0.32  0.21  0.21  0.00 to 0.73   Cis-vaccenic FA, %  566.6  0.38  0.29  0.20  0.04 to 0.78   Linoleic FA, %  8.9  0.37  0.29  0.21  0.01 to 0.77   Arachidonic FA, %  2.9  0.36  0.31  0.23  0.01 to 0.82   n-6 FA, %  1.1  0.24  0.14  0.13  0.001 to 0.49   n-3 FA, %  2.0  0.22  0.16  0.14  0.002 to 0.50  Trait  Bayes factor1  Heritability2  Mean  Mode  PSD  HPD95  Longissimus thoracis et lumborum muscle             Intramuscular fat, %  1,152.3  0.55  0.65  0.18  0.18 to 0.91   CHOL3 content, mg/g  3.9  0.30  0.20  0.20  0.01 to 0.65   Myristic FA, %  0.2  0.25  0.09  0.21  0.00 to 0.67   Palmitic FA, %  15.6  0.47  0.30  0.26  0.08 to 0.88   Palmitoleic FA, %  0.8  0.30  0.12  0.19  0.00 to 0.60   Stearic FA, %  883.0  0.45  0.33  0.22  0.09 to 0.86   Oleic FA, %  1.3  0.30  0.15  0.21  0.00 to 0.69   Cis-vaccenic FA, %  37.1  0.41  0.32  0.21  0.05 to 0.84   Linoleic FA, %  0.6  0.25  0.14  0.20  0.00 to 0.68   Arachidonic FA, %  0.3  0.26  0.12  0.22  0.00 to 0.74   n-6 FA, %  0.6  0.24  0.14  0.19  0.001 to 0.64   n-3 FA, %  0.4  0.28  0.14  0.21  0.001 to 0.79  Gluteus medius muscle             Intramuscular fat, %  992.9  0.47  0.58  0.17  0.18 to 0.86   CHOL content, mg/g  4.8  0.35  0.22  0.21  0.02 to 0.70   Myristic FA, %  0.1  0.16  0.08  0.16  0.00 to 0.50   Palmitic FA, %  40.4  0.44  0.30  0.24  0.06 to 0.90   Palmitoleic FA, %  0.9  0.27  0.12  0.18  0.01 to 0.62   Stearic FA, %  1,575.0  0.43  0.53  0.22  0.09 to 0.81   Oleic FA, %  1.6  0.32  0.21  0.21  0.00 to 0.73   Cis-vaccenic FA, %  566.6  0.38  0.29  0.20  0.04 to 0.78   Linoleic FA, %  8.9  0.37  0.29  0.21  0.01 to 0.77   Arachidonic FA, %  2.9  0.36  0.31  0.23  0.01 to 0.82   n-6 FA, %  1.1  0.24  0.14  0.13  0.001 to 0.49   n-3 FA, %  2.0  0.22  0.16  0.14  0.002 to 0.50  1Bayes factor of the model with additive polygenic effects against the same model without additive polygenic effects following García-Cortés et al. (2001). 2The posterior distribution of heritability was characterized by several basic statistics: mean, mode, posterior SD (PSD), and highest posterior density region at 95% (HPD95). 3CHOL = cholesterol. View Large Focusing on meat quality traits, BF estimates for the genetic background of the muscular fat content and composition showed a consistent pattern across muscles (Table 4). The additive genetic variability for the percentage of IMF was clearly demonstrated with a BF of 1,152.3 and 992.9 for longissimus thoracis et lumborum and gluteus medius muscles, respectively. Additionally, this trait reached the greatest heritability estimates, with modal values around 0.6 in both muscles. Although the small sample size led to wide HPD95, they were far apart from the null estimate, starting around 0.18 (Table 4). Also CHOLmusc showed inheritable patterns in both muscles, but BF values provided less relevant evidences of genetic determinism with modal heritabilities centered around approximately 0.20. The additive genetic background for the different FA was not homogeneous. The percentage of stearic FA was very heritable in both muscles (0.53 and 0.33 in gluteus medius and longissimus thoracis and lumborum, respectively), providing decisive evidence (BF >100) on the basis of the Jeffreys (1984) scale. Also the cis-vaccenic acid content provided decisive (BF = 566.6; h2 = 0.38) and very strong (BF = 37.1; h2 = 0.32) evidence of an additive genetic background in gluteus medius and longissimus thoracis et lumborum muscles, respectively. The palmitic FA revealed strong (longissimus thoracis et lumborum; h2 = 0.30) and very strong (gluteus medius; h2 = 0.30) evidence of additive genetic variance. Heritability estimates for those FA with relevant BF were moderate, with modal estimates ranging between 0.29 (cis-vaccenic) and 0.53 (stearic). For the oleic acid content a small but BF >1 was observed. In the light of these results, and despite the medium values (from 0.28 to 0.32) obtained for the posterior mode of the heritability, it is difficult to conclude about the genetic determinism of oleic content. Linoleic and arachidonic FA revealed moderate BF (BF <10) in gluteus medius, suggesting a certain level of genetic control. The remaining FA scored nonrelevant BF, failing to provide evidence about polygenic additive genetic effects (Table 4). Note that n-3 and n-6 FA in longissimus thoracis et lumborum were included in this last group of FA because BF failed to identify evidence of genetic control. DISCUSSION Phenotypic Pattern Pork is one of the most consumed meats in Western cultures, with 35 kg/(habitant/yr) in the European Union, 30 kg/(habitant/yr) in the United States, 27 kg/(habitant/yr) in Canada and 21 kg/(habitant/yr) in Australia. Moreover, pork is also relevant for Eastern countries like China [35 kg/(habitant/yr)] or Japan [18 kg/(habitant/yr)], and has an average worldwide consumption of 15 kg per habitant and year (Food and Agriculture Organization of the United Nations; http://www.fao.org). Within this context, concern about pork quality increased during last decades (Tarrant, 1998) concerning sensorial, nutritional, and technological variables. In our sample of Duroc barrows, longissimus thoracis et lumborum and gluteus medius muscles showed substantial differences in terms of IMF and CHOLmusc, with gluteus medius being fatter and with a greater CHOL content, as previously reported by Fiedler et al. (2003) and Kim et al. (2008). Apart from their incidence on quality of fresh meat, IMF content becomes a variable of paramount importance in the production of dry-cured products, where an increased IMF content has a key role in flavor and slow dehydration during the curing process (Ruiz-Carrascal et al., 2000). Despite differences in fat content, longissimus thoracis et lumborum and gluteus medius muscles are a relevant source of oleic (34.61 and 34.85%, respectively), palmitic (23.42 and 23.25%, respectively), and linoleic (14.44 and 15.11%, respectively) FA, without significant departures across muscles in terms of percentage. Although some metabolic differences were described previously between longissimus thoracis et lumborum and gluteus medius muscles (Mora et al., 2008), fat composition differences between both muscles focused on less abundant FA. It is important to highlight the significant increase in n-3 FA found in gluteus medius. Given that FA profile is largely determined by genotype and diet, our estimates showed substantial differences when compared with those provided by other authors (e.g., Cameron and Enser, 1991; Suzuki et al., 2006; Zhang et al., 2007), although the most abundant FA yielded values that agreed with previous studies in pigs (Cameron et al., 2000; Tejeda et al., 2002). Heritabilities for Serum Lipid and Meat Composition Traits The heritability of serum CHOL in swine in the current study was medium-to-large and comparable with that in humans (Feitosa et al., 2005). This result agreed with estimates previously reported in other pig populations (Rothschild and Chapman, 1976; Pond et al., 1986; Pond and Mersmann, 1996) and also with swine selection experiments with effective changes on serum CHOL (Pond et al., 1993; Young et al., 1993). Conversely, heritabilities for TG concentrations at both ages were also large but less than those reported by Pond et al. (1986). Heritabilities for HDL and LDL are, to the best of our knowledge, the first reported for pigs, with moderate values agreeing with the previous values reported in humans (Kaess et al., 2008). Even in humans, little information exists about the inheritance aspects of HDL- and LDL-bound CHOL concentrations, although their genetic background at older ages is undoubted (Heller et al., 1993; Bosse et al., 2004; Kaess et al., 2008). As a whole, our heritability estimates and the corresponding BF provide evidence of the existence of genetic factors controlling serum lipid concentrations at 190 d of age, but these were somewhat weaker for HDL90. Similar conclusions might be reached for serum lipid concentrations at 45 d of age, although the smaller BF and the wide HPD95 advocate for a cautious interpretation of these results. In any case, this genetic component agreed well with previous results demonstrating that serum lipid QTL segregate in this population (Gallardo et al., 2008), thus corroborating that pigs are a good model for studying genetic pathways involved in lipid metabolism. As previously mentioned, the IMF content has an important impact on various features of the sensory (Fernandez et al., 2000) and technological quality of pork meat (Ruiz-Carrascal et al., 2000). Heritabilities of IMF for gluteus medius and longissimus thoracis et lumborum muscles were included in the wide range of estimates reported in the literature for commercial breeds (from 0.26 to 0.86; Sellier, 1998). More specifically, our estimates match up with those obtained by Solanes et al. (2009) in another sample of the same commercial Duroc line (h2 = 0.57). In any case, the genetic background for IMF was clearly demonstrated, providing enough genetic variability for selection programs focused on meat-quality traits, as demonstrated by Suzuki et al. (2005) and Schwab et al. (2009) in other swine populations. Fatty acid profile has profound effects on meat quality because it determines the nutritional value, shelf life, and processing characteristics of meat (Sheard et al., 2000). Fatty acid composition also influences the firmness/oiliness of adipose tissue and the oxidative stability of muscle, which in turn affects flavor and muscle color (Wood et al., 2008). Several authors (e.g., Cameron et al., 2000; Nguyen et al., 2003) pointed out that the influence of nutrition is stronger than genetic effects on the FA composition of adipose tissues and also IMF, but few reports have addressed genetic parameter estimates of FA profile of IMF to corroborate this point. In the present study, all animals were subjected to the same dietary conditions, so no differences in the FA deposition resulting from differences in dietary FA were expected. Under these circumstances, some FA showed strong and moderate evidences of genetic determinism, whereas the BF failed to provide statistical evidences of additive polygenic effects in the remaining FA. The existence of additive genetic variability for the main SFA present in IMF and palmitic and stearic acids was beyond any doubt in longissimus thoracis et lumborum and gluteus medius muscles. Heritability estimates obtained for these FA matched previous estimates by Suzuki et al. (2006) in another Duroc population. In close resemblance with our data, Fernández et al. (2003) showed that the greatest heritability content of subcutaneous fat in the Iberian breed corresponded to stearic (0.41) and palmitic (0.38) FA. Sellier (1998) reported mean heritability estimates of 0.51 (0.42 to 0.57) for stearic FA of subcutaneous fat. On the other hand, genetic influences for the myristic FA were not observed in our study, disagreeing with the weak, but significant heritability reported by Suzuki et al. (2006). In nonruminant animals, dietary FA may be oxidized or deposited in fat tissues, but there is also de novo synthesis of FA from acetyl-CoA derived from carbohydrate or protein breakdown or both (Acheson et al., 1988). In that way, palmitic, stearic and oleic acids are present in the meat composition (Fischer, 2005). According with results obtained in this and in previous studies, the genetic determinism of palmitic and stearic acids content in IMF is largely demonstrated, but the evidence for oleic FA is far more elusive. Medium to high heritabilities had been obtained for the oleic content of several fat tissues (from 0.26 to 0.44) by Fernández et al. (2003) and Suzuki et al. (2006) in Iberian and Duroc populations, respectively. In the present study, similar but not conclusive results regarding heritability of IMF oleic content were obtained in both analyzed muscles. Concerning other relevant MUFA, the cis-vaccenic FA showed polygenic genetic control in longissimus thoracis et lumborum and gluteus medius muscles, with posterior mean and mode estimates ranging from 0.29 to 0.41, which are the first estimates obtained in pigs for these traits. Opposite to the previously mentioned FA, the n-3 and n-6 PUFA are labeled as essential FA because the biosynthetic pathway for n-3 and n-6 FA does not hold in mammalian cells and they cannot be synthesized de novo in pigs (El-Badry et al., 2007). These FA are involved in multiple metabolic routes with direct incidence on human and animal health (Van Oeckel et al., 1997; Trivedi, 2006). As they can be obtained only from diet, several authors (e.g., Nguyen et al., 2003) confirmed that the percentage of essential FA in the subcutaneous and intramuscular fat of pigs is directly related to the percentage of these FA in the dietary fat. According to this statement, we would not expect to find evidence of genetic variability on n-3 and n-6 FA content. Our results corroborated this hypothesis within the context of the longissimus thoracis et lumborum muscle, with less-than-1 BF for both groups of essential FA, as well as for the majority n-6 FA (linoleic and arachidonic FA), which were analyzed separately. Nevertheless, a relevant genetic determinism with moderate heritabilities was suggested for linoleic and arachidonic n-6 FA in the gluteus medius muscle. Besides, Suzuki et al. (2006) obtained a relevant heritability for the linoleic content in several fat tissues of pig including IMF, and previous works showed heritabilities from 0.47 to 0.70 for linoleic acid of subcutaneous fat (Sellier, 1998). Evidence of genetic determinism were also found for n-3 FA in the gluteus medius muscle. Note that these heritabilities for n-3 FA agreed with the one reported by Greeff et al. (2006). Given that n-3 FA cannot be synthesized de novo by pigs, this genetic determinism must be related to the genetic architecture of intestinal absorption from diet or organ-specific distribution of n-3 FA. Our results confirm the complex genetic control of FA and relevant influences of environmental factors. The existence of an important genetic determinism affecting nonessential FA deposition has been confirmed, and also a relevant heritability for some essential FA has been demonstrated in the gluteus medius muscle, suggesting that its content in pig hams could be genetically modified. Nevertheless, further studies are required to evaluate genetic relationships between the lipid traits studied in this research. LITERATURE CITED Acheson K. J. Schultz Y. Bessard T. Ananatharaman K. Flatt J. P. Jéquier E. 1988. Glycogen storage capacity and de novo lipogenesis during massive carbohydrate overfeeding in man. Am. J. Clin. Nutr.  48: 240– 247. https://doi.org/3165600 Google Scholar CrossRef Search ADS PubMed  Averette Gatlin L. See M. T. Hansen J. A. Odle J. 2003. Hydrogenated dietary fat improves pork quality of pigs from two lean genotypes. J. Anim. Sci.  81: 1989– 1997. https://doi.org/12926781 Google Scholar CrossRef Search ADS PubMed  Bosse Y. Perusse L. Vohl M. C. 2004. Genetics of LDL particle heterogeneity: From genetic epidemiology to DNA-based variations. J. Lipid Res.  45: 1008– 1026. https://doi.org/15060093 Google Scholar CrossRef Search ADS PubMed  Cameron N. D. Enser M. 1991. Fatty acid composition of lipid in longissimus dorsi muscle of Duroc and British Landrace pigs and its relationship with eating quality. Meat Sci.  29: 295– 307. Google Scholar CrossRef Search ADS PubMed  Cameron N. D. Enser M. Nute G. R. Whittington F. M. Penman J. C. Fisken A. C. Perry A. M. Wood J. D. 2000. Genotype with nutrition interaction on fatty acid composition of intramuscular fat and the relationship with flavor of pig meat. Meat Sci.  55: 187– 195. Google Scholar CrossRef Search ADS PubMed  Cayuela J. M. Garrido M. D. Banón S. J. Ros J. M. 2003. Simultaneous HPLC analysis of α-tocopherol and cholesterol in fresh pig meat. J. Agric. Food Chem.  51: 1120– 1124. https://doi.org/12590444 Google Scholar CrossRef Search ADS PubMed  Chizzolini R. Novelli E. Zanardi E. 1998. Oxidation in traditional Mediterranean meat products. Meat Sci.  49: S87– S99. Google Scholar CrossRef Search ADS   El-Badry A. M. Graf R. Clavien P.-A. 2007. Omega 3 - omega 6: What is right for the liver? J. Hepatol.  47: 718– 725. https://doi.org/17869370 Google Scholar CrossRef Search ADS PubMed  Feitosa M. F. Rice T. Rankinen T. Almasy L. Leon A. S. Skinner J. S. Wilmore J. H. Bouchard C. Rao D. C. 2005. Common genetic and environmental effects on lipid phenotypes: The HERITAGE family study. Hum. Hered.  59: 34– 40. https://doi.org/15802920 Google Scholar CrossRef Search ADS PubMed  Fernández A. de Pedro E. Núñez N. Silió L. García-Casco J. Rodríguez C. 2003. Genetic parameters for meat and fat quality and carcass composition traits in Iberian pigs. Meat Sci.  64: 405– 410. Google Scholar CrossRef Search ADS PubMed  Fernandez X. Mourot J. Lebret B. Gilbert S. Monin G. 2000. Influence of intramuscular fat content on lipid composition, sensory qualities and consumer acceptability of cured cooked ham. J. Sci. Food Agric.  80: 705– 710. Google Scholar CrossRef Search ADS   Fiedler I. Nürnberg K. Hardge T. Nürnberg G. Ender K. 2003. Phenotypic variations of muscle fibre and intramuscular fat traits in Longissimus muscle of F2 population Duroc×Berlin Miniature pig and relationships to meat quality. Meat Sci.  63: 131– 139. Google Scholar CrossRef Search ADS PubMed  Fischer K. 2005. Consumer-relevant aspects of pork quality. Anim. Sci. Pap. Rep.  23: 269– 280. Fossati P. Prencipe L. 1982. Serum triglycerides determined colorimetrically with an enzyme that produces hydrogen peroxide. Clin. Chem.  28: 2077– 2080. https://doi.org/6812986 Google Scholar PubMed  Framstad T. Sjaastad Ø. Aass R. A. 1988. Blodprøvetaking på gris. Norsk Veterinærtidsskrift  100: 265– 272. Friedewald W. T. Levy R. I. Fredrickson D. S. 1972. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin. Chem.  18: 499– 502. https://doi.org/4337382 Google Scholar PubMed  Gallardo D. Pena R. N. Amills M. Varona L. Ramírez O. Reixach J. Díaz I. Tibau J. Soler J. Prat-Cuffí J. M. Noguera J. L. Quintanilla R. 2008. Mapping of quantitative trait loci for colesterol, LDL, HDL and triglyceride serum concentrations in pigs. Physiol. Genomics  35: 199– 209. https://doi.org/18812458 Google Scholar CrossRef Search ADS PubMed  García-Cortés L. A. Cabrillo C. Moreno C. Varona L. 2001. Hypothesis testing for the genetic background of quantitative traits. Genet. Sel. Evol.  33: 3– 16. https://doi.org/11268311 Google Scholar CrossRef Search ADS PubMed  Gelfand A. Smith A. F. M. 1990. Sampling based approaches to calculating marginal densities. J. Am. Stat. Assoc.  85: 398– 409. Google Scholar CrossRef Search ADS   Good I. J. 1979. Studies in the history of probability and statistics. XXXVII A. M. Turing's statistical work in World War II. Biometrika  66: 393– 396. Google Scholar CrossRef Search ADS   Greeff, J. C., P. Young, S. Kitessa, and M. Dowling 2006. Preliminary heritability estimates of individual fatty acids in sheep meat.  Page 36 in Proc. 26th Aust. Soc. Anim. Prod. Aust. Soc. Anim. Prod., Perth, Australia. Hastings W. K. 1970. Monte Carlo sampling methods using Markov chains and their application. Biometrika  57: 97– 109. Google Scholar CrossRef Search ADS   Heller D. A. de Faire U. Pedersen N. L. Dahlen G. McClearn G. E. 1993. Genetic and environmental influences on serum lipid levels in twins. N. Engl. J. Med.  328: 1150– 1156. https://doi.org/8455681 Google Scholar CrossRef Search ADS PubMed  Jarratt J. Mahaffie J. B. 2002. Key trends affecting the dietetics profession and the American Dietetic Association. J. Am. Diet. Assoc.  102: S1821– SS1839. Google Scholar CrossRef Search ADS   Jeffreys, H. 1984. Theory of Probability.  Clarendon Press, Oxford, UK. Kaess B. Fischer M. Baessler A. Stark K. Huber F. Kremer W. Kalbitzer H. R. Schunkert H. Riegger G. Hengstenberg C. 2008. The lipoprotein subfraction profile: Heritability and identification of quantitative trait loci. J. Lipid Res.  49: 715– 723. https://doi.org/18165655 Google Scholar CrossRef Search ADS PubMed  Kass R. E. Raftery A. E. 1995. Bayes factors. J. Am. Stat. Assoc.  90: 773– 795. Google Scholar CrossRef Search ADS   Kim J. H. Seong P. N. Cho S. H. Park B. Y. Hah K. H. Yu L. H. Lim D. G. Hwang I. H. Kim D. H. Lee J. L. Ahn C. N. 2008. Characterization of nutritional value for twenty-one pork muscles. Asian-australas. J. Anim. Sci.  21: 138– 144. Google Scholar CrossRef Search ADS   López-Bote C. 1998. Sustained utilization of the Iberian pig breed. Meat Sci.  49( Suppl. 1): S17– S27. Google Scholar CrossRef Search ADS   Mach N. Devant M. Bach A. Díaz I. Font M. Oliver M. A. García J. A. 2006. Increasing the amount of n-3 fatty acid in meat from young Holstein bulls through nutrition. J. Anim. Sci.  84: 3039– 3048. https://doi.org/17032798 Google Scholar CrossRef Search ADS PubMed  Mora L. Sentandreu M. A. Toldrá F. 2008. Contents of creatine, creatinine and carnosine in porcine muscles of different metabolic types. Meat Sci.  79: 709– 715. Google Scholar CrossRef Search ADS PubMed  Nguyen L. Q. Nuijens M. C. G. A. Everts H. Salden N. Beynen A. C. 2003. Mathematical relationships between the intake of n-6 and n-3 polyunsaturated fatty acids and their contents in adipose tissue of growing pigs. Meat Sci.  65: 1399– 1406. Google Scholar CrossRef Search ADS PubMed  Pond W. G. Mersmann H. J. 1996. Genetically diverse pig models for neonatal cholesterol nutrition: A review. Nutr. Res.  16: 707– 721. Google Scholar CrossRef Search ADS   Pond W. G. Mersmann H. J. Klein P. D. Ferlic L. L. Wong W. W. Hachey D. L. Schoknecht P. A. Zhang S. 1993. Bodyweight gain is correlated with serum cholesterol at 8 weeks of age in pigs selected for four generations for low or high serum cholesterol. J. Anim. Sci.  71: 2406– 2411. https://doi.org/8407652 Google Scholar CrossRef Search ADS PubMed  Pond W. G. Mersmann H. J. Young L. D. 1986. Heritability of plasma cholesterol and triglyceride concentrations in swine. Proc. Soc. Exp. Med.  182: 221– 224. Google Scholar CrossRef Search ADS   Rafai, N., and G. R. Warnick 1994. Lipoproteins and Apolipoproteins. Pages 91– 105 in Laboratory Measurement of Lipids.  AACC Press, Washington, DC. Raftery, A. E., and S. M. Lewis 1992. How many iterations in the Gibbs sampler? Pages 763– 773 in Bayesian Statistics IV.  J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith ed. Oxford Univ. Press, Oxford, UK. Richmond W. 1992. Analytical reviews in clinical biochemistry: The quantitative analysis of cholesterol. Ann. Clin. Biochem.  29: 577– 597. https://doi.org/1489157 Google Scholar CrossRef Search ADS PubMed  Rothschild M. Chapman A. B. 1976. Factors influencing serum cholesterol levels in swine. J. Hered.  67: 47– 48. https://doi.org/944207 Google Scholar CrossRef Search ADS PubMed  Ruiz-Carrascal J. Ventanas J. Cava R. Andrés A. I. García C. 2000. Texture and appearance of dry cured ham as affected by fat content and fatty acid composition. Food Res. Int.  33: 91– 95. Google Scholar CrossRef Search ADS   Schwab C. R. Baas T. J. Stalder K. J. Nettleton D. 2009. Results from six generations of selection for intramuscular fat in Duroc swine using real-time ultrasound. I. Direct and correlated phenotypic responses to selection. J. Anim. Sci.  87: 2774- 2780. Google Scholar CrossRef Search ADS PubMed  Sellier, P. 1998. Genetics of meat and carcass traits. Pages 463– 510 in The Genetics of the Pig.  M. F. Rothschild, and A. Ruvinsky ed. CAB Int., New York, NY. Sheard P. R. Enser M. Wood J. D. Nute G. R. Gill B. P. Richardson R. I. 2000. Shelf life and quality of pork and pork products with raised n-3 PUFA. Meat Sci.  55: 213– 221. Google Scholar CrossRef Search ADS PubMed  Solanes F. X. Reixach J. Tor M. Tibau J. Estany J. 2009. Genetic correlations and expected response for intramuscular fat content in a Duroc pig line. Livest. Sci.  123: 63– 69. Google Scholar CrossRef Search ADS   Suzuki K. Ishida M. Kadowaki H. Shibata T. Uchida H. Nishida A. 2006. Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs. J. Anim. Sci.  84: 2026– 2034. https://doi.org/16864861 Google Scholar CrossRef Search ADS PubMed  Suzuki K. Kadowaki H. Shibata T. Uchida H. Nishida A. 2005. Selection for daily gain, loin-area, backfat thickness and intramuscular fat based on desired gains over seven generations of Duroc pigs. Livest. Prod. Sci.  97: 193– 202. Google Scholar CrossRef Search ADS   Tarrant P. V. 1998. Some recent advances and future priorities for the meat industry. Meat Sci.  49( Suppl. 1): S1– S16. Google Scholar CrossRef Search ADS   Tejeda J. F. Gandemer G. Antequera T. Viau M. García C. 2002. Lipid traits of muscles as related to genotype and fattening diet in Iberian pigs: Total intramuscular lipids and triacylglycerols. Meat Sci.  60: 357– 363. Google Scholar CrossRef Search ADS PubMed  Tribole E. F. 2006. Excess omega-6 fats thwart health benefits from omega-3 fats. BMJ  332: 752– 760. https://doi.org/16809710 Google Scholar CrossRef Search ADS PubMed  Trivedi B. 2006. The good, the fad, and the unhealthy. New Sci.  2570: 42– 49. Google Scholar CrossRef Search ADS   Van Oeckel M. J. Casteels M. Warnants N. Boucqué C. V. 1997. Omega-3 fatty acids in pig nutrition: Implications for zootechnical performances, carcass and fat quality. Arch. Tierernahr.  50: 31– 42. https://doi.org/9205735 Google Scholar CrossRef Search ADS PubMed  Varona L. García-Cortés L. A. Pérez-Enciso M. 2001. Bayes factor for the detection of quantitative trait loci. Genet. Sel. Evol.  33: 133– 152. https://doi.org/11333831 Google Scholar CrossRef Search ADS PubMed  Wood J. D. Enser M. Fisher A. V. Nute G. R. Sheard P. R. Richardson R. I. Hughes S. I. Whittington F. M. 2008. Fat deposition, fatty acid composition and meat quality: A review. Meat Sci.  78: 343– 358. Google Scholar CrossRef Search ADS PubMed  Young L. D. Pond W. G. Mersmann H. J. 1993. Direct and correlated responses to divergent selection for serum cholesterol concentration on day 56 in swine. J. Anim. Sci.  71: 1742– 1753. https://doi.org/8349502 Google Scholar CrossRef Search ADS PubMed  Zhang S. Knight T. J. Stalder K. J. Goodwin R. N. Lonergan S. M. Beitz D. C. 2007. Effects of breed, sex, and halothane genotype on fatty acid composition of pork longissimus muscle. J. Anim. Sci.  85: 583– 591. https://doi.org/17060410 Google Scholar CrossRef Search ADS PubMed  APPENDIX Bayes Factor for Testing the Additive Polygenic Background of Quantitative Traits Heritability was evaluated by calculating the Bayes factor (Kass and Raftery, 1995) of a model accounting for infinitesimal polygenic effects,  against a reduced model without genetic effects,  by applying the García-Cortés et al. (2001) and Varona et al. (2001) method. Note that y was the vector of phenotypic data, e was the vector of residuals, β was the vector storing the systematic effects described above, a was the vector of additive genetic effects, and X and Z were appropriate incidence matrices. Following Varona et al. (2001), only the analysis of the most complex model is required to calculate the Bayes factor, after reparameterizing it as  where is assumed to follow a multivariate normal distribution with mean 0 and variance Note that A was the numerator relationship matrix between individuals, I was an identity matrix with dimensions equal to the number of data, was the phenotypic variance, was the additive genetic variance, and Under a standard Bayesian development, the posterior probability of all the parameters in model was proportional to   where and the remaining a priori distributions were assumed flat as in Varona et al. (2001). Random samples from all unknowns in model were obtained by Gibbs sampling (Gelfand and Smith, 1990), with the exception of h2, which required a Metropolis-Hastings step (Hastings, 1970). The Bayes factor was calculated from the Markov chain Monte Carlo sampling by averaging the full conditional densities of each cycle at h2 = 0 (see Varona et al., 2001 for a detailed description of this methodology). The Bayes factor provides the ratio of posterior probabilities between the 2 tested models. A Bayes factor >1 suggests a relevant genetic background for the analyzed trait, whereas a Bayes factor <1 shows that the model without genetic effects is more probable. Jeffreys (1984) Scale of Evidence for Bayes Factors The BF is the ratio of posterior probabilities between 2 competing models placed as numerator and denominator in the ratio. Following Jeffreys (1984), the BF can be classified according to 6 levels of evidence:  Note that these specific cutoff values were defined on the basis of a 5-unit dB increase, a base-10 logarithmic unit that measures information and entropy (Good, 1979). The BF can be transformed to a dB value by applying and inversely, BF = 10 × log10 American Society of Animal Science

De Conto, C.;Oevermann, A.;Burgener, I. A.;Doherr, M. G.;Blum, J. W.

doi: 10.2527/jas.2009-2511pmid: 20228237

ABSTRACT In this study, the hypothesis was tested that the size of gastrointestinal tract (GIT) mucosal components and rates of epithelial cell proliferation and apoptosis change with increasing age. The aims were to quantitatively examine GIT histomorphology and to determine mucosal epithelial cell proliferation and apoptosis rates in neonatal (<48 h old) and adult (8 to 11.5 yr old) dogs. Morphometrical analyses were performed by light microscopy with a video-based, computer-linked system. Cell proliferation and apoptosis of the GIT epithelium were evaluated by counting the number of Ki-67 and caspase-3-positive cells, respectively, using immunohistochemical methods. Thickness of mucosal, glandular, subglandular, submucosal and muscular layers, crypt depths, villus heights, and villus widths were consistently greater (P < 0.05 to P < 0.001), whereas villus height/crypt depth ratios were smaller (P < 0.001) in adult than in neonatal dogs. The number of Ki-67-positive cells in stomach, small intestine, and colon crypts, but not in villi, was consistently greater (P < 0.01) in neonatal than in adult dogs. In contrast, the number of caspase-3-positive cells in crypts of the stomach, small intestine, and colon and in villi was not significantly influenced by age. In conclusion, canine GIT mucosal morphology and epithelial cell proliferation rates, but not apoptosis rates, change markedly from birth until adulthood is reached. INTRODUCTION After birth, total mass and mucosal weight of the gastrointestinal (GI) tract (GIT) of dogs continue to increase and differentiate (Lützen et al., 1976; Paulsen et al., 2003). Epithelial cells of the GIT experience permanent renewal that includes cell proliferation, migration, differentiation, apoptosis, and cell shedding into the intestinal lumen (Creamer et al., 1961; Johnson, 1988; Hall et al., 1994; Potten, 1997; Bjerknes and Cheng, 2005). Homeostasis of these activities is essential for the maintenance of GI mass and for structural and functional properties (Johnson, 1988; Hall et al., 1994; Karan, 1999). Knowledge of proliferation and apoptotic rates is therefore important for the understanding of the normal developmental and aging processes and may contribute to the understanding of GI diseases, such as inflammatory bowel disease, food-responsive diarrhea, and malignancies (Hall and Batt, 1990; Que and Gores, 1996; Allenspach and Gaschen, 2003; Edelblum et al., 2006). In the dog, the morphology of the small intestine (SI) and proliferation rates have been found to change ontogenetically (Schwarz and Heird, 1994; Ward and Torihashi, 1995; Paulsen et al., 2003; Baum et al., 2007). There are also marked developmental changes of the immune system, such as in lymphocytes (Pfammatter et al., 2008). To the best of our knowledge, studies on histomorphometrical changes of the GI mucosa, in combination with evaluations of epithelial cell proliferation and of apoptotic rates, have not yet been performed in dogs at different ages. Therefore, we have tested the hypothesis that the size of GIT mucosal components and rates of epithelial cell proliferation and apoptosis change with increasing age. The aim of this study was to perform histomorphometrical analyses of the GI mucosa, combined with the assessment of proliferation and apoptotic changes of epithelial cells, in the GIT in very young (<48 h old) compared with adult (8 to 11.5 yr old) dogs. MATERIALS AND METHODS The necessary authorizations for the project and euthanasia (of neonatal and adult dogs) were obtained from the Committee for Animal Experimentation of the Canton of Bern, Switzerland. Dogs, Feeding, and GI Tissue Collection Neonatal Beagle dogs (<48 h old; 2 males and 4 females) were supernumerary dogs from a research breeding facility and were obtained from Research and Consulting Company (Füllinsdorf, Switzerland). Adult Beagle dogs (8 to 11.5 yr old; 2 males and 4 females) were obtained from Novartis Animal Health (St. Aubin, Switzerland). The adult dogs were killed for reasons independent of our study: 3 of them because of age, 2 because of neoplastic disease not involving the GIT, and 1 because of age and rupture of the cruciate ligament. Dogs were killed with pentobarbital (150 mg/kg of BW), administered intravenously in adult dogs and intracardially in neonatal dogs after adequate sedation with medetomidine (Orion Corporation, Espoo, Finland) and ketamine (E. Gräub AG, Bern, Switzerland) subcutaneously in neonatal and adult dogs. The adult dogs in the study initially suckled on their dam (for about 2 mo). After weaning, these dogs were fed a diet for growing dogs, and upon reaching the mature weight (at the age of about 1 yr), they were fed a commercial premium pet food for normally active adult dogs (#830, Biomill, Granges-Marnand, Switzerland). According to the information from the producer, the food contained, in descending order: poultry meal, wheat, maize, poultry fat, rice, sugar beet, whole soya flour, maize gluten, liver hydrolysate, dried vegetable, fishmeal, oligosaccharides (acacia fibers), cod liver oil, brewer's dried yeast, lecithin, yucca extract, and premixes of minerals and vitamins. The key values were water, 9%; CP, 26%; crude fat, 15%; N-free extract, 40.5%; crude fiber, 2.5%; crude ash, 7%; calcium, 1.15%; inorganic phosphorus, 0.8%; n-6 fatty acids, 3%; and n-3 fatty acids, 6%. We did not have control over feeding. After euthanasia, the abdominal cavity of the dogs was immediately opened. Pieces of the GIT (2 to 3 cm long) were taken from the antrum pylori, flexura duodeni, mid jejunum, ileum, and flexura coli and immediately immersed in a phosphate-buffered paraformaldehyde (40 g/L) solution. After 24 h, a 4-mm-thick cross-sectional piece was cut from each sample and embedded in a paraffin block. Cuts 3- to 4-µm thick were made from each block. Histomorphometry of the GI Mucosa For histomorphometric analyses, hematoxylin- and eosin-stained slides were used. Morphometrical analyses were performed with a light microscope connected to a video-based, computer-linked system with the act2U software (Nikon, Tokyo, Japan) that allowed us to measure lengths and widths. Measurements were made for each location (stomach, duodenum, jejunum, ileum, and colon), as shown in Figure 1A to 1C. At 4 sites per location that were equally distant from each other by localization in a clockwise manner around the center of the selected tissue area, we measured the thickness (in μm) of the tunica mucosae, the tunica submucosa, and the tunica muscularis. The tunica mucosae was subdivided into 2 compartments: the lamina propria (LP) was measured together with the lamina muscularis mucosae and defined as the subglandular mucosal layer. The rest of the mucosa, defined as the glandular layer, was obtained by subtracting the values of the measurements of the subglandular mucosal layer from the values of the measurements of the tunica mucosae. In the SI, for villi and crypt measurements, we evaluated the first 5 well-oriented villi and their adjacent 10 crypts. Villus heights and widths, as well as crypt depths, were measured. In the colon, 10 well-oriented crypts were chosen and their depths were determined. Figure 1. View largeDownload slide Histological sites and locations of stomach (panel A), small intestine (panel B), and colon (panel C) used for morphometrical analyses and for the evaluation of cell proliferation and apoptosis rates. Figure 1. View largeDownload slide Histological sites and locations of stomach (panel A), small intestine (panel B), and colon (panel C) used for morphometrical analyses and for the evaluation of cell proliferation and apoptosis rates. Proliferation and Apoptosis of GI Epithelial Cells The proliferation of GI mucosal cells was evaluated by counting cells that were positive for protein Ki-67 (Wong and Wright, 1999). This protein is exclusively expressed in the nuclei of all proliferating cells, from phase G1 to phase M (Scholzen and Gerdes, 2000). To detect the Ki-67 protein, a monoclonal antibody against Ki-67 was used as described previously (Blättler et al., 2001) and visualized using a peroxidase-labeled polymer (Envision, Dako, Baar, Switzerland). The apoptosis of intestinal mucosal cells was evaluated by counting cells that were positive for activated caspase-3. Caspase-3 belongs to the group of effector caspases and is a central player in mediating apoptosis (Chang and Yang, 2000; Otsuki et al., 2003). To detect the active caspase, an affinity-purified polyclonal rabbit antibody was used that is able to specifically recognize the cleaved active form of caspase-3 and was shown to detect also canine caspase-3 (Dandrieux et al., 2008). The positive reaction was visualized by the labelled strept-avidin-biotin-method technique. In more detail, mounted specimens were dewaxed, rinsed with water, and then with Tris-buffered saline (TBS; 0.01 mol/L; anti-Ki-67 antibody) and PBS (0.01 mol/L; anti-cleaved caspase-3) 3 times for 5 min. Antigen retrieval was performed using a steam autoclave at 1 bar for 20 min and citrate buffer (pH 6). Endogenous peroxidase was blocked with 3% H2O2 in methanol for 10 min. The slides were rinsed with water and TBS and PBS, respectively. Slides for anti-cleaved caspase-3 were incubated for 20 min with 5% goat serum. Anti-Ki-67 antibody (monoclonal mouse anti-human, clone MIB-1, Dako; diluted 1:100 with TBS) and anti-active caspase-3 antibody (directed against a peptide from the p18 fragment of the human caspase-3, diluted 1:125 with PBS; Promega, Dübendorf, Switzerland), respectively, were added to each section and incubated for 30 and 90 min at room temperature, respectively. The sections were then rinsed with TBS and PBS, respectively. Slides for the detection of Ki-67 were incubated for 30 min with peroxydase-labeled polymer (Envision + Dual Link; Dako) and slides for the detection of activated caspase-3 with a biotinylated secondary antibody (Dako) for 10 min at room temperature, respectively. For the detection of cleaved caspase-3 the specimens were then incubated for 10 min with streptavidin horseradish peroxidase and rinsed with PBS. The specimens were incubated with Liquid Diaminobenzidine + Substrate Chromogen system (Dako; anti-Ki-67) and AEC chromogen (β-amino-9-ethylcarbazole; Dako; anti-caspase-3) for 5 min, rinsed in distilled water, and counterstained with hematoxylin. The sections were finally observed under a microscope connected to a video-based, computer-linked system with the act2U software that allowed determination of areas of selected tissue parts. In the stomach (Figure 1A), cell proliferation and apoptotic rates were evaluated in 5 areas that were localized at equal distances from each other. The measurement was made with a 20-fold magnification from the mucosal surface to the base of the glandular layer (i.e., stomach glands, which were considered as crypts of the stomach). The Ki-67 and cleaved caspase-3-positive cells were counted per measured field and separately in the lamina epithelialis (LE) and in the interglandular LP of the mucosa and expressed per 10,000 μm2 in the LE, in the LP, and as a total (LE + LP). In the SI (Figure 1B), cell proliferation and apoptotic rates were evaluated in the crypt base and in the villi. For crypts, 5 areas of the mucosa, localized at equal distances from each other, were selected. The Ki-67 and cleaved caspase-3-positive cells were counted with a 40-fold magnification per measured field and expressed per 10,000 μm2 of crypt mucosa. The Ki-67 and cleaved caspase-3-positive crypt cells of the LE were counted separately from those in the LP. This resulted in the number of proliferating cells per 10,000 μm2 mucosa in the crypt LE and in the crypt LP and served as a measure of the total number of mitotic cells in the crypt mucosa (LE + LP). For villi, 5 areas were selected, each one of which delineated 1 well-oriented villus. The Ki-67 and cleaved caspase-3-positive cells were counted with a 40-fold magnification per measured field and expressed per 10,000 μm2 of villus mucosa. The Ki-67-positive villus cells of the LE were counted separately from those in the LP and served together as a measure of the total number of mitotic cells (LE + LP). In the colon (Figure 1C), areas of the mucosa were measured at 5 places, localized at equal distances from each other. The measurement was made from the crypt base to the highest and widest points still visible with a 40-fold magnification. Then, Ki-67- and cleaved caspase-3-positive cells were counted per measured field, separately in the LE and in the LP, and served together as a measure of the total number of mitotic cells per 10,000 μm2 of crypt mucosa (LE + LP). Statistical Analyses For statistical analyses, NCSS2007 (Number Cruncher Statistical Systems, Kaysville, UT) was used. Histomorphometric, proliferative, and apoptotic values are expressed as means ± SEM. Histomorphometric values and the number of Ki-67- and caspase-3-positive cells for each group and differences between groups (adults vs. neonatals) were analyzed using a repeated measures ANOVA procedure with age group as the between-groups and intestinal site as the within-subjects repetition factor. Effects of the sex were also evaluated. However, because of the small number of dogs studied, and because of the uneven distribution of males and females within the 2 groups, the interpretation was difficult and possibly misleading. We have therefore decided not to present results of or to comment on sex effects. The ANOVA was according to the following model: Yijk = µ + ai + bk + (a × b)ik + εijk, where Yijk = prediction for the ith subject or dependent variable, µ = grand mean, ai = between-group factor, such as age or sex, bk = within-subject repetition factor (here GI site), (a × b)ik = interaction between a and b, and εijk = residual error (difference between predicted and observed Y). When the overall F-test for a main effect was significant, group differences were identified by post-hoc Tukey-Kramer multiple-comparison test. RESULTS Histomorphometrical Analyses of the GIT in Neonatal and Adult Dogs As shown in Table 1, there were age effects on all traits (P < 0.05 to P < 0.001). The thickness of mucosal, glandular, subglandular, submucosal and muscular layers, crypt depths, villus heights, and villus widths at all intestinal sites was consistently greater (P < 0.05 to P < 0.001) in adult than in neonatal dogs, whereas villus heights/crypt depths ratios were much smaller (P < 0.001) in adult than neonatal dogs. For all traits, there were differences (P < 0.05) among intestinal sites within groups, but these characteristics were not consistent between neonatal and adult dogs across GIT sites. For all GIT locations, except for villus width, there were group and GIT site interactions (P < 0.05 to P < 0.001). Table 1. Histomorphometrical analyses of the gastrointestinal tract in neonatal and adult dogs Trait  Intestinal site1  Neonatal (n = 6)  Adult (n = 6)  ANOVA2  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age× site  Mucosal layer, μm  STO  119.4 ± 5.3  STO, COL < DUO, JEJ, ILE3  782.9 ± 63.3  STO, COL < ILE, JEJ, DUO3  ***  ***  ***  DUO  846.5 ± 57.1    3,613.0 ± 170.6  ILE < DUO, JEJ        JEJ  894.2 ± 84.6    2,534.9 ± 194.3  JEJ < DUO        ILE  676.4 ± 56.0    1,851.7 ± 180.5          COL  236.3 ± 20.3    671.0 ± 56.1          Glandular layer, μm  STO  91.7 ± 16.2  STO, COL < DUO, JEJ, ILE  650.2 ± 56.4  STO, COL < DUO, JEJ, ILE  ***  ***  ***  DUO  829.9 ± 142.2    3,475.2 ± 168.8    ILE < JEJ, DUO        JEJ  883.8 ± 207.8    2,432.6 ± 191.2  JEJ < DUO        ILE  663.0 ± 139.7    1,746.1 ± 195.2          COL  222.9 ± 48.7    597.5 ± 51.5          Subglandular layer, μm  STO  27.7 ± 2.9  DUO, JEJ, ILE, COL < STO  132.8 ± 11.0  COL < STO, DUO  ***  ***  *  DUO  16.6 ± 1.4    137.8 ± 9.2          JEJ  10.5 ± 0.5    102.3 ± 11.4          ILE  13.4 ± 1.1    105.6 ± 19.8          COL  13.4 ± 1.1    73.5 ± 10.8          Submucosal layer, μm  STO  127.0 ± 23.1  DUO, JEJ, ILE, COL < STO  610.3 ± 73.2  DUO, JEJ, ILE, COL < STO  ***  ***  ***  DUO  51.9 ± 12.5    227.0 ± 16.8          JEJ  36.0 ± 2.1    148.9 ± 9.2          ILE  74.5 ± 13.1    237.8 ± 13.2          COL  60.0 ± 8.3    228.6 ± 37.4          Muscular layer, μm  STO  304.8 ± 11.1  DUO, JEJ, ILE, COL < STO  2,951.5 ± 170.0  DUO, JEJ < STO, ILE, COL  ***  ***  ***  DUO  135.7 ± 28.6  JEJ, ILE < COL  1,186.3 ± 100.1  COL < STO          JEJ  80.8 ± 59.2    921.6 ± 109.3          ILE  101.6 ± 28.6    1,778.7 ± 179.7          COL  200.2 ± 30.5    1,871.9 ± 77.0          Crypt depth, μm  DUO  89.3 ± 7.4  ILE < DUO, COL  2,069.5 ± 110.0  COL < DUO, JEJ  ***  ***  ***  JEJ  64.5 ± 6.7  JEJ, DUO < COL  1,131.5 ± 94.3  ILE < DUO, JEJ          ILE  59.4 ± 3.7    747.6 ± 49.3  JEJ < DUO        COL  223.1 ± 16.8    634.3 ± 32.3          Villus height, μm  DUO  688.9 ± 58.2  1,134.7 ± 97.0  ILE < DUO, JEJ  *  ***  **  JEJ  748.4 ± 58.4    937.5 ± 111.5          ILE  579.9 ± 49.3    589.4 ± 44.4          Villus area, μm2  DUO  64,332 ± 5,053  ILE < DUO  150,568 ± 14,539  ILE < DUO  ***  ***  *  JEJ  55,019 ± 8,566    113,162 ± 9,950          ILE  41,445 ± 5,266    79,478 ± 7,105          Villus width, μm  DUO  132.6 ± 6.5  219.1 ± 11.0      ***  *  NS  JEJ  121.6 ± 9.1    184.4 ± 11.5          ILE  111.2 ± 8.5    204.4 ± 16.4          Villus height/crypt depth  DUO  8.0 ± 1.0  DUO < JEJ  0.6 ± 0.1    ***  **  *  JEJ  11.9 ± 0.9    0.8 ± 0.1          ILE  10.1 ± 1.4    0.8 ± 0.1          Trait  Intestinal site1  Neonatal (n = 6)  Adult (n = 6)  ANOVA2  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age× site  Mucosal layer, μm  STO  119.4 ± 5.3  STO, COL < DUO, JEJ, ILE3  782.9 ± 63.3  STO, COL < ILE, JEJ, DUO3  ***  ***  ***  DUO  846.5 ± 57.1    3,613.0 ± 170.6  ILE < DUO, JEJ        JEJ  894.2 ± 84.6    2,534.9 ± 194.3  JEJ < DUO        ILE  676.4 ± 56.0    1,851.7 ± 180.5          COL  236.3 ± 20.3    671.0 ± 56.1          Glandular layer, μm  STO  91.7 ± 16.2  STO, COL < DUO, JEJ, ILE  650.2 ± 56.4  STO, COL < DUO, JEJ, ILE  ***  ***  ***  DUO  829.9 ± 142.2    3,475.2 ± 168.8    ILE < JEJ, DUO        JEJ  883.8 ± 207.8    2,432.6 ± 191.2  JEJ < DUO        ILE  663.0 ± 139.7    1,746.1 ± 195.2          COL  222.9 ± 48.7    597.5 ± 51.5          Subglandular layer, μm  STO  27.7 ± 2.9  DUO, JEJ, ILE, COL < STO  132.8 ± 11.0  COL < STO, DUO  ***  ***  *  DUO  16.6 ± 1.4    137.8 ± 9.2          JEJ  10.5 ± 0.5    102.3 ± 11.4          ILE  13.4 ± 1.1    105.6 ± 19.8          COL  13.4 ± 1.1    73.5 ± 10.8          Submucosal layer, μm  STO  127.0 ± 23.1  DUO, JEJ, ILE, COL < STO  610.3 ± 73.2  DUO, JEJ, ILE, COL < STO  ***  ***  ***  DUO  51.9 ± 12.5    227.0 ± 16.8          JEJ  36.0 ± 2.1    148.9 ± 9.2          ILE  74.5 ± 13.1    237.8 ± 13.2          COL  60.0 ± 8.3    228.6 ± 37.4          Muscular layer, μm  STO  304.8 ± 11.1  DUO, JEJ, ILE, COL < STO  2,951.5 ± 170.0  DUO, JEJ < STO, ILE, COL  ***  ***  ***  DUO  135.7 ± 28.6  JEJ, ILE < COL  1,186.3 ± 100.1  COL < STO          JEJ  80.8 ± 59.2    921.6 ± 109.3          ILE  101.6 ± 28.6    1,778.7 ± 179.7          COL  200.2 ± 30.5    1,871.9 ± 77.0          Crypt depth, μm  DUO  89.3 ± 7.4  ILE < DUO, COL  2,069.5 ± 110.0  COL < DUO, JEJ  ***  ***  ***  JEJ  64.5 ± 6.7  JEJ, DUO < COL  1,131.5 ± 94.3  ILE < DUO, JEJ          ILE  59.4 ± 3.7    747.6 ± 49.3  JEJ < DUO        COL  223.1 ± 16.8    634.3 ± 32.3          Villus height, μm  DUO  688.9 ± 58.2  1,134.7 ± 97.0  ILE < DUO, JEJ  *  ***  **  JEJ  748.4 ± 58.4    937.5 ± 111.5          ILE  579.9 ± 49.3    589.4 ± 44.4          Villus area, μm2  DUO  64,332 ± 5,053  ILE < DUO  150,568 ± 14,539  ILE < DUO  ***  ***  *  JEJ  55,019 ± 8,566    113,162 ± 9,950          ILE  41,445 ± 5,266    79,478 ± 7,105          Villus width, μm  DUO  132.6 ± 6.5  219.1 ± 11.0      ***  *  NS  JEJ  121.6 ± 9.1    184.4 ± 11.5          ILE  111.2 ± 8.5    204.4 ± 16.4          Villus height/crypt depth  DUO  8.0 ± 1.0  DUO < JEJ  0.6 ± 0.1    ***  **  *  JEJ  11.9 ± 0.9    0.8 ± 0.1          ILE  10.1 ± 1.4    0.8 ± 0.1          1Gastrointestinal sites: STO = stomach, DUO = duodenum, JEJ = jejunum, ILE = ileum, COL = colon. 2ANOVA: age = effect of group (neonatal vs. adult dogs); site = effect of gastrointestinal site for both groups (neonatal and adult dogs); age × site = group and gastrointestinal site interaction. *P < 0.05; **P < 0.01; ***P < 0.001; NS, P > 0.05. 3Differences (P < 0.05) among gastrointestinal sites. View Large Table 1. Histomorphometrical analyses of the gastrointestinal tract in neonatal and adult dogs Trait  Intestinal site1  Neonatal (n = 6)  Adult (n = 6)  ANOVA2  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age× site  Mucosal layer, μm  STO  119.4 ± 5.3  STO, COL < DUO, JEJ, ILE3  782.9 ± 63.3  STO, COL < ILE, JEJ, DUO3  ***  ***  ***  DUO  846.5 ± 57.1    3,613.0 ± 170.6  ILE < DUO, JEJ        JEJ  894.2 ± 84.6    2,534.9 ± 194.3  JEJ < DUO        ILE  676.4 ± 56.0    1,851.7 ± 180.5          COL  236.3 ± 20.3    671.0 ± 56.1          Glandular layer, μm  STO  91.7 ± 16.2  STO, COL < DUO, JEJ, ILE  650.2 ± 56.4  STO, COL < DUO, JEJ, ILE  ***  ***  ***  DUO  829.9 ± 142.2    3,475.2 ± 168.8    ILE < JEJ, DUO        JEJ  883.8 ± 207.8    2,432.6 ± 191.2  JEJ < DUO        ILE  663.0 ± 139.7    1,746.1 ± 195.2          COL  222.9 ± 48.7    597.5 ± 51.5          Subglandular layer, μm  STO  27.7 ± 2.9  DUO, JEJ, ILE, COL < STO  132.8 ± 11.0  COL < STO, DUO  ***  ***  *  DUO  16.6 ± 1.4    137.8 ± 9.2          JEJ  10.5 ± 0.5    102.3 ± 11.4          ILE  13.4 ± 1.1    105.6 ± 19.8          COL  13.4 ± 1.1    73.5 ± 10.8          Submucosal layer, μm  STO  127.0 ± 23.1  DUO, JEJ, ILE, COL < STO  610.3 ± 73.2  DUO, JEJ, ILE, COL < STO  ***  ***  ***  DUO  51.9 ± 12.5    227.0 ± 16.8          JEJ  36.0 ± 2.1    148.9 ± 9.2          ILE  74.5 ± 13.1    237.8 ± 13.2          COL  60.0 ± 8.3    228.6 ± 37.4          Muscular layer, μm  STO  304.8 ± 11.1  DUO, JEJ, ILE, COL < STO  2,951.5 ± 170.0  DUO, JEJ < STO, ILE, COL  ***  ***  ***  DUO  135.7 ± 28.6  JEJ, ILE < COL  1,186.3 ± 100.1  COL < STO          JEJ  80.8 ± 59.2    921.6 ± 109.3          ILE  101.6 ± 28.6    1,778.7 ± 179.7          COL  200.2 ± 30.5    1,871.9 ± 77.0          Crypt depth, μm  DUO  89.3 ± 7.4  ILE < DUO, COL  2,069.5 ± 110.0  COL < DUO, JEJ  ***  ***  ***  JEJ  64.5 ± 6.7  JEJ, DUO < COL  1,131.5 ± 94.3  ILE < DUO, JEJ          ILE  59.4 ± 3.7    747.6 ± 49.3  JEJ < DUO        COL  223.1 ± 16.8    634.3 ± 32.3          Villus height, μm  DUO  688.9 ± 58.2  1,134.7 ± 97.0  ILE < DUO, JEJ  *  ***  **  JEJ  748.4 ± 58.4    937.5 ± 111.5          ILE  579.9 ± 49.3    589.4 ± 44.4          Villus area, μm2  DUO  64,332 ± 5,053  ILE < DUO  150,568 ± 14,539  ILE < DUO  ***  ***  *  JEJ  55,019 ± 8,566    113,162 ± 9,950          ILE  41,445 ± 5,266    79,478 ± 7,105          Villus width, μm  DUO  132.6 ± 6.5  219.1 ± 11.0      ***  *  NS  JEJ  121.6 ± 9.1    184.4 ± 11.5          ILE  111.2 ± 8.5    204.4 ± 16.4          Villus height/crypt depth  DUO  8.0 ± 1.0  DUO < JEJ  0.6 ± 0.1    ***  **  *  JEJ  11.9 ± 0.9    0.8 ± 0.1          ILE  10.1 ± 1.4    0.8 ± 0.1          Trait  Intestinal site1  Neonatal (n = 6)  Adult (n = 6)  ANOVA2  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age× site  Mucosal layer, μm  STO  119.4 ± 5.3  STO, COL < DUO, JEJ, ILE3  782.9 ± 63.3  STO, COL < ILE, JEJ, DUO3  ***  ***  ***  DUO  846.5 ± 57.1    3,613.0 ± 170.6  ILE < DUO, JEJ        JEJ  894.2 ± 84.6    2,534.9 ± 194.3  JEJ < DUO        ILE  676.4 ± 56.0    1,851.7 ± 180.5          COL  236.3 ± 20.3    671.0 ± 56.1          Glandular layer, μm  STO  91.7 ± 16.2  STO, COL < DUO, JEJ, ILE  650.2 ± 56.4  STO, COL < DUO, JEJ, ILE  ***  ***  ***  DUO  829.9 ± 142.2    3,475.2 ± 168.8    ILE < JEJ, DUO        JEJ  883.8 ± 207.8    2,432.6 ± 191.2  JEJ < DUO        ILE  663.0 ± 139.7    1,746.1 ± 195.2          COL  222.9 ± 48.7    597.5 ± 51.5          Subglandular layer, μm  STO  27.7 ± 2.9  DUO, JEJ, ILE, COL < STO  132.8 ± 11.0  COL < STO, DUO  ***  ***  *  DUO  16.6 ± 1.4    137.8 ± 9.2          JEJ  10.5 ± 0.5    102.3 ± 11.4          ILE  13.4 ± 1.1    105.6 ± 19.8          COL  13.4 ± 1.1    73.5 ± 10.8          Submucosal layer, μm  STO  127.0 ± 23.1  DUO, JEJ, ILE, COL < STO  610.3 ± 73.2  DUO, JEJ, ILE, COL < STO  ***  ***  ***  DUO  51.9 ± 12.5    227.0 ± 16.8          JEJ  36.0 ± 2.1    148.9 ± 9.2          ILE  74.5 ± 13.1    237.8 ± 13.2          COL  60.0 ± 8.3    228.6 ± 37.4          Muscular layer, μm  STO  304.8 ± 11.1  DUO, JEJ, ILE, COL < STO  2,951.5 ± 170.0  DUO, JEJ < STO, ILE, COL  ***  ***  ***  DUO  135.7 ± 28.6  JEJ, ILE < COL  1,186.3 ± 100.1  COL < STO          JEJ  80.8 ± 59.2    921.6 ± 109.3          ILE  101.6 ± 28.6    1,778.7 ± 179.7          COL  200.2 ± 30.5    1,871.9 ± 77.0          Crypt depth, μm  DUO  89.3 ± 7.4  ILE < DUO, COL  2,069.5 ± 110.0  COL < DUO, JEJ  ***  ***  ***  JEJ  64.5 ± 6.7  JEJ, DUO < COL  1,131.5 ± 94.3  ILE < DUO, JEJ          ILE  59.4 ± 3.7    747.6 ± 49.3  JEJ < DUO        COL  223.1 ± 16.8    634.3 ± 32.3          Villus height, μm  DUO  688.9 ± 58.2  1,134.7 ± 97.0  ILE < DUO, JEJ  *  ***  **  JEJ  748.4 ± 58.4    937.5 ± 111.5          ILE  579.9 ± 49.3    589.4 ± 44.4          Villus area, μm2  DUO  64,332 ± 5,053  ILE < DUO  150,568 ± 14,539  ILE < DUO  ***  ***  *  JEJ  55,019 ± 8,566    113,162 ± 9,950          ILE  41,445 ± 5,266    79,478 ± 7,105          Villus width, μm  DUO  132.6 ± 6.5  219.1 ± 11.0      ***  *  NS  JEJ  121.6 ± 9.1    184.4 ± 11.5          ILE  111.2 ± 8.5    204.4 ± 16.4          Villus height/crypt depth  DUO  8.0 ± 1.0  DUO < JEJ  0.6 ± 0.1    ***  **  *  JEJ  11.9 ± 0.9    0.8 ± 0.1          ILE  10.1 ± 1.4    0.8 ± 0.1          1Gastrointestinal sites: STO = stomach, DUO = duodenum, JEJ = jejunum, ILE = ileum, COL = colon. 2ANOVA: age = effect of group (neonatal vs. adult dogs); site = effect of gastrointestinal site for both groups (neonatal and adult dogs); age × site = group and gastrointestinal site interaction. *P < 0.05; **P < 0.01; ***P < 0.001; NS, P > 0.05. 3Differences (P < 0.05) among gastrointestinal sites. View Large GI Epithelial Cell Proliferation, Based on Ki-67-Positive Cells, in Neonatal and Adult Dogs Cells positive for Ki-67 in the epithelium were mainly present in crypts (and thus in the LE) and in the SI at the base of the villi [i.e., in the generally known proliferation compartment (not shown)]. As shown in Table 2, there were age effects on stomach, all sites of the SI and colon (total, LE, and LP), but not in villi (P < 0.01 to P < 0.001). The number of Ki-67-positive cells was consistently greater (P < 0.01 to P < 0.001) in neonatal than in adult dogs in crypts of the stomach and SI, but not in colon (total and LE) and not in villi. The number of Ki-67-positive cells in crypts, but not in villi, was also significantly influenced by GIT sites, but the differences between neonatal and adult dogs were not consistent across GIT sites. For crypts of all GIT locations and for villus LE, there were group and intestinal site interactions (P < 0.05). Table 2. Gastrointestinal epithelial cell proliferation, based on number of Ki-67-positive cells, in neonatal and adult dogs Gastrointestinal site1  Mucosal site2  Neonatal (n = 6)  Adult (n = 6)  ANOVA3  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age× site  STO  Crypts total  26.16 ± 4.62  STO, COL < JEJ, ILE, DUO4  3.93 ± 0.42  STO, COL, DUO < ILE, JEJ4  ***  ***  **  DUO    94.57 ± 11.91    45.61 ± 3.42          JEJ    77.18 ± 9.68    59.94 ± 3.65          ILE    92.94 ± 8.27    55.95 ± 2.35          COL    34.11 ± 9.87    42.10 ± 2.84          STO  Crypts LE  23.22 ± 4.62  STO, COL < JEJ, ILE, DUO  3.52 ± 0.40  STO < COL, DUO < ILE, JEJ  **  ***  ***  DUO    90.18 ± 11.78    42.88 ± 3.12          JEJ    72.91 ± 9.50    58.20 ± 3.47          ILE    87.18 ± 8.08    54.78 ± 2.27          COL    30.65 ± 8.88    40.75 ± 2.85          STO  Crypts LP  2.94 ± 0.51    0.42 ± 0.06  STO < DUO  ***  ***  ***  DUO    4.40 ± 0.56    2.72 ± 0.87          JEJ    4.27 ± 0.67    1.74 ± 0.35          ILE    5.75 ± 0.48    1.17 ± 0.23          COL    3.45 ± 1.00    1.35 ± 0.23          DUO  Villi total5  9.22 ± 0.89    6.68 ± 0.78    NS  NS  NS  JEJ    8.16 ± 1.65    6.70 ± 0.53          ILE    6.84 ± 1.04    6.20 ± 0.81          DUO  Villi LE  1.62 ± 0.35    0.95 ± 0.33    NS  NS  **  JEJ    0.93 ± 0.23    1.51 ± 0.25          ILE    1.40 ± 0.18    0.83 ± 0.21          DUO  Villi LP  7.59 ± 0.85  ILE < DUO, JEJ  5.73 ± 0.54    NS  NS  NS  JEJ    7.23 ± 1.47    5.20 ± 0.43          ILE    5.44 ± 0.91    5.37 ± 0.67          Gastrointestinal site1  Mucosal site2  Neonatal (n = 6)  Adult (n = 6)  ANOVA3  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age× site  STO  Crypts total  26.16 ± 4.62  STO, COL < JEJ, ILE, DUO4  3.93 ± 0.42  STO, COL, DUO < ILE, JEJ4  ***  ***  **  DUO    94.57 ± 11.91    45.61 ± 3.42          JEJ    77.18 ± 9.68    59.94 ± 3.65          ILE    92.94 ± 8.27    55.95 ± 2.35          COL    34.11 ± 9.87    42.10 ± 2.84          STO  Crypts LE  23.22 ± 4.62  STO, COL < JEJ, ILE, DUO  3.52 ± 0.40  STO < COL, DUO < ILE, JEJ  **  ***  ***  DUO    90.18 ± 11.78    42.88 ± 3.12          JEJ    72.91 ± 9.50    58.20 ± 3.47          ILE    87.18 ± 8.08    54.78 ± 2.27          COL    30.65 ± 8.88    40.75 ± 2.85          STO  Crypts LP  2.94 ± 0.51    0.42 ± 0.06  STO < DUO  ***  ***  ***  DUO    4.40 ± 0.56    2.72 ± 0.87          JEJ    4.27 ± 0.67    1.74 ± 0.35          ILE    5.75 ± 0.48    1.17 ± 0.23          COL    3.45 ± 1.00    1.35 ± 0.23          DUO  Villi total5  9.22 ± 0.89    6.68 ± 0.78    NS  NS  NS  JEJ    8.16 ± 1.65    6.70 ± 0.53          ILE    6.84 ± 1.04    6.20 ± 0.81          DUO  Villi LE  1.62 ± 0.35    0.95 ± 0.33    NS  NS  **  JEJ    0.93 ± 0.23    1.51 ± 0.25          ILE    1.40 ± 0.18    0.83 ± 0.21          DUO  Villi LP  7.59 ± 0.85  ILE < DUO, JEJ  5.73 ± 0.54    NS  NS  NS  JEJ    7.23 ± 1.47    5.20 ± 0.43          ILE    5.44 ± 0.91    5.37 ± 0.67          1Intestinal sites: STO = stomach; DUO = duodenum; JEJ = jejunum; ILE = ileum; COL = colon. 2Number of Ki-67-positive cells per 10,000 µm2 of mucosal site, based on immunohistochemical analysis using an antibody against Ki-67 protein. LE = lamina epithelialis; LP = lamina propria. 3ANOVA: age = effect of group (neonatal vs. adult dogs); site = effect of gastrointestinal site for both groups; age × site = group and gastrointestinal site interaction. **P < 0.01; ***P < 0.001; NS, P > 0.05. 4Differences (P < 0.05) among gastrointestinal sites. 5Total mucosa comprising LE and LP. View Large Table 2. Gastrointestinal epithelial cell proliferation, based on number of Ki-67-positive cells, in neonatal and adult dogs Gastrointestinal site1  Mucosal site2  Neonatal (n = 6)  Adult (n = 6)  ANOVA3  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age× site  STO  Crypts total  26.16 ± 4.62  STO, COL < JEJ, ILE, DUO4  3.93 ± 0.42  STO, COL, DUO < ILE, JEJ4  ***  ***  **  DUO    94.57 ± 11.91    45.61 ± 3.42          JEJ    77.18 ± 9.68    59.94 ± 3.65          ILE    92.94 ± 8.27    55.95 ± 2.35          COL    34.11 ± 9.87    42.10 ± 2.84          STO  Crypts LE  23.22 ± 4.62  STO, COL < JEJ, ILE, DUO  3.52 ± 0.40  STO < COL, DUO < ILE, JEJ  **  ***  ***  DUO    90.18 ± 11.78    42.88 ± 3.12          JEJ    72.91 ± 9.50    58.20 ± 3.47          ILE    87.18 ± 8.08    54.78 ± 2.27          COL    30.65 ± 8.88    40.75 ± 2.85          STO  Crypts LP  2.94 ± 0.51    0.42 ± 0.06  STO < DUO  ***  ***  ***  DUO    4.40 ± 0.56    2.72 ± 0.87          JEJ    4.27 ± 0.67    1.74 ± 0.35          ILE    5.75 ± 0.48    1.17 ± 0.23          COL    3.45 ± 1.00    1.35 ± 0.23          DUO  Villi total5  9.22 ± 0.89    6.68 ± 0.78    NS  NS  NS  JEJ    8.16 ± 1.65    6.70 ± 0.53          ILE    6.84 ± 1.04    6.20 ± 0.81          DUO  Villi LE  1.62 ± 0.35    0.95 ± 0.33    NS  NS  **  JEJ    0.93 ± 0.23    1.51 ± 0.25          ILE    1.40 ± 0.18    0.83 ± 0.21          DUO  Villi LP  7.59 ± 0.85  ILE < DUO, JEJ  5.73 ± 0.54    NS  NS  NS  JEJ    7.23 ± 1.47    5.20 ± 0.43          ILE    5.44 ± 0.91    5.37 ± 0.67          Gastrointestinal site1  Mucosal site2  Neonatal (n = 6)  Adult (n = 6)  ANOVA3  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age× site  STO  Crypts total  26.16 ± 4.62  STO, COL < JEJ, ILE, DUO4  3.93 ± 0.42  STO, COL, DUO < ILE, JEJ4  ***  ***  **  DUO    94.57 ± 11.91    45.61 ± 3.42          JEJ    77.18 ± 9.68    59.94 ± 3.65          ILE    92.94 ± 8.27    55.95 ± 2.35          COL    34.11 ± 9.87    42.10 ± 2.84          STO  Crypts LE  23.22 ± 4.62  STO, COL < JEJ, ILE, DUO  3.52 ± 0.40  STO < COL, DUO < ILE, JEJ  **  ***  ***  DUO    90.18 ± 11.78    42.88 ± 3.12          JEJ    72.91 ± 9.50    58.20 ± 3.47          ILE    87.18 ± 8.08    54.78 ± 2.27          COL    30.65 ± 8.88    40.75 ± 2.85          STO  Crypts LP  2.94 ± 0.51    0.42 ± 0.06  STO < DUO  ***  ***  ***  DUO    4.40 ± 0.56    2.72 ± 0.87          JEJ    4.27 ± 0.67    1.74 ± 0.35          ILE    5.75 ± 0.48    1.17 ± 0.23          COL    3.45 ± 1.00    1.35 ± 0.23          DUO  Villi total5  9.22 ± 0.89    6.68 ± 0.78    NS  NS  NS  JEJ    8.16 ± 1.65    6.70 ± 0.53          ILE    6.84 ± 1.04    6.20 ± 0.81          DUO  Villi LE  1.62 ± 0.35    0.95 ± 0.33    NS  NS  **  JEJ    0.93 ± 0.23    1.51 ± 0.25          ILE    1.40 ± 0.18    0.83 ± 0.21          DUO  Villi LP  7.59 ± 0.85  ILE < DUO, JEJ  5.73 ± 0.54    NS  NS  NS  JEJ    7.23 ± 1.47    5.20 ± 0.43          ILE    5.44 ± 0.91    5.37 ± 0.67          1Intestinal sites: STO = stomach; DUO = duodenum; JEJ = jejunum; ILE = ileum; COL = colon. 2Number of Ki-67-positive cells per 10,000 µm2 of mucosal site, based on immunohistochemical analysis using an antibody against Ki-67 protein. LE = lamina epithelialis; LP = lamina propria. 3ANOVA: age = effect of group (neonatal vs. adult dogs); site = effect of gastrointestinal site for both groups; age × site = group and gastrointestinal site interaction. **P < 0.01; ***P < 0.001; NS, P > 0.05. 4Differences (P < 0.05) among gastrointestinal sites. 5Total mucosa comprising LE and LP. View Large GI Epithelial Cell Apoptosis, Based on Cleaved Caspase-3-Positive Cells, in Neonatal and Adult Dogs Cleaved caspase-3-positive cells in the epithelium were mainly present in crypts and were randomly distributed along epithelial cell migration routes; in the stomach they were mainly found at the base of gastric glands and in part at the surface of the mucosa, in the SI in the crypts base and in the villus epithelium, and in the colon in crypts (not shown). As shown in Table 3, there were no age effects on the number of caspase-3-positive cells in crypts of stomach or SI and colon, and in villi. The number of caspase-3-positive cells in crypts of stomach, SI, and colon (total and LE, but not LP), and in villi (total and LP, but not LE) was influenced (P < 0.05) by GIT sites, but differences between neonatal and adult dogs were not consistent across GIT sites. For caspase-3-positive cells in crypts of the LP, there were group and intestinal site interactions (P < 0.05). Table 3. Gastrointestinal epithelial cell apoptosis, based on number of caspase-3-positive cells, in neonatal and adult dogs Gastrointestinal site1  Mucosal site2  Neonatal (n = 6)  Adult (n = 6)  ANOVA3  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age ×site  STO  Crypts total  0.48 ± 0.07  STO, DUO, JEJ, COL < ILE4  0.10 ± 0.03  STO, DUO, JEJ, COL < ILE4  NS  ***  NS  DUO    0.16 ± 0.05    0.17 ± 0.05          JEJ    0.71 ± 0.11    0.59 ± 0.11          ILE    1.50 ± 0.19    1.32 ± 0.29          COL    0.18 ± 0.05    0.22 ± 0.07          STO  Crypt LE  0.39 ± 0.08  STO, DUO, JEJ, COL < ILE  0.09 ± 0.03  STO, DUO, JEJ, COL < ILE  NS  ***  NS  DUO    0.15 ± 0.05    0.15 ± 0.05          JEJ    0.65 ± 0.11    0.56 ± 0.11          ILE    1.34 ± 0.17    1.30 ± 0.28          COL    0.15 ± 0.04    0.15 ± 0.04          STO  Crypt LP  0.09 ± 0.02    0.01 ± 0.01    NS  NS  **  DUO    0.01 ± 0.01    0.02 ± 0.01          JEJ    0.06 ± 0.01    0.03 ± 0.01          ILE    0.15 ± 0.05    0.02 ± 0.01          COL    0.03 ± 0.02    0.07 ± 0.05          DUO  Villi total5  0.16 ± 0.06  DUO, JEJ < ILE  0.15 ± 0.07    NS  *  NS  JEJ    0.19 ± 0.08    0.44 ± 0.19          ILE    0.36 ± 0.07    0.49 ± 0.12          DUO  Villi LE  0.13 ± 0.06    0.14 ± 0.07    NS  NS  NS  JEJ    0.15 ± 0.07    0.41 ± 0.19          ILE    0.27 ± 0.06    0.40 ± 0.12          DUO  Villi LP  0.03 ± 0.01  DUO < ILE  0.02 ± 0.01  DUO < ILE  NS  ***  NS  JEJ    0.04 ± 0.01    0.04 ± 0.02          ILE    0.09 ± 0.02    0.09 ± 0.02          Gastrointestinal site1  Mucosal site2  Neonatal (n = 6)  Adult (n = 6)  ANOVA3  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age ×site  STO  Crypts total  0.48 ± 0.07  STO, DUO, JEJ, COL < ILE4  0.10 ± 0.03  STO, DUO, JEJ, COL < ILE4  NS  ***  NS  DUO    0.16 ± 0.05    0.17 ± 0.05          JEJ    0.71 ± 0.11    0.59 ± 0.11          ILE    1.50 ± 0.19    1.32 ± 0.29          COL    0.18 ± 0.05    0.22 ± 0.07          STO  Crypt LE  0.39 ± 0.08  STO, DUO, JEJ, COL < ILE  0.09 ± 0.03  STO, DUO, JEJ, COL < ILE  NS  ***  NS  DUO    0.15 ± 0.05    0.15 ± 0.05          JEJ    0.65 ± 0.11    0.56 ± 0.11          ILE    1.34 ± 0.17    1.30 ± 0.28          COL    0.15 ± 0.04    0.15 ± 0.04          STO  Crypt LP  0.09 ± 0.02    0.01 ± 0.01    NS  NS  **  DUO    0.01 ± 0.01    0.02 ± 0.01          JEJ    0.06 ± 0.01    0.03 ± 0.01          ILE    0.15 ± 0.05    0.02 ± 0.01          COL    0.03 ± 0.02    0.07 ± 0.05          DUO  Villi total5  0.16 ± 0.06  DUO, JEJ < ILE  0.15 ± 0.07    NS  *  NS  JEJ    0.19 ± 0.08    0.44 ± 0.19          ILE    0.36 ± 0.07    0.49 ± 0.12          DUO  Villi LE  0.13 ± 0.06    0.14 ± 0.07    NS  NS  NS  JEJ    0.15 ± 0.07    0.41 ± 0.19          ILE    0.27 ± 0.06    0.40 ± 0.12          DUO  Villi LP  0.03 ± 0.01  DUO < ILE  0.02 ± 0.01  DUO < ILE  NS  ***  NS  JEJ    0.04 ± 0.01    0.04 ± 0.02          ILE    0.09 ± 0.02    0.09 ± 0.02          1Intestinal sites: STO = stomach; DUO = duodenum; JEJ = jejunum; ILE = ileum; COL = colon. 2Number of caspase-3-positive cells per 10,000 µm2 of mucosal site, based on immunohistochemical analysis using antibody against caspase-3 protein. LE = lamina epithelialis; LP = lamina propria. 3ANOVA: Age = effect of group (neonatal vs. adult dogs); Site = effect of gastrointestinal site for both groups (P-value); Age × site = group and gastrointestinal site interaction. *P < 0.05; **P < 0.01; ***P < 0.001; NS, P > 0.05. 4Differences (P < 0.05) among gastrointestinal sites. 5Total mucosa comprising LE and LP. View Large Table 3. Gastrointestinal epithelial cell apoptosis, based on number of caspase-3-positive cells, in neonatal and adult dogs Gastrointestinal site1  Mucosal site2  Neonatal (n = 6)  Adult (n = 6)  ANOVA3  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age ×site  STO  Crypts total  0.48 ± 0.07  STO, DUO, JEJ, COL < ILE4  0.10 ± 0.03  STO, DUO, JEJ, COL < ILE4  NS  ***  NS  DUO    0.16 ± 0.05    0.17 ± 0.05          JEJ    0.71 ± 0.11    0.59 ± 0.11          ILE    1.50 ± 0.19    1.32 ± 0.29          COL    0.18 ± 0.05    0.22 ± 0.07          STO  Crypt LE  0.39 ± 0.08  STO, DUO, JEJ, COL < ILE  0.09 ± 0.03  STO, DUO, JEJ, COL < ILE  NS  ***  NS  DUO    0.15 ± 0.05    0.15 ± 0.05          JEJ    0.65 ± 0.11    0.56 ± 0.11          ILE    1.34 ± 0.17    1.30 ± 0.28          COL    0.15 ± 0.04    0.15 ± 0.04          STO  Crypt LP  0.09 ± 0.02    0.01 ± 0.01    NS  NS  **  DUO    0.01 ± 0.01    0.02 ± 0.01          JEJ    0.06 ± 0.01    0.03 ± 0.01          ILE    0.15 ± 0.05    0.02 ± 0.01          COL    0.03 ± 0.02    0.07 ± 0.05          DUO  Villi total5  0.16 ± 0.06  DUO, JEJ < ILE  0.15 ± 0.07    NS  *  NS  JEJ    0.19 ± 0.08    0.44 ± 0.19          ILE    0.36 ± 0.07    0.49 ± 0.12          DUO  Villi LE  0.13 ± 0.06    0.14 ± 0.07    NS  NS  NS  JEJ    0.15 ± 0.07    0.41 ± 0.19          ILE    0.27 ± 0.06    0.40 ± 0.12          DUO  Villi LP  0.03 ± 0.01  DUO < ILE  0.02 ± 0.01  DUO < ILE  NS  ***  NS  JEJ    0.04 ± 0.01    0.04 ± 0.02          ILE    0.09 ± 0.02    0.09 ± 0.02          Gastrointestinal site1  Mucosal site2  Neonatal (n = 6)  Adult (n = 6)  ANOVA3  Mean ± SE  Site difference  Mean ± SE  Site difference  Age  Site  Age ×site  STO  Crypts total  0.48 ± 0.07  STO, DUO, JEJ, COL < ILE4  0.10 ± 0.03  STO, DUO, JEJ, COL < ILE4  NS  ***  NS  DUO    0.16 ± 0.05    0.17 ± 0.05          JEJ    0.71 ± 0.11    0.59 ± 0.11          ILE    1.50 ± 0.19    1.32 ± 0.29          COL    0.18 ± 0.05    0.22 ± 0.07          STO  Crypt LE  0.39 ± 0.08  STO, DUO, JEJ, COL < ILE  0.09 ± 0.03  STO, DUO, JEJ, COL < ILE  NS  ***  NS  DUO    0.15 ± 0.05    0.15 ± 0.05          JEJ    0.65 ± 0.11    0.56 ± 0.11          ILE    1.34 ± 0.17    1.30 ± 0.28          COL    0.15 ± 0.04    0.15 ± 0.04          STO  Crypt LP  0.09 ± 0.02    0.01 ± 0.01    NS  NS  **  DUO    0.01 ± 0.01    0.02 ± 0.01          JEJ    0.06 ± 0.01    0.03 ± 0.01          ILE    0.15 ± 0.05    0.02 ± 0.01          COL    0.03 ± 0.02    0.07 ± 0.05          DUO  Villi total5  0.16 ± 0.06  DUO, JEJ < ILE  0.15 ± 0.07    NS  *  NS  JEJ    0.19 ± 0.08    0.44 ± 0.19          ILE    0.36 ± 0.07    0.49 ± 0.12          DUO  Villi LE  0.13 ± 0.06    0.14 ± 0.07    NS  NS  NS  JEJ    0.15 ± 0.07    0.41 ± 0.19          ILE    0.27 ± 0.06    0.40 ± 0.12          DUO  Villi LP  0.03 ± 0.01  DUO < ILE  0.02 ± 0.01  DUO < ILE  NS  ***  NS  JEJ    0.04 ± 0.01    0.04 ± 0.02          ILE    0.09 ± 0.02    0.09 ± 0.02          1Intestinal sites: STO = stomach; DUO = duodenum; JEJ = jejunum; ILE = ileum; COL = colon. 2Number of caspase-3-positive cells per 10,000 µm2 of mucosal site, based on immunohistochemical analysis using antibody against caspase-3 protein. LE = lamina epithelialis; LP = lamina propria. 3ANOVA: Age = effect of group (neonatal vs. adult dogs); Site = effect of gastrointestinal site for both groups (P-value); Age × site = group and gastrointestinal site interaction. *P < 0.05; **P < 0.01; ***P < 0.001; NS, P > 0.05. 4Differences (P < 0.05) among gastrointestinal sites. 5Total mucosa comprising LE and LP. View Large DISCUSSION The focus of this study was the GIT epithelium because it is the first line of interaction with ingested nutritional factors and microorganisms and is involved in chronic enteropathies, such as inflammatory bowel disease and food-responsive diarrhea. Although quantitative evaluations of the canine intestinal mucosa have been made before (Hart and Kidder, 1978; Paulsen et al., 2003; Baum et al., 2007), our current study on histomorphometrical changes of the GIT mucosa is expanded by the inclusion of epithelial cell proliferation, as well as apoptosis rates in neonatal and adult dogs. Importantly, all dogs were Beagles, thus excluding possible breed effects. Values of all histomorphometrical traits (except villus height/crypt depth ratios) were markedly greater in adult than in neonatal dogs, indicating important ontogenetic changes. These were likely the consequence of adaptations to nutritional and other factors (such as microbes). The overall increase of the measured traits was associated with an increased thickness of the GIT, to which the muscular layer contributed most and the subglandular layer least. The thickness of the stomach did not increase more than other parts of the GIT, although its size is known to increase exceptionally after birth (Widdowson et al., 1976). The 23-, 18-, 13-, and 3-fold increases of the crypt depth in duodenum, jejunum, ileum, and colon, respectively, express a very different growth potential that decreased from the duodenum to the colon. Paulsen et al. (2003) also found a decreased crypt depth along the SI in dogs. However, because the total mucosa thickness of the duodenum, jejunum, ileum, and colon increased 4.2-, 2.8-, 2.7-, and 2.8-fold, respectively, the change was not only dependent on crypt depths, indicating that other factors contributed as well. Whereas Paulsen et al. (2003) found smaller villus heights in the SI of Beagle dogs, we found increased villus heights (besides villus widths and areas) in adult than neonatal dogs. The reduced villus height/crypt depth ratios in the SI in adult as compared with neonatal dogs, as also found by Paulsen et al. (2003), express a relatively reduced efficiency of the crypt cell potential for villus growth in adult compared with neonatal dogs. This might be the result of reduced epithelial cell proliferation rates, reduced migration rates of epithelial cells from the crypts to the villus tip, or enhanced apoptosis and exfoliation rates of epithelial cells. The data in the current study indicate that epithelial crypt cell proliferation, but not apoptosis rates, were possibly involved. Enhanced exfoliation rates also may have been the result of the abrasive effects by nutritional components and contributed to the reduced villus height/crypt depth ratios. As expected, for all traits, there were significant, but variable, histomorphometrical differences among intestinal sites. For all GIT locations, except for villus width, there were significant group and intestinal site interactions. Because histomorphometrical characteristics between neonatal and adult dogs were not consistent, factors influencing GIT traits in neonatal and adult dogs must have been different. Based on the number of Ki-67-positive cells, epithelial cell proliferation in the GIT mucosa occurred in the LE (i.e., in crypts) rather than in the LP, as found previously (Blättler et al., 2001; Baum et al., 2007). Importantly, there were considerable differences in cell proliferation rates in different parts of the GIT. Differences in the cell proliferation rate in crypts between jejunum and colon of dogs have also been described by Baum et al. (2007). Sex effects on the number of Ki-67-positive cells were inconsistent. Cell proliferation in crypts of the newborn dogs was much greater than in adult dogs, except in crypts (total and LE) of the colon and in villi. Baum et al. (2007) demonstrated that the cell proliferation rate decreased with age in dogs and especially in the LP mucosae and in the lamina muscularis at the 2 GIT sites that they studied (jejunum and colon). In their study, the epithelial cell proliferation was, however, only weakly correlated with age. Correlations of cell proliferation rates and age could not be calculated in our study. The much greater villus height/crypt depth ratios (as indices of potential growth rate efficiency) and the greater Ki-67 expression in our study in neonatal than in adult dogs shows that the size of the villi is not negatively affected by reduced proliferation rates in the crypts in older compared with newborn dogs. Cleaved caspase-3 was chosen as a marker of apoptosis because it is the final member in the chain of activated effector caspases that trigger the apoptotic process (Gown and Willingham, 2002). It is realized, however, that there are also caspase-3-independent mechanisms of apoptosis (Cregan et al., 2004). Apoptotic epithelial cells were found in the proliferative and nonproliferative zones (i.e., in the stomach mainly at the base of gastric glands and in part at the surface of the mucosa, in the SI mainly in crypts base and unevenly distributed in villi, and in the colon in crypts), as found previously (Hall et al., 1994; Potten, 1997). Thus, there exist differences in the distribution of Ki-67- and caspase-3-positive cells. Furthermore, the absolute number of caspase-3-positive cells was a small fraction relative to the normal (non-caspase-3-positive) epithelial cells, in accordance with Potten (1997). There were significant differences in the number of caspase-3-positive cells among GIT sites on total crypts, total villi, and LP, suggesting considerable regional differences in apoptotic rates. Relatively small numbers of apoptotic cells in the colonic epithelium, as in our study, seem to be typical, as shown for other species (Potten, 1997). The greater number of caspase-3-positive cells in the crypts of the ileum than at other sites of the GIT, together with a relatively large number of Ki-67-positive cells in newborn and adult dogs at the same site, speaks for an increased epithelial cell turnover. In contrast to Ki-67-positive epithelial cells, age did not affect the number of caspase-3-positive GIT epithelial cells. In rats, colonic crypt cell apoptotic rates did not change dependent on age (Lee et al., 2000). The data from apoptosis rates in rat colon are conflicting with reports of no change (Lee et al., 2000) and decreased rates (Xiao et al., 2001). The lack of difference in the number of apoptotic cells between newborn and adult dogs was in marked contrast to the decrease of Ki-67-positive epithelial cells with increasing age. The greater size of the villi in adult than in neonatal dogs was therefore not the result of enhanced proliferation and reduced apoptosis rates of epithelial cells. Studies in rats indicate that the epithelial cell loss in the healthy GIT is mainly due to apoptosis and not to cell shedding (Hall et al., 1994). Reduced cell shedding (without apoptosis) in the SI of epithelial cells from villi into the gut lumen seems unlikely because of the abrasive effect of nutrient components, which would expectedly have an opposite effect on villus size. However, an enhanced cell migration rate of epithelial cells from the crypts to the villus tips in adult dogs may contribute to their increased villus sizes. The number of caspase-3-positive cells was much less than of Ki-67-positive cells (i.e., the proliferative cell/apoptotic cell ratio was consistently increased). If true, epithelial cell proliferation rates would be greater than apoptotic rates. As a consequence, growth or maintenance of the GIT mass would be out of control. This was obviously not the case. This discrepancy can most likely be explained by differences in the kinetics of proliferative and apoptotic events that were measured with the 2 methods used in this study. Both methods determined only part of proliferative and apoptotic mechanisms and events, whose duration is known to be different. This study confirms the hypothesis that the size of GIT mucosal components and rates of epithelial cell proliferation change with increasing age, whereas this is not the case with apoptosis rates. In the dog, differential diagnosis of chronic GI diseases, such as food-responsive diarrhea, inflammatory bowel disease, and malignancy, is routinely based on histological evaluations of biopsies from the GIT. The present study may provide a better basis for the evaluation of canine enteropathies. LITERATURE CITED Allenspach K. Gaschen F. 2003. Chronische Darmerkrankungen beim Hund: Eine Uebersicht. Schweiz. Arch. Tierheilk.  145: 209– 222. Google Scholar CrossRef Search ADS   Baum B. Meneses F. Kleinschmidt S. Nolte I. Hewicker-Trautwein M. 2007. Age-related histomorphologic changes in the canine intestinal tract: A histologic and immunhistologic study. World J. Gastroenterol.  13: 152– 157. https://doi.org/17206763 Google Scholar CrossRef Search ADS PubMed  Bjerknes M. Cheng H. 2005. Gastrointestinal stem cells. II. Intestinal stem cells. Am. J. Physiol. Gastrointest. Liver Physiol.  289: G381– G387. https://doi.org/16093419 Google Scholar CrossRef Search ADS PubMed  Blättler U. Hammon H. M. Morel C. Philipona C. Rauprich A. Romé V. Le-Huërou-Luron I. Guilloteau P. Blum J. W. 2001. Feeding colostrum, its composition and feeding duration variably modify proliferation and morphology of the intestine and digestive enzyme activities of neonatal calves. J. Nutr.  131: 1256– 1263. https://doi.org/11285335 Google Scholar CrossRef Search ADS PubMed  Chang H. Y. Yang X. 2000. Proteases for cell suicide: Functions and regulation of caspases. Microbiol. Mol. Biol. Rev.  64: 821– 846. https://doi.org/11104820 Google Scholar CrossRef Search ADS PubMed  Creamer B. Shorter G. Bamforth J. 1961. The turnover and shedding of epithelial cells. I. The turnover in the gastro-intestinal tract. Gut  2: 110– 118. https://doi.org/13696345 Google Scholar CrossRef Search ADS PubMed  Cregan S. P. Dawson V. L. Slack R. S. 2004. Role of AIF in caspase dependent and caspase independent cell death. Oncogene  23: 2785– 2796. https://doi.org/15077142 Google Scholar CrossRef Search ADS PubMed  Dandrieux J. R. Bornand V. F. Doherr M. G. Kano R. Zurbriggen A. Burgener I. A. 2008. Evaluation of lymphocytes apoptosis in dogs with inflammatory bowel disease. Am. J. Vet. Res.  69: 1279– 1285. https://doi.org/18828683 Google Scholar CrossRef Search ADS PubMed  Edelblum K. L. Yan F. Yamaoka T. Polk D. B. 2006. Regulation of apoptosis during homeostasis and disease in the intestinal epithelium. Inflamm. Bowel Dis.  12: 413– 424. https://doi.org/16670531 Google Scholar CrossRef Search ADS PubMed  Gown A. M. Willingham M. C. 2002. Improved detection of apoptotic cells in archival paraffin sections: Immunohistochemistry using antibodies to cleaved caspase-3. J. Histochem. Cytochem.  50: 449– 454. Google Scholar CrossRef Search ADS PubMed  Hall E. J. Batt R. M. 1990. Development of wheat-sensitive enteropathy in Irish Setters: Morphologic changes. Am. J. Vet. Res.  51: 978– 982. https://doi.org/2389896 Google Scholar PubMed  Hall P. A. Coates P. J. Ansari B. Hopwood D. 1994. Regulation of cell number in the mammalian gastrointestinal tract: The importance of apoptosis. J. Cell Sci.  107: 3569– 3577. https://doi.org/7706406 Google Scholar PubMed  Hart I. R. Kidder D. E. 1978. The quantitative assessment of normal canine small intestinal mucosa. Res. Vet. Sci.  25: 157– 162. https://doi.org/364574 Google Scholar PubMed  Johnson L. R. 1988. Regulation of gastrointestinal mucosal growth. Physiol. Rev.  68: 456– 502. https://doi.org/3282244 Google Scholar CrossRef Search ADS PubMed  Karan S. M. 1999. Lineage commitment and maturation of epithelial cells in the gut. Front. Biosci.  4: 286– 298. Google Scholar CrossRef Search ADS   Lee H.-M. Greeley G. H.Jr. Englander E. W. 2000. Effects of aging on expression of genes involved in regulation of proliferation and apoptosis in the colonic epithelium. Mech. Ageing Dev.  155: 139– 155. Google Scholar CrossRef Search ADS   Lützen L. Trieb G. Pappritz G. 1976. Allometric analysis of organ weights: II Beagle dogs. Toxicol. Pharmacol.  35: 543– 551. Google Scholar CrossRef Search ADS   Otsuki Y. Li Z. Shibata M. A. 2003. Apoptotic detection methods—From morphology to gene. Prog. Histochem. Cytochem.  38: 275– 339. https://doi.org/12756893 Google Scholar CrossRef Search ADS PubMed  Paulsen D. B. Buddington K. K. Buddington R. K. 2003. Dimensions and histologic characteristics of the small intestine of dogs during postnatal development. Am. J. Vet. Res.  64: 618– 626. https://doi.org/12755303 Google Scholar CrossRef Search ADS PubMed  Pfammatter, N. S., N. Luckschander, S. Jakob, S. Hartnack, T. Brunner, and I. A. Burgener 2008. Horizontal and vertical distribution of CD3+ T lymphocytes in the intestine of healthy adult and neonatal dogs.  Thesis for DVM. Univ. Bern, Bern, Switzerland. Potten C. S. 1997. Epithelial cell growth and differentiation. II. Intestinal apoptosis. Am. J. Physiol.  273: 253– 257. Que F. G. Gores G. J. 1996. Cell death by apoptosis. Basic concepts and disease relevance for the gastroenterologist. Gastroenterology  110: 1238– 1243. https://doi.org/8613014 Google Scholar CrossRef Search ADS PubMed  Scholzen T. Gerdes J. 2000. The Ki-67 protein: From the known and the unknown. J. Cell. Physiol.  182: 311– 322. https://doi.org/10653597 Google Scholar CrossRef Search ADS PubMed  Schwarz S. M. Heird W. C. 1994. Effects of feeding on the small intestinal mucosa of beagle pups during the first 5 d of life. Am. J. Clin. Nutr.  60: 879– 886. https://doi.org/7985628 Google Scholar CrossRef Search ADS PubMed  Ward S. M. Torihashi S. 1995. Morphological changes during ontogeny of the canine proximal colon. Cell Tissue Res.  282: 93– 108. https://doi.org/8581930 Google Scholar CrossRef Search ADS PubMed  Widdowson E. M. Colombo V. E. Artavanis C. A. 1976. Changes in the organs of pigs in response to feeding for the first 24 hours after birth. Biol. Neonate  28: 272– 281. Google Scholar CrossRef Search ADS   Wong W. M. Wright N. A. 1999. Cell proliferation in gastrointestinal mucosa. J. Clin. Pathol.  52: 321– 333. https://doi.org/10560350 Google Scholar CrossRef Search ADS PubMed  Xiao Z.-Q. Moragoda L. Jaszewski R. Hatfield J. A. Fligiel S. E. G. Majumdar A. P. N. 2001. Aging is associated with increased proliferation and decreased apoptosis in the colonic mucosa. Mech. Ageing Dev.  122: 1849– 1864. https://doi.org/11557285 Google Scholar CrossRef Search ADS PubMed  American Society of Animal Science

Cabrera, R. A.;Boyd, R. D.;Jungst, S. B.;Wilson, E. R.;Johnston, M. E.;Vignes, J. L.;Odle, J.

doi: 10.2527/jas.2009-2121pmid: 20190163

ABSTRACT A total of 1,034 pigs produced by breeding PIC sows to 2 different PIC terminal sires were used to create 3 distinct weaning weight populations so that postweaning growth to 125 kg could be studied. The rearing strategies resulted in BW that ranged from 4.1 to 11.5 kg by 20 d of age. Sows and corresponding litters were allocated to 3 treatments: sow reared (SR; n = 367) for 20 d, sow reared for 14 d (14W; n = 330), and sow reared for 2 d (2W; n = 337). Sows were removed from 2W and 14W groups, but progeny remained in the crates and received milk replacer ad libitum (for 18 and 6 d, respectively) until the contemporary SR pigs were weaned at 20 d of age. The SR pigs (6.49 ± 0.15 kg) weighed 1.01 kg less than 14W pigs (7.5 ± 0.14 kg) and 2.26 kg less than 2W pigs (8.75 ± 0.14 kg; P < 0.05). The 14W pigs weighed 1.25 kg less than 2W pigs (P < 0.05). Nursery ADG for the 2W group (547 g/d) was 35 g/d less (P < 0.05) than 14W pigs. The 14W pigs (165 d) required 3 fewer (P < 0.05) days to reach 125 kg of BW compared with SR pigs. The SR and 14W pigs gained BW 24 and 20 g/d faster (P < 0.05) in the postnursery period when compared with 2W pigs. The SR and 2W pigs consumed 0.10 and 0.12 kg/d less (P < 0.05) during this period when compared with 14W pigs (2.32 kg/d). Gain:feed of SR was improved (P < 0.05) when compared with the 14W and 2W pigs over 167 d of age (0.44 vs. 0.42 and 0.42, respectively). Lean percentage was 0.7% greater (P < 0.05) in carcasses from SR pigs (55.0%) compared with carcasses from 2W pigs (54.3%) when adjusted to a constant HCW. A study of the effect of weaning weight on days to 125 kg was limited to SR and 14W groups because maternal deprivation compromised the 2W group postweaning growth. Six weaning-weight groups were defined using a normal distribution: 4.6, 5.5, 6.4, 7.3, 8.2, and 9.5 kg. Pigs weighing 5.5 kg at 20 d of age were able to reach 125 kg 8 d sooner (168.8 d) than those weighing 4.6 kg (176.8 d). There was a linear relationship (P < 0.05) between weaning weight and ADG in the postnursery phase of growth. We conclude that 1) a weaning weight of less than 5.0 kg imposes the greatest marginal loss in production output for a 20-d weaning and 2) lactation length influences long-term growth, composition of growth, and viability of progeny. INTRODUCTION Weaning weight is an important factor influencing postweaning growth. Pigs with heavier weaning weights grow more rapidly after weaning. Mahan and Lepine (1991) reported that pigs weighing 6.8 to 8.2 kg at weaning reached 105 kg approximately 10 d earlier than pigs weighing 4.1 to 5.5 kg. This finding is consistent with that of Azain (1997), who observed pigs weighing an average of 5.65 kg reached 104 kg of BW 7 d sooner than those weighing 4.5 kg. However, it is not clear how weaning weight affected subsequent and viability performance when the confounding influence of birth weight was removed. The immunity acquired by piglets during the time they spend with the sow is critical for the protection of the piglets against diseases after weaning (Morrow, 2004). The influence of maternal age on the immune competence of her progeny is known to be important but has been more extensively studied in dairy cattle than pigs. Blecha et al. (1983) reported that in vivo and in vitro cellular immune responses were compromised in early weaned piglets. Our study examines how long-term growth and viability of piglets beyond the colostrum period are influenced by the amount of time they spend nursing their mother. The objectives of this experiment were 1) to determine the relationship between weaning weight and growth to 125 kg of BW and 2) to determine the impact of time spent nursing the sow on long-term growth and viability of her progeny. The confounding effect of birth weight was removed by our procedure. MATERIALS AND METHODS All protocols were under the supervision of licensed veterinarians. Standard operating procedures for animal care were in accordance with published guidelines for animal care (FASS, 1999). The experimental animals were not subjected to prolonged constraint or surgical procedures and were humanely treated throughout the experiment. This study was conducted at the PIC USA research farm located in Gold City, KY. The distance separating the sow farm (site I) from nursery and finish facilities was no more than 500 m. Nursery and finish facilities were on the same site (site II). The health status (farrowing, nursery, and finishing) of the farm was defined as porcine reproductive and respiratory syndrome-negative, Mycoplasma-positive, and pseudorabies virus- and brucellosis-negative based on serological sampling by the veterinary team in charge of health. A total of 112 litters produced by breeding PIC sows to terminal sires were assigned to 3 treatment groups involving different nursing lengths with their sow. The groups remained in their respective crates for 20 ± 0.2 d, but the time that the sow remained with the litter was varied: sow reared from birth to weaning (SR), 14 d with litter (14W), and 2 d with litter (2W). The litters of pigs assigned to the 2W and 14W groups remained as intact litters and in their crates after the sow was removed. Sow feces was not removed. Piglets in the 2W and 14W groups were fed an acidified medicated milk replacer (MR) for 18 and 6 d, respectively. The MR (Advanced Birthright Nutrition, Delano, MN, and Ralco Nutrition Inc., Marshall, MN) was medicated with 55 mg/kg of oxytetracycline and 110 mg/kg of neomycin. It was formulated to contain 24.1% CP and 18.1% crude fat (Table 1) and was based on dairy milk products and purified soya. The MR was delivered using the Supp-Le-Mate C.S. semi-automated milking system (Soppe Systems Inc., Manchester, IA). Fresh milk was mixed daily and chlorinated with calcium hypochlorite pellets (Better Water Industry Inc., Tyler, MN) at a ratio of 3.75 g/L of Cl, added at a central reservoir, and then circulated in a pressurized line (138 to 172 kPa) to a series of pig-activated drinkers using a pneumatic pump. Table 1. Nutrient composition (as-fed basis) of the acidified milk replacer (calculated values) fed to piglets Item  Units  Concentration  Nutrient       CP  %  24.10   Fat  %  18.10   Fiber  %  0.02   Ash  %  7.74   Carbohydrates  %  46.92   DM  %  96.88  Energy       ME of swine  kcal/kg  3,868  AA       Alanine  %  1.04   Arginine  %  0.80   Cystine  %  0.55   Glycine  %  0.50   Histidine  %  0.58   Isoleucine  %  1.23   Leucine  %  2.26   Lysine  %  2.10   Methionine  %  0.49   Phenylalanine  %  0.72   Threonine  %  1.54   Tryptophan  %  0.41   Tyrosine  %  0.72   Valine  %  1.32  Mineral       Calcium  %  0.91   Phosphorus  %  0.73   Sodium  %  0.73   Chlorine  %  0.06   Magnesium  %  0.16   Potassium  %  1.22   Sulfur  %  0.47   Cobalt  mg/kg  0.85   Copper  mg/kg  254.36   Iodine  mg/kg  3.56   Iron  mg/kg  160.40   Manganese  mg/kg  44.45   Zinc  mg/kg  96.0   Selenium  mg/kg  0.357  Vitamin       Vitamin A  IU/kg  99,000   Vitamin D3  IU/kg  33,000   Vitamin E  IU/kg  220   Vitamin K  mg/kg  1.83   Biotin  mg/kg  0.31   Vitamin B12  mg/kg  0.09   Folic acid  mg/kg  1.12   Niacin  mg/kg  73.35   Pantothenic acid  mg/kg  57.05   Pyridoxine  mg/kg  9.48   Riboflavin  mg/kg  26.99   Thiamine  mg/kg  9.09   Choline  g/kg  1.30   Vitamin C  mg/kg  170.70  Medication       Oxytetracycline  mg/kg  50.00   Neomycin  mg/kg  100.00  Item  Units  Concentration  Nutrient       CP  %  24.10   Fat  %  18.10   Fiber  %  0.02   Ash  %  7.74   Carbohydrates  %  46.92   DM  %  96.88  Energy       ME of swine  kcal/kg  3,868  AA       Alanine  %  1.04   Arginine  %  0.80   Cystine  %  0.55   Glycine  %  0.50   Histidine  %  0.58   Isoleucine  %  1.23   Leucine  %  2.26   Lysine  %  2.10   Methionine  %  0.49   Phenylalanine  %  0.72   Threonine  %  1.54   Tryptophan  %  0.41   Tyrosine  %  0.72   Valine  %  1.32  Mineral       Calcium  %  0.91   Phosphorus  %  0.73   Sodium  %  0.73   Chlorine  %  0.06   Magnesium  %  0.16   Potassium  %  1.22   Sulfur  %  0.47   Cobalt  mg/kg  0.85   Copper  mg/kg  254.36   Iodine  mg/kg  3.56   Iron  mg/kg  160.40   Manganese  mg/kg  44.45   Zinc  mg/kg  96.0   Selenium  mg/kg  0.357  Vitamin       Vitamin A  IU/kg  99,000   Vitamin D3  IU/kg  33,000   Vitamin E  IU/kg  220   Vitamin K  mg/kg  1.83   Biotin  mg/kg  0.31   Vitamin B12  mg/kg  0.09   Folic acid  mg/kg  1.12   Niacin  mg/kg  73.35   Pantothenic acid  mg/kg  57.05   Pyridoxine  mg/kg  9.48   Riboflavin  mg/kg  26.99   Thiamine  mg/kg  9.09   Choline  g/kg  1.30   Vitamin C  mg/kg  170.70  Medication       Oxytetracycline  mg/kg  50.00   Neomycin  mg/kg  100.00  View Large Table 1. Nutrient composition (as-fed basis) of the acidified milk replacer (calculated values) fed to piglets Item  Units  Concentration  Nutrient       CP  %  24.10   Fat  %  18.10   Fiber  %  0.02   Ash  %  7.74   Carbohydrates  %  46.92   DM  %  96.88  Energy       ME of swine  kcal/kg  3,868  AA       Alanine  %  1.04   Arginine  %  0.80   Cystine  %  0.55   Glycine  %  0.50   Histidine  %  0.58   Isoleucine  %  1.23   Leucine  %  2.26   Lysine  %  2.10   Methionine  %  0.49   Phenylalanine  %  0.72   Threonine  %  1.54   Tryptophan  %  0.41   Tyrosine  %  0.72   Valine  %  1.32  Mineral       Calcium  %  0.91   Phosphorus  %  0.73   Sodium  %  0.73   Chlorine  %  0.06   Magnesium  %  0.16   Potassium  %  1.22   Sulfur  %  0.47   Cobalt  mg/kg  0.85   Copper  mg/kg  254.36   Iodine  mg/kg  3.56   Iron  mg/kg  160.40   Manganese  mg/kg  44.45   Zinc  mg/kg  96.0   Selenium  mg/kg  0.357  Vitamin       Vitamin A  IU/kg  99,000   Vitamin D3  IU/kg  33,000   Vitamin E  IU/kg  220   Vitamin K  mg/kg  1.83   Biotin  mg/kg  0.31   Vitamin B12  mg/kg  0.09   Folic acid  mg/kg  1.12   Niacin  mg/kg  73.35   Pantothenic acid  mg/kg  57.05   Pyridoxine  mg/kg  9.48   Riboflavin  mg/kg  26.99   Thiamine  mg/kg  9.09   Choline  g/kg  1.30   Vitamin C  mg/kg  170.70  Medication       Oxytetracycline  mg/kg  50.00   Neomycin  mg/kg  100.00  Item  Units  Concentration  Nutrient       CP  %  24.10   Fat  %  18.10   Fiber  %  0.02   Ash  %  7.74   Carbohydrates  %  46.92   DM  %  96.88  Energy       ME of swine  kcal/kg  3,868  AA       Alanine  %  1.04   Arginine  %  0.80   Cystine  %  0.55   Glycine  %  0.50   Histidine  %  0.58   Isoleucine  %  1.23   Leucine  %  2.26   Lysine  %  2.10   Methionine  %  0.49   Phenylalanine  %  0.72   Threonine  %  1.54   Tryptophan  %  0.41   Tyrosine  %  0.72   Valine  %  1.32  Mineral       Calcium  %  0.91   Phosphorus  %  0.73   Sodium  %  0.73   Chlorine  %  0.06   Magnesium  %  0.16   Potassium  %  1.22   Sulfur  %  0.47   Cobalt  mg/kg  0.85   Copper  mg/kg  254.36   Iodine  mg/kg  3.56   Iron  mg/kg  160.40   Manganese  mg/kg  44.45   Zinc  mg/kg  96.0   Selenium  mg/kg  0.357  Vitamin       Vitamin A  IU/kg  99,000   Vitamin D3  IU/kg  33,000   Vitamin E  IU/kg  220   Vitamin K  mg/kg  1.83   Biotin  mg/kg  0.31   Vitamin B12  mg/kg  0.09   Folic acid  mg/kg  1.12   Niacin  mg/kg  73.35   Pantothenic acid  mg/kg  57.05   Pyridoxine  mg/kg  9.48   Riboflavin  mg/kg  26.99   Thiamine  mg/kg  9.09   Choline  g/kg  1.30   Vitamin C  mg/kg  170.70  Medication       Oxytetracycline  mg/kg  50.00   Neomycin  mg/kg  100.00  View Large A total of 698 pigs (out of 1,034 pigs) met our weaning weight criteria for the 3 different rearing strategies; 11 to 12 litters farrowed each week. Each litter was randomly assigned before birth within parity to 1 of the 3 groups. Sows were fed lactation diets that exceeded NRC (1998) nutrient specifications assuming pig litter size of 11 and litter growth rate of 2,550 g/d. Litters assigned to the SR control group remained with their sow until the piglets were weaned. Average age at weaning for this group was 19.5 ± 0.2 d of age. Supplemental MR was not provided to SR litters. For this group, the objective was to produce pigs that weighed between 4.1 and 6.6 kg at weaning. Litters assigned to the 14W group were weaned at 14 d of age by removing the sow. Progeny were then fed an acidified medicated MR ad libitum for 6 d until their contemporary SR litters were weaned. The objective of this group was to produce pigs that weighed between 6.8 to 8.4 kg at weaning. Litters assigned to 2W group were weaned 2 d after birth and then were fed the acidified medicated MR for 18 d. The objective of this group was to produce pigs that grew without maternal constraint and weighed more than 8.6 kg at 20 d of age. Piglets were processed within 24 h of birth. Each pig was individually weighed using a digital balance (model 4, Mosdal Scale Systems Inc., Broadview, MT) and identified with numbered ear tags in the left and right ears for individual identification. Each pig was given 1 mL of a mixture of iron dextran and penicillin, and 1 mL of Garacin (Gentamicin Sulfate, Schering-Plough Animal Health Corporation, Whitehouse Station, NJ). All pigs received creep feed during the last week of the lactation period to acclimate them to dry food. At 10 d of age, all pigs were vaccinated intramuscularly with 2 mL of Suvaxyn Respifend HPS (Hemophilus Parasuis Bacterin, Fort Dodge, IA). A second vaccination of HPS was given 2 wk later, after relocation to the nursery. Pigs from the 3 groups were individually weighed at weaning (20 ± 0.2 d of age) using the Mosdal scale. Each pig was tagged in the left ear with a red (SR), green (2W), or yellow (14W) button tag to designate the group. Pigs were then moved from the farrowing site to the nursery site where they were allotted to pens by group, sex, and size. Pigs were housed at a density of 0.23 to 0.28 m2/pig and were allotted to each pen from the 3 groups. Pigs remained in nursery rooms for approximately 49 d and received a 4-stage nursery feeding program. The first 2 diets were pellets and purchased from a commercial company. The last 2 diets were formulated internally (Table 2). A feed budget was implemented by feeding strict amounts of each diet phase: 0.23, 2.0, 5.4, and 18 kg/pig, respectively. After pigs completed their nursery period, they were moved to adjacent finish facilities. The nursery and finish buildings were separated by a weighing area. Table 2. Composition of nursery diets for the growth periods of 7 to 11 kg and 11 to 27 kg (as-fed basis)1 Item  Nursery 7 to 11 kg  Nursery 11 to 27 kg  Ingredient, %       Corn, 8.5  57.38  66.03   Soybean meal, 48  19.65  25.0   Fish menhaden, 60  9.20  2.21   Fat, animal-vegetable blend  4.37  2.90   L-Lysine  0.15  0.40   DL-Methionine  0.07  0.17   Limestone  0.10  0.44   Dicalcium, 18P 21Ca  0.61  1.19   Salt  0.44  0.51   Medication Neo-Terramycin 10–10  1.00  0.10   Base Mix Nursery 7–112,3  7.04  —   L-Threonine  —  0.10   Copper sulfate  —  0.08   Vitamin and trace mineral premix3  —  0.86   Vitamin E, 20,000  —  0.02  Calculated composition       NRC ME, kcal/kg  3,440  3,440   Total lysine, %  1.45  1.38   Calcium, %  0.80  0.70   Phosphorus, %  0.73  0.64  Item  Nursery 7 to 11 kg  Nursery 11 to 27 kg  Ingredient, %       Corn, 8.5  57.38  66.03   Soybean meal, 48  19.65  25.0   Fish menhaden, 60  9.20  2.21   Fat, animal-vegetable blend  4.37  2.90   L-Lysine  0.15  0.40   DL-Methionine  0.07  0.17   Limestone  0.10  0.44   Dicalcium, 18P 21Ca  0.61  1.19   Salt  0.44  0.51   Medication Neo-Terramycin 10–10  1.00  0.10   Base Mix Nursery 7–112,3  7.04  —   L-Threonine  —  0.10   Copper sulfate  —  0.08   Vitamin and trace mineral premix3  —  0.86   Vitamin E, 20,000  —  0.02  Calculated composition       NRC ME, kcal/kg  3,440  3,440   Total lysine, %  1.45  1.38   Calcium, %  0.80  0.70   Phosphorus, %  0.73  0.64  1Diet compositions of the early weaned and phase 1 diets (pigs weighing between 5 and 7 kg of BW) are not displayed and were purchased from Hubbard Feeds Inc. (Mankato, MN). Early weaned diets (designated for pigs less than 5.5 kg) were budgeted to provide 0.2 kg/pig and the phase 1 diet (5- to 7-kg pigs) was budgeted to provide 2.0 kg/pig. 2Base Mix Nursery 7 to 11 includes the vitamin and trace mineral and other ingredients purchased from Hubbard Feeds Inc. 3Base mix and premix supplied per kilogram of diet: vitamin A, 9,923 IU; vitamin D3, 1,654 IU; vitamin E, 77 IU; vitamin K (menadione activity), 4.0 mg; riboflavin, 9.9 mg; D-pantothenic acid, 33 mg; niacin, 55 mg; vitamin B12,, 44 µg; D-biotin, 0.28 mg; folic acid, 1.0 mg; thiamine, 3.3 mg; pyridoxine, 5.5 mg; Zn, 275 mg (ZnSO4); Cu, 33 mg (CuSO4); Fe, 220 mg (FeSO4); Mn, 99 mg (MnSO4); I, 1.5 mg (ethanediamine dihydroiodide); and Se, 0.30 mg (Na2Se). View Large Table 2. Composition of nursery diets for the growth periods of 7 to 11 kg and 11 to 27 kg (as-fed basis)1 Item  Nursery 7 to 11 kg  Nursery 11 to 27 kg  Ingredient, %       Corn, 8.5  57.38  66.03   Soybean meal, 48  19.65  25.0   Fish menhaden, 60  9.20  2.21   Fat, animal-vegetable blend  4.37  2.90   L-Lysine  0.15  0.40   DL-Methionine  0.07  0.17   Limestone  0.10  0.44   Dicalcium, 18P 21Ca  0.61  1.19   Salt  0.44  0.51   Medication Neo-Terramycin 10–10  1.00  0.10   Base Mix Nursery 7–112,3  7.04  —   L-Threonine  —  0.10   Copper sulfate  —  0.08   Vitamin and trace mineral premix3  —  0.86   Vitamin E, 20,000  —  0.02  Calculated composition       NRC ME, kcal/kg  3,440  3,440   Total lysine, %  1.45  1.38   Calcium, %  0.80  0.70   Phosphorus, %  0.73  0.64  Item  Nursery 7 to 11 kg  Nursery 11 to 27 kg  Ingredient, %       Corn, 8.5  57.38  66.03   Soybean meal, 48  19.65  25.0   Fish menhaden, 60  9.20  2.21   Fat, animal-vegetable blend  4.37  2.90   L-Lysine  0.15  0.40   DL-Methionine  0.07  0.17   Limestone  0.10  0.44   Dicalcium, 18P 21Ca  0.61  1.19   Salt  0.44  0.51   Medication Neo-Terramycin 10–10  1.00  0.10   Base Mix Nursery 7–112,3  7.04  —   L-Threonine  —  0.10   Copper sulfate  —  0.08   Vitamin and trace mineral premix3  —  0.86   Vitamin E, 20,000  —  0.02  Calculated composition       NRC ME, kcal/kg  3,440  3,440   Total lysine, %  1.45  1.38   Calcium, %  0.80  0.70   Phosphorus, %  0.73  0.64  1Diet compositions of the early weaned and phase 1 diets (pigs weighing between 5 and 7 kg of BW) are not displayed and were purchased from Hubbard Feeds Inc. (Mankato, MN). Early weaned diets (designated for pigs less than 5.5 kg) were budgeted to provide 0.2 kg/pig and the phase 1 diet (5- to 7-kg pigs) was budgeted to provide 2.0 kg/pig. 2Base Mix Nursery 7 to 11 includes the vitamin and trace mineral and other ingredients purchased from Hubbard Feeds Inc. 3Base mix and premix supplied per kilogram of diet: vitamin A, 9,923 IU; vitamin D3, 1,654 IU; vitamin E, 77 IU; vitamin K (menadione activity), 4.0 mg; riboflavin, 9.9 mg; D-pantothenic acid, 33 mg; niacin, 55 mg; vitamin B12,, 44 µg; D-biotin, 0.28 mg; folic acid, 1.0 mg; thiamine, 3.3 mg; pyridoxine, 5.5 mg; Zn, 275 mg (ZnSO4); Cu, 33 mg (CuSO4); Fe, 220 mg (FeSO4); Mn, 99 mg (MnSO4); I, 1.5 mg (ethanediamine dihydroiodide); and Se, 0.30 mg (Na2Se). View Large Each pig was individually weighed (model 700, True Test, Auckland, New Zealand) and ear tagged with an electronic transponder for feed intake recording by Feed Intake Recording Equipment (FIRE) feeders (Osborne Industries, Osborne, KS). When pigs were weighed on test, real-time ultrasound (RTUS) backfat was determined using an Aloka real-time ultrasound meter (model SSD 500 B Aloka, Wallingford, CT) at the first rib, last rib, and last lumbar vertebrae by a trained technician. Loin depth was estimated at the last lumbar vertebrae. The pigs were fed using a 4-phase finish program (Table 3); diets were corn-soy based and met PIC USA specifications for commercial PIC pigs (PIC, 1999) that exceeded NRC specifications (NRC, 1998). Pigs were individually weighed every 14 d until they reached an average BW of 122.5 kg. Table 3. Composition of the growth-finish diets (as-fed basis)1 Item  27 to 45 kg  45 to 73 kg  73 to 95 kg  95 to 118 kg  Ingredient, %           Corn, 8.5  60.69  60.04  68.32  73.17   Soybean meal 48  28.71  23.98  16.29  11.72   Wheat middlings  5.00  10.00  10.00  10.00   Fat, animal and vegetable blend  2.59  3.15  2.59  2.31   L-Lysine, hydrochloric  0.15  0.09  0.18  0.20   DL-Methionine  0.06  —  —  —   L-Threonine  0.03  0.02  0.06  0.09   Limestone  0.69  0.70  0.60  0.64   Dicalcium, 18P 21Ca  0.82  0.75  0.69  0.61   Salt  0.46  0.46  0.46  0.46   Copper sulfate  0.07  0.06  0.06  0.06   Vitamin and trace mineral premix2  0.75  0.75  0.75  0.75  Calculated composition           NRC ME, kcal/kg  3,300  3,300  3,300  3,300   Total lysine, %  1.21  1.05  0.91  0.79   Calcium, %  0.62  0.60  0.52  0.50   Phosphorus, %  0.57  0.57  0.53  0.49  Item  27 to 45 kg  45 to 73 kg  73 to 95 kg  95 to 118 kg  Ingredient, %           Corn, 8.5  60.69  60.04  68.32  73.17   Soybean meal 48  28.71  23.98  16.29  11.72   Wheat middlings  5.00  10.00  10.00  10.00   Fat, animal and vegetable blend  2.59  3.15  2.59  2.31   L-Lysine, hydrochloric  0.15  0.09  0.18  0.20   DL-Methionine  0.06  —  —  —   L-Threonine  0.03  0.02  0.06  0.09   Limestone  0.69  0.70  0.60  0.64   Dicalcium, 18P 21Ca  0.82  0.75  0.69  0.61   Salt  0.46  0.46  0.46  0.46   Copper sulfate  0.07  0.06  0.06  0.06   Vitamin and trace mineral premix2  0.75  0.75  0.75  0.75  Calculated composition           NRC ME, kcal/kg  3,300  3,300  3,300  3,300   Total lysine, %  1.21  1.05  0.91  0.79   Calcium, %  0.62  0.60  0.52  0.50   Phosphorus, %  0.57  0.57  0.53  0.49  1All diets were fed in a meal form and were switched with the average BW of the pigs. 2Supplied per kilogram of diet: vitamin A, 9,900 IU; vitamin D3, 1,760 IU; vitamin E, 66 IU; vitamin K (menadione activity), 4.4 mg; riboflavin, 9.9 mg; D-pantothenic acid, 100 mg; niacin, 44 mg; vitamin B12,, 37.4 µg; D-biotin, 0.22 mg; folic acid, 1.32 mg; choline, 0.66 g; thiamine, 2.2 mg; pyridoxine, 3.3 mg; Zn, 125 mg (ZnSO4); Cu, 15 mg (CuSO4); Fe, 100 mg (FeSO4); Mn, 50 mg (MnSO4); I, 0.35 mg (ethanediamine dihydroiodide); and Se, 0.30 mg (Na2Se). View Large Table 3. Composition of the growth-finish diets (as-fed basis)1 Item  27 to 45 kg  45 to 73 kg  73 to 95 kg  95 to 118 kg  Ingredient, %           Corn, 8.5  60.69  60.04  68.32  73.17   Soybean meal 48  28.71  23.98  16.29  11.72   Wheat middlings  5.00  10.00  10.00  10.00   Fat, animal and vegetable blend  2.59  3.15  2.59  2.31   L-Lysine, hydrochloric  0.15  0.09  0.18  0.20   DL-Methionine  0.06  —  —  —   L-Threonine  0.03  0.02  0.06  0.09   Limestone  0.69  0.70  0.60  0.64   Dicalcium, 18P 21Ca  0.82  0.75  0.69  0.61   Salt  0.46  0.46  0.46  0.46   Copper sulfate  0.07  0.06  0.06  0.06   Vitamin and trace mineral premix2  0.75  0.75  0.75  0.75  Calculated composition           NRC ME, kcal/kg  3,300  3,300  3,300  3,300   Total lysine, %  1.21  1.05  0.91  0.79   Calcium, %  0.62  0.60  0.52  0.50   Phosphorus, %  0.57  0.57  0.53  0.49  Item  27 to 45 kg  45 to 73 kg  73 to 95 kg  95 to 118 kg  Ingredient, %           Corn, 8.5  60.69  60.04  68.32  73.17   Soybean meal 48  28.71  23.98  16.29  11.72   Wheat middlings  5.00  10.00  10.00  10.00   Fat, animal and vegetable blend  2.59  3.15  2.59  2.31   L-Lysine, hydrochloric  0.15  0.09  0.18  0.20   DL-Methionine  0.06  —  —  —   L-Threonine  0.03  0.02  0.06  0.09   Limestone  0.69  0.70  0.60  0.64   Dicalcium, 18P 21Ca  0.82  0.75  0.69  0.61   Salt  0.46  0.46  0.46  0.46   Copper sulfate  0.07  0.06  0.06  0.06   Vitamin and trace mineral premix2  0.75  0.75  0.75  0.75  Calculated composition           NRC ME, kcal/kg  3,300  3,300  3,300  3,300   Total lysine, %  1.21  1.05  0.91  0.79   Calcium, %  0.62  0.60  0.52  0.50   Phosphorus, %  0.57  0.57  0.53  0.49  1All diets were fed in a meal form and were switched with the average BW of the pigs. 2Supplied per kilogram of diet: vitamin A, 9,900 IU; vitamin D3, 1,760 IU; vitamin E, 66 IU; vitamin K (menadione activity), 4.4 mg; riboflavin, 9.9 mg; D-pantothenic acid, 100 mg; niacin, 44 mg; vitamin B12,, 37.4 µg; D-biotin, 0.22 mg; folic acid, 1.32 mg; choline, 0.66 g; thiamine, 2.2 mg; pyridoxine, 3.3 mg; Zn, 125 mg (ZnSO4); Cu, 15 mg (CuSO4); Fe, 100 mg (FeSO4); Mn, 50 mg (MnSO4); I, 0.35 mg (ethanediamine dihydroiodide); and Se, 0.30 mg (Na2Se). View Large The finish building consisted of 6 rooms of pens with 4 pens per side in each room. Fifteen castrates or 15 gilts were allotted to each pen. Pigs from the 3 groups were randomly allotted to each pen by size and sex. Differences in BW within a pen between the lightest and heaviest pigs did not exceed 6.8 kg at placement. A total of 698 pigs were started on test. Temperature in the finish building was maintained at 21 ± 0.5°C. Feed intake recording equipment feeders were used to record daily feed intake for each pig. Two pens of pigs shared the same FIRE feeder with a swinging fence-line gate between the 2 pens. The position of the gate was changed once per week to control which pen of pigs had access to the FIRE feeder during the week. Age at 125 kg was calculated as age + [(125 – off-test BW) × (age – 41.84)]/age. Pigs that were removed early from test because of death, injury, or illness were assigned a value of 1 for the early removal variable, and a 0 was assigned to all pigs that were weighed off test at the completion of the experiment. The pigs were humanely slaughtered a commercial pork processing plant that was located in the Midwest. Hot carcass weight recorded for each carcass, and a Fat-O-Meat'er (FOM; SFK Technology, Herlev, Denmark) was used to record backfat depth and loin depth for each carcass. From these variables, carcass lean percentage was estimated using the following equation (PIC formula): lean % = 58.91586 – (0.56074 × backfat, mm) + (0.10585 × loin depth, mm). To investigate the relationship between 20-d weaning weight and age to 125 kg of BW, growth rate, feed intake, feed conversion, and carcass lean, data from only the SR and 14W groups were used. Relative growth and other variables were similar for these treatments despite the population shift in weaning weight. The 2W group was excluded from this analysis because their performance was inferior due to the short time of being with the sow even though the nutritional restriction for growth was removed. The SR and 14W populations performed relatively similarly, with only a slight advantage to SR pigs in measures of long-term growth. For this reason, they were pooled with a total of 468 pigs being grouped into 1 of 6 BW classifications based on their BW at 20 d of age. Body weight classifications were fitted to a normal distribution: 4.1 to 5.0 (9%), 5.0 to 5.9 (17%), 5.9 to 6.8 (25%), 6.8 to 7.7 (24%), 7.7 to 8.6 (16%), and 8.7 to 11.5 kg (9%). Least squares means were calculated for each BW classification, and orthogonal polynomials were fitted to determine linear and quadratic responses for each trait. Pigs in the 4.1- to 5.0-kg classification were largely pigs from the SR group (91%), whereas pigs in the 8.7- to 11.5-kg classification were represented to a greater extent by the 14W group (70%). The other BW classifications consisted of a balanced mixture of SR and 14W pigs. Statistical Analysis The PROC MIXED (SAS Inst. Inc., Cary, NC) was used to complete the statistical analyses comparing differences among the SR, 14W, and 2W groups for all traits except for survival rate, mortality, and early removal percentage. For these traits, SAS PROC GLIMMIX was used to complete the analyses. Source of variation accounted for in the analyses are presented in Table 4. Litter nested within sow line and farrowing group was considered a random effect and was the error term for testing group differences in the analyses completed using PROC MIXED. Group differences were tested using the PDIFF option in PROC MIXED and PROC GLIMMIX whenever there was a significant F-test for the group source of variation in the ANOVA. In all statistical models that included covariates, except those models used to analyze the carcass traits, separate linear and quadratic covariates were fit for each of the 3 groups. Statistical models used to estimate the effects of 20-d BW on growth, feed intake, and feed conversion during the finishing period and on carcass composition were the same as those used to compare differences among the SR, 14W, and 2W groups. Table 4. Sources of variation that were included in statistical models1 Source of variation  df  Birth wt, 20-d BW, age entering nursery  Nursery ADG  BW and age entering finish  Ultrasound backfat and loin depth entering finish  BW, ADG, ADFI, and G:F2  ADG3  Age at 125 kg, off-test wt  Ultrasound backfat and loin depth  Survival rate, death loss, early removal percentage  Carcass trait  Breed  2  X  X  X  X  X  X  X  X  X  X  Group (TRT)  2  X  X  X  X  X  X  X  X  X  X  Sex  1  X  X  X  X  X  X  X  X  X  X  Slaughter date  4  —  —  —  —  —  —  —  —  —  X  Age (linear)  1  X4  —  X5  —  X6  —  —  —  —  —  Age (quadratic)  1  —  —  —  —  X6  —  —  —  —  —  TRT × nursery wt (linear)  3  —  X  —  —  —  X  —  —  —  —  TRT × on-test wt (linear)  3  —  —  X7  X  X8  —  —  —  —  —  TRT × off-test wt (linear)  3  —  —  —  —  —  —  —  X  —  —  TRT × off-test wt (quadratic)  3  —  —  —  —  —  —  —  X9  —  —  HCW (linear)  1  —  —  —  —  —  —  —  —  —  X  Random effect                         Litter (breed TRT)    X  X  X  X  X  X  X  X    X  Source of variation  df  Birth wt, 20-d BW, age entering nursery  Nursery ADG  BW and age entering finish  Ultrasound backfat and loin depth entering finish  BW, ADG, ADFI, and G:F2  ADG3  Age at 125 kg, off-test wt  Ultrasound backfat and loin depth  Survival rate, death loss, early removal percentage  Carcass trait  Breed  2  X  X  X  X  X  X  X  X  X  X  Group (TRT)  2  X  X  X  X  X  X  X  X  X  X  Sex  1  X  X  X  X  X  X  X  X  X  X  Slaughter date  4  —  —  —  —  —  —  —  —  —  X  Age (linear)  1  X4  —  X5  —  X6  —  —  —  —  —  Age (quadratic)  1  —  —  —  —  X6  —  —  —  —  —  TRT × nursery wt (linear)  3  —  X  —  —  —  X  —  —  —  —  TRT × on-test wt (linear)  3  —  —  X7  X  X8  —  —  —  —  —  TRT × off-test wt (linear)  3  —  —  —  —  —  —  —  X  —  —  TRT × off-test wt (quadratic)  3  —  —  —  —  —  —  —  X9  —  —  HCW (linear)  1  —  —  —  —  —  —  —  —  —  X  Random effect                         Litter (breed TRT)    X  X  X  X  X  X  X  X    X  1X = effect included in statistical model; — = effect not included in statistical model. 2After 4, 8, and 12 wk in finish and nursery – finish test period. 3From nursery to 4, 8, and 12 wk in finisher and finish test period. 4Linear covariate included only in statistical analysis of 20-d BW. 5Linear covariate included in statistical analysis of BW entering finish. 6Linear and quadratic covariates included in analyses of BW after 4, 8, and 12 wk in the finish. 7Linear covariates included in statistical analysis of age entering finish. 8Linear covariates included in statistical models in analyses of ADG after 4, 8, and 12 wk in the finish. 9Quadratic linear covariate included in statistical model in analysis of ultrasound loin depth. View Large Table 4. Sources of variation that were included in statistical models1 Source of variation  df  Birth wt, 20-d BW, age entering nursery  Nursery ADG  BW and age entering finish  Ultrasound backfat and loin depth entering finish  BW, ADG, ADFI, and G:F2  ADG3  Age at 125 kg, off-test wt  Ultrasound backfat and loin depth  Survival rate, death loss, early removal percentage  Carcass trait  Breed  2  X  X  X  X  X  X  X  X  X  X  Group (TRT)  2  X  X  X  X  X  X  X  X  X  X  Sex  1  X  X  X  X  X  X  X  X  X  X  Slaughter date  4  —  —  —  —  —  —  —  —  —  X  Age (linear)  1  X4  —  X5  —  X6  —  —  —  —  —  Age (quadratic)  1  —  —  —  —  X6  —  —  —  —  —  TRT × nursery wt (linear)  3  —  X  —  —  —  X  —  —  —  —  TRT × on-test wt (linear)  3  —  —  X7  X  X8  —  —  —  —  —  TRT × off-test wt (linear)  3  —  —  —  —  —  —  —  X  —  —  TRT × off-test wt (quadratic)  3  —  —  —  —  —  —  —  X9  —  —  HCW (linear)  1  —  —  —  —  —  —  —  —  —  X  Random effect                         Litter (breed TRT)    X  X  X  X  X  X  X  X    X  Source of variation  df  Birth wt, 20-d BW, age entering nursery  Nursery ADG  BW and age entering finish  Ultrasound backfat and loin depth entering finish  BW, ADG, ADFI, and G:F2  ADG3  Age at 125 kg, off-test wt  Ultrasound backfat and loin depth  Survival rate, death loss, early removal percentage  Carcass trait  Breed  2  X  X  X  X  X  X  X  X  X  X  Group (TRT)  2  X  X  X  X  X  X  X  X  X  X  Sex  1  X  X  X  X  X  X  X  X  X  X  Slaughter date  4  —  —  —  —  —  —  —  —  —  X  Age (linear)  1  X4  —  X5  —  X6  —  —  —  —  —  Age (quadratic)  1  —  —  —  —  X6  —  —  —  —  —  TRT × nursery wt (linear)  3  —  X  —  —  —  X  —  —  —  —  TRT × on-test wt (linear)  3  —  —  X7  X  X8  —  —  —  —  —  TRT × off-test wt (linear)  3  —  —  —  —  —  —  —  X  —  —  TRT × off-test wt (quadratic)  3  —  —  —  —  —  —  —  X9  —  —  HCW (linear)  1  —  —  —  —  —  —  —  —  —  X  Random effect                         Litter (breed TRT)    X  X  X  X  X  X  X  X    X  1X = effect included in statistical model; — = effect not included in statistical model. 2After 4, 8, and 12 wk in finish and nursery – finish test period. 3From nursery to 4, 8, and 12 wk in finisher and finish test period. 4Linear covariate included only in statistical analysis of 20-d BW. 5Linear covariate included in statistical analysis of BW entering finish. 6Linear and quadratic covariates included in analyses of BW after 4, 8, and 12 wk in the finish. 7Linear covariates included in statistical analysis of age entering finish. 8Linear covariates included in statistical models in analyses of ADG after 4, 8, and 12 wk in the finish. 9Quadratic linear covariate included in statistical model in analysis of ultrasound loin depth. View Large For the estimation of the effects of 20-d weaning weight, only data from the SR and 14W groups were used. Each pig was assigned to 1 of 6 groups based on its 20-d weaning weight. Linear and quadratic orthogonal polynomials were fit to estimate group responses. The distribution of observations for all 3 groups is shown in Table 5. The least squares means by group for the preweaning through nursery, postnursery (finishing), and RTUS and carcass traits are presented in Tables 6, 7, and 8, respectively. Table 5. Number of observations by treatment group for growth and carcass traits of pigs that were sow reared (SR), weaned at 14 d of age (14W), and weaned at 2 d of age (2W) Item  SR  14W  2W  Birth weight  367  330  337  BW entering nursery  299  290  263  Age entering nursery  299  290  263  Nursery ADG  244  226  228  Finish growth assay         BW  244  226  228   Age  244  226  228  RTUS1 backfat at first rib and last lumbar  244  226  228  RTUS loin depth  244  226  228  28-d BW and ADG  243  224  226  56-d BW and ADG  242  222  222  84-d BW and ADG  238  221  216  Off test         BW  233  219  210   Age off test  233  219  210   Age to 125 kg  233  219  210   ADG  233  219  210   ADG from 20 d of age  233  219  210   Daily feed intake  178  179  188   Feed conversion  178  179  188   % completing test  233  219  210   RTUS backfat at first rib and last lumbar  233  219  210   RTUS loin depth  233  219  210   % dead  233  219  210   % early removals  233  219  210  Carcass trait         HCW  233  210  210   Carcass yield  233  210  208   Fat-O-Meat'er2 backfat  232  204  207   Fat-O-Meat'er loin depth  232  204  207   Lean percentage  232  204  207  Item  SR  14W  2W  Birth weight  367  330  337  BW entering nursery  299  290  263  Age entering nursery  299  290  263  Nursery ADG  244  226  228  Finish growth assay         BW  244  226  228   Age  244  226  228  RTUS1 backfat at first rib and last lumbar  244  226  228  RTUS loin depth  244  226  228  28-d BW and ADG  243  224  226  56-d BW and ADG  242  222  222  84-d BW and ADG  238  221  216  Off test         BW  233  219  210   Age off test  233  219  210   Age to 125 kg  233  219  210   ADG  233  219  210   ADG from 20 d of age  233  219  210   Daily feed intake  178  179  188   Feed conversion  178  179  188   % completing test  233  219  210   RTUS backfat at first rib and last lumbar  233  219  210   RTUS loin depth  233  219  210   % dead  233  219  210   % early removals  233  219  210  Carcass trait         HCW  233  210  210   Carcass yield  233  210  208   Fat-O-Meat'er2 backfat  232  204  207   Fat-O-Meat'er loin depth  232  204  207   Lean percentage  232  204  207  1Real-time ultrasound. 2SFK Technology, Herlev, Denmark. View Large Table 5. Number of observations by treatment group for growth and carcass traits of pigs that were sow reared (SR), weaned at 14 d of age (14W), and weaned at 2 d of age (2W) Item  SR  14W  2W  Birth weight  367  330  337  BW entering nursery  299  290  263  Age entering nursery  299  290  263  Nursery ADG  244  226  228  Finish growth assay         BW  244  226  228   Age  244  226  228  RTUS1 backfat at first rib and last lumbar  244  226  228  RTUS loin depth  244  226  228  28-d BW and ADG  243  224  226  56-d BW and ADG  242  222  222  84-d BW and ADG  238  221  216  Off test         BW  233  219  210   Age off test  233  219  210   Age to 125 kg  233  219  210   ADG  233  219  210   ADG from 20 d of age  233  219  210   Daily feed intake  178  179  188   Feed conversion  178  179  188   % completing test  233  219  210   RTUS backfat at first rib and last lumbar  233  219  210   RTUS loin depth  233  219  210   % dead  233  219  210   % early removals  233  219  210  Carcass trait         HCW  233  210  210   Carcass yield  233  210  208   Fat-O-Meat'er2 backfat  232  204  207   Fat-O-Meat'er loin depth  232  204  207   Lean percentage  232  204  207  Item  SR  14W  2W  Birth weight  367  330  337  BW entering nursery  299  290  263  Age entering nursery  299  290  263  Nursery ADG  244  226  228  Finish growth assay         BW  244  226  228   Age  244  226  228  RTUS1 backfat at first rib and last lumbar  244  226  228  RTUS loin depth  244  226  228  28-d BW and ADG  243  224  226  56-d BW and ADG  242  222  222  84-d BW and ADG  238  221  216  Off test         BW  233  219  210   Age off test  233  219  210   Age to 125 kg  233  219  210   ADG  233  219  210   ADG from 20 d of age  233  219  210   Daily feed intake  178  179  188   Feed conversion  178  179  188   % completing test  233  219  210   RTUS backfat at first rib and last lumbar  233  219  210   RTUS loin depth  233  219  210   % dead  233  219  210   % early removals  233  219  210  Carcass trait         HCW  233  210  210   Carcass yield  233  210  208   Fat-O-Meat'er2 backfat  232  204  207   Fat-O-Meat'er loin depth  232  204  207   Lean percentage  232  204  207  1Real-time ultrasound. 2SFK Technology, Herlev, Denmark. View Large Table 6. Least squares means for performance traits from preweaning through nursery pigs that were sow reared (SR), weaned at 14 d of age (14W), and weaned at 2 d of age (2W) Trait  SR  14W  2W  Pooled SEM  No. of pigs placed postfarrow  367  330  337    No. of pigs weaned  299  290  263    Survival rate, %  81.5  87.9  78.0    Pig birth weight, kg  1.60  1.58  1.67  0.04  Pig weaning weight, kg  6.49c  7.50b  8.75a  0.13  Age at weaning, d  19.5b  20.4a  21.0a  0.2  Nursery ADG,1 g/d  569.0ab  582.0a  547.0b  7.7  Nursery final BW, kg  33.5b  35.8a  36.1a  0.43  Nursery final age, d  70.4a  68.8b  68.1b  0.5  Trait  SR  14W  2W  Pooled SEM  No. of pigs placed postfarrow  367  330  337    No. of pigs weaned  299  290  263    Survival rate, %  81.5  87.9  78.0    Pig birth weight, kg  1.60  1.58  1.67  0.04  Pig weaning weight, kg  6.49c  7.50b  8.75a  0.13  Age at weaning, d  19.5b  20.4a  21.0a  0.2  Nursery ADG,1 g/d  569.0ab  582.0a  547.0b  7.7  Nursery final BW, kg  33.5b  35.8a  36.1a  0.43  Nursery final age, d  70.4a  68.8b  68.1b  0.5  a–cWithin a row, means without a common superscript differ (P < 0.05). 1During the nursery period, 3 SR pigs, a 14W pig, and a 2W pig died. View Large Table 6. Least squares means for performance traits from preweaning through nursery pigs that were sow reared (SR), weaned at 14 d of age (14W), and weaned at 2 d of age (2W) Trait  SR  14W  2W  Pooled SEM  No. of pigs placed postfarrow  367  330  337    No. of pigs weaned  299  290  263    Survival rate, %  81.5  87.9  78.0    Pig birth weight, kg  1.60  1.58  1.67  0.04  Pig weaning weight, kg  6.49c  7.50b  8.75a  0.13  Age at weaning, d  19.5b  20.4a  21.0a  0.2  Nursery ADG,1 g/d  569.0ab  582.0a  547.0b  7.7  Nursery final BW, kg  33.5b  35.8a  36.1a  0.43  Nursery final age, d  70.4a  68.8b  68.1b  0.5  Trait  SR  14W  2W  Pooled SEM  No. of pigs placed postfarrow  367  330  337    No. of pigs weaned  299  290  263    Survival rate, %  81.5  87.9  78.0    Pig birth weight, kg  1.60  1.58  1.67  0.04  Pig weaning weight, kg  6.49c  7.50b  8.75a  0.13  Age at weaning, d  19.5b  20.4a  21.0a  0.2  Nursery ADG,1 g/d  569.0ab  582.0a  547.0b  7.7  Nursery final BW, kg  33.5b  35.8a  36.1a  0.43  Nursery final age, d  70.4a  68.8b  68.1b  0.5  a–cWithin a row, means without a common superscript differ (P < 0.05). 1During the nursery period, 3 SR pigs, a 14W pig, and a 2W pig died. View Large Table 7. Least squares means for performance traits during the finishing period of growth (33 kg to 125 kg) for pigs that were sow reared (SR), weaned at 14 d of age (14W), and weaned at 2 d of age (2W) Item  SR  14W  2W  Pooled SEM  Trait           1 to 28 d            Initial BW,1 kg  58.0b  58.9a  58.9a  0.6    ADG, g/d  836  834  825  12   29 to 56 d            Initial BW, kg  83.5b  86.7a  85.3ab  0.8    ADG, g/d  913a  913a  885b  10   57 to 84 d            Initial BW, kg  110.2b  112.6a  111.3ab  0.8    ADG, g/d  927  922  904  8  Final BW, kg  123.5  124.8  123.9  0.6  Final age (unadjusted for BW), d  166.6  164.7  165.2  0.8  Age to 125 kg, d  167.7b  164.7a  166.1ab  0.9  Finish ADG, g/d  936a  932a  912b  8  ADG from 20 d of age, g/d  816a  816a  785b  6  Finish daily feed intake, kg/d  2.22b  2.32a  2.20b  0.03  G:F  0.44a  0.42b  0.42b  0.01  % completing test  98.7  97.0  95.8  1.1  % dead  1.1  2.0  3.3  1.0  % early removals2  0.2  1.0  0.9  0.5  Item  SR  14W  2W  Pooled SEM  Trait           1 to 28 d            Initial BW,1 kg  58.0b  58.9a  58.9a  0.6    ADG, g/d  836  834  825  12   29 to 56 d            Initial BW, kg  83.5b  86.7a  85.3ab  0.8    ADG, g/d  913a  913a  885b  10   57 to 84 d            Initial BW, kg  110.2b  112.6a  111.3ab  0.8    ADG, g/d  927  922  904  8  Final BW, kg  123.5  124.8  123.9  0.6  Final age (unadjusted for BW), d  166.6  164.7  165.2  0.8  Age to 125 kg, d  167.7b  164.7a  166.1ab  0.9  Finish ADG, g/d  936a  932a  912b  8  ADG from 20 d of age, g/d  816a  816a  785b  6  Finish daily feed intake, kg/d  2.22b  2.32a  2.20b  0.03  G:F  0.44a  0.42b  0.42b  0.01  % completing test  98.7  97.0  95.8  1.1  % dead  1.1  2.0  3.3  1.0  % early removals2  0.2  1.0  0.9  0.5  a,bWithin a row, means without a common superscript differ (P < 0.05). 1Age of pigs was used as covariate when determining the final BW for the different analyzed periods. 2Reasons for removals were lameness, unthriftiness, and so on. View Large Table 7. Least squares means for performance traits during the finishing period of growth (33 kg to 125 kg) for pigs that were sow reared (SR), weaned at 14 d of age (14W), and weaned at 2 d of age (2W) Item  SR  14W  2W  Pooled SEM  Trait           1 to 28 d            Initial BW,1 kg  58.0b  58.9a  58.9a  0.6    ADG, g/d  836  834  825  12   29 to 56 d            Initial BW, kg  83.5b  86.7a  85.3ab  0.8    ADG, g/d  913a  913a  885b  10   57 to 84 d            Initial BW, kg  110.2b  112.6a  111.3ab  0.8    ADG, g/d  927  922  904  8  Final BW, kg  123.5  124.8  123.9  0.6  Final age (unadjusted for BW), d  166.6  164.7  165.2  0.8  Age to 125 kg, d  167.7b  164.7a  166.1ab  0.9  Finish ADG, g/d  936a  932a  912b  8  ADG from 20 d of age, g/d  816a  816a  785b  6  Finish daily feed intake, kg/d  2.22b  2.32a  2.20b  0.03  G:F  0.44a  0.42b  0.42b  0.01  % completing test  98.7  97.0  95.8  1.1  % dead  1.1  2.0  3.3  1.0  % early removals2  0.2  1.0  0.9  0.5  Item  SR  14W  2W  Pooled SEM  Trait           1 to 28 d            Initial BW,1 kg  58.0b  58.9a  58.9a  0.6    ADG, g/d  836  834  825  12   29 to 56 d            Initial BW, kg  83.5b  86.7a  85.3ab  0.8    ADG, g/d  913a  913a  885b  10   57 to 84 d            Initial BW, kg  110.2b  112.6a  111.3ab  0.8    ADG, g/d  927  922  904  8  Final BW, kg  123.5  124.8  123.9  0.6  Final age (unadjusted for BW), d  166.6  164.7  165.2  0.8  Age to 125 kg, d  167.7b  164.7a  166.1ab  0.9  Finish ADG, g/d  936a  932a  912b  8  ADG from 20 d of age, g/d  816a  816a  785b  6  Finish daily feed intake, kg/d  2.22b  2.32a  2.20b  0.03  G:F  0.44a  0.42b  0.42b  0.01  % completing test  98.7  97.0  95.8  1.1  % dead  1.1  2.0  3.3  1.0  % early removals2  0.2  1.0  0.9  0.5  a,bWithin a row, means without a common superscript differ (P < 0.05). 1Age of pigs was used as covariate when determining the final BW for the different analyzed periods. 2Reasons for removals were lameness, unthriftiness, and so on. View Large Table 8. Least squares means of real-time ultrasound (RTUS) and Fat-O-Meat'er1 (FOM) measures on whole-body and carcass data for pigs that were sow reared (SR), weaned at 14 d of age (14W), and weaned at 2 d of age (2W) Trait  SR  14W  2W  Pooled SEM  Initial RTUS backfat at first rib, last lumbar, mm  10.5  10.4  10.6  0.2  Initial RTUS loin depth, mm  29.3  29.1  28.6  0.3  Final RTUS backfat at first rib and last lumbar, mm  19.2  19.2  19.4  0.4  Final RTUS loin depth, mm  55.6b  53.8a  53.7a  0.5  HCW, kg  91.1  91.9  92.1  0.6  Carcass yield, %  73.7  73.5  73.7  0.1  FOM fat depth, mm  17.7  18.1  18.3  0.4  FOM loin depth, mm  57.9b  56.2a  55.9a  0.6  Calculated lean,2 %  55.0b  54.7ab  54.3a  0.3  Trait  SR  14W  2W  Pooled SEM  Initial RTUS backfat at first rib, last lumbar, mm  10.5  10.4  10.6  0.2  Initial RTUS loin depth, mm  29.3  29.1  28.6  0.3  Final RTUS backfat at first rib and last lumbar, mm  19.2  19.2  19.4  0.4  Final RTUS loin depth, mm  55.6b  53.8a  53.7a  0.5  HCW, kg  91.1  91.9  92.1  0.6  Carcass yield, %  73.7  73.5  73.7  0.1  FOM fat depth, mm  17.7  18.1  18.3  0.4  FOM loin depth, mm  57.9b  56.2a  55.9a  0.6  Calculated lean,2 %  55.0b  54.7ab  54.3a  0.3  a,bWithin a row, means without a common superscript differ (P < 0.05). 1SFK Technology, Herlev, Denmark. 2Lean % equation = 58.91586 – (0.56074 × backfat, mm) + (0.10585 × loin depth, mm). View Large Table 8. Least squares means of real-time ultrasound (RTUS) and Fat-O-Meat'er1 (FOM) measures on whole-body and carcass data for pigs that were sow reared (SR), weaned at 14 d of age (14W), and weaned at 2 d of age (2W) Trait  SR  14W  2W  Pooled SEM  Initial RTUS backfat at first rib, last lumbar, mm  10.5  10.4  10.6  0.2  Initial RTUS loin depth, mm  29.3  29.1  28.6  0.3  Final RTUS backfat at first rib and last lumbar, mm  19.2  19.2  19.4  0.4  Final RTUS loin depth, mm  55.6b  53.8a  53.7a  0.5  HCW, kg  91.1  91.9  92.1  0.6  Carcass yield, %  73.7  73.5  73.7  0.1  FOM fat depth, mm  17.7  18.1  18.3  0.4  FOM loin depth, mm  57.9b  56.2a  55.9a  0.6  Calculated lean,2 %  55.0b  54.7ab  54.3a  0.3  Trait  SR  14W  2W  Pooled SEM  Initial RTUS backfat at first rib, last lumbar, mm  10.5  10.4  10.6  0.2  Initial RTUS loin depth, mm  29.3  29.1  28.6  0.3  Final RTUS backfat at first rib and last lumbar, mm  19.2  19.2  19.4  0.4  Final RTUS loin depth, mm  55.6b  53.8a  53.7a  0.5  HCW, kg  91.1  91.9  92.1  0.6  Carcass yield, %  73.7  73.5  73.7  0.1  FOM fat depth, mm  17.7  18.1  18.3  0.4  FOM loin depth, mm  57.9b  56.2a  55.9a  0.6  Calculated lean,2 %  55.0b  54.7ab  54.3a  0.3  a,bWithin a row, means without a common superscript differ (P < 0.05). 1SFK Technology, Herlev, Denmark. 2Lean % equation = 58.91586 – (0.56074 × backfat, mm) + (0.10585 × loin depth, mm). View Large RESULTS Preweaning Performance Pig birth weight did not differ among the 3 treatments (Table 6); however, pigs from the 3 groups differed (P < 0.05) in their weaning weight, which was consistent with their weaning age differences. Older groups (i.e., 14W and 2W) weighed 1.01 and 2.26 kg more, respectively, when compared with pigs in the SR group (P < 0.05). Pigs from the 2W group weighed 1.25 kg more compared with pigs from the 14W group (P < 0.05). These differences in weaning weight show that the different nutrition strategies (sow lactation length, MR) were successful in creating mean separated BW groups at weaning. Survival rates during the prenursery period (20 ± 0.2 d) were 87.9, 81.5, and 78.0% for the 14W, SR, and 2W groups, respectively. Nursery Performance Pigs from the 14W group grew 35 g/d faster (P < 0.05) during the nursery period compared with pigs from the 2W group (Table 6). Despite the 2.2-kg advantage of the 2W group, the growth rate for SR pigs did not differ (569 vs. 547 g/d; P < 0.05) from that of the 2W group. Postnursery Performance Pigs averaged 69 d of age when they were placed in the grow-finish site to evaluate their growth performance. Pigs from the 14W and 2W groups were 2.3 and 2.6 kg heavier (P < 0.05), respectively, than the SR group to begin the test (Table 6). However, rate of BW gain for the SR and 14W groups (Table 7) was identical (816 g/d) for the entire period, but ADG for the 2W group was 31 g/d poorer (P < 0.05). The ADG for the SR and 14W groups was greater (P < 0.05) during finishing period when compared with the pigs from the 2W group. Pigs from the 14W group reached a BW of 125 kg 3.0 d sooner than SR pigs (P < 0.05), and SR pigs were not different from 2W pigs despite weighing 2.2 kg less at weaning. Pigs from the SR and 14W groups grew 24 and 20 g/d faster, respectively, during the finishing period (33 to 125 kg) than 2W pigs (P < 0.05). Feed conversion of SR pigs was improved (P < 0.05) over 14W pigs but did not differ from that of 2W pigs during the finishing period. The SR pigs had 2.0 and 1.7 mm more FOM loin depth (P < 0.05) than loins of the 2W and 14W pigs, respectively, at slaughter (Table 8). The carcass lean percentage of SR pigs was 0.7% greater (P < 0.05) than that of the 2W pigs (55.0 vs. 54.3%, respectively; Table 8). Impact of 20-d Weaning Weight Because progeny long-term growth and composition of growth was different for the 2W pigs as compared with SR counterparts, this analysis was confined to pigs that remained with the sow for at least 14 d (SR and 14W). A summary of nursery and finishing performance is provided in Table 9. Nursery ADG increased linearly (P < 0.001) with increasing weaning weight. Grow-finish final BW increased linearly (P < 0.001) with increasing weaning weight. Age to 125 kg was reduced (quadratic, P < 0.05) with increasing weaning weight. Pigs that weighed between 4.1 and 5.0 kg required 176.8 d to reach 125 kg, which was 8.0 d more compared with pigs weighing between 5.0 and 5.9 kg at 20 d of age. An extra 15.9 d were required when compared with pigs that weighed between 8.7 and 11.5 kg. Finish ADG increased (quadratic, P < 0.10) with increasing weaning weight. Pigs in the 4.1- to 5.0-kg classification gained 886 g/d during the postweaning period, which was 46 to 59 g/d less than pigs weighing 5.0 kg or more. Estimates of the linear and quadratic coefficients were 4.176 g/d (P < 0.05) and −2.326 g/d2 (P < 0.10), respectively. Feed intake was increased (linear, P < 0.05) with increasing weaning weight. Table 9. Least squares means for the combined data of sow-reared and 14-d weaned pigs for nursery and finishing phases of growth Item  Weaning weight classification, kg/pig  Linear  Quadratic  Pooled SEM  4.1 to 5.0  5.0 to 5.9  5.9 to 6.8  6.8 to 7.7  7.7 to 8.6  8.7 to 11.5  Trait                     No. of pigs  42  78  116  112  78  42         Population, %  9  17  24  24  17  9         Pig birth weight, kg  1.43  1.43  1.49  1.60  1.71  1.85  0.043***  0.011†  0.05   Weaning age, d  19.1  19.6  20.0  20.2  20.4  21.0  0.175***  −0.002  0.3   Nursery ADG, g/d  507  530  549  579  593  607  10.254***  −0.730  10  Test data                     Final BW, kg  119.4  122.7  125.1  123.9  125.8  126.5  0.623***  −0.183  1.0   Age off test, d  172.5  167.0  167.9  164.1  163.7  161.9  −0.955***  0.164  1.2   Age to 125 kg, d  176.8  168.8  167.8  164.8  162.9  160.9  −1.432***  0.319*  1.3   ADG, g/d  886  939  936  932  939  945  4.176*  −2.326†  11.3   ADG from 20 d, g/d  752  796  804  815  825  831  7.063***  −2.126*  8   Daily feed intake, kg/d  2.18  2.30  2.25  2.29  2.27  2.47  0.020*  0.006  0.05   G:F  0.42  0.41  0.42  0.41  0.42  0.39  0.010  0.007  0.01   % completing test  97.9  99.3  97.0  97.5  99.1  97.9  −0.005  0.027  1.9   % dead  2.1  0.8  3.1  1.5  1.0  0.0  −0.186  −0.127  1.7  Item  Weaning weight classification, kg/pig  Linear  Quadratic  Pooled SEM  4.1 to 5.0  5.0 to 5.9  5.9 to 6.8  6.8 to 7.7  7.7 to 8.6  8.7 to 11.5  Trait                     No. of pigs  42  78  116  112  78  42         Population, %  9  17  24  24  17  9         Pig birth weight, kg  1.43  1.43  1.49  1.60  1.71  1.85  0.043***  0.011†  0.05   Weaning age, d  19.1  19.6  20.0  20.2  20.4  21.0  0.175***  −0.002  0.3   Nursery ADG, g/d  507  530  549  579  593  607  10.254***  −0.730  10  Test data                     Final BW, kg  119.4  122.7  125.1  123.9  125.8  126.5  0.623***  −0.183  1.0   Age off test, d  172.5  167.0  167.9  164.1  163.7  161.9  −0.955***  0.164  1.2   Age to 125 kg, d  176.8  168.8  167.8  164.8  162.9  160.9  −1.432***  0.319*  1.3   ADG, g/d  886  939  936  932  939  945  4.176*  −2.326†  11.3   ADG from 20 d, g/d  752  796  804  815  825  831  7.063***  −2.126*  8   Daily feed intake, kg/d  2.18  2.30  2.25  2.29  2.27  2.47  0.020*  0.006  0.05   G:F  0.42  0.41  0.42  0.41  0.42  0.39  0.010  0.007  0.01   % completing test  97.9  99.3  97.0  97.5  99.1  97.9  −0.005  0.027  1.9   % dead  2.1  0.8  3.1  1.5  1.0  0.0  −0.186  −0.127  1.7  †P < 0.10. *P < 0.05. ***P < 0.001. View Large Table 9. Least squares means for the combined data of sow-reared and 14-d weaned pigs for nursery and finishing phases of growth Item  Weaning weight classification, kg/pig  Linear  Quadratic  Pooled SEM  4.1 to 5.0  5.0 to 5.9  5.9 to 6.8  6.8 to 7.7  7.7 to 8.6  8.7 to 11.5  Trait                     No. of pigs  42  78  116  112  78  42         Population, %  9  17  24  24  17  9         Pig birth weight, kg  1.43  1.43  1.49  1.60  1.71  1.85  0.043***  0.011†  0.05   Weaning age, d  19.1  19.6  20.0  20.2  20.4  21.0  0.175***  −0.002  0.3   Nursery ADG, g/d  507  530  549  579  593  607  10.254***  −0.730  10  Test data                     Final BW, kg  119.4  122.7  125.1  123.9  125.8  126.5  0.623***  −0.183  1.0   Age off test, d  172.5  167.0  167.9  164.1  163.7  161.9  −0.955***  0.164  1.2   Age to 125 kg, d  176.8  168.8  167.8  164.8  162.9  160.9  −1.432***  0.319*  1.3   ADG, g/d  886  939  936  932  939  945  4.176*  −2.326†  11.3   ADG from 20 d, g/d  752  796  804  815  825  831  7.063***  −2.126*  8   Daily feed intake, kg/d  2.18  2.30  2.25  2.29  2.27  2.47  0.020*  0.006  0.05   G:F  0.42  0.41  0.42  0.41  0.42  0.39  0.010  0.007  0.01   % completing test  97.9  99.3  97.0  97.5  99.1  97.9  −0.005  0.027  1.9   % dead  2.1  0.8  3.1  1.5  1.0  0.0  −0.186  −0.127  1.7  Item  Weaning weight classification, kg/pig  Linear  Quadratic  Pooled SEM  4.1 to 5.0  5.0 to 5.9  5.9 to 6.8  6.8 to 7.7  7.7 to 8.6  8.7 to 11.5  Trait                     No. of pigs  42  78  116  112  78  42         Population, %  9  17  24  24  17  9         Pig birth weight, kg  1.43  1.43  1.49  1.60  1.71  1.85  0.043***  0.011†  0.05   Weaning age, d  19.1  19.6  20.0  20.2  20.4  21.0  0.175***  −0.002  0.3   Nursery ADG, g/d  507  530  549  579  593  607  10.254***  −0.730  10  Test data                     Final BW, kg  119.4  122.7  125.1  123.9  125.8  126.5  0.623***  −0.183  1.0   Age off test, d  172.5  167.0  167.9  164.1  163.7  161.9  −0.955***  0.164  1.2   Age to 125 kg, d  176.8  168.8  167.8  164.8  162.9  160.9  −1.432***  0.319*  1.3   ADG, g/d  886  939  936  932  939  945  4.176*  −2.326†  11.3   ADG from 20 d, g/d  752  796  804  815  825  831  7.063***  −2.126*  8   Daily feed intake, kg/d  2.18  2.30  2.25  2.29  2.27  2.47  0.020*  0.006  0.05   G:F  0.42  0.41  0.42  0.41  0.42  0.39  0.010  0.007  0.01   % completing test  97.9  99.3  97.0  97.5  99.1  97.9  −0.005  0.027  1.9   % dead  2.1  0.8  3.1  1.5  1.0  0.0  −0.186  −0.127  1.7  †P < 0.10. *P < 0.05. ***P < 0.001. View Large The RTUS backfat and loin depth and carcass data are shown in Table 10 for the 6 weaning weight classifications. The RTUS loin depth decreased (−0.334 mm; linear, P < 0.01) with increasing weaning weight. Hot carcass weight increased (linear, P < 0.001) with increasing weaning weight. The FOM on carcass loin depth decreased (−0.24 mm; linear, P < 0.05) with increasing weaning weight. Table 10. Least squares means for the combined data of sow-reared and 14-d weaned groups for real-time ultrasound (RTUS), Fat-O-Meat'er (FOM),1 and carcass measures of pigs Item  Weaning weight classification, kg/pig  Linear  Quadratic  Pooled SEM  4.1 to 5.0  5.0 to 5.9  5.9 to 6.8  6.8 to 7.7  7.7 to 8.6  8.7 to 11.5  Trait                     RTUS backfat at shoulder, mm  23.0  21.0  21.7  21.9  21.2  22.8  −0.0003  0.144*  0.5   RTUS backfat at last lumbar, mm  16.3  15.4  16.4  16.8  16.7  16.4  0.065  −0.017  0.4   RTUS loin depth, mm  55.5  56.1  55.1  54.4  52.7  53.0  −0.336**  −0.054  0.7   % dead  2.1  0.8  3.1  1.5  1.0  0.0  −0.186  −0.127  1.7  Carcass data                     HCW, kg  88.6  90.6  91.9  91.7  91.9  94.4  0.471***  −0.028  0.8   Carcass yield, %  74.3  73.7  73.4  73.6  73.3  73.9  −0.048  0.071**  0.2   FOM backfat, mm  17.9  16.8  17.7  18.3  17.8  18.8  0.112  0.058  0.7   FOM loin depth, mm  57.4  58.1  57.6  56.4  56.1  55.4  −0.242*  −0.081  0.8   Carcass lean, %  54.7  55.5  55.0  54.9  54.8  54.1  −0.078  −0.065†  0.3  Item  Weaning weight classification, kg/pig  Linear  Quadratic  Pooled SEM  4.1 to 5.0  5.0 to 5.9  5.9 to 6.8  6.8 to 7.7  7.7 to 8.6  8.7 to 11.5  Trait                     RTUS backfat at shoulder, mm  23.0  21.0  21.7  21.9  21.2  22.8  −0.0003  0.144*  0.5   RTUS backfat at last lumbar, mm  16.3  15.4  16.4  16.8  16.7  16.4  0.065  −0.017  0.4   RTUS loin depth, mm  55.5  56.1  55.1  54.4  52.7  53.0  −0.336**  −0.054  0.7   % dead  2.1  0.8  3.1  1.5  1.0  0.0  −0.186  −0.127  1.7  Carcass data                     HCW, kg  88.6  90.6  91.9  91.7  91.9  94.4  0.471***  −0.028  0.8   Carcass yield, %  74.3  73.7  73.4  73.6  73.3  73.9  −0.048  0.071**  0.2   FOM backfat, mm  17.9  16.8  17.7  18.3  17.8  18.8  0.112  0.058  0.7   FOM loin depth, mm  57.4  58.1  57.6  56.4  56.1  55.4  −0.242*  −0.081  0.8   Carcass lean, %  54.7  55.5  55.0  54.9  54.8  54.1  −0.078  −0.065†  0.3  1SFK Technology, Herlev, Denmark. †P < 0 0.10. *P < 0.05. **P < 0 0.01. ***P < 0.001. View Large Table 10. Least squares means for the combined data of sow-reared and 14-d weaned groups for real-time ultrasound (RTUS), Fat-O-Meat'er (FOM),1 and carcass measures of pigs Item  Weaning weight classification, kg/pig  Linear  Quadratic  Pooled SEM  4.1 to 5.0  5.0 to 5.9  5.9 to 6.8  6.8 to 7.7  7.7 to 8.6  8.7 to 11.5  Trait                     RTUS backfat at shoulder, mm  23.0  21.0  21.7  21.9  21.2  22.8  −0.0003  0.144*  0.5   RTUS backfat at last lumbar, mm  16.3  15.4  16.4  16.8  16.7  16.4  0.065  −0.017  0.4   RTUS loin depth, mm  55.5  56.1  55.1  54.4  52.7  53.0  −0.336**  −0.054  0.7   % dead  2.1  0.8  3.1  1.5  1.0  0.0  −0.186  −0.127  1.7  Carcass data                     HCW, kg  88.6  90.6  91.9  91.7  91.9  94.4  0.471***  −0.028  0.8   Carcass yield, %  74.3  73.7  73.4  73.6  73.3  73.9  −0.048  0.071**  0.2   FOM backfat, mm  17.9  16.8  17.7  18.3  17.8  18.8  0.112  0.058  0.7   FOM loin depth, mm  57.4  58.1  57.6  56.4  56.1  55.4  −0.242*  −0.081  0.8   Carcass lean, %  54.7  55.5  55.0  54.9  54.8  54.1  −0.078  −0.065†  0.3  Item  Weaning weight classification, kg/pig  Linear  Quadratic  Pooled SEM  4.1 to 5.0  5.0 to 5.9  5.9 to 6.8  6.8 to 7.7  7.7 to 8.6  8.7 to 11.5  Trait                     RTUS backfat at shoulder, mm  23.0  21.0  21.7  21.9  21.2  22.8  −0.0003  0.144*  0.5   RTUS backfat at last lumbar, mm  16.3  15.4  16.4  16.8  16.7  16.4  0.065  −0.017  0.4   RTUS loin depth, mm  55.5  56.1  55.1  54.4  52.7  53.0  −0.336**  −0.054  0.7   % dead  2.1  0.8  3.1  1.5  1.0  0.0  −0.186  −0.127  1.7  Carcass data                     HCW, kg  88.6  90.6  91.9  91.7  91.9  94.4  0.471***  −0.028  0.8   Carcass yield, %  74.3  73.7  73.4  73.6  73.3  73.9  −0.048  0.071**  0.2   FOM backfat, mm  17.9  16.8  17.7  18.3  17.8  18.8  0.112  0.058  0.7   FOM loin depth, mm  57.4  58.1  57.6  56.4  56.1  55.4  −0.242*  −0.081  0.8   Carcass lean, %  54.7  55.5  55.0  54.9  54.8  54.1  −0.078  −0.065†  0.3  1SFK Technology, Herlev, Denmark. †P < 0 0.10. *P < 0.05. **P < 0 0.01. ***P < 0.001. View Large DISCUSSION The most important, but unexpected, finding of this trial was that the longer the sow nursed the litter, the better her progeny tended to perform in ADG, G:F, viability (mortality plus illness), and loin and fat depth. This is consistent with the work reported by Main et al. (2004) that weaned litters at 12, 15, 18, or 21 d of age. They found that increasing weaning age increased overall weaning-to-finish ADG, weight sold per pig weaned, and decreased mortality rate. Main et al. (2004) concluded that increasing weaning age from 12 to 21.5 d of age increased weight sold per pig weaned by 1.80 ± 0.12 kg for each day increase in weaning age. Main et al. (2004) suggested that increasing weaning age up to 21.5 d can be an effective management strategy to improve weaning-to-finish growth performance in multisite pig production. Another important finding was the failure of the 2W pigs to completely retain the substantial weaning weight advantage over the lighter SR and 14W pigs throughout the postweaning period. Early weaning at less than 10 d of age followed by removal of pigs to a second isolated site is known to reduce immunological stress, resulting in improved growth and feed efficiency (Johnson, 1997; Maxwell, 1999). Cabrera et al. (2002) applied this principle to small pigs at birth. We observed that the size of small pigs (less than 1.13 kg on average) can be reclaimed by separating them from their dam at the age of 2 d postbirth by placing them in a controlled isolated nursery environment receiving an unlimited amount of a properly fortified, formulated milk powder. These small pigs showed greater potential for growth and livability than was observed if they remained with the sow. This management practice allowed the weaning of more better quality pigs beyond the rearing capacity of the sow. However, when these small pigs were comingled with their siblings in the nursery and finishing phases, we observed a reduced survival rate than when reared separately. We concluded that they had a different health status (naïve) when compared with their sow-reared siblings. When considered in light of the present experiment, length of time spent with the sow is important to long-term viability even if progeny remained in the pen from which their mother was removed. A separate pig flow within a specific production system is best when rearing early weaning small pigs. An important observation in the present experiment was that long-term whole-body and carcass growth were benefited by piglets spending 20 d with the sow when compared with either 14 or 2 d. The influence of the sow beyond the colostrum period, and even to 14 d of age, is significant. The biological basis for the far-reaching effects of the sow on the performance of her progeny is not clear. The North American swine industry reduced nursing length during the 1990s from about 21 d to as little as 12 to 14 d because of proposed health advantages (segregated early weaning). However, this procedure compromised long-term piglet growth and viability. These effects result in a known decrease in the number of pigs weaned per sow per year. One issue with early weaning is the impact that the length of the lactation period may have on cellular immunity in the pig. Blecha et al. (1983) have shown that early weaning might compromise cellular immune function in young pigs. In their study, early weaned pigs had suppressed in vivo and in vitro cellular immune responses. The capability of lymphocytes to undergo blastogenesis in response to phytohemagglutinin was decreased in early weaned pigs. This requires further investigation because the physiological and immunological mechanism(s) responsible for weaning-impaired cellular immunity are unknown. Moeser et al. (2006) investigated the impact of weaning on gastrointestinal health in the pig to assess the role of stress signaling pathways in this response. Moeser et al. (2006) weaned 19-d-old pigs compared with age-matched, unweaned control pigs and assessed mucosal barrier function and ion transport in jejunal and colonic tissues mounted on Ussing chambers. Moeser et al. (2006) found that weaning caused marked disturbances in intestinal barrier function, as demonstrated by significant reductions in transepithelial electrical resistance and increases in intestinal permeability to [3H]-mannitol in the jejunum and colon. These 2 characteristics are an indication of a poor and leaky epithelial barrier. In a similar study, Moeser et al. (2007) also found the same results when comparing piglets weaned at 19 vs. 28 d; proper barrier function favored the latter. The advantages of SR pigs over the 2W pigs might be explained by components in the milk of the sow and controlled pathogen exposure, which could potentially improve long-term health of their progeny when compared with milk replacement. In humans, breast milk oligosaccharides have been shown to selectively stimulate the growth of bifidobacteria and lactobacilli in the intestine of infants (Moreno Villares, 2008). The intestinal microflora of breast-fed infants is an important physiological factor in gut function and the development of the immune system. Hosea Blewett et al. (2008) found that breast milk contains various antimicrobial substances, factors that promote immune development, and constituents that promote tolerance or priming of the infant immune system as well as anti-inflammatory components. In humans, breastfeeding has been proven to have a long-lasting effect on future health of babies (Lowdon, 2008). Bilenko et al. (2008) has shown that partial breastfeeding has protective effects against enteric infection (Cryptosporidium spp., Campylobacter spp., ear infections, and asthma) and associated morbidity. Bosnjak and Grguric (2007) have compiled data from 2001 and 2006 indicating that breastfeeding is likely to protect against later obesity, type 1 diabetes, celiac disease, inflammatory bowel diseases, and childhood cancer. These findings are consistent with those found by Schack-Nielsen and Michaelsen (2006). Loland et al. (2007) indicated that human milk possibly affects components of the metabolic syndrome and decreases the risk of autoimmune diseases. Given the established health benefits in human milk, we assume that the same benefits are inherent in sows nursing their progeny. The 3 rearing lactation strategies successfully produced the desired weaning weight subpopulations. This approach avoids the confounding problem of weaning weights that are a function of birth weight (Schinckel et al., 2007). In our case, birth weight represented less of a bias; however, nursing length needed to be re-examined. The latter could be controlled by eliminating the most disparate treatment (2W). The 14W strategy represents a commercially feasible result of genetically increased sow milk output. The 2W strategy represents an alternative scenario in which the genetic potential for growth by the piglet is not constrained by the mother. It was possible that 1 or both of the strategies (14W and 2W) might be compromised if the sow (milk, fecal microbe shedding) is important to lifetime immune competence. Early weaned groups (2W and 14W) received unlimited access to the MR for 18 and 6 d, respectively, which created an average increase of 2.2 and 1.0 kg/pig compared with SR pigs. This is consistent with other reports showing greatly accelerated growth of young pigs fed liquid milk products (Azain, 1997; Heo et al., 1999). The growth rate of the neonatal pig is largely determined by sow milk nutrient output (Noblet and Etienne, 1989). Boyd et al. (1995) reported that the nursing pig has substantially greater growth potential than is being realized because milk secretion is inadequate for maximum growth. The sow appears to be limiting maximum pig BW gain as early as 8 d after farrowing (Harrell et al., 1993). Harrell et al. (1993) found that in pigs with unlimited nutrient supply, the average growth rate is 400 g/d from birth to 21 d of age. This is 170 g/d greater than a typical growth rate for sow-reared pigs (230 g/d). More important is the observation that the rate of growth increased in a linear manner to an average of 521 g/d between 17 and 21 d of age. The accelerated growth resulting from ad libitum feeding a liquid diet early in the life of neonatal pigs is well documented (Harrell et al., 1993; Boyd et al., 1995; Kim et al., 2001). Our study shows that although the sow limits the biological maximum for growth, the sow is to this point indispensable to long-term growth and viability. The 14W pigs were able to maintain their weaning weight advantage over the SR pigs from weaning to 125 kg (3 d less required); however, composition of BW gain and viability numerically favored SR pigs. Spencer et al. (2003) reported that early weaned (d 14) piglets under extreme heat stress fed a MR were able to maintain their weaning weight advantage through the 47-d nursery period. In their study, primiparous and multiparous sows were exposed to a hot or thermoneutral temperature (32 and 21°C, respectively) and weaned them at 14 or 19 d of lactation. The objective of this regimen was to prevent extreme maternal BW loss by early weaning. When sows were removed at 14 d of lactation, pigs were allowed to consume milk supplement ad libitum. The growth rate of piglets in the thermoneutral and hot environment increased to 605 and 717 g/d, respectively, during these 5 d. The 1.8 kg/pig advantage at weaning increased to 3.9 kg/piglet at the end of 47 d of nursery. This increase (2.2:1) is consistent with the increase obtained in this study (2.3:1). Unfortunately, pigs were not grown to slaughter, which is important to understanding the outcome. In our study, the weaning weight advantage of 14W pigs over SR controls (1.0 kg/pig) was maintained to slaughter because ADG was similar from weaning to slaughter (816 g/d). This difference in weaning weight did not invoke a more rapid growth rate to slaughter. The net effect was to reduce the time to reach 125 kg whole-BW for 14W pigs. Our study showed that composition of BW gain was improved by a longer lactation. There was no difference in backfat or loin depth between milk-fed pigs (2W and 14W) and SR pigs at the end of the nursery period. However, at slaughter, SR pigs had greater RTUS and FOM loin depth when compared with 2W and 14W pigs. The SR pigs were 0.7% leaner than 2W pigs. Using data from SR and 14W subpopulations of pigs, the advantage of weaning heavier pigs became clear. We were also able to confirm the relationship between birth weight and weaning weight. Heavier pigs at 20 d of age did maintain their advantage (ADG, ADFI) and required less time to reach market BW. Mahan and Lepine (1991) observed that BW gains and feed intakes were greater as weaning weight increased during the nursery and finishing periods. In our study, pigs weighing between 5.0 and 5.9 kg at 20 d of age were able to reach 125 kg of BW 8 d sooner than pigs weighing between 4.1 and 5.0 kg. This is consistent with Azain (1997), who found that pigs weighing on average 5.65 kg reached 104 kg of BW 7 d sooner than those weighing 4.5 kg. Mahan et al. (1998) fed a complex nursery diet for 1, 2, or 3 wk to 23-d-old weaned pigs of 2 weaning weights (5.5 or 7.5 kg). Pigs with heavier weaning weight reached 105 kg of BW approximately 8 d sooner than those with lighter weaning weight. Mahan et al. (1998) concluded weaning heavier pigs seemed to have greater effect on postweaning performance than the feeding duration of the complex nursery diet. Unlike our results, Mahan et al. (1998) were able to show better feed utilization on heavier weaned pig in the growing period. In a similar study, Mahan (1993) found that pigs weighing between 6.8 and 8.2 kg reached 105 kg approximately 10 d earlier than pigs weighing between 4.1 and 5.5 kg of BW. The concern with each of these studies is that birth weight is confounded with weaning weight. Further, the growth curve for smaller birth weight pigs does not achieve the same level as for pigs with greater birth weight. We propose that the sow influences the performance of her progeny beyond the colostrum period and in a profound way. Based on what is known in human nutrition, the maternal influence of milk is important to the development of immune competence beyond the contribution of colostrum. This trial confirms the growth potential of the neonatal pig is significantly beyond the milking ability of the sow for a 20-d lactation period (2W group) and that pigs that weigh less than 5.0 kg at weaning represent the greatest marginal opportunity to decrease days to slaughter (8 d more to achieve 125 kg of BW or weigh 3.3 kg less than 5.5-kg pigs). Segregated early weaning technology not only failed to deliver improved health in practice but compromised piglet whole-body and composition of growth and viability, especially under situations of increased immune stress. These data can be used in economic models to determine the value of lactation length on progeny performance to slaughter, and the value of genetic improvements in milk production. The increment of greatest opportunity appears to lie between 4.6 and 5.5 kg at 20 d of age. The bottom 9% of the pigs (4.1 to 5.0 kg) imposes significant cost to a system because of reduced market weights because increased time to achieve target BW is a poor financial option. LITERATURE CITED Azain, M. J. 1997. Nutrition of the young pig, use of liquid diets.  Pages 1– 14 in Proc. 13th Annu. Carolina Swine Nutr. Conf. Raleigh, NC. Carolina Feed Ind. Assoc., Raleigh, NC. Bilenko N. Ghosh R. Levy A. Deckelbaum R. J. Fraser D. 2008. Partial breastfeeding protects Bedouin infants from infection and morbidity: Prospective cohort study. Asia Pac. J. Clin. Nutr.  17: 243– 249. https://doi.org/18586643 Google Scholar PubMed  Blecha F. Pollman D. S. Nichols D. A. 1983. Weaning pigs at an early age decreases cellular immunity. J. Anim. Sci.  56: 396– 400. https://doi.org/6841290 Google Scholar CrossRef Search ADS PubMed  Bosnjak A. P. Grguric J. 2007. Long-term health effects of breastfeeding. Lijec. Vjesn.  129: 293– 298. https://doi.org/18198630 Google Scholar PubMed  Boyd R. D. Kessinger R. S. Harrell R. J. Bauman D. E. 1995. Nutrient uptake and endocrine regulation of milk synthesis by mammary tissue of lactating sows. J. Anim. Sci.  73( Suppl. 2): 36– 56. Google Scholar CrossRef Search ADS   Cabrera, R., R. D. Boyd, J. Mencke, J. Vignes, and J. Mauney 2002. PICweaning Plus Technology: A paradigm shift in neonatal piglets care. PIC USA Techn. Memo No. 267.  PIC USA, Franklin, KY. FASS 1999. Guide for the Care and Use of Agricultural Animals in Agricultural Research and Teaching.  1st rev. ed. Fed. Anim. Sci. Soc., Champaign, IL. Harrell, R. J., M. J. Thomas, and R. D. Boyd 1993. Limitations of sow milk yield on baby pig growth.  Page 156 in Proc. Cornell Nutr. Conf., Ithaca, NY. Heo K. N. Odle J. Oliver W. Kim J. H. Han I. K. Jones E. 1999. Effects of milk replacer and ambient temperature on growth performance of 14-day-old early-weaned pigs. Asian-australas. J. Anim. Sci.  12: 908– 913. Google Scholar CrossRef Search ADS   Hosea Blewett H. J. Cicalo M. C. Holland C. D. Field C. J. 2008. The immunological components of human milk. Adv. Food Nutr. Res.  54: 45– 80. https://doi.org/18291304 Google Scholar CrossRef Search ADS PubMed  Johnson, R. W. 1997. Explanation why sick pigs neither eat well nor grow well.  Proc. Carolina Swine Nutr. Conf. p. 49. Kim J. H. Heo K. N. Odle J. Han I. K. Harrell R. J. 2001. Liquids diets accelerate the growth of early-weaned pigs and the effects are maintained to market weight. J. Anim. Sci.  79: 427– 434. https://doi.org/11219452 Google Scholar CrossRef Search ADS PubMed  Loland B. F. Baerug A. B. Nylander G. 2007. Human milk, immune response and health effects. Tidsskr. Nor. Laegeforen.  127: 2395– 2398. https://doi.org/17895946 Google Scholar PubMed  Lowdon J. 2008. Getting bone health right from the start: Pregnancy, lactation and weaning. J. Fam. Health Care  18: 137– 141. https://doi.org/18754554 Google Scholar PubMed  Mahan D. C. 1993. Effect of weight, split-weaning, and nursery feeding programs on performance responses of pigs to 105 kilograms body weight and subsequent effects on sow rebreeding interval. J. Anim. Sci.  71: 1991– 1995. https://doi.org/8376220 Google Scholar CrossRef Search ADS PubMed  Mahan D. C. Cromwell G. L. Hamilton R. C. Yen J. T. 1998. Evaluation of the feeding duration of a phase 1 nursery diet to three-week-old pigs of two weaning weights. J. Anim. Sci.  76: 578– 583. https://doi.org/9498368 Google Scholar CrossRef Search ADS PubMed  Mahan D. C. Lepine A. J. 1991. Effect of weaning weight and associated nursery feeding programs on subsequent performance to 105 kilograms of body weight. J. Anim. Sci.  69: 1370– 1378. https://doi.org/2071501 Google Scholar CrossRef Search ADS PubMed  Main R. G. Dritz S. S. Tokach M. D. Goodband R. D. Nelssen J. L. 2004. Increasing weaning age improves pig performance in a multisite production system. J. Anim. Sci.  82: 1499– 1507. https://doi.org/15144093 Google Scholar CrossRef Search ADS PubMed  Maxwell, C. V. 1999. Nutrition and management of the early-weaned pig.  Page 203 in Biotechnol. Feed Ind. Proc. Alltech's 15th Annu. Symp., Nicholasville, KY. Alltech Biotechnol., Nicholasville, KY. Moeser A. Klok C. V. Ryan K. A. Wooten J. G. Little D. Cook V. L. Blikslager A. T. 2006. Stress signaling pathways activated by weaning mediate intestinal dysfunction in the pig. Am. J. Physiol. Gastrointest. Liver Physiol.  292: 173– 181. Google Scholar CrossRef Search ADS   Moeser A. Ryan K. A. Nighot P. K. Blikslager A. T. 2007. Gastointestinal dysfunction induced by early weaning is attenuated by delayed weaning and mast cell blockade in pigs. Am. J. Physiol. Gastrointest. Liver Physiol.  293: 413– 421. Google Scholar CrossRef Search ADS   Moreno Villares J. M. 2008. Probiotics in infant formulas. Could we modify the immune response? An. Pediatr. (Barc.)  68: 286– 294. https://doi.org/18358143 Google Scholar CrossRef Search ADS PubMed  Morrow, M. 2004. What are the alternatives for saving our little piglets? International Pig Letter.  January, 2004. Volume 23, No. 11c. Noblet J. Etienne M. 1989. Estimation of sow milk nutrient output. J. Anim. Sci.  67: 3352– 3359. https://doi.org/2613581 Google Scholar CrossRef Search ADS PubMed  NRC 1998. Nutrient Requirements for Domestic Animals.  10th rev. ed. Natl. Acad. Sci., Washington, DC. Pig Improvement Company (PIC) 1999. PIC Nutrient Requirements. Technical Memo.  PIC, Franklin, KY. Schack-Nielsen L. Michaelsen K. F. 2006. Breast feeding and future health. Curr. Opin. Clin. Nutr. Metab. Care  9: 289– 296. https://doi.org/16607131 Google Scholar CrossRef Search ADS PubMed  Schinckel A. P. Cabrera R. A. Boyd R. D. Jungst S. Booher C. Johnston M. E. Preckel P. V. Einstein M. E. 2007. Modeling the impact of birth and twenty-day body weight on the post-weaning growth of pigs. Prof. Anim. Sci.  23: 211– 223. Spencer J. D. Boyd R. D. Cabrera R. A. Allee G. L. 2003. Early-weaning to reduce tissue mobilization in lactating sows and milk replacement to enhance pig weaning weight during extreme heat stress. J. Anim. Sci.  81: 2041– 2052. https://doi.org/12926786 Google Scholar CrossRef Search ADS PubMed  Footnotes 1 This research was conducted at the Pig Improvement Company (PIC) Research Farm (Gold City, KY). The authors recognize the contributions of C. Booher of PIC USA (Franklin, KY) for technical assistance with the computerized feed intake recording system; R. Graves and S. Coffey (PIC USA) for their technical assistance in collecting data; and S. Welbourne for installation of the semi-automated milk system at the research farm. This paper was presented at the national American Society of Animal Science meeting in Quebec, Canada, in 2002 (abstracts 793 and 794). 2 Milk replacer and financial support were provided by Advanced Birthright Nutrition (Delano, MN) and Ralco Nutrition Inc. (Marshall, MN). American Society of Animal Science This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]

Tucker, A. L.;Duncan, I. J. H.;Millman, S. T.;Friendship, R. M.;Widowski, T. M.

doi: 10.2527/jas.2009-2404pmid: 20154167

ABSTRACT The objectives of this study were to determine the effects of dentition on feed-oriented behavior and feed consumption before weaning at 28 d, and whether premolar eruption or occlusion at the time of weaning influenced postweaning growth or behavior. Over 3 trials, 24 litters of Yorkshire piglets (n = 233) were provided with creep feed marked with 1% chromic oxide on d 5. Dental exams were performed on d 2, 6, 9, 13, 16, 20, 23, and 27. Fecal samples were visually assessed for feed consumption (via fecal color) on the same day as dental exams, beginning on d 6. The duration of time spent at, and frequency of visits to, the creep feeder were determined from continuous video recordings on d 7, 10, 14, 17, 21, and 24 for 6 h/d (0700 to 1000 h, 1300 to 1600 h). After weaning, behavior was recorded every 5 min for three 2-h time periods (0600 to 0800 h, 1100 to 1300 h, and 1600 to 1800 h) on d 2, 4, 6, 8, 10, and 12. Piglets younger than 17 d with their premolars erupted and occluded spent less time at the creep feeder and visited it less often than piglets without their premolars erupted and occluded [duration: p3 (premolar position 3 on maxilla), d 7 (P = 0.005); p4 (premolar position 4 on mandible), d 7 (P < 0.0001), d 10 (P = 0.003); p4 (premolar position 4 on maxilla), d 17 (P = 0.012); occlusion, d 7 (P < 0.0001), d 10 (P = 0.0004); visits: p3, d 7 (P < 0.0001); p4, d 7 (P < 0.0001), d 10 (P = 0.001); p3 (premolar position 3 on mandible), d 14 (P = 0.037); p4, d 17 (P = 0.024); occlusion, d 7 (P < 0.0001), d 10 (P = 0.003)]. By d 21 of age, this trend reversed such that piglets with premolars erupted and occluded spent more time at the feeder and visited it more frequently [duration: p3, d 24 (P = 0.025); p4, d 24 (P = 0.0005); occlusion, d 21 (P = 0.001), d 24 (P = 0.0001); visits: p3, d 21 (P = 0.0002), d 24 (P < 0.0001); p4, d 24 (P = 0.0002); occlusion, d 21 (P < 0.0001), d 24 (P < 0.0001)]. The percentages of piglets with positive fecal scores were 0, 1.4, 4.6, 8.0, 29.0, 44.9, and 60.6% on d 7, 10, 14, 17, 21, 24, and 27, respectively (P < 0.0001 between each day). No associations were found between the eruption or occlusion of premolars and feed consumption before weaning (P > 0.05), and no dental measures influenced growth rates (P > 0.10) or behavior (P > 0.10) after weaning. A more precise method may be necessary for detecting associations between dental eruption and feed consumption. However, the behavioral results indicate that, before weaning at 28 d, younger piglets are inhibited from feeding when their premolars first erupt, whereas older piglets with a more advanced dentition are more attracted to feed. INTRODUCTION The performance and health of weaned piglets hinges on their ability to consume feed quickly after weaning (McCracken et al., 1995; Pluske et al., 1996), yet most piglets still consume an insufficient amount of feed and experience BW loss at this time (Berkeveld et al., 2009). To reduce these weaning-related problems, highly digestible creep diets are often provided before weaning. However, the usefulness of this practice appears equivocal because most piglets do not eat significant quantities of feed until after 19 d of age (Pajor et al., 1991; Fraser et al., 1994). Interestingly, piglets given a liquid or gruel diet vs. a dry diet at weaning have greater DMI and BW gain (Partridge et al., 1992; Kim et al., 2001). These diets allow piglets to meet their water and nutrient requirements simultaneously, and they also require very little chewing activity. Recently, we have shown that the majority of deciduous premolars erupt between the first and fifth week of the life of a piglet (Tucker and Widowski, 2009). Studies on miniature breeds have described in detail the changes in muscle, teeth, and bone that are required for suckling piglets to adapt to feeding and drinking (Herring, 1977, 1985; Herring and Wineski, 1986). Throughout the weaning process, the mode of intake changes and piglets must shift from using the jaw-opening muscles to generate suction power (i.e., to obtain milk) to using the jaw-closing muscles for the mechanical breakdown of solid food (Herring, 1985; Langenbach and van Eijden, 2001). Both motor learning and the eruption of teeth are important developmental precursors to the natural weaning process (Herring, 1985; Herring and Wineski, 1986), which begs the question: How does dental development relate to the onset of feeding in commercial piglets? Therefore, the aims of this study were to determine 1) the effects of dentition on feed-oriented behavior and feed consumption before weaning, and 2) whether premolar eruption or occlusion at the time of weaning influences growth or behavior after weaning at 28 d. The preweaning predictions were that piglets would spend more time at the creep feeder and consume more feed if their premolars were erupted and occluded. Similarly, those piglets with a more advanced dentition at weaning would experience better postweaning growth and would perform more feeding behavior and less belly nosing. MATERIALS AND METHODS The University of Guelph Animal Care Committee approved all procedures for this study in accordance with the Canadian Council on Animal Care guidelines (Canadian Council on Animal Care, 1993). Preweaning Animals A total of 233 Yorkshire piglets (3 trials of 8 litters each) were obtained from the University of Guelph Arkell Swine Research Station. Piglets were processed (tail-docked, injected with iron, ear-notched, boars castrated) before 3 d of age and were individually marked with nonammonia hair dye for identification on camera. To ensure natural dental development, teeth were not clipped. Litters remained with their sows in standard-sized farrowing crates (creep area: 152 × 208 cm) until weaning at 28 d. Housing and Feeding The farrowing room had natural lighting but was also illuminated with fluorescent light from 0530 to 1730 h to ensure clarity of video recordings. Room temperature was maintained between 23 and 25°C with floor heating being provided in the creep area. On d 5, a commercial starter creep feed (Starter Advance Crumble, Floradale Feed Mill Limited, Floradale, Ontario, Canada; Table 1) containing 1.0% chromic oxide (Sigma-Aldrich Canada Ltd., Oakville, Ontario, Canada) was added to a single 4-hole corner creep feeder (41 × 29 × 29 cm) that was placed at the anterior of each farrowing crate. Water was provided ad libitum to piglets via nipple drinkers. All feed added to creep feeders was weighed and replaced every other day to ensure freshness. Once feed was removed, it was dried in a Fisher Isotemp Drying Oven (model 255g, 200 Series, Fisher, Pittsburgh, PA) at 100°C for a minimum of 2 d and weighed back to calculate the amount eaten per pen. Feed spillage was collected and recorded for the first 10 d of each trial but was found to be negligible so was not accounted for. Table 1. Composition of diet (as-fed basis) Item  Amount  Creep diet1  Weaner diet2  Chemical analysis, %       CP  21.50  19.83   Crude fat  4.00  2.00   Crude fiber  2.50  3.18  Vitamins and minerals       Calcium, %  0.87  0.82   Phosphorus, %  0.75  0.30   Sodium, %  0.50  0.303   Copper, mg/kg  40.00  15.00   Zinc, mg/kg  3,000.00  104.00   Vitamin A, IU/kg  11,000.00  10,000.00   Vitamin D, IU/kg  1,500.00  1,000.00   Vitamin E, IU/kg  90.00  56.00   Selenium premix,4 mg/kg  0.50  0.00   Vitamin premix5  0.00  0.50   Mineral premix6  0.00  0.10   AA7  0.00  0.43   Antibacterial, mg/kg  4.408  0.109   Chromic oxide,10 %  1.00  0.00  Item  Amount  Creep diet1  Weaner diet2  Chemical analysis, %       CP  21.50  19.83   Crude fat  4.00  2.00   Crude fiber  2.50  3.18  Vitamins and minerals       Calcium, %  0.87  0.82   Phosphorus, %  0.75  0.30   Sodium, %  0.50  0.303   Copper, mg/kg  40.00  15.00   Zinc, mg/kg  3,000.00  104.00   Vitamin A, IU/kg  11,000.00  10,000.00   Vitamin D, IU/kg  1,500.00  1,000.00   Vitamin E, IU/kg  90.00  56.00   Selenium premix,4 mg/kg  0.50  0.00   Vitamin premix5  0.00  0.50   Mineral premix6  0.00  0.10   AA7  0.00  0.43   Antibacterial, mg/kg  4.408  0.109   Chromic oxide,10 %  1.00  0.00  1Starter Advance Crumble (Floradale Feed Mill Limited, Floradale, Ontario, Canada). 2Piglet Starter Advance II (Arkell Feed Mill, Arkell, Ontario, Canada). 3Iodized salt. 4Calculated to supply 0.50 mg/kg selenium to diet (as Na2SeO3·5H2O). 5Calculated to supply per kilogram of feed: 2.5 mg of menadione, 500 mg of choline, 15 mg of pantothenic acid, 5 mg of riboflavin, 2 mg of folic acid, 25 mg of niacin, 1.5 mg of pyridoxine, 1.5 mg of biotin, 0.025 mg of vitamin B12. 6Calculated to supply per kilogram: 100 mg of Fe, 19 mg of Mn. 7Calculated to supply per kilogram: 30 mg of lysine hydrochloride (79%), 1 mg of methionine, 12 mg of threonine. 8Contains per kilogram: 22 mg of lincomycin (Bio Agri Mix Ltd., Mitchell, Ontario, Canada) and 22 mg of spectinomycin. 9Contains per kilogram: 22 mg of tylosin (Bio Agri Mix Ltd.). 10Chromic oxide (Sigma-Aldrich Canada Ltd., Oakville, ON, Canada) was used as a creep feed consumption indicator. View Large Table 1. Composition of diet (as-fed basis) Item  Amount  Creep diet1  Weaner diet2  Chemical analysis, %       CP  21.50  19.83   Crude fat  4.00  2.00   Crude fiber  2.50  3.18  Vitamins and minerals       Calcium, %  0.87  0.82   Phosphorus, %  0.75  0.30   Sodium, %  0.50  0.303   Copper, mg/kg  40.00  15.00   Zinc, mg/kg  3,000.00  104.00   Vitamin A, IU/kg  11,000.00  10,000.00   Vitamin D, IU/kg  1,500.00  1,000.00   Vitamin E, IU/kg  90.00  56.00   Selenium premix,4 mg/kg  0.50  0.00   Vitamin premix5  0.00  0.50   Mineral premix6  0.00  0.10   AA7  0.00  0.43   Antibacterial, mg/kg  4.408  0.109   Chromic oxide,10 %  1.00  0.00  Item  Amount  Creep diet1  Weaner diet2  Chemical analysis, %       CP  21.50  19.83   Crude fat  4.00  2.00   Crude fiber  2.50  3.18  Vitamins and minerals       Calcium, %  0.87  0.82   Phosphorus, %  0.75  0.30   Sodium, %  0.50  0.303   Copper, mg/kg  40.00  15.00   Zinc, mg/kg  3,000.00  104.00   Vitamin A, IU/kg  11,000.00  10,000.00   Vitamin D, IU/kg  1,500.00  1,000.00   Vitamin E, IU/kg  90.00  56.00   Selenium premix,4 mg/kg  0.50  0.00   Vitamin premix5  0.00  0.50   Mineral premix6  0.00  0.10   AA7  0.00  0.43   Antibacterial, mg/kg  4.408  0.109   Chromic oxide,10 %  1.00  0.00  1Starter Advance Crumble (Floradale Feed Mill Limited, Floradale, Ontario, Canada). 2Piglet Starter Advance II (Arkell Feed Mill, Arkell, Ontario, Canada). 3Iodized salt. 4Calculated to supply 0.50 mg/kg selenium to diet (as Na2SeO3·5H2O). 5Calculated to supply per kilogram of feed: 2.5 mg of menadione, 500 mg of choline, 15 mg of pantothenic acid, 5 mg of riboflavin, 2 mg of folic acid, 25 mg of niacin, 1.5 mg of pyridoxine, 1.5 mg of biotin, 0.025 mg of vitamin B12. 6Calculated to supply per kilogram: 100 mg of Fe, 19 mg of Mn. 7Calculated to supply per kilogram: 30 mg of lysine hydrochloride (79%), 1 mg of methionine, 12 mg of threonine. 8Contains per kilogram: 22 mg of lincomycin (Bio Agri Mix Ltd., Mitchell, Ontario, Canada) and 22 mg of spectinomycin. 9Contains per kilogram: 22 mg of tylosin (Bio Agri Mix Ltd.). 10Chromic oxide (Sigma-Aldrich Canada Ltd., Oakville, ON, Canada) was used as a creep feed consumption indicator. View Large Growth Rate All animals were weighed on d 2, 6, 13, 20, 27, 31, and 35 (Pennsylvania M6400 Bench Platform Scale, Pennsylvania Scale Company, Lancaster, PA, with a Cardinal 738 Digital Indicator, Cardinal Scale Manufacturing Company, Webb City, MO). From BW data, preweaning ADG and ADG over the first 3 and 7 d of weaning were calculated. Fecal Scores On d 6, 9, 13, 16, 20, 23, and 27, a fecal sample was obtained from each piglet via a fecal swab (cotton tip applicator). The color of each sample was immediately visually assessed for the presence of chromic oxide (i.e., green color). When detected, the sample was assigned a positive score, indicating prior ingestion of creep feed (Barnett et al., 1989; Kuller et al., 2007). Occasionally, because of diarrhea or the inability to retrieve fecal material, a sample could not be assessed and was removed from data analysis (depending on the day, this ranged from 0 to 26 samples from a total 233 samples). Behavioral Observations Behavioral data were collected continuously via a digital recording system (Kodicom i31808WM, i3DVR International Inc., Scarborough, Ontario, Canada). Cameras (Panasonic WV-CP240, Panasonic Canada Inc., Mississauga, Ontario, Canada) were mounted from the ceiling directly over creep feeders, and piglets were considered to be at the feeder when their head and ears were directly over the feed trough. Video recordings were analyzed by 3 trained individuals, and continuous sampling of behavior occurred during two 3-h time periods, for a total of 6 h/d (0700 to 1000 h, 1300 to 1600 h) on d 7, 10, 14, 17, 21, and 24. These time periods were selected based on preliminary observations over a 24-h recording period, during which piglets were most active and observed most frequently at the creep feeder. The total number of visits and total duration of time spent at the creep feeder was determined for each piglet. If a piglet was observed with its head over the feeder trough and then withdrawing its head, it was considered to have left the feeder (i.e., 1 visit had occurred). Interobserver reliability was determined using one 3-h time period, which was watched by all observers (kappa = 0.91; Lehner, 1996). Dentition Procedures for dental exams are described by Tucker and Widowski (2009). All deciduous incisors, canines, and premolars are referred to by a lowercase i, c, and p, with a superscript (or subscript) number indicating its position within the maxilla (or mandible). For example, p3 is the third maxillary premolar, whereas i2 is the second mandibular incisor (Figure 1). Figure 1. View largeDownload slide Upper right and lower left deciduous dental arches of Sus scrofa [with permission from Hillson (2005)]. All deciduous incisors, canines, and premolars are referred to by a lowercase i, c, and p, with a superscript (or subscript) number indicating its position within the maxilla (or mandible). Figure 1. View largeDownload slide Upper right and lower left deciduous dental arches of Sus scrofa [with permission from Hillson (2005)]. All deciduous incisors, canines, and premolars are referred to by a lowercase i, c, and p, with a superscript (or subscript) number indicating its position within the maxilla (or mandible). Full oral examinations were performed on all piglets on d 2, 6, 9, 13, 16, 20, 23, and 27, with every deciduous tooth within the oral cavity being recorded as erupted or not. Eruption was considered to have occurred when any portion of the tooth crown had penetrated the gingiva (Smith et al., 1994). Dental measures included both the presence (vs. absence) of individual premolars (p3, p3, p4, p4) and the occlusion status of premolars within the entire dental arch. Occlusion (i.e., the contact between maxillary and mandibular premolars) was present at 2 stages within the population. Initial occlusion occurred when some (but not all) of the cusps touched between p4 and p3, and full occlusion occurred when all cusps on p4 and p3 made contact in addition to at least 2 of the cusps of p4 (Figure 2). Figure 2. View largeDownload slide Levels of premolar occlusion. Initial occlusion (a) is achieved when some (but not all) of the cusps on p4 and p3 make contact. Full occlusion (b) is achieved when all cusps on p4 and p3 make contact in addition to at least 2 of the cusps of p4. All deciduous premolars are referred to by a lowercase p, with a superscript (or subscript) number indicating its position within the maxilla (or mandible). Figure 2. View largeDownload slide Levels of premolar occlusion. Initial occlusion (a) is achieved when some (but not all) of the cusps on p4 and p3 make contact. Full occlusion (b) is achieved when all cusps on p4 and p3 make contact in addition to at least 2 of the cusps of p4. All deciduous premolars are referred to by a lowercase p, with a superscript (or subscript) number indicating its position within the maxilla (or mandible). Postweaning Housing and Feeding After weaning, 7 piglets from each litter (approximately balancing for birth weight and sex) were weaned as litter groups to an on-site nursery. Animals were housed in 1.2 × 2.4 m raised-deck pens (n = 24), each containing one 4-hole stainless steel feeder and standard nipple drinker. Both feed and water were provided ad libitum for the duration of the study. During the first 7 d, the same preweaning creep diet was provided but was then replaced gradually with a pelleted starter weaning diet (University of Guelph, Arkell Feed Mill, Guelph, Ontario, Canada; Table 1). Temperature was maintained at 26°C, and fluorescent lighting was provided from 0530 to 1830 h. To examine how preweaning feed intake affected behavior and growth after weaning, piglets were further classified as creep feed eaters, noneaters, and moderate eaters. Those individuals demonstrating prior ingestion of creep feed on d 23 and 27 were considered eaters, whereas those demonstrating ingestion on neither day were noneaters. Piglets having 1 positive and 1 negative sample on either day were moderate eaters. Behavioral Observations The same digital recording equipment was used as in the preweaning period, with cameras being moved from the farrowing room to the nursery. Behavior was recorded continuously, and data were collected by a single individual using scan sampling every 5 min for three 2-h time periods (0600 to 0800 h, 1100 to 1300 h, and 1600 to 1800 h) on d 2, 4, 6, 8, 10, and 12 after weaning. At each scan, piglets were recorded as performing 1 of 8 mutually exclusive behavior patterns (Table 2) and daily time budgets were estimated on a per-pig basis by calculating the number of scans devoted to each behavior pattern as a proportion of the total scans for that animal on that day. Table 2. Ethogram of behavior patterns Behavior  Description  Belly nosing  A repeated rhythmic up-and-down massage of the snout on the midsection of a pen mate (Fraser, 1978)  Pen-mate nosing  Nosing, sucking, chewing, or biting another pig in an arrhythmic manner (distinct from belly nosing; Fraser, 1978)  Rooting the pen  Nosing, sucking, chewing, or biting the floor, walls, or pen fixtures  At feeder  Head, including ears, in or over the feeder trough  At drinker  Snout in contact with the nipple drinker  Social  Fighting, aggressive, or play behavior between 2 or more pigs  Active  Standing, walking, or sitting without interaction with another pig  Lying  Weight of body not supported by legs, but excluding performance of any of the above-mentioned behaviors  Behavior  Description  Belly nosing  A repeated rhythmic up-and-down massage of the snout on the midsection of a pen mate (Fraser, 1978)  Pen-mate nosing  Nosing, sucking, chewing, or biting another pig in an arrhythmic manner (distinct from belly nosing; Fraser, 1978)  Rooting the pen  Nosing, sucking, chewing, or biting the floor, walls, or pen fixtures  At feeder  Head, including ears, in or over the feeder trough  At drinker  Snout in contact with the nipple drinker  Social  Fighting, aggressive, or play behavior between 2 or more pigs  Active  Standing, walking, or sitting without interaction with another pig  Lying  Weight of body not supported by legs, but excluding performance of any of the above-mentioned behaviors  View Large Table 2. Ethogram of behavior patterns Behavior  Description  Belly nosing  A repeated rhythmic up-and-down massage of the snout on the midsection of a pen mate (Fraser, 1978)  Pen-mate nosing  Nosing, sucking, chewing, or biting another pig in an arrhythmic manner (distinct from belly nosing; Fraser, 1978)  Rooting the pen  Nosing, sucking, chewing, or biting the floor, walls, or pen fixtures  At feeder  Head, including ears, in or over the feeder trough  At drinker  Snout in contact with the nipple drinker  Social  Fighting, aggressive, or play behavior between 2 or more pigs  Active  Standing, walking, or sitting without interaction with another pig  Lying  Weight of body not supported by legs, but excluding performance of any of the above-mentioned behaviors  Behavior  Description  Belly nosing  A repeated rhythmic up-and-down massage of the snout on the midsection of a pen mate (Fraser, 1978)  Pen-mate nosing  Nosing, sucking, chewing, or biting another pig in an arrhythmic manner (distinct from belly nosing; Fraser, 1978)  Rooting the pen  Nosing, sucking, chewing, or biting the floor, walls, or pen fixtures  At feeder  Head, including ears, in or over the feeder trough  At drinker  Snout in contact with the nipple drinker  Social  Fighting, aggressive, or play behavior between 2 or more pigs  Active  Standing, walking, or sitting without interaction with another pig  Lying  Weight of body not supported by legs, but excluding performance of any of the above-mentioned behaviors  View Large Statistical Analyses All data were analyzed using SAS software (SAS Inst. Inc., Cary, NC), with the individual piglet as the experimental unit. All data were formally examined using the UNIVARIATE procedure, with comprehensive residual analyses being conducted to assess the ANOVA assumptions and determine the most appropriate method for data transformation. Preweaning Analyses For duration of time spent at the feeder, a repeated measures ANOVA was performed using the MIXED procedure. Duration data were transformed by taking the square root after the addition of a bias correction factor of 0.25. For analyses of creep feeder visits as well as fecal score, the GLIMMIX procedure was used. The Poisson and binary distributions were most suitable for number of feeder visits and fecal score, respectively, with neither variable being transformed before analysis. Because the range of eruption times differed for each premolar (for example, p3 was erupted in all piglets by d 16, whereas p4 did not begin to erupt within the population until d 16), separate analyses were carried out for each premolar (and extent of occlusion). Duration of time spent at the feeder, number of visits to the feeder, and fecal score were evaluated using the following model:  where Yijkl is the response variable (duration, number of visits, fecal score, and l is the error term that represents the litter error), μ is the overall mean, Gi is the fixed effect of sex, Aj is the fixed effect of piglet age (in days), Dj is the fixed effect of dental condition (the presence or absence of a particular premolar, or the occlusion of premolars), BWj is BW, Bj is birth weight (on d 2), Dj × Aj is the interaction between dental condition and piglet age, Iij is other relevant interactions, Tk is the random effect of trial, Ll is the random effect of litter, and eijkl is the random error term. Pair-wise differences between treatment means were assessed using t-tests. To account statistically for the passage rate of feed through the gastrointestinal system, a piglet was considered to have consumed feed on the examination day before when a positive fecal score was actually observed. Postweaning Analyses Postweaning behavioral data were transformed by taking the arcsine of the square root after the addition of a bias correction factor of 0.15. To examine how dentition and preweaning creep feed ingestion affected piglet behavior (ingestive behavior: head in feeder, at drinker; oral-nasal behavior: belly nosing, pen-mate nosing, pen rooting), repeated measures analyses of variance were used with the MIXED procedure, with the following model:  where Yijkl is the time spent performing a specific behavior, μ is the overall mean, Gi is the fixed effect of sex, Dj is the fixed effect of dental condition (the presence or absence of premolars, the occlusion of premolars), Ej is the fixed effect of preweaning creep “eater” classification, Aj is the fixed effect of day (after weaning), BWj is BW, Bj is birth weight (on d 2), ADGj is the ADG, Iij is the relevant 2-way interactions, Tk is the random effect of trial, Ll is the random effect of litter, and eijkl is the random error term. Differences in behavior means across days were analyzed by linear contrasts (Kuehl, 1994). To examine how dentition and preweaning creep feed ingestion influenced growth in the first 3 and 7 d after weaning, ANOVA were used in the MIXED procedure with the following model:  where Yijkl is the growth performance trait, μ is the overall mean, Gi is the fixed effect of sex, Dj is the fixed effect of dental condition (the presence or absence of premolars, the occlusion of premolars), Ej is the fixed effect of preweaning creep eater classification, BWj is BW at weaning, Bj is birth weight (on d 2), ADGj is the ADG before weaning, Iij are the relevant 2-way interactions, Tk is the random effect of trial, Ll is the random effect of litter, and eijkl is the random error term. Pair-wise differences between means were assessed using a t-test. RESULTS Preweaning BW Gain and Feed Intake Mean BW, and weekly ADG and ADFI are listed in Table 3. Feed data could not be collected on d 21 to 26 for 1 trial, so total feed intake and ADFI for wk 3 are from 2 trials of 16 litters. Total creep feed intake over the 22-d period ranged from 1,026 to 3,040 g/litter, which gave an average total intake of 216 g/piglet (litter range: 131 to 334 g; SEM = 15.8 g). The majority of creep feed was consumed in the week before weaning (d 21 to 28), when intake averaged 122 g/piglet (litter range: 60 to 220 g; SEM = 11.8). Table 3. Mean BW, growth performance, and creep feed consumption of piglets Item  BW,1 kg  ADG,1 g/d  ADFI,2 g/pig  Preweaning period         d 2  1.60 ± 0.01  —  —   wk 1, d 2 to 7  2.36 ± 0.04  189 ± 4  —   wk 2, d 8 to 14  3.14 ± 0.05  252 ± 5  19.6 ± 2.7   wk 3, d 15 to 21  4.15 ± 0.06  223 ± 7  44.5 ± 4.3   wk 4, d 22 to 28  4.80 ± 0.08  268 ± 7  121.5 ± 11.8  Postweaning period         d 28  7.85 ± 0.13  —  —   d 28 to 31  —  0.153 ± 0.008  —   wk 1, d 28 to 35  —  0.202 ± 0.008  —  Item  BW,1 kg  ADG,1 g/d  ADFI,2 g/pig  Preweaning period         d 2  1.60 ± 0.01  —  —   wk 1, d 2 to 7  2.36 ± 0.04  189 ± 4  —   wk 2, d 8 to 14  3.14 ± 0.05  252 ± 5  19.6 ± 2.7   wk 3, d 15 to 21  4.15 ± 0.06  223 ± 7  44.5 ± 4.3   wk 4, d 22 to 28  4.80 ± 0.08  268 ± 7  121.5 ± 11.8  Postweaning period         d 28  7.85 ± 0.13  —  —   d 28 to 31  —  0.153 ± 0.008  —   wk 1, d 28 to 35  —  0.202 ± 0.008  —  1For BW and ADG, raw means ± SEM for the preweaning period were based on data from 233 piglets from 24 litters, whereas raw means ± SEM for the postweaning period were based on data from 112 piglets from 16 litters. 2For ADFI, raw means ± SEM for the preweaning and postweaning periods were based on data from 148 piglets from 16 litters. View Large Table 3. Mean BW, growth performance, and creep feed consumption of piglets Item  BW,1 kg  ADG,1 g/d  ADFI,2 g/pig  Preweaning period         d 2  1.60 ± 0.01  —  —   wk 1, d 2 to 7  2.36 ± 0.04  189 ± 4  —   wk 2, d 8 to 14  3.14 ± 0.05  252 ± 5  19.6 ± 2.7   wk 3, d 15 to 21  4.15 ± 0.06  223 ± 7  44.5 ± 4.3   wk 4, d 22 to 28  4.80 ± 0.08  268 ± 7  121.5 ± 11.8  Postweaning period         d 28  7.85 ± 0.13  —  —   d 28 to 31  —  0.153 ± 0.008  —   wk 1, d 28 to 35  —  0.202 ± 0.008  —  Item  BW,1 kg  ADG,1 g/d  ADFI,2 g/pig  Preweaning period         d 2  1.60 ± 0.01  —  —   wk 1, d 2 to 7  2.36 ± 0.04  189 ± 4  —   wk 2, d 8 to 14  3.14 ± 0.05  252 ± 5  19.6 ± 2.7   wk 3, d 15 to 21  4.15 ± 0.06  223 ± 7  44.5 ± 4.3   wk 4, d 22 to 28  4.80 ± 0.08  268 ± 7  121.5 ± 11.8  Postweaning period         d 28  7.85 ± 0.13  —  —   d 28 to 31  —  0.153 ± 0.008  —   wk 1, d 28 to 35  —  0.202 ± 0.008  —  1For BW and ADG, raw means ± SEM for the preweaning period were based on data from 233 piglets from 24 litters, whereas raw means ± SEM for the postweaning period were based on data from 112 piglets from 16 litters. 2For ADFI, raw means ± SEM for the preweaning and postweaning periods were based on data from 148 piglets from 16 litters. View Large Feeding Behavior Behavioral data could be collected only from 2 trials. The duration of time spent at the creep feeder and the number of visits to the feeder over the course of the experiment are presented in Table 4. Duration of time spent at the feeder on d 7, 10, and 14 was variable but did not differ until d 17 (P > 0.05), when it decreased (P < 0.05). Increases were then observed on each successive observation day (P < 0.0001). The frequency of visits to the creep feeder decreased from d 7 to 10 (P < 0.05) and also from d 14 to 17 (P = 0.0002), and then increased successively thereafter (P < 0.0001). Table 4. Mean duration of time spent at the feeder, number of visits to the feeder, and percentage of piglets with positive fecal scores Piglet age, d  Duration,1 s/6 h  No. of visits1 in 6 h  Piglets with positive fecal score,2 %  7  105 ± 25.9a  7.3 ± 0.6ad  0a  10  84 ± 10.6a  5.2 ± 0.4b  1.4b  14  83 ± 9.4a  6.2 ± 0.6ab  4.6c  17  75 ± 10.7b  4.2 ± 0.5c  8.0d  21  146 ± 18.1c  8.1 ± 0.7d  29.0e  24  281 ± 26.8d  11.7 ± 0.9e  44.9f  27  —  —  60.6g  Piglet age, d  Duration,1 s/6 h  No. of visits1 in 6 h  Piglets with positive fecal score,2 %  7  105 ± 25.9a  7.3 ± 0.6ad  0a  10  84 ± 10.6a  5.2 ± 0.4b  1.4b  14  83 ± 9.4a  6.2 ± 0.6ab  4.6c  17  75 ± 10.7b  4.2 ± 0.5c  8.0d  21  146 ± 18.1c  8.1 ± 0.7d  29.0e  24  281 ± 26.8d  11.7 ± 0.9e  44.9f  27  —  —  60.6g  a–gMeans within a column with different superscripts differ (P < 0.05). 1Raw means ± SEM based on data from 148 piglets. 2Raw means ± SEM based on data from 233 piglets. View Large Table 4. Mean duration of time spent at the feeder, number of visits to the feeder, and percentage of piglets with positive fecal scores Piglet age, d  Duration,1 s/6 h  No. of visits1 in 6 h  Piglets with positive fecal score,2 %  7  105 ± 25.9a  7.3 ± 0.6ad  0a  10  84 ± 10.6a  5.2 ± 0.4b  1.4b  14  83 ± 9.4a  6.2 ± 0.6ab  4.6c  17  75 ± 10.7b  4.2 ± 0.5c  8.0d  21  146 ± 18.1c  8.1 ± 0.7d  29.0e  24  281 ± 26.8d  11.7 ± 0.9e  44.9f  27  —  —  60.6g  Piglet age, d  Duration,1 s/6 h  No. of visits1 in 6 h  Piglets with positive fecal score,2 %  7  105 ± 25.9a  7.3 ± 0.6ad  0a  10  84 ± 10.6a  5.2 ± 0.4b  1.4b  14  83 ± 9.4a  6.2 ± 0.6ab  4.6c  17  75 ± 10.7b  4.2 ± 0.5c  8.0d  21  146 ± 18.1c  8.1 ± 0.7d  29.0e  24  281 ± 26.8d  11.7 ± 0.9e  44.9f  27  —  —  60.6g  a–gMeans within a column with different superscripts differ (P < 0.05). 1Raw means ± SEM based on data from 148 piglets. 2Raw means ± SEM based on data from 233 piglets. View Large Compared with d 10, piglets visited the feeder more frequently on d 7 (P < 0.05), although the duration of time did not differ (P > 0.05). As described by previous authors, this may reflect greater investigatory behavior toward a novel structure (or its edible contents, or both) that was introduced to the farrowing crates on d 5 (de Passillé et al., 1989; Delumeau and Meunier-Salaün, 1995). Dentition in Relation to Feeding Behavior The percentage of piglets with various premolars erupted and occluded over the course of the experiment is shown in Figure 3. Eruption times of all teeth, as well as factors that influence that timing, have been reported (Tucker and Widowski, 2009). Sex was not found to be a factor for either feeding behavior or feed ingestion before weaning (P > 0.05). Figure 3. View largeDownload slide Cumulative percentage of piglets with various premolars erupted and occluded (n = 233). Maxillary p3 (■-■); mandibular p4 (◆-◆); mandibular p3 (▲-▲); maxillary p4 (●-●); occlusion of p3 and p4 (---). All deciduous premolars are referred to by a lowercase p with a superscript (or subscript) number indicating its position within the maxilla (or mandible). Figure 3. View largeDownload slide Cumulative percentage of piglets with various premolars erupted and occluded (n = 233). Maxillary p3 (■-■); mandibular p4 (◆-◆); mandibular p3 (▲-▲); maxillary p4 (●-●); occlusion of p3 and p4 (---). All deciduous premolars are referred to by a lowercase p with a superscript (or subscript) number indicating its position within the maxilla (or mandible). The first premolar to erupt in the population was the maxillary p3. This tooth has 3 triangular elements (i.e., is trigonal), 2 major distal cusps (1 each on the buccal and lingual side), and 1 major mesial cusp on the buccal side (Figure 1). One piglet had this premolar erupted at 2 d of age, and all individuals had at least partial eruption by 16 d of age. From Tables 5 and 6, it is clear that on d 7, piglets with p3 erupted spent less time (P = 0.005) at the creep feeder and visited it less frequently (P < 0.0001 for number of visits on d 7 and P = 0.001 for number of visits on d 10) than those piglets without it. Table 5. Mean length of time (s) that piglets with (W) and without (WO) their premolars erupted and occluded spent with their heads in the creep feeder trough Piglet age, d  Premolar1 and eruption status  N  Mean2  Lower CI3  Upper CI  P-value  7  p3          0.005     W  77  24.69  10.44  49.90       WO  72  46.33  21.11  89.16      p4          <0.0001     W  36  17.35  8.02  38.19       WO  113  43.67  23.11  81.02      p3 and p4          <0.0001     W  11  11.09  3.06  28.77       WO  138  42.70  20.51  79.17    10  p3          0.206     W  118  31.20  14.19  60.08       WO  27  43.39  18.17  88.43      p4          0.003     W  49  24.36  12.11  49.22       WO  96  41.73  22.35  76.90      p3 and p4          0.0004     W  29  18.11  6.53  40.49       WO  116  39.77  19.11  73.75    14  p3          0.927     W  140  41.77  20.36  76.63       WO  3  39.69  8.55  119.09      p4          0.784     W  112  36.72  19.60  68.36       WO  31  39.23  18.72  79.10      p3          0.131     W  10  14.72  3.85  39.54       WO  133  28.12  13.29  52.68      p3 and p4          0.524     W  68  32.00  14.30  62.39       WO  75  36.11  16.45  69.48    17  p4          0.425     W  124  48.69  26.91  86.88       WO  4  37.44  15.11  85.21      p3          0.501     W  30  32.93  14.06  66.20       WO  98  39.30  20.14  69.58      p4          0.012     W  3  11.97  27.54  118.31       WO  125  48.38  17.62  66.92      p3 and p4          0.116     W  101  46.73  23.01  85.15       WO  27  33.53  14.11  68.10    21  p3          0.156     W  73  78.34  43.81  130.04       WO  52  59.00  30.48  103.86      p4          0.756     W  25  61.02  32.36  111.98       WO  100  56.49  1.01  5.53      p3 and p4          0.001     W  118  73.57  39.07  126.95       WO  7  30.57  10.90  68.10    24  p3          0.025     W  100  135.03  79.14  216.21       WO  28  78.01  38.75  141.37      p4          0.0005     W  54  148.77  78.01  259.22       WO  74  63.19  21.53  77.14      p3 and p4          0.0001     W  126  100.23  54.76  169.36       WO  2  28.06  8.65  69.12    Piglet age, d  Premolar1 and eruption status  N  Mean2  Lower CI3  Upper CI  P-value  7  p3          0.005     W  77  24.69  10.44  49.90       WO  72  46.33  21.11  89.16      p4          <0.0001     W  36  17.35  8.02  38.19       WO  113  43.67  23.11  81.02      p3 and p4          <0.0001     W  11  11.09  3.06  28.77       WO  138  42.70  20.51  79.17    10  p3          0.206     W  118  31.20  14.19  60.08       WO  27  43.39  18.17  88.43      p4          0.003     W  49  24.36  12.11  49.22       WO  96  41.73  22.35  76.90      p3 and p4          0.0004     W  29  18.11  6.53  40.49       WO  116  39.77  19.11  73.75    14  p3          0.927     W  140  41.77  20.36  76.63       WO  3  39.69  8.55  119.09      p4          0.784     W  112  36.72  19.60  68.36       WO  31  39.23  18.72  79.10      p3          0.131     W  10  14.72  3.85  39.54       WO  133  28.12  13.29  52.68      p3 and p4          0.524     W  68  32.00  14.30  62.39       WO  75  36.11  16.45  69.48    17  p4          0.425     W  124  48.69  26.91  86.88       WO  4  37.44  15.11  85.21      p3          0.501     W  30  32.93  14.06  66.20       WO  98  39.30  20.14  69.58      p4          0.012     W  3  11.97  27.54  118.31       WO  125  48.38  17.62  66.92      p3 and p4          0.116     W  101  46.73  23.01  85.15       WO  27  33.53  14.11  68.10    21  p3          0.156     W  73  78.34  43.81  130.04       WO  52  59.00  30.48  103.86      p4          0.756     W  25  61.02  32.36  111.98       WO  100  56.49  1.01  5.53      p3 and p4          0.001     W  118  73.57  39.07  126.95       WO  7  30.57  10.90  68.10    24  p3          0.025     W  100  135.03  79.14  216.21       WO  28  78.01  38.75  141.37      p4          0.0005     W  54  148.77  78.01  259.22       WO  74  63.19  21.53  77.14      p3 and p4          0.0001     W  126  100.23  54.76  169.36       WO  2  28.06  8.65  69.12    1All deciduous premolars are referred to by a lowercase p with a superscript (or subscript) number indicating its position within the maxilla (or mandible). 2Least squares means back transformed from square root + 0.25. 3CI = confidence interval. View Large Table 5. Mean length of time (s) that piglets with (W) and without (WO) their premolars erupted and occluded spent with their heads in the creep feeder trough Piglet age, d  Premolar1 and eruption status  N  Mean2  Lower CI3  Upper CI  P-value  7  p3          0.005     W  77  24.69  10.44  49.90       WO  72  46.33  21.11  89.16      p4          <0.0001     W  36  17.35  8.02  38.19       WO  113  43.67  23.11  81.02      p3 and p4          <0.0001     W  11  11.09  3.06  28.77       WO  138  42.70  20.51  79.17    10  p3          0.206     W  118  31.20  14.19  60.08       WO  27  43.39  18.17  88.43      p4          0.003     W  49  24.36  12.11  49.22       WO  96  41.73  22.35  76.90      p3 and p4          0.0004     W  29  18.11  6.53  40.49       WO  116  39.77  19.11  73.75    14  p3          0.927     W  140  41.77  20.36  76.63       WO  3  39.69  8.55  119.09      p4          0.784     W  112  36.72  19.60  68.36       WO  31  39.23  18.72  79.10      p3          0.131     W  10  14.72  3.85  39.54       WO  133  28.12  13.29  52.68      p3 and p4          0.524     W  68  32.00  14.30  62.39       WO  75  36.11  16.45  69.48    17  p4          0.425     W  124  48.69  26.91  86.88       WO  4  37.44  15.11  85.21      p3          0.501     W  30  32.93  14.06  66.20       WO  98  39.30  20.14  69.58      p4          0.012     W  3  11.97  27.54  118.31       WO  125  48.38  17.62  66.92      p3 and p4          0.116     W  101  46.73  23.01  85.15       WO  27  33.53  14.11  68.10    21  p3          0.156     W  73  78.34  43.81  130.04       WO  52  59.00  30.48  103.86      p4          0.756     W  25  61.02  32.36  111.98       WO  100  56.49  1.01  5.53      p3 and p4          0.001     W  118  73.57  39.07  126.95       WO  7  30.57  10.90  68.10    24  p3          0.025     W  100  135.03  79.14  216.21       WO  28  78.01  38.75  141.37      p4          0.0005     W  54  148.77  78.01  259.22       WO  74  63.19  21.53  77.14      p3 and p4          0.0001     W  126  100.23  54.76  169.36       WO  2  28.06  8.65  69.12    Piglet age, d  Premolar1 and eruption status  N  Mean2  Lower CI3  Upper CI  P-value  7  p3          0.005     W  77  24.69  10.44  49.90       WO  72  46.33  21.11  89.16      p4          <0.0001     W  36  17.35  8.02  38.19       WO  113  43.67  23.11  81.02      p3 and p4          <0.0001     W  11  11.09  3.06  28.77       WO  138  42.70  20.51  79.17    10  p3          0.206     W  118  31.20  14.19  60.08       WO  27  43.39  18.17  88.43      p4          0.003     W  49  24.36  12.11  49.22       WO  96  41.73  22.35  76.90      p3 and p4          0.0004     W  29  18.11  6.53  40.49       WO  116  39.77  19.11  73.75    14  p3          0.927     W  140  41.77  20.36  76.63       WO  3  39.69  8.55  119.09      p4          0.784     W  112  36.72  19.60  68.36       WO  31  39.23  18.72  79.10      p3          0.131     W  10  14.72  3.85  39.54       WO  133  28.12  13.29  52.68      p3 and p4          0.524     W  68  32.00  14.30  62.39       WO  75  36.11  16.45  69.48    17  p4          0.425     W  124  48.69  26.91  86.88       WO  4  37.44  15.11  85.21      p3          0.501     W  30  32.93  14.06  66.20       WO  98  39.30  20.14  69.58      p4          0.012     W  3  11.97  27.54  118.31       WO  125  48.38  17.62  66.92      p3 and p4          0.116     W  101  46.73  23.01  85.15       WO  27  33.53  14.11  68.10    21  p3          0.156     W  73  78.34  43.81  130.04       WO  52  59.00  30.48  103.86      p4          0.756     W  25  61.02  32.36  111.98       WO  100  56.49  1.01  5.53      p3 and p4          0.001     W  118  73.57  39.07  126.95       WO  7  30.57  10.90  68.10    24  p3          0.025     W  100  135.03  79.14  216.21       WO  28  78.01  38.75  141.37      p4          0.0005     W  54  148.77  78.01  259.22       WO  74  63.19  21.53  77.14      p3 and p4          0.0001     W  126  100.23  54.76  169.36       WO  2  28.06  8.65  69.12    1All deciduous premolars are referred to by a lowercase p with a superscript (or subscript) number indicating its position within the maxilla (or mandible). 2Least squares means back transformed from square root + 0.25. 3CI = confidence interval. View Large Table 6. Mean number of times that piglets with (W) and without (W) their premolars erupted and occluded visited the creep feeder Piglet age, d  Premolar1 and eruption status  N  Mean2  Lower CI3  Upper CI  P-value  7  p3          <0.0001     W  77  1.3  1.02  1.58       WO  72  1.9  1.55  2.20      p4          <0.0001     W  36  1.1  0.79  1.40       WO  113  1.8  1.50  2.07      p3 and p4          <0.0001     W  11  1.0  0.67  1.38       WO  138  1.7  1.41  1.96    10  p3          0.238     W  118  1.4  1.17  1.69       WO  27  1.6  1.24  2.02      p4          0.001     W  49  1.3  0.99  1.55       WO  96  1.6  1.35  1.89      p3 and p4          0.003     W  29  1.2  0.91  1.54       WO  116  1.6  1.33  1.85    14  p3          0.418     W  140  1.6  1.35  1.86       WO  3  1.3  0.55  2.06      p4          0.475     W  112  1.5  1.25  1.77       WO  31  1.4  1.05  1.76      p3          0.037     W  10  1.2  0.72  1.61       WO  133  1.6  1.32  1.83      p3 and p4          0.822     W  68  1.5  1.21  1.76       WO  75  1.5  1.18  1.74    17  p4          0.029     W  124  1.7  1.44  1.94       WO  4  1.2  0.79  1.70      p3          0.670     W  30  1.5  1.17  1.86       WO  98  1.6  1.32  1.84      p4          0.024     W  3  1.0  0.34  1.66       WO  125  1.7  1.47  1.98      p3 and p4          0.010     W  101  1.7  1.42  1.94       WO  27  1.4  1.04  1.68    21  p3          0.0002     W  73  2.0  1.72  2.26       WO  52  1.6  1.30  1.86      p4          0.979     W  25  1.8  1.44  2.17       WO  100  1.8  1.54  2.07      p3 and p4          <0.0001     W  118  1.9  1.69  2.20       WO  7  1.2  0.84  1.63    24  p3          <0.0001     W  100  2.3  1.28  2.63       WO  28  1.6  1.28  1.89      p4          0.0002     W  54  2.4  2.07  2.74       WO  74  1.9  1.60  2.15      p3 and p4          <0.0001     W  126  2.1  1.88  2.41       WO  2  1.1  0.68  1.59    Piglet age, d  Premolar1 and eruption status  N  Mean2  Lower CI3  Upper CI  P-value  7  p3          <0.0001     W  77  1.3  1.02  1.58       WO  72  1.9  1.55  2.20      p4          <0.0001     W  36  1.1  0.79  1.40       WO  113  1.8  1.50  2.07      p3 and p4          <0.0001     W  11  1.0  0.67  1.38       WO  138  1.7  1.41  1.96    10  p3          0.238     W  118  1.4  1.17  1.69       WO  27  1.6  1.24  2.02      p4          0.001     W  49  1.3  0.99  1.55       WO  96  1.6  1.35  1.89      p3 and p4          0.003     W  29  1.2  0.91  1.54       WO  116  1.6  1.33  1.85    14  p3          0.418     W  140  1.6  1.35  1.86       WO  3  1.3  0.55  2.06      p4          0.475     W  112  1.5  1.25  1.77       WO  31  1.4  1.05  1.76      p3          0.037     W  10  1.2  0.72  1.61       WO  133  1.6  1.32  1.83      p3 and p4          0.822     W  68  1.5  1.21  1.76       WO  75  1.5  1.18  1.74    17  p4          0.029     W  124  1.7  1.44  1.94       WO  4  1.2  0.79  1.70      p3          0.670     W  30  1.5  1.17  1.86       WO  98  1.6  1.32  1.84      p4          0.024     W  3  1.0  0.34  1.66       WO  125  1.7  1.47  1.98      p3 and p4          0.010     W  101  1.7  1.42  1.94       WO  27  1.4  1.04  1.68    21  p3          0.0002     W  73  2.0  1.72  2.26       WO  52  1.6  1.30  1.86      p4          0.979     W  25  1.8  1.44  2.17       WO  100  1.8  1.54  2.07      p3 and p4          <0.0001     W  118  1.9  1.69  2.20       WO  7  1.2  0.84  1.63    24  p3          <0.0001     W  100  2.3  1.28  2.63       WO  28  1.6  1.28  1.89      p4          0.0002     W  54  2.4  2.07  2.74       WO  74  1.9  1.60  2.15      p3 and p4          <0.0001     W  126  2.1  1.88  2.41       WO  2  1.1  0.68  1.59    1All deciduous premolars are referred to by a lowercase p with a superscript (or subscript) number indicating its position within the maxilla (or mandible). 2Least squares means. 3CI = confidence interval. View Large Table 6. Mean number of times that piglets with (W) and without (W) their premolars erupted and occluded visited the creep feeder Piglet age, d  Premolar1 and eruption status  N  Mean2  Lower CI3  Upper CI  P-value  7  p3          <0.0001     W  77  1.3  1.02  1.58       WO  72  1.9  1.55  2.20      p4          <0.0001     W  36  1.1  0.79  1.40       WO  113  1.8  1.50  2.07      p3 and p4          <0.0001     W  11  1.0  0.67  1.38       WO  138  1.7  1.41  1.96    10  p3          0.238     W  118  1.4  1.17  1.69       WO  27  1.6  1.24  2.02      p4          0.001     W  49  1.3  0.99  1.55       WO  96  1.6  1.35  1.89      p3 and p4          0.003     W  29  1.2  0.91  1.54       WO  116  1.6  1.33  1.85    14  p3          0.418     W  140  1.6  1.35  1.86       WO  3  1.3  0.55  2.06      p4          0.475     W  112  1.5  1.25  1.77       WO  31  1.4  1.05  1.76      p3          0.037     W  10  1.2  0.72  1.61       WO  133  1.6  1.32  1.83      p3 and p4          0.822     W  68  1.5  1.21  1.76       WO  75  1.5  1.18  1.74    17  p4          0.029     W  124  1.7  1.44  1.94       WO  4  1.2  0.79  1.70      p3          0.670     W  30  1.5  1.17  1.86       WO  98  1.6  1.32  1.84      p4          0.024     W  3  1.0  0.34  1.66       WO  125  1.7  1.47  1.98      p3 and p4          0.010     W  101  1.7  1.42  1.94       WO  27  1.4  1.04  1.68    21  p3          0.0002     W  73  2.0  1.72  2.26       WO  52  1.6  1.30  1.86      p4          0.979     W  25  1.8  1.44  2.17       WO  100  1.8  1.54  2.07      p3 and p4          <0.0001     W  118  1.9  1.69  2.20       WO  7  1.2  0.84  1.63    24  p3          <0.0001     W  100  2.3  1.28  2.63       WO  28  1.6  1.28  1.89      p4          0.0002     W  54  2.4  2.07  2.74       WO  74  1.9  1.60  2.15      p3 and p4          <0.0001     W  126  2.1  1.88  2.41       WO  2  1.1  0.68  1.59    Piglet age, d  Premolar1 and eruption status  N  Mean2  Lower CI3  Upper CI  P-value  7  p3          <0.0001     W  77  1.3  1.02  1.58       WO  72  1.9  1.55  2.20      p4          <0.0001     W  36  1.1  0.79  1.40       WO  113  1.8  1.50  2.07      p3 and p4          <0.0001     W  11  1.0  0.67  1.38       WO  138  1.7  1.41  1.96    10  p3          0.238     W  118  1.4  1.17  1.69       WO  27  1.6  1.24  2.02      p4          0.001     W  49  1.3  0.99  1.55       WO  96  1.6  1.35  1.89      p3 and p4          0.003     W  29  1.2  0.91  1.54       WO  116  1.6  1.33  1.85    14  p3          0.418     W  140  1.6  1.35  1.86       WO  3  1.3  0.55  2.06      p4          0.475     W  112  1.5  1.25  1.77       WO  31  1.4  1.05  1.76      p3          0.037     W  10  1.2  0.72  1.61       WO  133  1.6  1.32  1.83      p3 and p4          0.822     W  68  1.5  1.21  1.76       WO  75  1.5  1.18  1.74    17  p4          0.029     W  124  1.7  1.44  1.94       WO  4  1.2  0.79  1.70      p3          0.670     W  30  1.5  1.17  1.86       WO  98  1.6  1.32  1.84      p4          0.024     W  3  1.0  0.34  1.66       WO  125  1.7  1.47  1.98      p3 and p4          0.010     W  101  1.7  1.42  1.94       WO  27  1.4  1.04  1.68    21  p3          0.0002     W  73  2.0  1.72  2.26       WO  52  1.6  1.30  1.86      p4          0.979     W  25  1.8  1.44  2.17       WO  100  1.8  1.54  2.07      p3 and p4          <0.0001     W  118  1.9  1.69  2.20       WO  7  1.2  0.84  1.63    24  p3          <0.0001     W  100  2.3  1.28  2.63       WO  28  1.6  1.28  1.89      p4          0.0002     W  54  2.4  2.07  2.74       WO  74  1.9  1.60  2.15      p3 and p4          <0.0001     W  126  2.1  1.88  2.41       WO  2  1.1  0.68  1.59    1All deciduous premolars are referred to by a lowercase p with a superscript (or subscript) number indicating its position within the maxilla (or mandible). 2Least squares means. 3CI = confidence interval. View Large The second premolar to erupt was the mandibular p4. This is the only tricuspid tooth in the deciduous dentition, having 3 cusps on both the buccal and lingual sides that are fused into transverse ridges (Figure 1; Herring, 1976). Again, 1 piglet (the same individual as mentioned above) had this tooth erupted by d 2, and all individuals had it erupted by d 20 of age. As with the p3, those piglets having p4 erupted made fewer visits to (P < 0.0001), and spent less time at (P < 0.0001 for duration on d 7 and P = 0.003 for duration on d 10), the creep feeder on d 7 and d 10 compared with those piglets with premolars at a lesser developmental stage. However, by age 17 d, piglets having this premolar erupted began to visit the feeder more frequently (P = 0.024). The third premolar to erupt was the mandibular p3. Unlike the others seen to erupt in the time course of this experiment, this premolar is not molariform in shape but sectorial, with an elongated shearing cusp (Figure 1). No individual had this tooth present until 13 d of age, and even by 27 d, it was still not erupted in 6 animals. On d 14, piglets with this tooth erupted visited the feeder less (P = 0.037) than piglets without it, but by d 21 and 24, they visited the feeder more (P = 0.0002 on d 21 and P < 0.0001 on d 24). Likewise, piglets having this tooth by d 24 spent more time at the feeder (P = 0.025). The last premolar to erupt during the course of this experiment was the maxillary p4. This tooth has 4 main bunodont cusps and hypoconulid lobes, with subsidiary cusps and crenulations surrounding them (Figure 1; Herring and Scapino, 1973). Within the current population, this tooth erupted in piglets as young as 16 d, although by 27 d, 53 individuals (27.5% of the population) still did not have it erupted. Piglets that had their p4 at 17 d of age spent less (P = 0.012) time at the feeder and visited it less frequently (P = 0.024), but at age 24 d, they spent more time (P = 0.0005) at the feeder and visited it more frequently (P = 0.0002). The percentage of piglets displaying initial occlusion between p3 and p4 is shown in Figure 3. The same individual having both p3 and p4 erupted at 2 d of age also had initial occlusion between these teeth. Only 1 individual had not yet obtained initial occlusion by d 27. On d 7 and d 10, negative associations were found between measures of feed-oriented behavior (duration, P < 0.0001 for d 7 and P = 0.0004 for d 10; feeder visits, P < 0.0001 for d 7 and P = 0.003 for d 10) and premolar occlusion. By d 17, piglets with occlusion visited the feeder more (P = 0.010), and on d 21 and 24, both the number of visits (P < 0.0001 for d 21 and 24) and the duration of time (P = 0.001 for d 21 and P = 0.0001 for d 24) were positively associated with occlusion. Dentition in Relation to Feed Consumption The percentage of piglets having positive fecal scores over the course of the study is shown in Table 4. No piglet was seen to have consumed feed by 6 d of age, and only 44.9% of piglets demonstrated (by fecal analysis) consumption of feed at 24 d of age, and only 60.6% demonstrated feed consumption by 27 d of age. Linear increases in consumption were seen for every day that fecal scores were examined (P < 0.0001); however, no association was found between creep feed consumption and the eruption or occlusion of premolars (P > 0.05). Postweaning Relationships Between Dentition, Growth, and Behavior Because of equipment failure, postweaning data were collected from only 2 trials of 16 litters (112 piglets in total). Over the first week, gilts tended to gain more BW than barrows (P = 0.065). The percentages of noneaters, moderate eaters, and eaters during the preweaning period were 57.1, 30.4, and 12.5%, respectively. All 112 piglets had their p3 and p4 erupted and had attained initial occlusion before weaning. Full occlusion was achieved by 48.2% of the population by d 27, whereas eruption of both p3 and p4 had occurred in 96.4 and 81.2% of the population, respectively. None of the dental measures influenced postweaning growth rates (P > 0.05) or the performance of any behavior in the postweaning period (P > 0.05). Similarly, classifying piglets as noneaters, moderate eaters, or eaters before weaning did not influence either growth or the time spent performing any behavior after weaning (P > 0.05). DISCUSSION Preweaning We predicted that piglets with erupted and occluded premolars would spend more time at the creep feeder, visit the feeder more frequently, and show earlier consumption of solid feed. Our hypotheses were correct regarding feed-oriented behavior; however, the effects were age dependent. Two general trends were seen: first, animals 17 d of age and younger spent less time at the feeder and visited it less often if they had premolars erupted and occluded, and second, piglets 21 d of age and older spent more time at the feeder and visited it more frequently if they had premolars erupted and occluded. These findings indicate that younger piglets may be inhibited from feeding as their first premolars begin erupting and occluding. In the examination of the gums during oral exams, it was often noted that bleeding and inflammation were present around the erupting premolars, especially at the early stages. On a cellular level, eruption is a localized and programmed event that is a direct result of alveolar bone resorption. In addition to the influx of bone-mediating cells to the gingiva, there is a host of signaling proteins that accompany them (i.e., eicosanoids, cytokines, growth factors; Sandy, 1992). These compounds are common mediators of inflammation, which, after induction, are known to cause localized discomfort, pain, and protective behaviors (Funk, 2001; Shapira et al., 2003; Sommer and Kress, 2004). In cattle, and to a lesser extent in sheep and goats, this localized reaction is termed an “eruptive collar” (Andrews, 1985; Cocquyt et al., 2005) and is thought to cause a loss of appetite and reduced body condition in what would otherwise be healthy individuals (Andrews, 1985). The symptoms attributable to the initial cutting or eruption of teeth (i.e., teething) have been best documented in human infants and include increased biting, drooling, gum and ear rubbing, sucking, irritability, wakefulness, decreased appetite for solid foods, and mildly raised body temperature (Macknin et al., 2000; Wake et al., 2000). Interestingly, many of these behavior patterns are common in the young pig and might therefore be related to dental discomfort. Although many animal practitioners assume that teething afflicts other mammals (Hale, 2000), whether a true loss of appetite is occurring or animals choose to avoid solid food remains to be determined. The reversal of the trend between premolar eruption and feed aversion that was observed when piglets reached 21 d of age and older may indicate that a point of desensitization had been reached or a method of coping had developed with regard to oral discomfort. For example, if piglets became accustomed to their painful gums or found that chewing on items (such as creep feed) offered relief, they may have increased feed-oriented behaviors as further premolars continued to erupt. Alternatively, the discomfort of eruption may have diminished or ceased altogether by this age. However, because piglets with erupted premolars spent more than double the amount of time at the feeder compared with piglets without premolars, it seems probable that feeding motivation became significantly enhanced as this part of their oral physiology developed. Nevertheless, we did not find any associations between the eruption or occlusion of premolars and feed ingestion during the preweaning period. Often, it was noted that piglets appeared clumsy in their feed-handling abilities and dropped much of it from their mouths as they attempted to consume it. This has been reported by researchers studying feeding behavior in miniature breeds of swine (Herring and Wineski, 1986) and may explain why, during the first few days after weaning, commercial piglets appear to be eating (i.e., they have their heads in the feeder trough), yet are not actually consuming appreciable quantities of feed (Gardner et al., 2001; McGlone and Anderson, 2002; Torrey and Widowski, 2004). These observations indicate that motor learning is an important aspect in the development of feeding behavior in young pigs. For example, the periodontal mechanoreceptors within the ligaments attaching tooth roots to alveolar bone are the main tactile sensors providing tooth load information to the brain. As opposing teeth erupt into initial contact, these receptors will undoubtedly require a period of readjustment. Likewise, handling and chewing of food items requires significant coordination of the tongue muscles in a fashion distinct from that needed for suckling (Gordon and Herring, 1987), and also varies significantly based on the properties of food (Kakizaki et al., 2002). Within miniature breeds, the masticatory muscles themselves have been well documented as requiring periods of motor learning in the development of effective and coordinated chewing behavior (Herring and Scapino, 1973; Herring and Wineski, 1986; Herring et al., 1991). Huang et al. (1994) went further to classify 3 separate stages of premolar occlusion and then related them, using electromyography, to masticatory ability and efficiency. Miniature piglets with 1 cheek tooth per quadrant in occlusion (first stage) chewed more slowly, required more chewing cycles to process a food bolus, and had less regular alternation in the chewing side compared with piglet in further stages. To our knowledge, no studies have yet examined this period of adjustment in commercial piglets. The lack of association between feed intake and premolar eruption may also be due to the method by which we accounted for passage rate of feed through the gastrointestinal system. Piglets were considered to have consumed feed on the examination day before when a positive fecal score was actually observed, assuming a passage rate of between 72 and 96 h. Kuller et al. (2007), who orally administered chromic oxide-marked creep feed to nursing piglets, reported occasional intermittent excretion of marked feed within the same day and concluded that the best time to sample feces was 48 to 58 h after ingestion. However, they also noted that when piglets are allowed continuous access to marked feed, chances of accurate detection for creep-eating individuals will increase because those who ingest feed tend to continue to do so (Appleby et al., 1991; Pajor et al., 1991; Kuller et al., 2004). Because fecal samples were collected only once every few days in our study, it is possible that the optimal detection period for marked feces was missed. Furthermore, ingestion of small quantities of marked feed may be masked by large intakes of milk; hence, the absence of color may not reliably indicate that no feed was eaten (Barnett et al., 1989). It should also be noted that only one 4-hole creep feeder was placed in each farrowing crate so that simultaneous feeding of the whole litter was not possible. Because litter size ranged from 7 to 13 piglets, competition may have varied between litters. Although not recorded, displacement was frequently seen whereby 1 piglet would push another away from the creep feeder; however, the dentition of these piglets was not determined. Regarding the dentition itself, it should be noted that although occlusion could be determined in this study from visual assessment of contact between premolars, the quality of occlusion could not be assessed. The animals were not sedated, and even the slightest resistance or movement in jaw muscles would give a misrepresentation of how well premolars aligned between the maxilla and mandible; therefore, the quality of occlusion was undetermined. In addition, piglets in this study were categorized simply as having their premolars either erupted or not erupted. This means that piglets with only small portions of their premolar cusps erupted through the gingiva would be statistically grouped with those having larger proportions of their premolar cusps erupted. Postweaning The postweaning phase of this study was designed to investigate whether dentition influenced postweaning growth or behavior. However, no evidence was found for an influence of dentition on the transition at weaning when piglets were weaned at 28 d of age. One possible explanation for the lack of relationship between dental development and feeding behavior or growth after weaning was the small variation in many of these dental measures across the population by 27 d of age. Except for full occlusion, which was present in only 48.2% of the population at weaning, all other measures were present in at least 80% of piglets. Additionally, the variation in the number of teeth and premolars was skewed toward the maximum possible number by this age. It has been suggested that the maturity of the piglet is critically important for their ability to adapt at weaning (de Passillé et al., 1989; Pajor et al., 1991). Piglets generally have smaller distress responses (Weary et al., 2008) and better performance when weaned at older ages (Leibbrandt et al., 1975; Armstrong and Clawson, 1980; Main et al., 2004). In addition, the period of BW loss after weaning has been shown to be age dependent, with 21-d-weaned piglets taking twice as long to recover compared with piglets weaned at 28 d (Colson et al., 2006). Studies also indicate more feeding behavior among piglets weaned at 28 vs. 14 d of age (Metz and Gonyou, 1990) and at 28 vs. 21 d of age (Worobec, et al. 1999). Therefore, any effect of dentition on feeding and growth at weaning may be more likely when piglets are weaned at younger ages (e.g., between 17 and 21 d). Any effect of dentition on ingestive behavior patterns in the postweaning period may have been masked by the husbandry conditions imposed. Piglets were given access to solid feed for an unusually long period (from the age of 5 d), and 54% of piglets were known to be ingesting feed on the day before weaning. Housing piglets in litter groups may also have allowed for increased learning opportunities for both feeding and drinking behaviors because social stresses would be reduced under these conditions (Olesen et al., 1996; Hessel et al., 2006). Morgan et al. (2001) found that food-naïve piglets housed with established eaters were more likely to discover and consume feed than when only food-naïve piglets were housed together. Retrospective analysis in the current study showed that at least 1 piglet in every weaning pen had demonstrated previous feed ingestion. Interestingly, classifying piglets as creep eaters based on their preweaning feed ingestion did not affect either their time at the feeder or their postweaning growth. This is contrary to the results of Bruininx et al. (2002), who reported that creep eaters ingested more feed on their visits to the feeder and also had greater ADG compared with noneaters. Although we could not determine whether piglets were actually consuming feed while visiting the feeder, the fact that we found no difference in BW gain suggests preweaning feed consumption had little effect on postweaning feeding behavior. However, it should be noted that Bruininx et al. (2002) classified piglets as creep eaters by examining fecal scores on d 18, 22, and 27, and found that 17.4% of piglets were positive on all 3 d. If using the same methodology, only 3 individuals (2.7%) would have been considered creep eaters in the current study. With regard to our predictions on the performance of belly nosing after weaning, it was thought that because this abnormal behavior is considered to be a juvenile form of ingestive behavior (i.e., redirected suckling; Torrey and Widowski, 2006), piglets developing it may also possess a less developed dentition. However, no association was revealed in this study to support this hypothesis. In fact, the occurrence of belly nosing was minimal throughout the duration of this study, with overall means remaining below 1% of the scans. This is comparable with values reported by Metz and Gonyou (1990) and Worobec et al. (1999), who also weaned piglets at 28 d of age. Although belly-nosing events were minimal, there was a characteristic increase during the second week after weaning (Metz and Gonyou, 1990; Gonyou et al., 1998; Worobec et al., 1999; Bruni et al., 2008). Unfortunately, no observations were made on the condition of the gingiva or the dentition during that period, when belly nosing was seen to spike within the population. Conclusions The results of this study showed that although the majority of piglets had many of their deciduous premolars erupted by 27 d of age, and almost all piglets had initial occlusion between their p4 and p3 by 23 d of age, only 45 and 61% of piglets were consuming creep feed by d 24 and 27, respectively. No associations were found between the eruption or occlusion of premolars and feed consumption; however, a more precise methodology is suggested for examining this relationship in the future. Feeding behavior was associated with dental eruption, with age-dependent responses being revealed. Younger piglets were inhibited from feeding when their premolars began erupting, possibly because it was their first experience with oral sensitivity. However, by 21 d of age, piglets with erupted premolars were more attracted to feed, indicating enhanced motivation for independent ingestion after the eruption of their teeth. At the time of weaning at 28 d, neither the eruption nor the occlusion of premolars was found to influence postweaning growth or behavior. Very little variation in dental measures existed within this population by this age. Further studies using more commercially relevant conditions such as younger weaning ages, mixing of pen mates, varying genetics, and limited preweaning feed exposure would be beneficial in elucidating the effect of dentition on weaning transition. LITERATURE CITED Andrews, A. H. 1985. Anatomy of the oral cavity, eruption and developmental abnormalities in ruminants. Pages 235– 267 in Veterinary Dentistry.  C. E. Harvey ed. W. B. Saunders, Philadelphia, PA. Appleby M. C. Pajor E. A. Fraser D. 1991. Effects of management options on creep feeding by piglets. Anim. Prod.  53: 361– 366. Google Scholar CrossRef Search ADS   Armstrong W. D. Clawson A. J. 1980. Nutrition and management of early weaned pigs: Effect of increased nutrient concentrations and (or) supplemental liquid feeding. J. Anim. Sci.  50: 377– 384. https://doi.org/7364673 Google Scholar CrossRef Search ADS PubMed  Barnett K. L. Kornegay E. T. Risley C. R. Lindemann M. D. Schurig G. G. 1989. Characterization of creep feed consumption and its subsequent effects on immune response, scouring index and performance of weanling pigs. J. Anim. Sci.  67: 2698– 2708. https://doi.org/2808171 Google Scholar CrossRef Search ADS PubMed  Berkeveld M. Langendijk P. Soed N. M. Kemp B. Taverne M. A. M. Verheijden J. H. M. Kuijken N. Koets A. P. 2009. Improving adaptation to weaning: Effect of intermittent suckling regimens on piglet feed intake, growth, and gut characteristics. J. Anim. Sci.  87: 3156– 3166. https://doi.org/19617520 Google Scholar CrossRef Search ADS PubMed  Bruininx E. M. A. M. Binnendijk G. P. van der Peet-Schweing C. M. C. Schama J. W. den Hartog L. A. Everts H. Beynen A. C. 2002. Effect of creep feed consumption on individual feed intake characteristics and performance of group-housed weanling pigs. J. Anim. Sci.  80: 1413– 1418. https://doi.org/12078720 Google Scholar CrossRef Search ADS PubMed  Bruni A. Quinton V. M. Widowski T. M. 2008. The effect of feed restriction on belly nosing behaviour in weaned piglets. Appl. Anim. Behav. Sci.  110: 203– 215. Google Scholar CrossRef Search ADS   Canadian Council on Animal Care 1993. Guide to the Care and Use of Experimental Animals.  2nd ed. Canadian Council on Animal Care, Ottawa, Ontario, Canada. Cocquyt G. Driessen B. Simoens P. 2005. Variability in the eruption of the permanent incisor teeth in sheep. Vet. Rec.  157: 619– 623. https://doi.org/16284330 Google Scholar CrossRef Search ADS PubMed  Colson V. Orgeur P. Foury A. Mormède P. 2006. Consequences of weaning piglets at 21 and 28 d on growth, behaviour and hormonal responses. Appl. Anim. Behav. Sci.  98: 70– 88. Google Scholar CrossRef Search ADS   Delumeau O. Meunier-Salaün M. C. 1995. Effect of early trough familiarity on the creep feeding behavior in suckling piglets and after weaning. Behav. Processes  34: 185– 196. Google Scholar CrossRef Search ADS PubMed  de Passillé A. M. B. Pelletier G. Menard J. 1989. Relationships of weight gain and behavior to digestive organ weight and enzyme activities in piglets. J. Anim. Sci.  67: 2921– 2929. https://doi.org/2480340 Google Scholar CrossRef Search ADS PubMed  Fraser D. 1978. Observations on the behavioral development of suckling and early-weaned piglets during the first 6 weeks after birth. Anim. Behav.  26: 22– 30. Google Scholar CrossRef Search ADS   Fraser D. Feddes J. J. R. Pajor E. A. 1994. The relationship between creep feeding behavior of piglets and adaptation to weaning: Effect of diet quality. Can. J. Anim. Sci.  74: 1– 6. Google Scholar CrossRef Search ADS   Funk C. D. 2001. Prostaglandins and leukotrienes: Advances in eicosanoid biology. Science  294: 1871– 1875. https://doi.org/11729303 Google Scholar CrossRef Search ADS PubMed  Gardner J. M. de Lange C. F. M. Widowski T. M. 2001. Belly nosing in early-weaned pigs is not influenced by diet quality or the presence of milk in the diet. J. Anim. Sci.  79: 73– 80. https://doi.org/11204718 Google Scholar CrossRef Search ADS PubMed  Gonyou H. W. Beltranena E. Whittington D. L. Patience J. F. 1998. The behavior of pigs weaned at 12 and 21 d of age from weaning to market. Can. J. Anim. Sci.  78: 517– 523. Google Scholar CrossRef Search ADS   Gordon K. R. Herring S. W. 1987. Activity patterns within the genioglossus during suckling in domestic dogs and pigs: Interspecific and intraspecific plasticity. Brain Behav. Evol.  30: 249– 262. https://doi.org/3427406 Google Scholar CrossRef Search ADS PubMed  Hale, F. A. 2000. Juvenile dentistry. In Recent Advances in Small Animal Dentistry.  D. T. Carmichael ed. Int. Vet. Inf. Serv., Ithaca, NY. http://www.ivis.org/advances/Dentistry_Carmichael/hale/chapter_frm.asp?LA=1.PDF Accessed Oct. 26, 2009. Herring S. W. 1976. The dynamics of mastication in pigs. Arch. Oral Biol.  21: 473– 480. https://doi.org/823928 Google Scholar CrossRef Search ADS PubMed  Herring S. W. 1977. Mastication and maturity: A longitudinal study in pigs. J. Dent. Res.  56: 1377– 1382. https://doi.org/274463 Google Scholar CrossRef Search ADS PubMed  Herring S. W. 1985. The ontogeny of mammalian mastication. Am. Zool.  25: 339– 349. Google Scholar CrossRef Search ADS   Herring S. W. Anapol F. C. Wineski L. E. 1991. Motor-unit territories in the masseter muscle of infant pigs. Arch. Oral Biol.  36: 867– 873. https://doi.org/1837450 Google Scholar CrossRef Search ADS PubMed  Herring S. W. Scapino R. P. 1973. Physiology and feeding in miniature pigs. J. Morphol.  141: 427– 460. https://doi.org/4760635 Google Scholar CrossRef Search ADS PubMed  Herring S. W. Wineski L. E. 1986. Development of the masseter muscle and oral behavior in the pig. J. Exp. Zool.  237: 191– 207. https://doi.org/3950565 Google Scholar CrossRef Search ADS PubMed  Hessel E. F. Reiners K. Van den Weghe H. F. A. 2006. Socializing piglets before weaning: Effects on behavior of lactating sows, pre- and postweaning behavior, and performance of piglets. J. Anim. Sci.  84: 2847– 2855. https://doi.org/16971588 Google Scholar CrossRef Search ADS PubMed  Hillson, S. 2005. Tooth forms in mammals. Page 129 in Teeth.  S. Hillson ed. Cambridge Univ. Press, Cambridge, UK. Google Scholar CrossRef Search ADS   Huang X. Zhang G. Herring S. W. 1994. Age changes in mastication in the pig. Comp. Biochem. Physiol.  107A: 647– 654. Kakizaki Y. Uchida K. Yamamura K. Yamada Y. 2002. Coordination between the masticatory and tongue muscles as seen with different foods in consistency and in reflex activities during natural chewing. Brain Res.  929: 210– 217. https://doi.org/11864626 Google Scholar CrossRef Search ADS PubMed  Kim J. H. Heo K. N. Odle J. Han I. K. Harrell R. J. 2001. Liquid diets accelerate the growth of early-weaned pigs and the effects are maintained to market weight. J. Anim. Sci.  79: 427– 434. https://doi.org/11219452 Google Scholar CrossRef Search ADS PubMed  Kuehl, R. O. 1994. Statistical Principles of Research Design and Analysis.  Duxbury Press, Belmont, CA. Kuller W. I. Soede N. M. van Beers-Schreurs H. M. G. Langendijk P. Taverne M. A. M. Verheijden J. H. M. Kemp B. 2004. Intermittent suckling: Effects on piglet and sow performance before and after weaning. J. Anim. Sci.  82: 405– 413. Google Scholar CrossRef Search ADS PubMed  Kuller W. I. van Beers-Schreurs H. M. G. Soede N. M. Taverne M. A. M. Kemp B. Verheijden J. H. M. 2007. Addition of chromic oxide to creep feed as a fecal marker for selection of creep feed-eating suckling pigs. Am. J. Vet. Res.  68: 748– 752. https://doi.org/17605610 Google Scholar CrossRef Search ADS PubMed  Langenbach G. E. J. van Eijden T. M. G. J. 2001. Mammalian feeding motor patterns. Am. Zool.  41: 1338– 1351. Lehner, P. N. 1996. Handbook of Ethological Methods.  2nd ed. Cambridge Univ. Press, New York, NY. Leibbrandt V. D. Ewan R. C. Speer V. C. Zimmerman D. R. 1975. Effect of weaning and age at weaning on baby pig performance. J. Anim. Sci.  40: 1077– 1080. Google Scholar CrossRef Search ADS   Macknin M. L. Piedmonte M. Jacobs J. Skibinski C. 2000. Symptoms associated with infant teething: A prospective study. Pediatrics  105: 747– 752. https://doi.org/10742315 Google Scholar CrossRef Search ADS PubMed  Main R. G. Dritz S. S. Tokach M. D. Goodband R. D. Nelssen J. L. 2004. Increasing weaning age improves pig performance in a multisite production system. J. Anim. Sci.  82: 1499– 1507. https://doi.org/15144093 Google Scholar CrossRef Search ADS PubMed  McCracken B. A. Gaskins H. R. Ruwe-Kaiser P. J. Klasing K. C. Jewell D. E. 1995. Diet-dependent and diet-independent metabolic responses underlie growth stasis of pigs at weaning. J. Nutr.  125: 2838– 2845. https://doi.org/7472664 Google Scholar PubMed  McGlone J. J. Anderson D. L. 2002. Synthetic maternal pheromone stimulates feeding behavior and weight gain in weaned pigs. J. Anim. Sci.  80: 3179– 3183. https://doi.org/12542158 Google Scholar CrossRef Search ADS PubMed  Metz J. H. M. Gonyou H. W. 1990. Effect of age and housing conditions on the behavioural and haemolytic reaction of piglets to weaning. Appl. Anim. Behav. Sci.  27: 299– 309. Google Scholar CrossRef Search ADS   Morgan C. A. Lawrence A. B. Chirnside J. Deans L. A. 2001. Can information about solid food be transmitted from one piglet to another? Anim. Sci.  73: 471– 478. Google Scholar CrossRef Search ADS   Olesen L. S. Nygaard C. M. Friend T. H. Bushong D. Knabe D. A. Vestergaard K. S. Vaughan R. K. 1996. Effect of partitioning pens on aggressive behavior of pigs regrouped at weaning. Appl. Anim. Behav. Sci.  46: 167– 174. Google Scholar CrossRef Search ADS   Pajor E. A. Fraser D. Kramer D. L. 1991. Consumption of solid food by suckling piglets: Individual variation and relation to weight gain. Appl. Anim. Behav. Sci.  32: 139– 155. Google Scholar CrossRef Search ADS   Partridge G. C. Fisher J. Gregory H. Prior S. G. 1992. Automated wet feeding of weaner pigs vs. conventional dry diet feeding: Effect on growth rate and food consumption. Anim. Prod.  54: 484. (Abstr.) Pluske J. R. Williams I. H. Aherne F. X. 1996. Maintenance of villous height and crypt depth in piglets by providing continuous nutrition after weaning. Anim. Sci.  62: 131– 144. Google Scholar CrossRef Search ADS   Sandy J. R. 1992. Tooth eruption and orthodontic movement. Br. Dent. J.  172: 141– 149. https://doi.org/1543616 Google Scholar CrossRef Search ADS PubMed  Shapira J. Berenstein-Ajzman G. Engelhard D. Cahan S. Kalickman I. Barak V. 2003. Cytokine levels in gingival crevicular fluid of erupting primary teeth correlated with systemic disturbances accompanying teething. Pediatr. Dent.  25: 441– 448. https://doi.org/14649607 Google Scholar PubMed  Smith B. H. Crummett T. L. Brant K. L. 1994. Ages of eruption of primate teeth: A compendium for aging individuals and comparing life histories. Yearb. Phys. Anthropol.  37: 177– 231. Google Scholar CrossRef Search ADS   Sommer C. Kress M. 2004. Recent findings on how proinflammatory cytokines cause pain: Peripheral mechanisms in inflammatory and neuropathic hyperalgesia. Neurosci. Lett.  361: 184– 187. https://doi.org/15135924 Google Scholar CrossRef Search ADS PubMed  Torrey S. Widowski T. M. 2004. Effect of drinker type and sound stimuli on early-weaned pig performance and behavior. J. Anim. Sci.  82: 2105– 2114. https://doi.org/15309958 Google Scholar CrossRef Search ADS PubMed  Torrey S. Widowski T. M. 2006. Is belly nosing redirected suckling behaviour? Appl. Anim. Behav. Sci.  101: 288– 304. Google Scholar CrossRef Search ADS   Tucker A. L. Widowski T. M. 2009. Normal profiles for deciduous dental eruption in domestic piglets: Effect of sow, litter and piglet characteristics. J. Anim. Sci.  87: 2274– 2281. https://doi.org/19329477 Google Scholar CrossRef Search ADS PubMed  Wake M. Hesketh K. Lucas J. L. 2000. Teething and tooth eruption in infants: A cohort study. Pediatrics  106: 1374– 1379. https://doi.org/11099591 Google Scholar CrossRef Search ADS PubMed  Weary D. M. Jasper J. Hötzel M. J. 2008. Understanding weaning distress. Appl. Anim. Behav. Sci.  110: 24– 41. Google Scholar CrossRef Search ADS   Worobec E. K. Duncan I. J. H. Widowski T. M. 1999. The effects of weaning at 7, 14 and 28 d on piglet behaviour. Appl. Anim. Behav. Sci.  62: 173– 182. Google Scholar CrossRef Search ADS   American Society of Animal Science

Wertz-Lutz, A. E.;Jennings, J. S.;Clapper, J. A.

doi: 10.2527/jas.2009-2447pmid: 20190164

ABSTRACT Previous research demonstrated increased plasma ghrelin concentrations in beef cattle when intake of a high-grain diet was restricted. Two experiments were conducted to determine whether differences in DMI influenced plasma ghrelin concentrations when energy intake was similar but cattle were in either an anabolic or a catabolic state. In Exp. 1, five steers (BW = 592.6 ± 9.3 kg) were offered dietary treatments of 1) 50:50 hay:concentrate (HY) to meet the NEm requirement and were supplied an additional 3.4 Mcal of NEg daily, 2) or a diet composed of 10:90 hay:concentrate but were limit-fed to achieve an energy intake similar to that of the HY steers (LFC). The LFC treatment met the NEm requirement of each steer and supplied 3.6 Mcal of NEg daily. The experiment was conducted as a crossover design composed of 2 21-d periods. In the first period, 2 steers were assigned to the HY treatment and 3 steers were assigned to the LFC treatment. On d 21 after initiation of the dietary treatment, serial blood samples were collected via indwelling jugular catheter, using periods of frequent sampling in which samples were collected at 15-min intervals. The periods of frequent sampling were spread throughout the beginning, middle, and end of the 12-h feeding interval. After the first period, steers were weighed, dietary treatments were switched between steer groups, and intake amounts were recalculated on the basis of the first-period ending BW. The second-period adaptation and sampling were repeated as described for the first period. Plasma samples were assayed for ghrelin, insulin, GH, and NEFA concentrations. Experiment 2 was conducted using the same methodology as Exp. 1, except that steers were in a catabolic state. Five steers (BW = 718.3 ± 12.8 kg) were offered the HY or LFC diet at an amount that would supply 80% of the NEm required to maintain BW. For Exp. 1, energy intake was sufficient to result in similar (P = 0.14) BW gains between treatment groups. Experiment 2 energy intake resulted in a loss of BW that was similar (P = 0.66) between treatment groups. In both experiments, the decreased energy density of the HY diet resulted in greater (P ≤ 0.001) DMI for HY steers compared with LFC steers. Regardless of catabolic or anabolic state, plasma ghrelin, GH, and insulin were similar (P ≥ 0.44) when energy intakes were similar despite differences in DMI between HY and LFC steers. Plasma NEFA concentrations were similar (P ≥ 0.45) between treatment groups in an anabolic state but tended to differ (P = 0.09) as a result of treatment for cattle in the catabolic state. These data are consistent with the hypothesis that quantity of DMI does not influence plasma ghrelin concentrations of steers when energy intake is similar. INTRODUCTION As cattle approach maturity, BW gain slows and shifts toward fat deposition. During this time, feed intake is decreased and feed efficiency is compromised. Numerous physiological factors contribute to the changes in feed intake and body composition. One hormone that may influence feed intake and body composition is ghrelin. Ghrelin is a peptide hormone that is involved in the central nervous system regulation of feed intake and body composition in rodents (Tschöp et al., 2000; Nakazato et al., 2001; Shintani et al., 2001). The receptor to which ghrelin binds is found in hypothalamic, adipose, skeletal muscle, and liver tissues (Wang et al., 2002). Expression of this receptor has been reported to decrease in the hypothalamus and pituitary gland of sheep as they become fatter (Kurose et al., 2005; French et al., 2006). Ghrelin has been reported to alter energy metabolism and favor fat accretion in rodents (Tschöp et al., 2000; Patel et al., 2006). The potential influence of ghrelin on feed intake and body composition is relevant in improving the efficiency of cattle and has resulted in this investigation. Research from our laboratory has demonstrated that ghrelin concentrations are increased in cattle during acute feed deprivation and during prolonged, moderate nutrient restriction that results in decreased BW (Wertz-Lutz et al., 2006, 2008). However, these experiments were not designed to determine whether dietary ingredient composition and DMI, or specific nutrient intake, resulted in differences in plasma ghrelin concentrations. It was hypothesized that regardless of metabolic state, plasma ghrelin concentrations would be similar when energy intakes were similar, despite differences in the quantity of feed consumed. The objective of this experiment was to determine plasma ghrelin concentrations when energy intake was similar but DMI differed in cattle that were in either an anabolic or a catabolic state. MATERIALS AND METHODS Animal procedures were approved by the South Dakota State University Institutional Animal Care and Use Committee. Exp. 1 Animals and Feeding Practices Five Simmental × Angus 2-yr-old, ruminally cannulated steers [initial shrunk BW (SBW) = 592.6 ± 9.3 kg] were used in a crossover design. Steers were adapted to a climate-controlled facility and a specific feeding schedule during a 21-d pretreatment period. Room temperature was maintained at 15°C. Cattle were allowed 16 h of light exposure daily from 0600 to 2200 h. The light-dark cycle was controlled by automation. These conditions mimicked the natural environment to which cattle were accustomed before entering the research facility. Steers were offered feed twice daily (0800 and 2000 h). All mixing and weighing of feed was done in a room separate from the room that housed the cattle. All feed for the morning meal was weighed and aliquoted the evening before as cattle were consuming their evening meal. Likewise, all feed for the evening meal was weighed and aliquoted each morning as cattle were consuming their morning meal. These measures were taken to minimize external stimuli that might trigger the release of ghrelin in anticipation of a meal. Sugino et al. (2002b) have suggested that the increased plasma ghrelin concentration before feeding in meal-fed sheep is the result of a conditioned reflex. However, research from our laboratory demonstrated a gradual increase in plasma ghrelin concentrations from approximately 3 h postfeeding until the next feed offering, indicating that fluctuation in plasma ghrelin concentrations relative to other physiological factors warrants further investigation. Dietary Treatments The forage used in this experiment was a mixed prairie grass hay. A preliminary sample of the hay and concentrates used in Exp. 1 was collected before initiation of the trial. For all ingredients, CP was determined based on Kjeldahl N analysis, and DM was determined (AOAC, 2007). Additionally, fiber analyses (ADF and NDF) were performed on the hay, using the procedures of Goering and Van Soest (1970). The ADF content of the hay was used to estimate percentage of TDN, using the equation [96.35 − (% ADF × 1.15)]. Percentage of TDN was then used to estimate NEm and NEg values for the hay according to Beef NRC (2000) equations. For concentrate ingredients, tabular energy values from Beef NRC (2000) were used in estimating energy of the diet. Nutrient and energy values obtained from analyses of the preliminary samples were used to calculate the desired intake at trial initiation. Throughout the experiment, hay samples were collected weekly, and other dietary ingredients were sampled as a new batch was received. On completion of the experiment, samples collected throughout were analyzed for CP and DM. Additionally, ADF and NDF content was determined for hay samples. Nutrient composition of diets reported in Table 1 is based on the average of the nutritional value of dietary ingredients from samples collected throughout the experiment, and thus reflect actual nutrient composition of the diet throughout the experiment, as opposed to that used to predict intakes at trial initiation. Table 1. Ingredient and nutrient composition of experimental diets1 Item  HY  LFC  Ingredient, % (DM basis)       Prairie grass hay  50.00  10.00   Cracked corn  23.12  65.00   Beet pulp  10.00  10.00   DDGS2  11.00  8.00   Soybean meal  5.25  5.67   Limestone  0.50  1.00   Trace mineral salt3  0.10  0.30   Premix4  0.03  0.03  Nutrient composition, % (DM basis)       Exp. 1        CP,5 %  13.0  12.7    NDF, %  45.3  22.5    NRC NEm,6 Mcal/kg  1.77  2.26    NRC NEg,6 Mcal/kg  0.98  1.36   Exp. 2        CP,5 %  11.6  12.0    NDF, %  43.0  22.1    NRC NEm,6 Mcal/kg  1.82  2.27    NRC NEg,6 Mcal/kg  1.02  1.37  Item  HY  LFC  Ingredient, % (DM basis)       Prairie grass hay  50.00  10.00   Cracked corn  23.12  65.00   Beet pulp  10.00  10.00   DDGS2  11.00  8.00   Soybean meal  5.25  5.67   Limestone  0.50  1.00   Trace mineral salt3  0.10  0.30   Premix4  0.03  0.03  Nutrient composition, % (DM basis)       Exp. 1        CP,5 %  13.0  12.7    NDF, %  45.3  22.5    NRC NEm,6 Mcal/kg  1.77  2.26    NRC NEg,6 Mcal/kg  0.98  1.36   Exp. 2        CP,5 %  11.6  12.0    NDF, %  43.0  22.1    NRC NEm,6 Mcal/kg  1.82  2.27    NRC NEg,6 Mcal/kg  1.02  1.37  1HY = a 50:50 hay:concentrate diet (DM basis). LFC = a 10:90 hay:concentrate diet (DM basis). Intake of the 2 diets was manipulated to supply the desired amount of energy intake for Exp. 1, where cattle were fed to achieve an anabolic state, and Exp. 2, where cattle were fed to achieve a catabolic state. 2Dried distillers grains with solubles. 3NaCl, 94.0 to 98.5%; Zn, 0.35%; Fe, 0.20%; Co, 0.005%; Mn, 0.20%; Cu, 0.30%; I, 0.007%. 4Formulated to supply a final dietary concentration of 30 g/t of Rumensin (Elanco Animal Health, Greenfield, IN), 20 mg/kg of Zn, 25 IU/kg of vitamin E, 2,600 IU/kg of vitamin A, and 290 IU/kg of vitamin D. 5Kjeldahl N analysis was performed on all ingredients sampled throughout the experiment and used to estimate CP. 6For forage samples, laboratory analyses of ADF and NDF were entered into the Beef NRC (2000) model feed composition tables, and TDN of the forage was calculated from the ADF content using the equation [96.35 − (% ADF × 1.15)]. The TDN value for forages was entered into the Beef NRC (2000) model feed composition table, which calculated NEm and NEg values of the forage. For concentrate feeds, tabular Beef NRC (2000) values were used for estimating energy. For all dietary ingredients, tabular values for effective NDF were used. View Large Table 1. Ingredient and nutrient composition of experimental diets1 Item  HY  LFC  Ingredient, % (DM basis)       Prairie grass hay  50.00  10.00   Cracked corn  23.12  65.00   Beet pulp  10.00  10.00   DDGS2  11.00  8.00   Soybean meal  5.25  5.67   Limestone  0.50  1.00   Trace mineral salt3  0.10  0.30   Premix4  0.03  0.03  Nutrient composition, % (DM basis)       Exp. 1        CP,5 %  13.0  12.7    NDF, %  45.3  22.5    NRC NEm,6 Mcal/kg  1.77  2.26    NRC NEg,6 Mcal/kg  0.98  1.36   Exp. 2        CP,5 %  11.6  12.0    NDF, %  43.0  22.1    NRC NEm,6 Mcal/kg  1.82  2.27    NRC NEg,6 Mcal/kg  1.02  1.37  Item  HY  LFC  Ingredient, % (DM basis)       Prairie grass hay  50.00  10.00   Cracked corn  23.12  65.00   Beet pulp  10.00  10.00   DDGS2  11.00  8.00   Soybean meal  5.25  5.67   Limestone  0.50  1.00   Trace mineral salt3  0.10  0.30   Premix4  0.03  0.03  Nutrient composition, % (DM basis)       Exp. 1        CP,5 %  13.0  12.7    NDF, %  45.3  22.5    NRC NEm,6 Mcal/kg  1.77  2.26    NRC NEg,6 Mcal/kg  0.98  1.36   Exp. 2        CP,5 %  11.6  12.0    NDF, %  43.0  22.1    NRC NEm,6 Mcal/kg  1.82  2.27    NRC NEg,6 Mcal/kg  1.02  1.37  1HY = a 50:50 hay:concentrate diet (DM basis). LFC = a 10:90 hay:concentrate diet (DM basis). Intake of the 2 diets was manipulated to supply the desired amount of energy intake for Exp. 1, where cattle were fed to achieve an anabolic state, and Exp. 2, where cattle were fed to achieve a catabolic state. 2Dried distillers grains with solubles. 3NaCl, 94.0 to 98.5%; Zn, 0.35%; Fe, 0.20%; Co, 0.005%; Mn, 0.20%; Cu, 0.30%; I, 0.007%. 4Formulated to supply a final dietary concentration of 30 g/t of Rumensin (Elanco Animal Health, Greenfield, IN), 20 mg/kg of Zn, 25 IU/kg of vitamin E, 2,600 IU/kg of vitamin A, and 290 IU/kg of vitamin D. 5Kjeldahl N analysis was performed on all ingredients sampled throughout the experiment and used to estimate CP. 6For forage samples, laboratory analyses of ADF and NDF were entered into the Beef NRC (2000) model feed composition tables, and TDN of the forage was calculated from the ADF content using the equation [96.35 − (% ADF × 1.15)]. The TDN value for forages was entered into the Beef NRC (2000) model feed composition table, which calculated NEm and NEg values of the forage. For concentrate feeds, tabular Beef NRC (2000) values were used for estimating energy. For all dietary ingredients, tabular values for effective NDF were used. View Large During the adaptation period, it was established that ad libitum intake of the steer with the least DMI on the 50:50 hay:concentrate (HY) diet was enough feed to meet its NEm requirement and an additional 3.5 Mcal/d of NEg. It was assumed that physical fill was limiting intake of this animal, and the intake of that animal was therefore used as the benchmark to calculate a common energy intake for all steers. Intake for each of the remaining steers was adjusted such that all steers consumed 3.5 Mcal of NEg daily, in addition to that required to meet their individual NEm requirement. Dietary treatments therefore were 1) the HY diet (DM basis), which was offered at an amount that would meet the NEm requirement and supply an additional 3.5 Mcal of NEg daily for each steer, or 2) a diet composed of 10:90 hay:concentrate (DM basis) but limit-fed (LFC) to achieve a caloric intake similar to that of the HY steers (Table 1). At trial initiation, hand calculations were used to determine how much feed to offer each steer. Steers were weighed before the morning feeding, and this BW was multiplied by 0.96 to obtain an SBW. The SBW was then multiplied by 0.891 to determine empty BW. The equation 0.077 × empty BW0.75 (kg) was used to estimate the calories needed to maintain BW (NEm, Mcal/d; Beef NRC, 2000). This NEm requirement (Mcal/d) was then divided by the NEm density of the diet (Mcal/kg) to determine the amount of feed (kg/d) necessary to meet the maintenance requirement of each particular steer based on its own BW. The amount of feed needed to supply the additional 3.5 Mcal/d of NEg was calculated by dividing 3.5 Mcal/d by the NEg density (Mcal/kg) of each test diet. The quantity of feed needed to meet the energy requirement of each steer was divided into 2 equal aliquots. One aliquot was offered at 0800 h and the second was offered at 2000 h. This amount of feed was sufficient for each steer to be in an anabolic state and gain BW. On completion of the experiment, the Beef NRC (2000) computer model level 1 was used to evaluate the dietary treatments. For each steer, average SBW for the 21-d period was calculated and used in the prediction of nutrient requirements and intake. Additionally, average nutrient values predicted from laboratory analyses of ingredients collected throughout the experiment were entered into the Beef NRC (2000) feed composition table. Simmental × Angus was used as the breed, and the environmental condition of the climate-controlled facility and the use of an ionophore were included. Cattle were not implanted. These variables were used to predict the NEm requirement, NEm intake, NEg intake, MP requirement, and MP intake of the test diets. Energy-allowable ADG and MP-allowable ADG also were predicted using the computer model. Sampling Period 1 For period 1, two steers were assigned to the HY treatment and 3 steers were assigned to the LFC treatment. Steers were allowed 21 d to acclimate to the assigned treatment before being sampled. On d 20 of treatment acclimation, steers were fitted with an indwelling jugular catheter and allowed a minimum of 12 h to recover before the sampling period was initiated. On d 21, blood samples were collected via the indwelling jugular catheter from 1 h before feed offering in the morning to 1 h before feed offering in the evening. Blood samples were collected at 15-min intervals from 0700 to 1145 h, 1300 to 1345 h, 1600 to 1645 h, and 1800 to 1845 h. Crossover and Sampling Period 2 On the day after sampling period 1, steers were weighed before the morning feeding. This BW was multiplied by 0.96 to obtain an SBW for calculating energy requirements. The SBW was then multiplied by 0.891 to determine empty BW. Dietary treatments were switched between steer groups, and feed intake required to meet the NEm requirement and supply an additional 3.5 Mcal/d of NEg was recalculated as described for period 1 but on the basis of steer empty BW at the end of sampling period 1. Steers were adapted to the change in dietary ingredients for 20 d, at which point an indwelling jugular catheter was again inserted, and sampling period 2 was conducted on d 21 of the period, as described for period 1. Exp. 2 Animals Five Simmental × Angus 3-year-old, ruminally cannulated steers (initial BW 718.3 ± 12.8 kg) were used in a crossover design. Experiment 2 was conducted using the same animals as in Exp. 1. The cattle used in Exp. 2 were housed in a climate-controlled facility. Room temperature and light-dark exposure were similar to those described for Exp. 1. Dietary Treatments Steers were offered feed twice daily (0800 and 2000 h). Feed mixing and delivery practices described in Exp. 1 were adhered to in Exp. 2. Dietary treatments differed in ingredient composition and were HY (DM basis) or LFC (DM basis; Table 1). With the preliminary calculations described below, the amount of each diet offered to a steer was 80% of that required to meet the NEm requirement to maintain BW. Thus, cattle in this experiment were in a catabolic state. Although ingredient composition of the diets used in Exp. 2 was the same as that used in Exp. 1, the sources of the hay and concentrates used were different because the experiments were conducted in 2 different years. Thus, the hay and concentrates used in Exp. 2 were analyzed for chemical composition. The forage used in this experiment was a mixed prairie grass hay. A preliminary sample of the hay and concentrates used in Exp. 2 was collected before the trial was initiated. Chemical analyses and estimates of energy values for these ingredients were completed the same as described for Exp. 1. Nutrient and energy values obtained from analyses of the preliminary samples were used to calculate the desired intake at trial initiation. Throughout the experiment, hay samples were collected weekly, and other dietary ingredients were sampled as a new batch was received. On completion of Exp. 2, chemical analyses of the ingredients sampled throughout the experiment were conducted as described for Exp. 1. Nutrient composition of diets reported in Table 1 is based on the average nutritional value of dietary ingredients from samples collected throughout the experiment. To determine how much feed to offer each steer, steers were weighed before the morning feeding, and this BW was multiplied by 0.96 to obtain an SBW. The SBW was then multiplied by 0.891 to determine the empty BW. The NEm requirement (Mcal/d) of each animal was estimated, using the equation 0.077 × empty BW0.75 (kg) (Beef NRC, 2000). This NEm requirement (Mcal/d) was multiplied by 0.80 and was then divided by the NEm density of the diet (Mcal/kg) to determine the amount of feed (kg/d) necessary to meet 80% of the NEm of each particular steer based on its own BW. This amount of feed was not adequate to maintain BW, and steers were expected to lose BW. On completion of the experiment, the Beef NRC (2000) computer model level 1 was used to evaluate the dietary treatments. For each steer, the average SBW for the 21-d period was calculated and used in the prediction of nutrient requirements and intake. Additionally, nutrient values predicted from laboratory analyses of ingredients collected throughout the experiment were entered into the Beef NRC (2000) feed composition table. Simmental × Angus was used as the breed, and environmental condition of the climate-controlled facility and use of an ionophore were included. Cattle were not implanted. These variables were used to predict the NEm requirement, NEm intake, MP requirement, and MP intake. Sampling Period 1 For period 1, three steers were assigned to the HY treatment and two steers were assigned to the LFC treatment. Steers were acclimated to the climate-controlled facility and to the respective diet for 21 d before sampling was initiated. On d 20 of the acclimation period, steers were fitted with an indwelling jugular catheter and allowed a minimum of 12 h to recover before the sampling period was initiated. On d 21, blood samples were collected via the indwelling jugular catheter at 15-min intervals from 0700 to 1145 h, 1300 to 1345 h, 1600 to 1645 h, and 1800 to 1845 h. Crossover and Sampling Period 2 On the morning after sampling period 1, steers were weighed before feeding and dietary treatments were switched between steer groups. Dry matter intake was recalculated as described for period 1 but on the basis of steer BW at the end of sampling period 1. Steers were adapted to the change in dietary ingredients for 20 d, at which point an indwelling jugular catheter was again inserted and steers were sampled again as described for period 1. Hormone Concentration Data Blood collection and plasma separation were similar for Exp. 1 and 2 and are described below. Two 10-mL aliquots of blood were collected into a glass tube containing K3EDTA (12.15 mg, supplied by a 15% solution) for plasma separation. Tubes were placed on ice and were then centrifuged at 4°C for 20 min at 1,100 × g within 1 h of collection. A 1.0-mL aliquot of plasma was treated with 50 μL of 1 N HCl and 100 µg of phenylmethylsulfonyl fluoride (Sigma-Aldrich, St. Louis, MO) to preserve the integrity of the octanoyl moiety of ghrelin. These plasma samples were stored at −20°C for subsequent measurement of ghrelin. The remaining plasma was separated into 1.0-mL aliquots and stored at −20°C for subsequent analyses of GH, NEFA, and insulin (INS). Plasma hormone and metabolite analyses were performed as described previously by Wertz-Lutz et al. (2008). Hormone and metabolite analyses were performed on samples collected at 15-min intervals. Subsequent to analyses, average hourly hormone or metabolite concentrations were determined using the 4 samples collected within the hour. Statistical Analyses Statistical analyses were similar for Exp. 1 and 2 and are described below. Each experiment was analyzed separately because the experiments were conducted in different years. Plasma ghrelin, GH, INS, and NEFA data were analyzed as repeated measures using the MIXED procedure (SAS Inst. Inc, Cary, NC). Fixed variables included dietary treatment, sampling time relative to feeding, and their interaction. Steer within period by dietary treatment was used as the random variable. The repeated statement was used with the autoregressive-1 covariance structure and the Kenward-Roger method of calculating SE. The autoregressive-1 covariance structure was chosen because it resulted in the most robust fit for all measured variables. The Kenward-Roger method calculates SE by accounting for missing values. Differences in hormone and metabolite concentrations that resulted from dietary treatment, sampling time relative to feed offering, or their interaction were separated using least squares means with the PDIFF option of SAS. Growth performance, DMI, nutrient intake, and variables predicted using the Beef NRC (2000) computer model were analyzed as a crossover design accounting for variation that resulted from steer, period, and dietary treatment. Data were analyzed using the MIXED procedure of SAS. Dietary treatment was a fixed variable, whereas period and steer were random variables. Differences in growth performance, DMI, or nutrient intake that resulted from dietary treatment were separated using least squares means with the PDIFF option of SAS. Mean differences were considered significant at P ≤ 0.05. RESULTS Exp. 1 Growth Performance Evaluations of nutrient intake and growth performance are reported in Table 2. By experimental design, a greater DMI (P < 0.001) was required by HY steers compared with LFC steers (9.5 vs. 7.3 kg; SEM = 0.09 kg) to achieve the desired energy intake. Using the Beef NRC (2000) computer model to evaluate the diet, we predicted NEg to be 3.4 vs. 3.6 Mcal/d (SEM 0.04 Mcal/d) for HY compared with LFC. Energy intake in excess of that needed to maintain BW was predicted to support 0.68 kg/d of BW gain for HY compared with 0.72 kg/d of BW gain for LFC (SEM 0.01 kg/d). Although calculations using preliminary nutrient analyses of ingredients indicated that energy intake was similar between treatment groups, energy-allowable BW gain and NEg intake predicted using average period BW and ingredient analyses in the Beef NRC (2000) computer model differed (P ≤ 0.01) between treatment groups. This difference is an artifact of the small SE that resulted from the way in which treatments were assigned at trial initiation. When total NEm intake was expressed as a percentage of that required to maintain BW, energy intake was similar (P = 0.15) between treatments [158 vs. 157% (SEM 0.68%) for HY vs. LFC, respectively]. Initial SBW was similar between treatment groups; however, final SBW was greater (P = 0.004) for HY steers. Differences in final SBW may be attributed to differences in fill because intakes were not standardized between treatments before BW was recorded. Steers in both dietary treatment groups were in positive energy balance throughout the experiment, as indicated by a positive change in BW during the 21-d period. Shrunk BW change was similar (P = 0.14) for HY and LFC steers [1.67 vs. 0.73 kg/d (SEM 0.46 kg/d), respectively]. For LFC steers, ADG was similar to that predicted by the Beef NRC (2000) computer model (0.72 kg/d), whereas ADG for HY steers did not reflect that predicted by the Beef NRC (2000) computer model (0.68 kg/d), which supports the view that the differences in final SBW were attributable to differences in fill. Diets were not formulated to be isonitrogenous. Thus, MP intake differed (P < 0.001) as a result of dietary treatment. However, for either dietary treatment, MP intake was in excess of the amount needed to support the energy-allowable BW gain predicted by the Beef NRC (2000) computer model. Table 2. Growth performance characteristics of steers consuming similar energy from a hay-based diet (HY) or a limit-fed concentrate diet (LFC) Item  HY  LFC  SE  P-value  Exp. 11           Initial SBW,2 kg  578.6  581.3  11.78  ≤0.84   Final SBW, kg  614.2  596.1  2.23  ≤0.004   SBW change, kg/d  1.67  0.73  0.46  ≤0.14   DMI, kg/d  9.5  7.3  0.09  ≤0.001   NRC NEm intake,3 Mcal/d  10.6  10.5  0.11  ≤0.52   NRC NEg intake,3 Mcal/d  3.4  3.6  0.04  ≤0.01   ME-allowable ADG,3 kg/d  0.68  0.72  0.01  ≤0.003   NRC MP requirement,3 g/d  626.0  633.0  2.66  ≤0.08   NRC MP intake,3 g/d  1,091.3  788.1  12.55  ≤0.001   MP-allowable ADG,3 kg/d  2.59  1.36  0.08  ≤0.001  Exp. 24           Initial SBW, kg  730.8  728.7  11.78  ≤0.87   Final SBW, kg  710.5  703.4  2.02  ≤0.04   SBW change, kg  −20.3  −25.2  10.03  ≤0.66   DMI, kg/d  5.4  4.3  0.06  ≤0.001   NRC NEm intake,3 Mcal/d  9.8  9.7  0.09  ≤0.39   NRC NEm requirement,3 Mcal/d  12.3  12.2  0.09  ≤0.46   NRC MP requirement,3 g/d  528.3  525.7  3.66  ≤0.52   NRC MP intake,3 g/d  589.9  440.9  6.76  ≤0.001  Item  HY  LFC  SE  P-value  Exp. 11           Initial SBW,2 kg  578.6  581.3  11.78  ≤0.84   Final SBW, kg  614.2  596.1  2.23  ≤0.004   SBW change, kg/d  1.67  0.73  0.46  ≤0.14   DMI, kg/d  9.5  7.3  0.09  ≤0.001   NRC NEm intake,3 Mcal/d  10.6  10.5  0.11  ≤0.52   NRC NEg intake,3 Mcal/d  3.4  3.6  0.04  ≤0.01   ME-allowable ADG,3 kg/d  0.68  0.72  0.01  ≤0.003   NRC MP requirement,3 g/d  626.0  633.0  2.66  ≤0.08   NRC MP intake,3 g/d  1,091.3  788.1  12.55  ≤0.001   MP-allowable ADG,3 kg/d  2.59  1.36  0.08  ≤0.001  Exp. 24           Initial SBW, kg  730.8  728.7  11.78  ≤0.87   Final SBW, kg  710.5  703.4  2.02  ≤0.04   SBW change, kg  −20.3  −25.2  10.03  ≤0.66   DMI, kg/d  5.4  4.3  0.06  ≤0.001   NRC NEm intake,3 Mcal/d  9.8  9.7  0.09  ≤0.39   NRC NEm requirement,3 Mcal/d  12.3  12.2  0.09  ≤0.46   NRC MP requirement,3 g/d  528.3  525.7  3.66  ≤0.52   NRC MP intake,3 g/d  589.9  440.9  6.76  ≤0.001  1The HY (50:50 hay:concentrate) diet was offered at an amount that would meet the NEm requirement of each animal and supplied an additional 3.4 Mcal/d of NEg. The LFC (10:90 hay:concentrate) diet was limit-fed to meet the NEm requirement of each animal and supplied an additional 3.6 Mcal/d of NEg. Both treatment groups in Exp. 1 were in positive energy balance. 2Shrunk BW (SBW) = full BW × 0.96. 3Predicted using the Beef NRC (2000) model level 1 with average shrunk BW for a 21-d period and nutrient composition of dietary ingredients calculated from nutrient analyses. 4The HY (50:50 hay:concentrate) diet was offered at an amount that would supply 80% of the NEm requirement of the animal. The LFC (10:90 hay:concentrate) diet was limit-fed to achieve a similar caloric intake as the HY treatment. Cattle in Exp. 2 were in negative energy balance. View Large Table 2. Growth performance characteristics of steers consuming similar energy from a hay-based diet (HY) or a limit-fed concentrate diet (LFC) Item  HY  LFC  SE  P-value  Exp. 11           Initial SBW,2 kg  578.6  581.3  11.78  ≤0.84   Final SBW, kg  614.2  596.1  2.23  ≤0.004   SBW change, kg/d  1.67  0.73  0.46  ≤0.14   DMI, kg/d  9.5  7.3  0.09  ≤0.001   NRC NEm intake,3 Mcal/d  10.6  10.5  0.11  ≤0.52   NRC NEg intake,3 Mcal/d  3.4  3.6  0.04  ≤0.01   ME-allowable ADG,3 kg/d  0.68  0.72  0.01  ≤0.003   NRC MP requirement,3 g/d  626.0  633.0  2.66  ≤0.08   NRC MP intake,3 g/d  1,091.3  788.1  12.55  ≤0.001   MP-allowable ADG,3 kg/d  2.59  1.36  0.08  ≤0.001  Exp. 24           Initial SBW, kg  730.8  728.7  11.78  ≤0.87   Final SBW, kg  710.5  703.4  2.02  ≤0.04   SBW change, kg  −20.3  −25.2  10.03  ≤0.66   DMI, kg/d  5.4  4.3  0.06  ≤0.001   NRC NEm intake,3 Mcal/d  9.8  9.7  0.09  ≤0.39   NRC NEm requirement,3 Mcal/d  12.3  12.2  0.09  ≤0.46   NRC MP requirement,3 g/d  528.3  525.7  3.66  ≤0.52   NRC MP intake,3 g/d  589.9  440.9  6.76  ≤0.001  Item  HY  LFC  SE  P-value  Exp. 11           Initial SBW,2 kg  578.6  581.3  11.78  ≤0.84   Final SBW, kg  614.2  596.1  2.23  ≤0.004   SBW change, kg/d  1.67  0.73  0.46  ≤0.14   DMI, kg/d  9.5  7.3  0.09  ≤0.001   NRC NEm intake,3 Mcal/d  10.6  10.5  0.11  ≤0.52   NRC NEg intake,3 Mcal/d  3.4  3.6  0.04  ≤0.01   ME-allowable ADG,3 kg/d  0.68  0.72  0.01  ≤0.003   NRC MP requirement,3 g/d  626.0  633.0  2.66  ≤0.08   NRC MP intake,3 g/d  1,091.3  788.1  12.55  ≤0.001   MP-allowable ADG,3 kg/d  2.59  1.36  0.08  ≤0.001  Exp. 24           Initial SBW, kg  730.8  728.7  11.78  ≤0.87   Final SBW, kg  710.5  703.4  2.02  ≤0.04   SBW change, kg  −20.3  −25.2  10.03  ≤0.66   DMI, kg/d  5.4  4.3  0.06  ≤0.001   NRC NEm intake,3 Mcal/d  9.8  9.7  0.09  ≤0.39   NRC NEm requirement,3 Mcal/d  12.3  12.2  0.09  ≤0.46   NRC MP requirement,3 g/d  528.3  525.7  3.66  ≤0.52   NRC MP intake,3 g/d  589.9  440.9  6.76  ≤0.001  1The HY (50:50 hay:concentrate) diet was offered at an amount that would meet the NEm requirement of each animal and supplied an additional 3.4 Mcal/d of NEg. The LFC (10:90 hay:concentrate) diet was limit-fed to meet the NEm requirement of each animal and supplied an additional 3.6 Mcal/d of NEg. Both treatment groups in Exp. 1 were in positive energy balance. 2Shrunk BW (SBW) = full BW × 0.96. 3Predicted using the Beef NRC (2000) model level 1 with average shrunk BW for a 21-d period and nutrient composition of dietary ingredients calculated from nutrient analyses. 4The HY (50:50 hay:concentrate) diet was offered at an amount that would supply 80% of the NEm requirement of the animal. The LFC (10:90 hay:concentrate) diet was limit-fed to achieve a similar caloric intake as the HY treatment. Cattle in Exp. 2 were in negative energy balance. View Large Plasma Hormone and Metabolite Concentrations The interaction of time relative to feeding and dietary treatment was not significant (P ≥ 0.84). Therefore, the main effects have been reported separately. Plasma ghrelin, GH, INS, and NEFA concentrations did not differ as a result of dietary treatments (Table 3). However, plasma ghrelin and GH concentrations differed (P < 0.001) as a result of time relative to feeding (Figure 1A and 1B). Plasma concentrations of both ghrelin and GH decreased subsequent to feeding. Plasma ghrelin concentrations were decreased (P ≤ 0.001) and remained less for 3 h postfeeding. By 5 h postfeeding, plasma ghrelin concentrations had returned to prefeeding concentrations. Plasma GH concentrations decreased (P ≤ 0.001) postfeeding, reaching a nadir at 2 h after feed was offered. By 5 h postfeeding, plasma GH concentrations were similar to preprandial concentrations. Plasma INS and NEFA concentrations did not differ (P ≥ 0.45) as a result of time relative to feeding (Figure 1C and 1D) for cattle in a positive energy balance. Table 3. Plasma hormone and metabolite concentrations for cattle consuming a similar amount of dietary energy from hay (HY) or limit-fed concentrate (LFC) Item  HY  LFC  SE  P-value  Exp. 11           Plasma ghrelin, pg/mL  116.0  105.7  10.26  ≤0.81   Plasma GH, ng/mL  4.2  5.1  1.07  ≤0.44   Plasma insulin, ng/mL  1.3  1.4  0.22  ≤0.76   Plasma NEFA, μEq/L  76.9  68.2  10.91  ≤0.45  Exp. 22           Plasma ghrelin, pg/mL  110.7  117.8  37.42  ≤0.85   Plasma GH, ng/mL  2.3  2.5  0.42  ≤0.66   Plasma insulin, ng/mL  1.2  1.0  0.31  ≤0.57   Plasma NEFA, μEq/L  153.2  123.6  15.55  ≤0.09  Item  HY  LFC  SE  P-value  Exp. 11           Plasma ghrelin, pg/mL  116.0  105.7  10.26  ≤0.81   Plasma GH, ng/mL  4.2  5.1  1.07  ≤0.44   Plasma insulin, ng/mL  1.3  1.4  0.22  ≤0.76   Plasma NEFA, μEq/L  76.9  68.2  10.91  ≤0.45  Exp. 22           Plasma ghrelin, pg/mL  110.7  117.8  37.42  ≤0.85   Plasma GH, ng/mL  2.3  2.5  0.42  ≤0.66   Plasma insulin, ng/mL  1.2  1.0  0.31  ≤0.57   Plasma NEFA, μEq/L  153.2  123.6  15.55  ≤0.09  1The HY (50:50 hay:concentrate) diet was offered at an amount that would meet the NEm requirement of each animal and supply an additional 3.4 Mcal/d of NEg, as determined using the Beef NRC (2000) model. The LFC (10:90 hay:concentrate) diet was limit-fed to meet the NEm requirement for BW maintenance for each steer and supplied an additional 3.6 Mcal/d of NEg as determined using the Beef NRC (2000) model. Both treatment groups in Exp. 1 were in positive energy balance. 2The HY (50:50 hay:concentrate) diet was offered at an amount that would supply 80% of the NEm requirement of each animal as determined using the Beef NRC (2000) model. The LFC (10:90 hay:concentrate) diet was limit-fed to supply 80% of the NEm requirement of each steer, as determined using the Beef NRC (2000) model. Cattle in Exp. 2 were in negative energy balance. View Large Table 3. Plasma hormone and metabolite concentrations for cattle consuming a similar amount of dietary energy from hay (HY) or limit-fed concentrate (LFC) Item  HY  LFC  SE  P-value  Exp. 11           Plasma ghrelin, pg/mL  116.0  105.7  10.26  ≤0.81   Plasma GH, ng/mL  4.2  5.1  1.07  ≤0.44   Plasma insulin, ng/mL  1.3  1.4  0.22  ≤0.76   Plasma NEFA, μEq/L  76.9  68.2  10.91  ≤0.45  Exp. 22           Plasma ghrelin, pg/mL  110.7  117.8  37.42  ≤0.85   Plasma GH, ng/mL  2.3  2.5  0.42  ≤0.66   Plasma insulin, ng/mL  1.2  1.0  0.31  ≤0.57   Plasma NEFA, μEq/L  153.2  123.6  15.55  ≤0.09  Item  HY  LFC  SE  P-value  Exp. 11           Plasma ghrelin, pg/mL  116.0  105.7  10.26  ≤0.81   Plasma GH, ng/mL  4.2  5.1  1.07  ≤0.44   Plasma insulin, ng/mL  1.3  1.4  0.22  ≤0.76   Plasma NEFA, μEq/L  76.9  68.2  10.91  ≤0.45  Exp. 22           Plasma ghrelin, pg/mL  110.7  117.8  37.42  ≤0.85   Plasma GH, ng/mL  2.3  2.5  0.42  ≤0.66   Plasma insulin, ng/mL  1.2  1.0  0.31  ≤0.57   Plasma NEFA, μEq/L  153.2  123.6  15.55  ≤0.09  1The HY (50:50 hay:concentrate) diet was offered at an amount that would meet the NEm requirement of each animal and supply an additional 3.4 Mcal/d of NEg, as determined using the Beef NRC (2000) model. The LFC (10:90 hay:concentrate) diet was limit-fed to meet the NEm requirement for BW maintenance for each steer and supplied an additional 3.6 Mcal/d of NEg as determined using the Beef NRC (2000) model. Both treatment groups in Exp. 1 were in positive energy balance. 2The HY (50:50 hay:concentrate) diet was offered at an amount that would supply 80% of the NEm requirement of each animal as determined using the Beef NRC (2000) model. The LFC (10:90 hay:concentrate) diet was limit-fed to supply 80% of the NEm requirement of each steer, as determined using the Beef NRC (2000) model. Cattle in Exp. 2 were in negative energy balance. View Large Figure 1. View largeDownload slide Experiment 1: Plasma hormone and metabolite concentrations relative to feed offering for steers in a positive energy state. Bars at each time point represent 4 samples collected at 15-min intervals during that hour and averaged after sample analyses. The arrow indicates the time at which feed was offered in the morning. Feed also was offered at 2000 h in the evening. The 50:50 hay:concentrate treatment was fed to meet the NEm requirement and supplied an additional 3.4 Mcal/d of NEg, as calculated based on individual steer BW using Beef NRC (2000) equations. The 10:90 hay:concentrate treatment was limit-fed to meet the NEm requirement and supplied an additional 3.6 Mcal/d of NEg, as calculated based on individual steer BW using the Beef NRC (2000) equations. Means for plasma ghrelin or GH concentrations that lack a common letter (a–e) differ (P ≤ 0.001) as a result of sampling time relative to feeding. Plasma NEFA concentrations (P = 0.25) and plasma insulin concentrations (P = 0.12) did not differ as a result of time relative to feeding for cattle in positive energy balance. Figure 1. View largeDownload slide Experiment 1: Plasma hormone and metabolite concentrations relative to feed offering for steers in a positive energy state. Bars at each time point represent 4 samples collected at 15-min intervals during that hour and averaged after sample analyses. The arrow indicates the time at which feed was offered in the morning. Feed also was offered at 2000 h in the evening. The 50:50 hay:concentrate treatment was fed to meet the NEm requirement and supplied an additional 3.4 Mcal/d of NEg, as calculated based on individual steer BW using Beef NRC (2000) equations. The 10:90 hay:concentrate treatment was limit-fed to meet the NEm requirement and supplied an additional 3.6 Mcal/d of NEg, as calculated based on individual steer BW using the Beef NRC (2000) equations. Means for plasma ghrelin or GH concentrations that lack a common letter (a–e) differ (P ≤ 0.001) as a result of sampling time relative to feeding. Plasma NEFA concentrations (P = 0.25) and plasma insulin concentrations (P = 0.12) did not differ as a result of time relative to feeding for cattle in positive energy balance. Exp. 2 Growth Performance Evaluations of nutrient intake and growth performance are reported in Table 2. The NEm intake predicted using the Beef NRC (2000) computer model was similar (P = 0.39) between treatment groups [9.8 and 9.7 Mcal of NEm/d (SEM 0.09 Mcal of NEm/d) for HY and LFC, respectively). A greater DMI (P < 0.001) was required by HY steers compared with LFC steers to achieve the desired energy intake. The amount of NEm consumed was less than that required to maintain BW and, as a result, steers in both treatment groups were in negative energy balance throughout the experiment and lost BW. The net change in BW during the 21-d period [−20.3 and −25.2 kg (SEM 10.03 kg) for HY and LFC steers, respectively] did not differ (P = 0.66) as a result of dietary treatment. Evaluation of the treatments using the Beef NRC (2000) computer model predicted that NEm intake was 80% of that needed to maintain BW [79.7 vs. 79.5% of NEm requirement (SEM 0.36%) for HY vs. LFC, respectively]. Diets were not formulated to be isonitrogenous. As a result, MP intake predicted using the Beef NRC (2000) computer model indicated that MP intake differed (P ≤ 0.001) as a result of dietary treatment. The HY steers consumed MP adequate to meet the requirement for maintenance of BW (112% of the requirements for BW maintenance), whereas MP intake for LFC steers was less than that required for BW maintenance (84% of the requirement for BW maintenance). The Beef NRC (2000) computer model does not predict MP requirements for cattle losing BW, as was the case in this experiment. Plasma Hormone and Metabolite Concentrations The interaction of time relative to feeding and dietary treatment was not significant (P ≥ 0.48). The main effects have been reported separately. Plasma ghrelin, GH, and INS concentrations did not differ (P ≥ 0.57) as a result of dietary treatment (Table 3). Plasma NEFA concentrations (P = 0.09) tended to be greater for HY compared with LFC steers. Plasma ghrelin, GH, INS, and NEFA concentrations differed (P < 0.05) as a result of time relative to feeding. Plasma concentrations of both ghrelin and GH decreased (P ≤ 0.05) subsequent to feeding (Figure 2A and 2B). Plasma ghrelin concentrations reached a nadir at 1 h postfeeding. By 3 h postfeeding, plasma ghrelin concentrations had returned to preprandial concentrations and remained similar throughout the remainder of the sampling period. Plasma GH concentrations reached a nadir 1 h postfeeding but returned to prefeeding concentrations by 3 h postfeeding. Plasma INS concentrations were decreased (P ≤ 0.05) at 5 h postfeeding but remained similar at other sampling times (Figure 2C). Plasma NEFA concentrations were increased (P ≤ 0.05) before feeding times and reached a nadir between 2 and 3 h postfeeding (Figure 2D). Figure 2. View largeDownload slide Experiment 2: Plasma hormone and metabolite concentrations relative to feed offering for steers in negative energy balance. Bars at each time point represent the concentration of 4 samples collected at 15-min intervals during that hour and averaged after analyses. The arrow indicates the time at which feed was offered in the morning. An equal aliquot of fed was also offered at 2000 h. The 50:50 hay:concentrate treatment was fed to meet 80% of the daily NEm requirement, as calculated using the Beef NRC (2000) equations with individual steer BW. The 10:90 hay:concentrate treatment was limit-fed to meet 80% of the daily NEm requirement, as calculated based on individual steer BW using the Beef NRC (2000) equations. Means that lack a common letter (a–e) differ (P ≤ 0.05) as a result of sampling time relative to feeding for cattle in negative energy balance. Figure 2. View largeDownload slide Experiment 2: Plasma hormone and metabolite concentrations relative to feed offering for steers in negative energy balance. Bars at each time point represent the concentration of 4 samples collected at 15-min intervals during that hour and averaged after analyses. The arrow indicates the time at which feed was offered in the morning. An equal aliquot of fed was also offered at 2000 h. The 50:50 hay:concentrate treatment was fed to meet 80% of the daily NEm requirement, as calculated using the Beef NRC (2000) equations with individual steer BW. The 10:90 hay:concentrate treatment was limit-fed to meet 80% of the daily NEm requirement, as calculated based on individual steer BW using the Beef NRC (2000) equations. Means that lack a common letter (a–e) differ (P ≤ 0.05) as a result of sampling time relative to feeding for cattle in negative energy balance. DISCUSSION Energy value calculated from average chemical analyses of the prairie grass hay collected throughout Exp. 1 was less than that of the hay sample collected before trial initiation. Because the prairie grass hay was a greater percentage of the HY diet compared with the LFC diet, an energy value less than originally calculated resulted for the HY treatment. Preliminary calculations at the initiation of Exp. 1 predicted that cattle in both treatment groups were consuming enough feed to meet their energy requirement for BW maintenance and an additional 3.5 Mcal/d of NEg. Subsequent evaluation of the treatment diets using the Beef NRC (2000) computer model indicated that cattle in the HY treatment consumed 3.4 Mcal/d of NEg, whereas cattle in the LFC treatment consumed 3.6 Mcal/d of NEg (SEM 0.04 Mcal/d). The NEg intake differed between treatments; however, this was an artifact of the small SE associated with the treatments based on original calculations. When NEm intake was expressed as a percentage of that required to maintain BW, NEm was similar between treatments. Thus, it was concluded that cattle were in a similar, positive metabolic state. Hormone and metabolite data support the conclusion that, despite statistical differences in predicted energy intakes for the treatment groups, the metabolic state of the cattle was similar. Diets were not formulated to be isonitrogenous; thus, MP intakes differed between dietary treatments. However, for both treatment groups, MP intake was greater than that required to meet the need for the energy-allowable ADG. Thus, it was concluded that MP was not altering the growth of the cattle. The Beef NRC (2000) computer model predicted that cattle in the HY treatment received enough energy to gain 0.68 kg/d, whereas those in the LFC treatment were predicted to gain 0.72 kg/d. Observed BW gains for the LFC steers (0.73 kg/d) were similar to those predicted by the Beef NRC (2000) computer model. In contrast, observed BW gains for HY steers (1.67 kg/d) were greater than those predicted by the Beef NRC (2000) computer model. The large SEM (0.46 kg/d) associated with the observed BW change resulted in a similar BW change between treatments. The numeric difference in BW gain between dietary treatment groups may be explained by intakes not being standardized between treatment groups before BW were recorded. When evaluating these data relative to the Beef NRC (2000) computer model, it should be considered that data were collected on a small number of cannulated cattle that were approaching maturity and therefore may not match as well the population of cattle on which the Beef NRC (2000) model was based. Energy Source and Physical Fill Previous research from our laboratory demonstrated that plasma ghrelin concentrations were increased when intake of a high-grain diet was restricted to result in a prolonged moderate energy and protein restriction sufficient to result in loss of BW (Wertz-Lutz et al., 2008). It was concluded that differences in plasma ghrelin concentration were the result of differences in metabolic state that resulted from differences in nutrient intake. However, DMI and nutrient intake were confounded in the experiment reported by Wertz-Lutz et al. (2008); thus, observed differences in plasma ghrelin concentrations that were attributed to differences in the metabolic state of the cattle could also have been attributed to differences in physical fill. In Exp. 1 herein, energy intake was similar between treatment groups and metabolic state was similar, as indicated by similar plasma INS, GH, and NEFA concentrations. Plasma ghrelin concentrations also were similar between treatment groups despite differences in amount of DMI. It was concluded that plasma ghrelin concentrations were similar when the metabolic state was similar, regardless of differences in DMI. Plasma NEFA and INS concentrations were similar to those reported for beef cattle in positive energy balance (Wertz-Lutz et al., 2008). A state of positive energy balance is supported by observed BW gains. These data are consistent with the hypothesis that for cattle in positive energy balance, plasma ghrelin concentrations are similar when the metabolic state is similar, regardless of the dietary energy source or resulting differences in DMI. It was speculated, on the basis of previous research in our laboratory, that differences in plasma ghrelin concentrations observed for steers fed different amounts of a common high-grain diet could be attributed to differences in nutrient intake (Wertz-Lutz et al., 2008). Rumen distention has been implicated, given that the vagus nerve has been reported to be involved in the release of ghrelin in sheep. Sugino et al. (2003) reported that cholinergic activity suppresses ghrelin release in sheep and concluded that ghrelin secretion is regulated by cholinergic neurons of the vagus nerve. However, researchers have demonstrated that the stimulatory effect of ghrelin on feeding behavior is not the result of the vagus nerve. Arnold et al. (2006) reported that activities of only a small fraction of vagal afferents were sensitive to intraperitoneal ghrelin injection. These researchers concluded that vagal afferent signaling was not required for the acute stimulatory influence of ghrelin on feed intake in rats (Arnold et al., 2006). In Exp. 1, plasma ghrelin concentrations did not differ when metabolic state was similar but the DMI and dietary ingredient composition differed. These data further support the hypothesis that differences in plasma ghrelin concentrations previously observed by Wertz-Lutz et al. (2008) for cattle experiencing prolonged nutrient restriction were not the result of differences in DMI. Experiment 2 was designed to use diets of similar ingredient composition as Exp. 1, but to offer cattle an amount of the diet that would result in a negative energy balance, and thus BW loss, for the cattle. In Exp. 2, HY steers received 9.8 Mcal/d of NEm and LFC steers received 9.7 Mcal/d of NEm, which were similar between treatment groups. This amount of energy intake was 80% of that needed to maintain BW; thus, cattle lost BW during the experiment. The Beef NRC (2000) computer model does not predict MP requirements for cattle that are losing BW. In the current experiment, MP intake differed between treatment groups. For HY cattle, MP intake was above (112%) that required for BW maintenance, whereas for LFC cattle, MP intake was below (84%) that required for BW maintenance. The experiment was designed for cattle to lose BW, and the MP requirement would be expected to be less than that required for BW maintenance. The decreased MP intake of LFC steers did not affect the BW compared with HY cattle. The MP intake was 84% of that needed for BW maintenance; thus, energy likely was the first-limiting nutrient in both diets. Cattle losing BW may be breaking down muscle in addition to fat stores, thus supplying additional proteins to the body for use. Body weight loss was similar between the 2 treatment groups, indicating that the energy restriction imposed by the different dietary energy sources was similar. A tendency for plasma NEFA concentrations to be greater for HY cattle compared with LFC cattle warrants further investigation. Plasma NEFA concentrations were similar to those reported previously for beef cattle receiving 80% of the dietary energy needed for BW maintenance (Wertz-Lutz et al., 2008). Plasma ghrelin concentrations also did not differ as a result of dietary energy source (HY vs. LFC). These data are consistent with the hypothesis that for cattle in a negative energy balance, plasma ghrelin concentrations are similar when energy intake is similar, regardless of the dietary energy source or resulting differences in DMI. Energy state (positive vs. negative) is confounded by year in the current set of experiments. Thus, plasma ghrelin concentrations relative to energy status cannot by compared statistically. However, marked differences in plasma ghrelin concentrations are not apparent between cattle in a positive energy balance (Exp. 1) and a negative energy balance (Exp. 2), as was reported previously by Wertz-Lutz et al. (2008). This observation warrants further investigation. Plausible explanations for the differences in plasma ghrelin concentrations between experiments could be the result of age of the animal and the point of the animals on the growth curve at sampling. Additionally, the magnitude of difference in energy intake between the experiments must also be considered. Wertz-Lutz et al. (2008) reported statistically different plasma ghrelin concentrations between cattle consuming 0.8- and 2.4-fold of the energy needed to maintain BW. For the experiments reported herein, cattle in a positive energy balance were consuming 1.6-fold the energy required for BW maintenance, whereas cattle in a negative energy balance were consuming 0.80-fold. Time Relative to Feeding For steers in a positive energy balance, a characteristic increase in plasma ghrelin concentrations before feeding time and a decline in plasma ghrelin concentrations subsequent to feeding were observed. This pattern of fluctuating plasma ghrelin concentrations relative to meal feeding has been observed previously in cattle (Hayashida et al., 2001; Miura et al., 2004; Wertz-Lutz et al., 2006) and sheep (Sugino et al.,b). Sugino et al. (2002b) concluded that temporal differences were a conditioned response. That cannot be ruled out because a strict feeding routine was adhered to in this experiment. Rate of meal consumption was not measured in this experiment. However, for Exp. 1, the quantity of feed offered was sufficient to last throughout the majority of the 12-h feeding interval. Regardless of dietary energy source, which resulted in differences in DMI, a gradual increase in plasma ghrelin concentrations occurred from the nadir at 2 h postfeeding. A plasma ghrelin concentration similar to preprandial concentrations was reached by 5 h postfeeding. Previous research in this facility demonstrated that cattle consumed a large meal at feed offering and then small meals throughout the remainder of the feeding interval (Wertz-Lutz et al., 2006). A similar feeding pattern was observed but was not measured with this group of cattle. Plasma GH concentrations also were increased before feed was offered, declined subsequent to feeding, and began to increase as the evening feeding approached. Increased plasma GH concentrations associated with increased plasma ghrelin concentrations have been observed previously in sheep (Sugino et al., 2002a). Wertz-Lutz et al. (2006) also demonstrated that injection of exogenous ghrelin resulted in a GH surge in cattle. Plasma INS and NEFA concentrations did not fluctuate significantly relative to feeding time for cattle in Exp. 1, in which energy was adequate. Plasma NEFA and INS concentrations were similar to those reported for beef cattle in a positive energy balance (Wertz-Lutz et al., 2008). In Exp. 2, plasma ghrelin concentrations were not as great as have been observed previously for cattle receiving 80% of the NEm required to maintain BW (Wertz-Lutz et al., 2008). A characteristic decrease in plasma ghrelin concentrations was observed subsequent to feeding, and plasma ghrelin concentrations returned to prefeeding concentrations by 3 h postfeeding and remained increased through the feeding interval. The amount of feed offered in Exp. 2 of this study was not sufficient to last throughout the majority of the 12-h feeding interval. Although rate of consumption was not measured in Exp. 2, cattle behaved as meal-fed animals, consuming their feed early in the 12-h feeding interval and thus having a period of time when their bunk was empty before the next feeding. Plasma GH concentrations were increased prefeeding and again from 3 h postfeeding. For cattle in positive energy balance, plasma ghrelin and GH increased gradually from the postprandial nadir to the next feeding time, whereas plasma ghrelin and GH concentrations had returned to preprandial concentrations by 3 h postfeeding. A similar response of plasma ghrelin and GH was expected because ghrelin is the endogenous ligand for the GH secretagogue receptor and is capable of releasing GH independent of the GHRH cascade (Kojima et al., 1999), and, as discussed previously, exogenous ghrelin has been reported to elicit a GH response when injected in cattle. Unlike plasma NEFA concentrations, which remained unchanged relative to feeding time for cattle in positive energy balance, plasma NEFA concentrations did fluctuate for cattle in negative energy balance. Plasma NEFA concentrations were similar to those reported by Wertz-Lutz et al. (2008) for cattle limit-fed a high-concentrate diet to achieved 80% of the NEm requirement, and were increased relative to cattle consuming 240% of the energy needed for BW maintenance. In summary, the current experiment demonstrates that plasma ghrelin concentrations were similar when the metabolic state of cattle, as indicated by other hormones and metabolites, was similar, regardless of differences in DMI to achieve a similar metabolic state. These data substantiate the previous report of Wertz-Lutz et al. (2008), which concluded that differences in plasma ghrelin concentration were the result of differences in the metabolic state and not the result of differences in DMI. Therefore, further research into the role of ghrelin in energy metabolism in cattle is warranted. In species other than cattle, ghrelin has been reported to influence body composition by altering energy metabolism (Tschöp et al., 2000). More recently, researchers have demonstrated that ghrelin can stimulate the differentiation of preadipocytes to mature adipocytes and increase PPARγ2 in rats (Choi et al., 2003). Peroxisome proliferator-activated receptor-γ2 is a nuclear transcription factor involved in the differentiation of preadipocytes (Sundvold et al., 1997). Additionally, ghrelin has been reported to stimulate INS-induced glucose uptake by adipocytes in certain adipose tissue depots in the rat (Patel et al., 2006). Thus, research to investigate the role of ghrelin in the development of economically important adipose depots in cattle is warranted. LITERATURE CITED AOAC 2007. Official Methods of Analysis.  18th ed. Assoc. Off. Anal. Chem., Arlington, VA. Arnold M. Mura A. Langhans W. Geary N. 2006. Gut vagal afferents are not necessary for the eating-stimulatory effect of interperitoneally injected ghrelin in the rat. J. Neurosci.  26: 11052– 11060. https://doi.org/17065447 Google Scholar CrossRef Search ADS PubMed  Beef NRC 2000. Nutrient Requirements of Beef Cattle.  7th rev. ed. Natl. Acad. Press, Washington, DC. PubMed PubMed  Choi K. Rho S.-G. Hong Y.-H. Shrestha Y. B. Hishikawa D. Chen C. Kojima M. Kangawa K. Sasaki S.-I. 2003. The role of ghrelin and growth hormone secretagogues receptor on rat adipogenesis. Endocrinology  144: 754– 759. Google Scholar CrossRef Search ADS PubMed  French M. C. Little John R. P. Greer G. J. Bain W. E. McEwam J. C. Tisdall D. J. 2006. Growth hormone and ghrelin receptor genes are differently expressed between genetically lean and fat selection lines of sheep. J. Anim. Sci.  84: 324– 331. https://doi.org/16424260 Google Scholar CrossRef Search ADS PubMed  Goering, H. K., and P. J. Van Soest 1970. Forage fiber analyses. Agric. Handb. 379.  USDA, ARS, Washington, DC. Hayashida T. Murakami K. Mogi K. Nishihara M. Nakazato M. Mondal M. S. Horii Y. Kojima M. Kangawa K. Murakami N. 2001. Ghrelin in domestic animals: Distribution in the stomach and its possible role. Domest. Anim. Endocrinol.  21: 17– 24. https://doi.org/11524171 Google Scholar CrossRef Search ADS PubMed  Kojima M. Hosoda H. Date Y. Nakazato M. Matsuo H. Kangawa K. 1999. Ghrelin is a growth-hormone-releasing acylated peptide from stomach. Nature  402: 656– 660. https://doi.org/10604470 Google Scholar CrossRef Search ADS PubMed  Kurose Y. Iqbal J. Rao A. Murata Y. Hasegawa Y. Terashima Y. Kojima M. Kangawa K. Clarke I. J. 2005. Changes in expression of the genes for the leptin receptor and the growth hormone-releasing peptide/ghrelin receptor in the hypothalamic arcuate nucleus with long-term manipulation of adiposity by dietary means. J. Neuroendocrinol.  17: 331– 340. Google Scholar CrossRef Search ADS PubMed  Miura H. Tsuchiya N. Sasaki I. Kikuchi M. Kojima M. Kangawa K. Hasegawa Y. Ohnami Y. 2004. Changes in plasma ghrelin and growth hormone concentrations in mature Holstein cows and three-month-old calves. J. Anim. Sci.  82: 1329– 1333. https://doi.org/15144072 Google Scholar CrossRef Search ADS PubMed  Nakazato M. Murakami N. Date Y. Kojima M. Matsuo H. Kangawa K. Matsukura S. 2001. A role for ghrelin in the central regulation of feeding. Nature  409: 194– 198. https://doi.org/11196643 Google Scholar CrossRef Search ADS PubMed  Patel A. D. Stanley S. A. Murphy K. G. Frost G. S. Gardiner J. V. Kent A. S. White N. E. Ghatei M. A. Bloom S. R. 2006. Ghrelin stimulates insulin-induced glucose uptake in adipocytes. Regul. Pept.  134: 17– 22. https://doi.org/16338009 Google Scholar CrossRef Search ADS PubMed  Shintani M. Ogawa Y. Ebihara K. Aizawa-Abe M. Miyanaga F. Hayashi T. T. Inoue G. Hosoda K. Kojima M. Kangawa K. Nakao K. 2001. Ghrelin, an endogenous growth hormone secretagogue, is a novel orexigenic peptide that antagonizes leptin action through the activation of hypothalamic neuropeptide Y/Y1 receptor pathway. Diabetes  50: 227– 232. https://doi.org/11272130 Google Scholar CrossRef Search ADS PubMed  Sugino T. Hasegawa Y. Kikkawa Y. Yamaura J. Yamagishi M. Kurose Y. Kojima M. Kangawa K. Terashima Y. 2002a. A transient ghrelin surge occurs just before feeding in a scheduled meal-fed sheep. Biochem. Biophys. Res. Commun.  295: 255– 260. https://doi.org/12150940 Google Scholar CrossRef Search ADS   Sugino T. Yamaura J. Yamagishi M. Kurose Y. Kojima M. Kangawa K. Hasegawa Y. Terashima Y. 2003. Involvement of cholinergic neurons in the regulation of the ghrelin secretory response to feeding in sheep. Biochem. Biophys. Res. Commun.  304: 308– 312. https://doi.org/12711315 Google Scholar CrossRef Search ADS PubMed  Sugino T. Yamaura J. Yamagishi M. Ogura A. Hayashi R. Kurose Y. Kojima M. Kangawa K. Hasegawa Y. Terashima Y. 2002b. A transient surge of ghrelin secretion before feeding is modified by different feeding regimens in sheep. Biochem. Biophys. Res. Commun.  298: 785– 788. https://doi.org/12419323 Google Scholar CrossRef Search ADS   Sundvold H. Brzozowska A. Lien S. 1997. Characterization of bovine peroxisome proliferator-activated receptors γ1 and γ2: Genetic mapping and differential expression of the two isoforms. Biochem. Biophys. Res. Commun.  239: 857– 861. https://doi.org/9367859 Google Scholar CrossRef Search ADS PubMed  Tschöp M. Smiley D. L. Heiman M. L. 2000. Ghrelin induces adiposity in rodents. Nature  407: 908– 913. https://doi.org/11057670 Google Scholar CrossRef Search ADS PubMed  Wang G. Lee H.-M. Englander E. Greeley G. H. Jr 2002. Ghrelin—Not just another stomach hormone. Regul. Pept.  105: 75– 81. https://doi.org/11891007 Google Scholar CrossRef Search ADS PubMed  Wertz-Lutz A. E. Daniel J. A. Clapper J. A. Smart A. J. Trenkle A. Beitz D. C. 2008. Prolonged feed intake restriction in beef cattle results in persistently elevated plasma ghrelin concentrations. J. Anim. Sci.  86: 564– 575. https://doi.org/18156362 Google Scholar CrossRef Search ADS PubMed  Wertz-Lutz A. E. Knight T. J. Pritchard R. H. Daniel J. A. Clapper J. A. Smart A. J. Beitz D. C. Trenkle A. 2006. Circulating ghrelin concentrations fluctuate relative to nutritional status and influence feeding behavior in cattle. J. Anim. Sci.  84: 3285– 3300. https://doi.org/17093221 Google Scholar CrossRef Search ADS PubMed  American Society of Animal Science

Atkins, J. A.;Smith, M. F.;Wells, K. J.;Geary, T. W.

doi: 10.2527/jas.2009-2531pmid: 20228240

ABSTRACT Cows induced to ovulate small dominant follicles were reported to have reduced pregnancy rates compared with cows that ovulated large follicles. The reason for the presence of small dominant follicles at the time of GnRH-induced ovulation in timed AI protocols is unknown. The objectives of this experiment were to examine the role of day of the estrous cycle at initiation of treatment on ovulation after the first GnRH injection (GnRH1) and associated effects on growth rate and final size of the ovulatory follicle at the second GnRH injection (GnRH2), serum concentrations of estradiol at GnRH2, and subsequent luteal concentrations of progesterone in suckled beef cows. Estrous cycles of cows were manipulated to be at 1 of 5 specific days of the cycle (d 2, 5, 9, 13, and 18, d 0 = estrus; n = 12 per treatment group) at the beginning of the CO-Synch protocol (GnRH1 on d −9, PGF2α on d −2, and GnRH2 on d 0). Day of the estrous cycle at GnRH1 did not affect the size of the preovulatory follicle or the proportion of cows ovulating at GnRH2 (P = 0.65 and 0.21, respectively). When all cows were included in the analysis, cows that ovulated after GnRH1 had similar follicle size at GnRH2 compared with cows that did not ovulate after GnRH1 (11.4 and 10.4 mm, respectively; P = 0.23). When only cows that could ovulate after GnRH1 (excluding cows treated on d 2) were included in the analysis, cows that ovulated to GnRH1 had a larger follicle at GnRH2 than cows that did not ovulate after GnRH1 (11.4 and 9.5 mm, respectively; P = 0.04). Follicle growth from d −5 to 0 was similar between cows that ovulated after GnRH1 and cows that did not (1.01 vs. 0.89 mm/d, respectively; P = 0.75). There was a tendency for faster follicle growth rate in cows that ovulated a large follicle (>11 mm) compared with cows that ovulated a small follicle (≤11 mm; 1.01 vs. 0.86 mm/d, respectively; P = 0.07). Serum concentrations of estradiol at GnRH2 and progesterone after ovulation were reduced in cows that ovulated small follicles compared with cows that ovulated large follicles (P = 0.006 and 0.005, respectively). In summary, day of the estrous cycle at initiation of synchronization did not affect ovulatory follicle size, but follicle growth rates affected the size of the follicle at GnRH2. Cows that ovulated a small follicle had reduced serum concentrations of estradiol at GnRH2 and progesterone after ovulation. INTRODUCTION Ovulation of a small, and presumably physiologically immature, dominant follicle reduced pregnancy rates (Lamb et al., 2001; Vasconcelos et al., 2001; Perry et al., 2005) and increased late embryonic or fetal loss (Perry et al., 2005) in beef and dairy cattle. Factors affecting ovulatory follicle size, or the mechanisms by which ovulatory follicle size affect fertility have not been determined. Administration of a first GnRH injection (GnRH1) 7 d before PGF2α and a second GnRH injection (GnRH2) has been used to breed beef cattle by appointment (CO-Synch; Geary and Whittier, 1998). The GnRH1 injection is expected to ovulate a dominant follicle and initiate a new follicular wave so that a viable preovulatory follicle is present at timed AI. It is logical that small dominant follicles present at the time of GnRH2 could result from failure to ovulate a dominant follicle and initiate a new follicular wave with GnRH1. Failure to initiate a new follicular wave with GnRH1 did not affect follicle diameter at GnRH2 or fertility in beef heifers (Atkins et al., 2008). Alternatively, slower growth rate of the follicle could result in a small dominant follicle at GnRH2. In the present study, estrous cycles of beef cows were manipulated so that cows were on specific days of the estrous cycle at the beginning of the study. These days were selected based on prediction of the presence of a dominant follicle that either would or would not respond to GnRH1 and ovulate a first-, second-, or third-wave dominant follicle in response to GnRH2. Our hypothesis was that day of the estrous cycle at GnRH1, and thus ovulatory response to GnRH1, would affect growth rate, health, and diameter of the ovulatory follicle at the GnRH-induced ovulation for timed AI. Our objective was to determine how day of the estrous cycle at GnRH1 would affect ovulation after GnRH1 and the associated effects on size and physiological maturity of the dominant follicle at GnRH2 in cycling beef cows. MATERIALS AND METHODS All protocols and procedures were approved by the Fort Keogh Livestock and Range Research Laboratory Animal Care and Use Committee. Animal Handling Postpartum suckled beef cows (n = 60) that had resumed cyclicity [based on electronic observation of estrus (described below) and formation of a corpus luteum via ultrasound] were assigned to 1 of 5 treatment groups based on age and days postpartum. The treatment groups (n = 12 per group) were defined as the day of the estrous cycle at the beginning of the CO-Synch protocol [d 2, 5, 9, 13, and 18 treatment groups (d 0 = estrus), referred to as D2, D5, D9, D13, and D18, respectively]. These days were selected based on prediction of the presence of a dominant follicle that either would or would not respond to GnRH1 and ovulate a first-, second-, or third-wave dominant follicle in response to GnRH2 (Ginther et al., 2001). All cows were treated with the CO-Synch protocol (GnRH1 on d −9, followed by PGF2α on d −2 and GnRH2 on d 0), except that cows were not bred. Presynchronization Cows were synchronized to be on their assigned day of the cycle using a controlled internal drug-releasing device (CIDR; EAZI-Breed CIDR containing 1.38 g of progesterone, Pfizer Animal Health, New York, NY) for 7 d with an injection of GnRH at insertion and PGF2α at CIDR removal. Cows that displayed estrus (±12 h) on the same day were included in the treatment groups. Estrous Detection The HeatWatch Estrous Detection System (CowChips LLC, Manalapan, NJ) was used to monitor estrus during the presynchronization period and throughout the experiment. Estrus was defined as 3 mounts lasting longer than 2 s per mount within a 4-h period. Transrectal Ultrasonography Ovarian structures were monitored using an Aloka 500V ultrasound instrument with a 7.5-MHz transducer (Aloka, Wallingford, CT). Follicles ≥5 mm in diameter and the presence of a corpus luteum were recorded. Follicle diameter was measured at the widest point and at a right angle to the first measurement. The follicular diameter was calculated as the average of the 2 measurements. Transrectal ultrasonography was performed on d −9 (GnRH1) and d 0 (GnRH2) to determine the diameter of the dominant follicle. The presence or absence of a class III follicle (>9 mm; Moreira et al., 2000) was recorded at each ultrasound scan and used as an indicator of a potentially ovulatory follicle. Ovulatory follicles ≤11 mm were considered small dominant follicles, whereas follicles >11 mm were considered large follicles. This cutoff was determined based on pregnancy rates in cows ovulating various follicle sizes reported previously in this herd (Perry et al., 2005). Ovarian ultrasound exams were performed daily from d −9 to 0 and on d 2 to characterize follicular waves and growth of dominant follicles and to confirm ovulation after GnRH1 and GnRH2, based on the disappearance of a dominant follicle and the formation of luteal tissue (after GnRH1). All ultrasound scans were recorded to video. Individual follicles were tracked beginning on d −5 to 0 to determine the long-term follicle growth rate. These days were chosen to monitor long-term growth because most cows had a follicle that could be tracked accurately during this period. In addition, ovaries were scanned daily beginning 9 (D18 group), 4 (D13 group), 2 (D9 and D5 groups), or 1 d (D2 group) before GnRH1 to characterize the dominant follicle before GnRH1 [i.e., follicle wave number and stage of growth (increasing, plateau, or regressing)]. Stage of growth at GnRH2 was assessed by fitting a polynomial curve to the follicle growth pattern of each cow. The first derivative of the polynomial was solved for zero to find the point on the curve where the follicle was no longer growing (±0.5 d; plateau). The follicle was considered increasing in size before the time of plateau and was considered decreasing after the plateau. Blood Collection and RIA Blood samples were collected via tail or jugular venipuncture into 10-mL vacuum tubes (Fisher Scientific, Pittsburgh, PA) daily from d −9 to 21. After collection, the blood was incubated for 24 h at 4°C, followed by centrifugation at 1,200 × g for 25 min at 4°C. Serum was collected and stored at −20°C until RIA. Serum concentrations of progesterone were measured in all samples using a Coat-a-Count RIA kit (Diagnostic Products Corporation, Los Angeles, CA; Kirby et al., 1997). Intra- and interassay CV were 2.9 and 9.8%, respectively. Sensitivity of the assay was 0.08 ng/mL. Two distinct luteal stages were characterized: 1) early luteolysis referred to cows that had undergone luteal regression before PGF2α, and 2) incomplete luteal regression referred to cows that had incomplete luteal regression after administration of PGF2α. Cows were determined to have undergone early luteolysis when serum concentrations of progesterone decreased below 1.0 ng/mL before PGF2α. Cows were determined to have incomplete luteal regression when serum concentrations of progesterone decreased after PGF2α but did not decrease below 1.0 ng/mL and then increased earlier than cows that had undergone complete luteal regression. Serum concentrations of estradiol-17β were measured using RIA (Kirby et al., 1997) in samples collected from d −9 to 0. Intra- and inter-assay CV were 13.1 and 17.6%, respectively. Sensitivity of the assay was 0.5 pg/mL. Statistical Analyses One-way ANOVA with day as the independent variable was used to test differences among treatment groups in average follicular diameter and serum concentrations of estradiol at GnRH1 and GnRH2 (SAS Inst. Inc., Cary, NC). Differences in the proportion of cows ovulating among treatment groups at GnRH1 and GnRH2, undergoing premature luteolysis, and in estrus early were tested using GENMOD with SAS. Differences in average follicular diameter at GnRH1 and GnRH2 between cows that did or did not ovulate after GnRH1 were analyzed with the 2-sample t-test. The percentage of cows with a class III follicle from d −9 through 0 was analyzed using the GENMOD procedure in SAS for repeated measures. Changes in serum concentrations of progesterone over time during the linear increase in progesterone (d 2 to 12 after GnRH2) were analyzed by ANOVA for repeated measures (PROC MIXED; Littell et al., 1998) between cows that ovulated small or large follicles after GnRH2. Because of unequal variances, the progesterone concentrations were log-transformed for the analysis; the actual concentrations were used for graphical display. Differences in long-term follicle growth rate (d −5 to 0) among treatment groups (day of the cycle), between cows that did or did not ovulate to GnRH1, and between cows with a small or large follicle at GnRH2 were analyzed by weighted ANOVA for repeated measures (PROC MIXED; Littell et al., 1998), in which time points were weighted based on the number of observations. A multiple regression with GLM in SAS was used to analyze the effect of ovulatory follicle diameter and display of estrus on serum concentrations of estradiol at the time of GnRH2. The correlations between ovulatory follicle diameter, follicle growth rate, and serum concentrations of estradiol were analyzed using the CORR procedure in SAS. Cows that underwent incomplete luteal regression after PGF2α were not included in the progesterone analysis after GnRH2 but were included in all other analyses. RESULTS Ovulatory Response and Follicle Diameter The percentages of cows that ovulated to GnRH1 and GnRH2 were 50 and 76%, respectively. The percentage of cows ovulating at GnRH2 was greater when the largest follicle was increasing in size (32/34; P < 0.001) or had reached a plateau in growth (10/11; P = 0.01) compared with cows with a decreasing follicle diameter (4/7; cows that were in estrus before GnRH2 were not included in this analysis). The proportion of cows ovulating to GnRH2 was not different between cows with an increase in follicle growth and cows that had reached a plateau in follicle growth (P = 0.71). The size of the follicle at GnRH1 and the proportion that ovulated after GnRH1 was decreased in D2 cows (P < 0.003 and 0.005, respectively) compared with the remaining treatment groups. There was no difference in the proportion ovulating to GnRH1 among D5, D9, D13, or D18 cows (P > 0.2; Table 1). Neither the proportion ovulating nor the size of the dominant follicle at GnRH2 was affected by cycle day at GnRH1 (P = 0.21 and 0.65, respectively; Table 1). Cycle day at GnRH1 affected the percentage of cows with a class III follicle between d −9 and d 0 of the treatment period (cycle day × time interaction P < 0.05; Figure 1a); however, the percentage of cows with a class III follicle at GnRH2 was not affected by cycle day at the beginning of treatment (P > 0.2; Figure 1a). Table 1. Mean diameter (mm) of the largest follicle (±SEM) at the first and second GnRH injection (GnRH1 and GnRH2, respectively), number (%) of cows ovulating to GnRH1 and GnRH2, serum estradiol at GnRH2 (pg/mL, mean ± SEM), number (%) of cows undergoing luteolysis before PGF2α, and number (%) of cows in estrus from GnRH1 to PGF2α1 Treatment group  GnRH1  GnRH2  Serum estradiolat GnRH2  Luteolysisbefore PGF2α2  In estrusbefore PGF2α  Largest folliclediameter  Ovulating  Largest folliclediameter  Ovulating  D2  6.5 ± 0.6a  0/12d (0)  11.7 ± 0.7  9/12 (75)  4.7 ± 1.0  0/12a (0)  0/12d (0)  D5  9.0 ± 0.3b  9/12e (75)    10.8 ± 0.5  9/12 (75)  3.2 ± 0.5  0/12a (0)  0/12d (0)  D9  11.5 ± 0.4c  8/12e (67)    10.8 ± 0.4  12/12 (100)  3.5 ± 0.5  0/12a (0)  0/12d (0)  D13  10.0 ± 0.4bc  6/12e (50)    11.4 ± 0.9  9/12 (75)  4.3 ± 0.6  11/12b (92)  2/12de (17)  D18  10.0 ± 0.5bc  7/12e (58)    9.8 ± 1.6  7/12 (58)  5.0 ± 1.0  5/12c (42)  4/12e (33)  Treatment group  GnRH1  GnRH2  Serum estradiolat GnRH2  Luteolysisbefore PGF2α2  In estrusbefore PGF2α  Largest folliclediameter  Ovulating  Largest folliclediameter  Ovulating  D2  6.5 ± 0.6a  0/12d (0)  11.7 ± 0.7  9/12 (75)  4.7 ± 1.0  0/12a (0)  0/12d (0)  D5  9.0 ± 0.3b  9/12e (75)    10.8 ± 0.5  9/12 (75)  3.2 ± 0.5  0/12a (0)  0/12d (0)  D9  11.5 ± 0.4c  8/12e (67)    10.8 ± 0.4  12/12 (100)  3.5 ± 0.5  0/12a (0)  0/12d (0)  D13  10.0 ± 0.4bc  6/12e (50)    11.4 ± 0.9  9/12 (75)  4.3 ± 0.6  11/12b (92)  2/12de (17)  D18  10.0 ± 0.5bc  7/12e (58)    9.8 ± 1.6  7/12 (58)  5.0 ± 1.0  5/12c (42)  4/12e (33)  a–cMeans or percentages within a column having different superscripts were different (P < 0.01). d,eMeans or percentages within a column having different superscripts were different (P < 0.05). 1Treatment groups were based on the day of the estrous cycle at the beginning of the CO-Synch protocol [a first GnRH injection (GnRH1) was administered, followed 7 d later with an injection of PGF2α, and 48 h after PGF2α, a second injection of GnRH (GnRH2) was administered]. 2Luteolysis was defined as the day when serum progesterone concentrations decreased below 1.0 ng/mL. View Large Table 1. Mean diameter (mm) of the largest follicle (±SEM) at the first and second GnRH injection (GnRH1 and GnRH2, respectively), number (%) of cows ovulating to GnRH1 and GnRH2, serum estradiol at GnRH2 (pg/mL, mean ± SEM), number (%) of cows undergoing luteolysis before PGF2α, and number (%) of cows in estrus from GnRH1 to PGF2α1 Treatment group  GnRH1  GnRH2  Serum estradiolat GnRH2  Luteolysisbefore PGF2α2  In estrusbefore PGF2α  Largest folliclediameter  Ovulating  Largest folliclediameter  Ovulating  D2  6.5 ± 0.6a  0/12d (0)  11.7 ± 0.7  9/12 (75)  4.7 ± 1.0  0/12a (0)  0/12d (0)  D5  9.0 ± 0.3b  9/12e (75)    10.8 ± 0.5  9/12 (75)  3.2 ± 0.5  0/12a (0)  0/12d (0)  D9  11.5 ± 0.4c  8/12e (67)    10.8 ± 0.4  12/12 (100)  3.5 ± 0.5  0/12a (0)  0/12d (0)  D13  10.0 ± 0.4bc  6/12e (50)    11.4 ± 0.9  9/12 (75)  4.3 ± 0.6  11/12b (92)  2/12de (17)  D18  10.0 ± 0.5bc  7/12e (58)    9.8 ± 1.6  7/12 (58)  5.0 ± 1.0  5/12c (42)  4/12e (33)  Treatment group  GnRH1  GnRH2  Serum estradiolat GnRH2  Luteolysisbefore PGF2α2  In estrusbefore PGF2α  Largest folliclediameter  Ovulating  Largest folliclediameter  Ovulating  D2  6.5 ± 0.6a  0/12d (0)  11.7 ± 0.7  9/12 (75)  4.7 ± 1.0  0/12a (0)  0/12d (0)  D5  9.0 ± 0.3b  9/12e (75)    10.8 ± 0.5  9/12 (75)  3.2 ± 0.5  0/12a (0)  0/12d (0)  D9  11.5 ± 0.4c  8/12e (67)    10.8 ± 0.4  12/12 (100)  3.5 ± 0.5  0/12a (0)  0/12d (0)  D13  10.0 ± 0.4bc  6/12e (50)    11.4 ± 0.9  9/12 (75)  4.3 ± 0.6  11/12b (92)  2/12de (17)  D18  10.0 ± 0.5bc  7/12e (58)    9.8 ± 1.6  7/12 (58)  5.0 ± 1.0  5/12c (42)  4/12e (33)  a–cMeans or percentages within a column having different superscripts were different (P < 0.01). d,eMeans or percentages within a column having different superscripts were different (P < 0.05). 1Treatment groups were based on the day of the estrous cycle at the beginning of the CO-Synch protocol [a first GnRH injection (GnRH1) was administered, followed 7 d later with an injection of PGF2α, and 48 h after PGF2α, a second injection of GnRH (GnRH2) was administered]. 2Luteolysis was defined as the day when serum progesterone concentrations decreased below 1.0 ng/mL. View Large Figure 1. View largeDownload slide Percentage of cows with a class III (>9 mm) follicle by cycle day at the first GnRH injection (GnRH1; panel a) and ovulatory response to GnRH1 (panel b) during the treatment period. Cows (n = 12 per treatment group) were on d 2, 5, 9, 13, or 18 (referred to as D2, D5, D9, D13, and D18, respectively) of their estrous cycle at the beginning of the CO-Synch protocol [GnRH injection (GnRH1) followed 7 d later with PGF2α, and a second GnRH injection (GnRH2) 48 h after PGF2α]. Cycle day at GnRH1 affected the percentage of cows with a class III follicle between d −9 and d 0 (time × cycle day interaction; P < 0.05); however, on d 0 (GnRH2) there was no effect of cycle day on the percentage of cows with a class III follicle (P > 0.2). In panel b, the analysis compared cows that ovulated to GnRH1 (solid line with diamond points) with cows that did not ovulate to GnRH1 either with (hatched line with square points) or without (hatched line with triangle points) the D2 cows but did not compare the latter 2 groups. Treatment groups with different letters (a–e) had differences in the percentage of cows with a class III follicle (P < 0.05). Figure 1. View largeDownload slide Percentage of cows with a class III (>9 mm) follicle by cycle day at the first GnRH injection (GnRH1; panel a) and ovulatory response to GnRH1 (panel b) during the treatment period. Cows (n = 12 per treatment group) were on d 2, 5, 9, 13, or 18 (referred to as D2, D5, D9, D13, and D18, respectively) of their estrous cycle at the beginning of the CO-Synch protocol [GnRH injection (GnRH1) followed 7 d later with PGF2α, and a second GnRH injection (GnRH2) 48 h after PGF2α]. Cycle day at GnRH1 affected the percentage of cows with a class III follicle between d −9 and d 0 (time × cycle day interaction; P < 0.05); however, on d 0 (GnRH2) there was no effect of cycle day on the percentage of cows with a class III follicle (P > 0.2). In panel b, the analysis compared cows that ovulated to GnRH1 (solid line with diamond points) with cows that did not ovulate to GnRH1 either with (hatched line with square points) or without (hatched line with triangle points) the D2 cows but did not compare the latter 2 groups. Treatment groups with different letters (a–e) had differences in the percentage of cows with a class III follicle (P < 0.05). The range in ovulatory follicle diameter at GnRH2 was 7.7 to 18.2 mm, with 33% ovulating small (≤11 mm) follicles. Ovulation to GnRH1 did not affect the proportion ovulating to GnRH2 or the size of the follicle at GnRH2 (P = 0.22 and 0.23, respectively; Table 2). Considering that cows on D2 of the cycle were unable to have a follicle capable of ovulating when administered GnRH1, a second analysis was performed to compare only cows capable of ovulating after GnRH1 (the D2 treatment group was removed). When cows on D2 of the estrous cycle were excluded, cows that ovulated after GnRH1 had larger follicle diameters at GnRH2 than cows that did not ovulate (P = 0.04; Table 2). Among cows that ovulated after GnRH2, the proportion of cows that ovulated a small follicle was not affected by ovulatory response to GnRH1 (7/25 and 10/21 of cows that did or did not ovulate to GnRH1 ovulated a small follicle at GnRH2, respectively; P = 0.17). When D2 cows were removed from the analysis, there was still no difference in the proportion of cows that ovulated a small follicle with respect to ovulatory response at GnRH1 (7/25 and 6/12 of the cows that did or did not ovulate to GnRH1 ovulated a small follicle at GnRH2; P = 0.19). Ovulation to GnRH1 affected the percentage of cows with a class III follicle throughout the synchronization period (P < 0.05; Figure 1b). When D2 cows were excluded from the analysis, the percentage of cows with a class III follicle at GnRH2 that ovulated to GnRH1 was greater than the percentage of cows that did not ovulate to GnRH1 (P = 0.04; Figure 1b). Table 2. Effect of ovulatory response at the first GnRH injection (GnRH1) in the CO-Synch protocol1 on mean (± SEM) follicular diameter (mm) at GnRH1 and GnRH2, serum estradiol (pg/mL, mean ± SEM) at GnRH2, number (%) of cows ovulating after GnRH2, number (%) of cows undergoing luteolysis before PGF2α, and number (%) of cows in estrus before PGF2α1 Ovulation, GnRH1  n  Follicle diameter, GnRH1  Follicle diameter, GnRH2  Estradiol at GnRH2  Ovulating after GnRH2  Luteolysis2before PGF2α  Estrus before PGF2α  Yes  30  10.6 ± 0.3  11.4 ± 0.5a  4.46 ± 0.5c  25/30 (83)  6/30e (20)  1/30e (3)  No with D2  30  8.4 ± 0.3  10.4 ± 0.7a  3.85 ± 0.4c  21/30 (70)  10/30e (33)  5/30e (17)  No without D2  18  9.7 ± 0.4  9.5 ± 0.8b  3.25 ± 0.3d  12/18 (67)  10/18f (56)  5/18f (28)  Ovulation, GnRH1  n  Follicle diameter, GnRH1  Follicle diameter, GnRH2  Estradiol at GnRH2  Ovulating after GnRH2  Luteolysis2before PGF2α  Estrus before PGF2α  Yes  30  10.6 ± 0.3  11.4 ± 0.5a  4.46 ± 0.5c  25/30 (83)  6/30e (20)  1/30e (3)  No with D2  30  8.4 ± 0.3  10.4 ± 0.7a  3.85 ± 0.4c  21/30 (70)  10/30e (33)  5/30e (17)  No without D2  18  9.7 ± 0.4  9.5 ± 0.8b  3.25 ± 0.3d  12/18 (67)  10/18f (56)  5/18f (28)  a–fMeans within columns having different superscripts were different between cows that ovulated (yes) or cows that did not ovulate (either “no with” cows at D2 of the estrous cycle at GnRH1, or “no without” cows at D2 of the estrous cycle at GnRH1; a,bP = 0.04,c,dP < 0.1,e,fP < 0.01). D2 = d 2 of treatment. 1The CO-Synch protocol included a first injection of GnRH (GnRH1) followed 7 d later with an injection of PGF2α, and 48 h after PGF2α, a second injection of GnRH (GnRH2). 2Luteolysis was defined as the day the serum progesterone concentrations decreased below 1.0 ng/mL. View Large Table 2. Effect of ovulatory response at the first GnRH injection (GnRH1) in the CO-Synch protocol1 on mean (± SEM) follicular diameter (mm) at GnRH1 and GnRH2, serum estradiol (pg/mL, mean ± SEM) at GnRH2, number (%) of cows ovulating after GnRH2, number (%) of cows undergoing luteolysis before PGF2α, and number (%) of cows in estrus before PGF2α1 Ovulation, GnRH1  n  Follicle diameter, GnRH1  Follicle diameter, GnRH2  Estradiol at GnRH2  Ovulating after GnRH2  Luteolysis2before PGF2α  Estrus before PGF2α  Yes  30  10.6 ± 0.3  11.4 ± 0.5a  4.46 ± 0.5c  25/30 (83)  6/30e (20)  1/30e (3)  No with D2  30  8.4 ± 0.3  10.4 ± 0.7a  3.85 ± 0.4c  21/30 (70)  10/30e (33)  5/30e (17)  No without D2  18  9.7 ± 0.4  9.5 ± 0.8b  3.25 ± 0.3d  12/18 (67)  10/18f (56)  5/18f (28)  Ovulation, GnRH1  n  Follicle diameter, GnRH1  Follicle diameter, GnRH2  Estradiol at GnRH2  Ovulating after GnRH2  Luteolysis2before PGF2α  Estrus before PGF2α  Yes  30  10.6 ± 0.3  11.4 ± 0.5a  4.46 ± 0.5c  25/30 (83)  6/30e (20)  1/30e (3)  No with D2  30  8.4 ± 0.3  10.4 ± 0.7a  3.85 ± 0.4c  21/30 (70)  10/30e (33)  5/30e (17)  No without D2  18  9.7 ± 0.4  9.5 ± 0.8b  3.25 ± 0.3d  12/18 (67)  10/18f (56)  5/18f (28)  a–fMeans within columns having different superscripts were different between cows that ovulated (yes) or cows that did not ovulate (either “no with” cows at D2 of the estrous cycle at GnRH1, or “no without” cows at D2 of the estrous cycle at GnRH1; a,bP = 0.04,c,dP < 0.1,e,fP < 0.01). D2 = d 2 of treatment. 1The CO-Synch protocol included a first injection of GnRH (GnRH1) followed 7 d later with an injection of PGF2α, and 48 h after PGF2α, a second injection of GnRH (GnRH2). 2Luteolysis was defined as the day the serum progesterone concentrations decreased below 1.0 ng/mL. View Large Luteolysis and Estrus An increased proportion of cows in the later part of the estrous cycle at the beginning of the CO-Synch protocol underwent luteolysis before PGF2α administration (P < 0.01; Table 1). An increased proportion of cows in the D18 treatment group were in estrus before PGF2α compared with cows in the D2, D5, or D9 treatment groups (P < 0.05; Table 1). When all cows were included in the analysis, the proportion of cows undergoing luteolysis or in estrus before PGF2α were not different between cows that ovulated or did not ovulate after GnRH1 (P = 0.24 and P = 0.09, respectively; Table 2). When cows in the D2 treatment group were excluded from the analysis, fewer cows that ovulated to GnRH1 underwent luteolysis or displayed estrus before PGF2α administration than cows that did not ovulate after GnRH1 (P < 0.001 and P < 0.001, respectively; Table 2). Follicle Growth Only cows ovulating after GnRH2 were used to analyze follicle growth rate. Follicle growth from d −5 to 0 was not affected by day of the cycle at GnRH1 (P = 0.82; Figure 2). When all cows were included in the analysis, there was no time × ovulation to GnRH1 interaction (P = 0.75; Figure 3). When D2 cows were removed from the analysis, there was also no time × ovulation to GnRH1 interaction (P = 0.57), but cows that ovulated after GnRH1 had a larger follicle by d −5 than cows that did not ovulate after GnRH1 (P = 0.05; Figure 3). There was no interaction in long-term follicle growth between ovulation after GnRH1 and the size of the ovulatory follicle (P = 0.25; data not shown). Mean follicle diameter ± SEM for small follicles (≤11 mm) was 9.3 ± 0.2 mm, with a range of 7.7 to 10.7 mm, and mean follicle diameter ± SEM for large follicles (>11 mm) was 12.9 ± 0.3 mm, with a range of 11.1 to 18.2 mm. Follicle growth rate tended to be faster (P = 0.07) from d −5 to 0 in cows ovulating large compared with small follicles (Figure 4), and follicle size was larger (P < 0.001) from d −5 to 0 in these cows. Short-term (from d −2 to 0) growth rate and follicle diameter at GnRH2 were positively correlated (r = 0.467; P < 0.0005). Follicle growth rate was slower from d −2 to 0 in D2 cows than in D9 cows (0.339 ± 0.14 vs. 1.29 ± 0.08 mm/d, respectively; P = 0.048) but was similar among all other treatment groups (1.06 ± 0.11, 1.13 ± 0.12, and 1.28 ± 0.11 mm/d for D5, D13, and D18 cows, respectively; P > 0.06; Figure 2). Figure 2. View largeDownload slide Largest follicle diameter (y-axis) for the 5 d preceding the second GnRH injection (GnRH2; d 0) by treatment group [day of the cycle at the beginning of the CO-Synch protocol (GnRH injection followed 7 d later with PGF2α, and a second GnRH injection 48 h after PGF2α)]. D2, D5, D9, D13, and D18 refer to treatment groups on d 2, 5, 9, 13, and 18, respectively. Follicle growth was similar among cows on different days of the cycle at the beginning of the CO-Synch protocol (P = 0.83 for the cycle day × time interaction). Only cows that ovulated after GnRH2 are included. Figure 2. View largeDownload slide Largest follicle diameter (y-axis) for the 5 d preceding the second GnRH injection (GnRH2; d 0) by treatment group [day of the cycle at the beginning of the CO-Synch protocol (GnRH injection followed 7 d later with PGF2α, and a second GnRH injection 48 h after PGF2α)]. D2, D5, D9, D13, and D18 refer to treatment groups on d 2, 5, 9, 13, and 18, respectively. Follicle growth was similar among cows on different days of the cycle at the beginning of the CO-Synch protocol (P = 0.83 for the cycle day × time interaction). Only cows that ovulated after GnRH2 are included. Figure 3. View largeDownload slide Growth of the preovulatory dominant follicle for 5 d preceding the second GnRH injection (GnRH2; d 0) between cows that did [first GnRH injection (GnRH1) yes; n = 25] or did not (GnRH1 no; n = 21) ovulate in response to GnRH1. Cows that ovulated to GnRH1 had a similar follicular growth pattern as cows that did not ovulate to GnRH1 when all treatment groups were included (GnRH1 yes and GnRH1 no; P = 0.75). However, when the d 2 cows were excluded from the analysis (GnRH1 no without d 2, n = 12), cows that ovulated to GnRH1 had a similar follicular growth rate from d −5 to 0 (P = 0.57), but the size of the ovulatory follicle was larger at d −5 compared with cows that did not ovulate (P = 0.05). Only cows that ovulated after GnRH2 are included. Figure 3. View largeDownload slide Growth of the preovulatory dominant follicle for 5 d preceding the second GnRH injection (GnRH2; d 0) between cows that did [first GnRH injection (GnRH1) yes; n = 25] or did not (GnRH1 no; n = 21) ovulate in response to GnRH1. Cows that ovulated to GnRH1 had a similar follicular growth pattern as cows that did not ovulate to GnRH1 when all treatment groups were included (GnRH1 yes and GnRH1 no; P = 0.75). However, when the d 2 cows were excluded from the analysis (GnRH1 no without d 2, n = 12), cows that ovulated to GnRH1 had a similar follicular growth rate from d −5 to 0 (P = 0.57), but the size of the ovulatory follicle was larger at d −5 compared with cows that did not ovulate (P = 0.05). Only cows that ovulated after GnRH2 are included. Figure 4. View largeDownload slide Growth of the preovulatory dominant follicle for 5 d preceding the second GnRH injection (GnRH2; d 0) among cows that ovulated a large (>11 mm; n = 29) or small (≤11 mm; n = 17) dominant follicle at GnRH2. Cows with a large follicle at GnRH2 had a faster rate of follicle growth leading up to GnRH2 than cows with a small dominant follicle at GnRH2 (P = 0.07), and the large ovulatory follicles were larger at d −5 than the small ovulatory follicles (P < 0.001). The complete regression equation is y = 9.8971 + 3.2812 (follicle size group, where 0 = small and 1 = large) + 0.4458 (day) − 0.1310 (day2). Only cows that ovulated after GnRH2 are included. Figure 4. View largeDownload slide Growth of the preovulatory dominant follicle for 5 d preceding the second GnRH injection (GnRH2; d 0) among cows that ovulated a large (>11 mm; n = 29) or small (≤11 mm; n = 17) dominant follicle at GnRH2. Cows with a large follicle at GnRH2 had a faster rate of follicle growth leading up to GnRH2 than cows with a small dominant follicle at GnRH2 (P = 0.07), and the large ovulatory follicles were larger at d −5 than the small ovulatory follicles (P < 0.001). The complete regression equation is y = 9.8971 + 3.2812 (follicle size group, where 0 = small and 1 = large) + 0.4458 (day) − 0.1310 (day2). Only cows that ovulated after GnRH2 are included. Serum Concentrations of Progesterone and Estradiol Serum concentrations of estradiol on the day of GnRH2 were not different among cows on different days of the cycle at GnRH1 (P = 0.33; Table 1). Serum concentrations of estradiol on the day of GnRH2 were not different among cows that ovulated to GnRH1 compared with cows that did not ovulate after GnRH1 (P = 0.35; Table 2). When the D2 cows were removed from the analysis, there was a tendency for cows that ovulated to GnRH1 to have increased serum concentrations of estradiol at GnRH2 compared with cows that did not ovulate to GnRH1 (P = 0.07). In cows that ovulated to GnRH2, the serum concentrations of estradiol were positively correlated with size of the dominant follicle at GnRH2 (r = 0.335; P = 0.006; Figure 5) independent of estrus. Figure 5. View largeDownload slide Scatter plot of serum concentration of estradiol (pg/mL) and diameter of the preovulatory follicle at the second GnRH injection (GnRH2). Serum concentration of estradiol was positively correlated (r = 0.335; P = 0.006) with diameter of the dominant follicle at GnRH2. Only cows that ovulated after GnRH2 are included (n = 46). Figure 5. View largeDownload slide Scatter plot of serum concentration of estradiol (pg/mL) and diameter of the preovulatory follicle at the second GnRH injection (GnRH2). Serum concentration of estradiol was positively correlated (r = 0.335; P = 0.006) with diameter of the dominant follicle at GnRH2. Only cows that ovulated after GnRH2 are included (n = 46). The variance in the mean serum concentrations of progesterone was not equal over time, so the concentrations of progesterone were log-transformed for the analysis. Actual values are graphed in Figure 6. Ovulation of follicles ≤11 mm led to reduced concentrations of serum progesterone over time (Figure 6; P = 0.005) compared with cows that ovulated follicles >11 mm in diameter. Some cows from the D2 (6/12) and D5 (5/12) treatment groups underwent incomplete luteal regression after PGF2α (Figure 7) and were not included in the progesterone analysis. Of the cows that underwent incomplete luteolysis, 2/6 and 2/5 of the D2 and D5 cows, respectively, were considered to have ovulated after GnRH2 based on disappearance of a dominant follicle and formation of luteal tissue. Figure 6. View largeDownload slide Mean serum concentrations of progesterone from d 1 to 12 after the second GnRH injection (GnRH2; d 0) in cows that ovulated small follicles (<11 mm; n = 14; circles) or large follicles (≥11 mm; n = 24; triangles). Because of unequal variance over time, the mean concentrations of progesterone were log-transformed for the analysis, but the nontransformed data are presented in this graph. Cows that ovulated large follicles had a greater increase in serum concentrations of progesterone than cows that ovulated small follicles (P = 0.005). Only cows that ovulated after GnRH2 and underwent complete luteal regression are included. Figure 6. View largeDownload slide Mean serum concentrations of progesterone from d 1 to 12 after the second GnRH injection (GnRH2; d 0) in cows that ovulated small follicles (<11 mm; n = 14; circles) or large follicles (≥11 mm; n = 24; triangles). Because of unequal variance over time, the mean concentrations of progesterone were log-transformed for the analysis, but the nontransformed data are presented in this graph. Cows that ovulated large follicles had a greater increase in serum concentrations of progesterone than cows that ovulated small follicles (P = 0.005). Only cows that ovulated after GnRH2 and underwent complete luteal regression are included. Figure 7. View largeDownload slide Mean serum concentrations of progesterone (ng/mL; error bars = SEM) during the treatment period and the subsequent estrous cycle. Some cows on treatment d 2 (6/12) and treatment d 5 (5/12) of the cycle at the beginning of the CO-Synch protocol [GnRH injection (GnRH1) followed 7 d later with PGF2α, and a second GnRH injection (GnRH2) 48 h after PGF2α] underwent incomplete luteolysis after PGF2α administration (open squares), whereas the remaining cows (from all treatment groups) had complete luteal regression (open triangles) based on serum concentrations of progesterone. Figure 7. View largeDownload slide Mean serum concentrations of progesterone (ng/mL; error bars = SEM) during the treatment period and the subsequent estrous cycle. Some cows on treatment d 2 (6/12) and treatment d 5 (5/12) of the cycle at the beginning of the CO-Synch protocol [GnRH injection (GnRH1) followed 7 d later with PGF2α, and a second GnRH injection (GnRH2) 48 h after PGF2α] underwent incomplete luteolysis after PGF2α administration (open squares), whereas the remaining cows (from all treatment groups) had complete luteal regression (open triangles) based on serum concentrations of progesterone. DISCUSSION In the present study, day of the estrous cycle at the beginning of the synchronization program did not affect follicle size or proportion ovulating at the second GnRH injection. In cows that were capable of ovulating to GnRH1 (i.e., excluding cows on D2 of the estrous cycle), cows that ovulated in response to GnRH1 had a larger follicle at GnRH2 than cows that did not ovulate to GnRH1. In anestrous beef cows, those that ovulated after the first GnRH injection also had larger follicles at the second GnRH injection than cows that did not ovulate after GnRH1 (Atkins et al., 2010). Vasconcelos et al. (1999) reported that day of the estrous cycle at the beginning of the Ovsynch program did not affect the percentage of dairy cows that ovulated at GnRH2, but cows in either the early (cycle d 1 to 4) or the later (cycle d 17 to 21) stage of the estrous cycle at the beginning of synchronization had larger follicles at GnRH2 than cows in the middle of the estrous cycle. These results differ slightly from those of the current study, but the current study used defined days of the estrous cycle instead of a range in days. Similar studies conducted in estrous cycling beef (Atkins et al., 2008) and dairy (Stevenson, 2008) heifers resulted in observations nearly opposite those of the current study. In beef heifers, ovulation to the first GnRH did not affect follicle size or ovulatory response at the second GnRH injection, but day of the estrous cycle at the beginning of the synchronization program did affect follicle size, percentage of heifers with a class III follicle at GnRH2, and ovulatory response after the second GnRH injection (Atkins et al., 2008). In dairy heifers, inclusion of the first GnRH injection did not improve response at the second GnRH or pregnancy rates (Stevenson, 2008), but day of the cycle did affect ovulation rate and follicle size at the second GnRH. The reason for the discrepancy between the heifer and cow responses is unknown. In the current study, nearly one-half of the cows in the early luteal phase at the beginning of the CO-Synch protocol underwent incomplete luteolysis after PGF2α (6/12 and 5/12 of the cows on D2 and D5 of the cycle at treatment initiation, respectively). Twagiramungu et al. (1994) reported that 4/18 cows treated with GnRH followed 7 d later with PGF2α underwent incomplete luteolysis. Similarly, Burke et al. (1996) reported that 8% of lactating dairy cows had incomplete luteal regression following PGF2α administered 7 d after GnRH. It is interesting that in the present study, only the cows in the early part of the estrous cycle had incomplete luteal regression, and all the cows on D9, D13, or D18 underwent complete luteolysis. Future experiments to confirm whether day of the cycle may contribute to the ability to undergo complete luteolysis after PGF2α administration could be important to improving the success of estrous synchronization protocols. Fortune et al. (1988) reported the growth rate of the dominant follicle in the first, second, and third follicular wave to be 1.6, 1.1, and 1.7 mm/d, respectively, in Holstein heifers. In the present study, follicle growth rate (0.89 mm/d; 5 d before GnRH2) was a little slower compared with the expected 1 to 2 mm/d. Follicle growth rate increased slightly after PGF2α administration (0.99 mm/d) compared with the long-term growth rate. The increase in follicle growth rate after PGF2α may be due to the loss of negative feedback from progesterone on LH (Beck et al., 1976); therefore, an increase in LH pulse frequency could drive follicle growth and estradiol production (Fortune, 1994). Although follicle growth rate from d −5 to 0 was similar between cows that did or did not ovulate after GnRH1, cows that ovulated after GnRH1 had a larger follicle by d −5 than cows that did not ovulate after GnRH1. The larger follicular diameter by d −5 may have been due to the synchronized growth of a follicular wave in cows ovulating to GnRH1. The reason for the reduced fertility associated with ovulation of a smaller dominant follicle may be related to the altered hormone profile in the cows after ovulation of an immature follicle. Britt and Holt (1988) described the 5 critical periods of changing hormone profiles that are important to fertility in lactating cows: 1) the luteal phase during the estrous cycle before insemination, 2) the period from the onset of luteolysis to estrus, 3) the preovulatory period, 4) the period from ovulation until progesterone increases, and 4) the luteal phase after insemination. Cows that are induced to ovulate a small dominant follicle may have altered endocrine profiles at several of these important periods, which could affect fertility. In the current study, cows that ovulated a small dominant follicle had reduced serum concentrations of estradiol during the preovulatory period and reduced serum concentrations of progesterone in the subsequent luteal phase. Cows that were induced to ovulate a small dominant follicle had reduced serum concentrations of estradiol at the time of ovulation compared with cows that ovulated large or small follicles spontaneously (Vasconcelos et al., 2001; Busch et al., 2008). Our results are supported by several others who reported reduced serum concentrations of progesterone in the subsequent luteal phase after induced ovulation of small (Perry et al., 2005; Atkins et al., 2008; Busch et al., 2008) or immature (Burke et al., 2001; Mussard et al., 2007) dominant follicles. Luteal secretion of progesterone is vital to embryo survival. Inadequate luteal function may impair interferon-τ production (Mann and Lamming, 2001) or uterine secretions important to embryo development (Garrett et al., 1988). The mechanism by which induced ovulation of small dominant follicles affects the establishment and maintenance of pregnancy is unclear, but may be related to an inadequate uterine environment supported by altered endocrine profiles of these cows, or to poor oocyte quality, gamete transport, or oviductal environment. Current studies are underway using reciprocal embryo transfer experiments to attempt to resolve this question. In summary, day of the estrous cycle at initiation of the CO-Synch protocol did not affect ovulatory follicle size or ovulatory response at induced ovulation for timed AI. Ovulatory response to GnRH1 also did not affect ovulatory response to GnRH2 and affected ovulatory size only when cows on d 2 of their estrous cycle at the start of synchronization (which did not have a follicle capable of ovulating at GnRH1) were removed from the analysis. Follicle growth rate leading up to GnRH2 tended to be faster in cows that ovulated a large follicle compared with cows that ovulated a small follicle. Ovulatory follicle diameter was positively correlated with serum concentrations of estradiol at GnRH2, and cows that ovulated small follicles had a smaller increase in serum concentrations of progesterone 12 d after GnRH. LITERATURE CITED Atkins J. A. Busch D. C. Bader J. F. Keisler D. H. Patterson D. J. Lucy M. C. Smith M. F. 2008. Gonadotropin-releasing hormone-induced ovulation and luteinizing hormone release in beef heifers: Effect of day of the cycle. J. Anim. Sci.  86: 83– 93. https://doi.org/17911234 Google Scholar CrossRef Search ADS PubMed  Beck T. W. Smith V. G. Seguin B. E. Convey E. M. 1976. Bovine serum LH, GH, and prolactin following chronic implantation of ovarian steroids and subsequent ovariectomy. J. Anim. Sci.  42: 461– 468. https://doi.org/1262266 Google Scholar CrossRef Search ADS PubMed  Britt J. H. Holt L. C. 1988. Endocrinological screening of embryo donors and embryo transfer recipients: A review of research with cattle. Theriogenology  29: 189– 202. Google Scholar CrossRef Search ADS   Burke C. R. Mussard M. L. Grum D. E. Day M. L. 2001. Effects of maturity of the potential ovulatory follicle on induction of oestrus and ovulation in cattle with oestradiol benzoate. Anim. Reprod. Sci.  66: 161– 174. https://doi.org/11348779 Google Scholar CrossRef Search ADS PubMed  Burke J. M. De la Sota R. L. Risco C. A. Staples C. R. Schmitt E. J.-P. Thatcher W. W. 1996. Evaluation of timed insemination using a gonadotropin-releasing hormone agonist in lactating dairy cows. J. Dairy Sci.  79: 1385– 1393. https://doi.org/8880462 Google Scholar CrossRef Search ADS PubMed  Busch D. C. Atkins J. A. Bader J. F. Schafer D. J. Patterson D. J. Geary T. W. Smith M. F. 2008. Effect of ovulatory follicle size and expression of estrus on progesterone secretion in beef cows. J. Anim. Sci.  86: 553– 563. https://doi.org/18156357 Google Scholar CrossRef Search ADS PubMed  Fortune J. E. 1994. Ovarian follicular growth and development in mammals. Biol. Reprod.  50: 225– 232. https://doi.org/8142540 Google Scholar CrossRef Search ADS PubMed  Fortune J. E. Sirois J. Quirk S. M. 1988. The growth and differentiation of ovarian follicles during the bovine estrous cycle. Theriogenology  29: 95– 109. Google Scholar CrossRef Search ADS   Garrett J. E. Geisert R. D. Zavy M. T. Morgan G. L. 1988. Evidence for maternal regulation of early conceptus growth and development in beef cattle. J. Reprod. Fertil.  84: 437– 446. https://doi.org/3199361 Google Scholar CrossRef Search ADS PubMed  Geary T. W. Whittier J. C. 1998. Effects of a timed insemination following synchronization of ovulation using the Ovsynch or CO-Synch protocol in beef cows. Prof. Anim. Sci.  14: 217– 220. Ginther O. J. Beg M. A. Bergfelt D. R. Donadeu F. X. Kot K. 2001. Follicle selection in monovular species. Biol. Reprod.  65: 638– 647. https://doi.org/11514323 Google Scholar CrossRef Search ADS PubMed  Kirby C. J. Smith M. F. Keisler D. H. Lucy M. C. 1997. Follicular function in lactating dairy cows treated with sustained-release bovine somatotropin. J. Dairy Sci.  80: 273– 285. https://doi.org/9058268 Google Scholar CrossRef Search ADS PubMed  Lamb G. C. Stevenson J. S. Kesler D. J. Garverick H. A. Brown D. R. Salfen B. E. 2001. Inclusion of an intravaginal progesterone insert plus GnRH and prostaglandin F2α for ovulation control in postpartum suckled beef cows. J. Anim. Sci.  79: 2253– 2259. https://doi.org/11583411 Google Scholar CrossRef Search ADS PubMed  Littell R. C. Henry P. R. Ammerman C. B. 1998. Statistical analysis of repeated measures data using SAS procedures. J. Anim. Sci.  76: 1216– 1231. https://doi.org/9581947 Google Scholar CrossRef Search ADS PubMed  Mann G. E. Lamming G. E. 2001. Relationship between maternal endocrine environment, early embryo development and inhibition of the luteolytic mechanism in cows. Reproduction  121: 175– 180. https://doi.org/11226041 Google Scholar CrossRef Search ADS PubMed  Moreira F. de la Sota R. L. Diaz T. Thatcher W. W. 2000. Effect of day of the estrous cycle at the initiation of a timed artificial insemination protocol on reproductive responses in dairy heifers. J. Anim. Sci.  78: 1568– 1576. https://doi.org/10875641 Google Scholar CrossRef Search ADS PubMed  Mussard M. L. Burke C. R. Behlke E. J. Gasser C. L. Day M. L. 2007. Influence of premature induction of a luteinizing hormone surge with gonadotropin-releasing hormone on ovulation, luteal function, and fertility in cattle. J. Anim. Sci.  85: 937– 943. https://doi.org/17145968 Google Scholar CrossRef Search ADS PubMed  Perry G. A. Smith M. F. Lucy M. C. Green J. A. Parks T. E. MacNeil M. D. Roberts A. J. Geary T. W. 2005. Relationship between follicle size at insemination and pregnancy success. Proc. Natl. Acad. Sci. USA  102: 5268– 5273. https://doi.org/15795381 Google Scholar CrossRef Search ADS   Stevenson J. S. 2008. Progesterone, follicular and estrual responses to progesterone-based estrus and ovulation synchronization protocols at five stages of the estrous cycle. J. Dairy Sci.  91: 4640– 4650. https://doi.org/19038940 Google Scholar CrossRef Search ADS PubMed  Twagiramungu H. Guilbault L. A. Proulx J. G. Dufour J. J. 1994. Influence of corpus luteum and induced ovulation on ovarian follicular dynamics in postpartum cyclic cows treated with buserelin and cloprostenol. J. Anim. Sci.  72: 1796– 1805. https://doi.org/7928759 Google Scholar CrossRef Search ADS PubMed  Vasconcelos J. L. Sartori R. Oliveira H. N. Guenther J. G. Wiltbank M. C. 2001. Reduction in size of the ovulatory follicle reduces subsequent luteal size and pregnancy rate. Theriogenology  56: 307– 314. https://doi.org/11480622 Google Scholar CrossRef Search ADS PubMed  Vasconcelos J. L. Silcox R. W. Rosa G. J. M. Pursley J. R. Wiltbank M. C. 1999. Synchronization rate, size of the ovulatory follicle, and pregnancy rate after synchronization of ovulation beginning on different days of the estrous cycle in lactating dairy cows. Theriogenology  52: 1067– 1078. https://doi.org/10735113 Google Scholar CrossRef Search ADS PubMed  American Society of Animal Science

Atkins, J. A.;Smith, M. F.;Wells, K. J.;Geary, T. W.

doi: 10.2527/jas.2009-2532pmid: 20348374

ABSTRACT There is large variation in dominant follicle diameter at the time of GnRH-induced ovulation in the CO-Synch protocol [a first GnRH injection on d −9 (GnRH1), followed by PGF2α on d −2, and a second GnRH injection (GnRH2) with timed AI on d 0], and the reason for the presence of small dominant follicles at GnRH2 is not known. Our hypothesis was that ovulatory response to GnRH1 and progesterone exposure [controlled intravaginal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY)] would affect ovulatory follicle size at GnRH2 in anestrous cows. This study used a 2 × 2 factorial arrangement of treatments in which anestrous suckled beef cows (n = 55) either ovulated (Ov1+) or failed to ovulate (Ov1−) after GnRH1 and either received (CIDR+) or did not receive (CIDR−) a 7-d CIDR treatment (from GnRH1 to PGF2α), resulting in the following treatment groups: Ov1+CIDR+, Ov1−CIDR+, Ov1+CIDR−, and Ov1−CIDR− (n = 9, 17, 11, and 18, respectively). The Ov1+ cows had larger follicles at GnRH2 (12.3 vs. 11.0 mm; P = 0.04), a decreased proportion of small follicles within cows that ovulated to GnRH2 (2/16 vs. 14/23; P = 0.003), and a similar growth rate of the ovulatory follicle from d −5 to 0 (d 0 = GnRH2; 1.1 ± 0.06 vs. 1.1 ± 0.07 mm/d; P = 0.99) compared with Ov1− cows. Administration of a CIDR had no effect on follicle diameter at GnRH2 (11.8 vs. 11.2 mm; P = 0.3), proportion of small ovulatory follicles at GnRH2 (7/19 vs. 9/20; P = 0.6), and follicular growth rate from d −5 to 0 (d 0 = GnRH2; 1.2 ± 0.07 vs. 1.1 ± 0.07 mm/d; P = 0.76). Administration of a CIDR, but not ovulation to GnRH1, increased follicle growth from d −2 to 0 (d 0 = GnRH2; P = 0.03 and 0.9, respectively). Large follicles (>11 mm) had a similar growth rate from d −5 to 0 (d 0 = GnRH2; P = 0.44) compared with small follicles (1.1 ± 0.07 vs. 1.2 ± 0.07 mm/d), but the large ovulatory follicles were larger at d −5 compared with small ovulatory follicles (P < 0.001). Follicle diameter was positively correlated with serum concentrations of estradiol at GnRH2 (r = 0.622; P < 0.0001). In summary, ovulation to GnRH1, but not CIDR administration, resulted in increased dominant follicle diameter at GnRH2 in anestrous suckled beef cows. Large follicles were already larger 5 d before GnRH2 but grew at a rate similar to small follicles; follicle size was positively correlated with serum concentrations of estradiol at the time of GnRH-induced ovulation. INTRODUCTION Multiple reports indicated that cows induced to ovulate small dominant follicles have reduced pregnancy rates (Lamb et al., 2001; Vasconcelos et al., 2001; Perry et al., 2005). There is large variation in the diameter of the largest follicle (Lamb et al., 2001; Perry et al., 2005) at the time of GnRH-induced ovulation in the CO-Synch protocol [GnRH on d 9 (GnRH1), PGF2α on d 2, and GnRH on d 0 (GnRH2); Geary and Whittier, 1998] and other timed AI protocols [CO-Synch plus controlled intravaginal drug-releasing insert (CIDR; Lamb et al., 2001)]. Additionally, cows that were induced to ovulate a small dominant follicle had an increased occurrence of late embryonic or fetal mortality (Perry et al., 2005). The reason for the presence of small dominant follicles at GnRH2 is unknown, but may be due to failure to control the initiation of a new follicular wave before GnRH2. This lack of control of the follicle wave could result in more variation in the age and size of the follicle at GnRH2. Alternatively, varied follicle growth rates leading up to GnRH2 could contribute to variation in preovulatory follicle diameter. In anestrous cows, the pulse frequency and mean concentrations of circulating LH were less than those in cycling cows, which could reduce the rate of dominant follicle growth (reviewed by Yavas and Walton, 2000). Administration of a progestin increased the frequency of LH pulses in postpartum beef cows (Williams et al., 1983; Garcia-Winder et al., 1986). Addition of exogenous progesterone to the CO-Synch protocol may affect follicle growth rate and the preovulatory follicle diameter at GnRH2. We hypothesized that failure to initiate a new follicular wave after GnRH1 and absence of progesterone would increase the occurrence of small preovulatory follicles at the GnRH-induced ovulation. The objectives of the study were to determine the effect of ovulation after GnRH1 and exogenous progesterone treatment on the size and growth rate of the largest follicle at GnRH2 in anestrous beef cows. MATERIALS AND METHODS All protocols and procedures were approved by the Fort Keogh Livestock and Range Research Laboratory Animal Care and Use Committee. Animal Handling Anestrous suckled beef cows (n = 55) at the Fort Keogh Livestock and Range Research Laboratory were administered the CO-Synch protocol, with the exception that no cows were bred. Anestrous status was based on serum concentrations of progesterone 10 d before the beginning of treatment and the absence of a corpus luteum (CL) at treatment initiation. Approximately one-half of the cows were administered exogenous progesterone (EAZI-Breed CIDR containing 1.38 g of progesterone, Pfizer Animal Health, New York, NY; n = 26) from GnRH1 to PGF2α, resulting in a 2 × 2 factorial arrangement of treatments based on ovulation (OV1+) or failure to ovulate (Ov1−) to GnRH1 and the presence (CIDR+) or absence (CIDR−) of a CIDR (Ov1+CIDR+, Ov1−CIDR+, Ov1+CIDR−, Ov1−CIDR−; n = 9, 17, 11, and 18, respectively). There were no differences in average days postpartum, age, or BCS (1 to 9, where 1 = emaciated and 9 = obese) among the treatment groups (Table 1). Estrus was detected visually twice daily from d −9 to 20 (d 0 = GnRH2) and was aided with the use of Estrus Alert (Western Point Inc., Apple Valley, MN) estrous detection patches. Table 1. Mean (±SEM) days postpartum (DPP), age, and BCS for each treatment group Treatment1  n  DPP, d  Age, yr  BCS2  Ov1+CIDR+  9  42 ± 2.3  3.4 ± 0.8  4.3 ± 0.1  Ov1+CIDR−  11  42 ± 1.8  3.9 ± 0.8  4.6 ± 0.1  Ov1−CIDR+  17  44 ± 1.5  2.9 ± 0.4  4.2 ± 0.1  Ov1−CIDR−  18  44 ± 1.7  2.7 ± 0.3  4.3 ± 0.1  Treatment1  n  DPP, d  Age, yr  BCS2  Ov1+CIDR+  9  42 ± 2.3  3.4 ± 0.8  4.3 ± 0.1  Ov1+CIDR−  11  42 ± 1.8  3.9 ± 0.8  4.6 ± 0.1  Ov1−CIDR+  17  44 ± 1.5  2.9 ± 0.4  4.2 ± 0.1  Ov1−CIDR−  18  44 ± 1.7  2.7 ± 0.3  4.3 ± 0.1  1Ovulation to the first GnRH injection (GnRH1; Ov1+) or failure to ovulate (Ov1−) and presence (CIDR+) or absence (CIDR−) of a controlled internal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY). There were no treatment differences (P > 0.6). 2BCS was based on a 1 to 9 scale, where 1 = emaciated and 9 = obese. View Large Table 1. Mean (±SEM) days postpartum (DPP), age, and BCS for each treatment group Treatment1  n  DPP, d  Age, yr  BCS2  Ov1+CIDR+  9  42 ± 2.3  3.4 ± 0.8  4.3 ± 0.1  Ov1+CIDR−  11  42 ± 1.8  3.9 ± 0.8  4.6 ± 0.1  Ov1−CIDR+  17  44 ± 1.5  2.9 ± 0.4  4.2 ± 0.1  Ov1−CIDR−  18  44 ± 1.7  2.7 ± 0.3  4.3 ± 0.1  Treatment1  n  DPP, d  Age, yr  BCS2  Ov1+CIDR+  9  42 ± 2.3  3.4 ± 0.8  4.3 ± 0.1  Ov1+CIDR−  11  42 ± 1.8  3.9 ± 0.8  4.6 ± 0.1  Ov1−CIDR+  17  44 ± 1.5  2.9 ± 0.4  4.2 ± 0.1  Ov1−CIDR−  18  44 ± 1.7  2.7 ± 0.3  4.3 ± 0.1  1Ovulation to the first GnRH injection (GnRH1; Ov1+) or failure to ovulate (Ov1−) and presence (CIDR+) or absence (CIDR−) of a controlled internal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY). There were no treatment differences (P > 0.6). 2BCS was based on a 1 to 9 scale, where 1 = emaciated and 9 = obese. View Large Transrectal Ultrasonography Ovarian structures were monitored using an Aloka 500V ultrasound instrument with a 7.5-MHz transducer (Aloka, Wallingford, CT). Follicles ≥5 mm in diameter and the presence of a CL were recorded. Follicle diameter was measured at the widest point and at a right angle to the first measurement, and the average of these measurements was recorded as the follicle diameter. Transrectal ultrasonography was performed on d −9 (GnRH1) and d 0 (GnRH2) to determine the diameter of the ovulatory follicle. The presence of a class III follicle (>9 mm; Moreira et al., 2000) was recorded at each ultrasound exam and used as an indicator of a follicle that might ovulate. Ovulatory follicles ≤11 mm were considered small dominant follicles and follicles >11 mm were considered large follicles based on previous research defining the optimal follicle diameter for pregnancy in this herd (Perry et al., 2005). Ovulation after GnRH1 and GnRH2 was determined on d −7 and 2, respectively, and was based on the disappearance of a dominant follicle and, in some cases, formation of new luteal tissue. Daily ultrasound exams from d −9 to 0 were performed and recorded to monitor the growth of dominant follicles during the treatment period. The long-term (d −5 to 0) follicle growth pattern required backtracking specific follicles using the recorded ovarian sonograms. Most cows had an individual follicle from a single follicular wave that was tracked beginning on d −5, so the growth of the ovulatory follicle was calculated from d −5 to 0. A polynomial equation was fit to the follicle growth curve, and the first derivative of the polynomial equation was determined for each cow. The first derivative was solved for zero to determine the day the follicle had reached a plateau in growth (±0.5 d). Follicles were considered to be increasing in size before the plateau and decreasing in size after the plateau. Blood Collection and RIA Blood samples were collected daily from d −9 to 20 by tail or jugular venipuncture into 10-mL vacuum tubes (Fisher Scientific, Pittsburgh, PA). After collection, the blood was stored for 24 h at 4°C, followed by centrifugation at 1,200 × g for 25 min at 4°C. Serum was harvested and stored at −20°C until RIA. Serum concentrations of progesterone were measured in all samples by using a Coat-a-Count RIA kit (Diagnostic Products Corporation, Los Angeles, CA; Kirby et al., 1997). The intra- and interassay CV for the progesterone RIA were 3.7 and 8.4%, respectively. The sensitivity of the assay was 0.08 ng/mL. Serum concentrations of estradiol-17β were measured by using RIA (Kirby et al., 1997) in samples collected from d −9 to 0. The intra- and interassay CV were 9.5 and 18.8%, respectively. The sensitivity of the assay was 0.5 pg/mL. Resumption of the Estrous Cycle Based on the serum concentrations of progesterone from d 0 to 20 and estrous data, cows were classified as cycling or anestrus (serum concentrations of progesterone remained below 1.0 ng/mL for the duration of the experiment). The cycling cows were further separated into cows with a normal estrous cycle (≥16 d) or cows with a short estrous cycle (<16 d; Rantala et al., 2009). Statistical Analyses Throughout the analyses, the main effects of CIDR administration and ovulation to GnRH1 were analyzed, followed by interaction of the main effects. The proportions of cows that ovulated to GnRH, had a small ovulatory follicle, and resumed estrous cycling were analyzed using the GENMOD procedure (SAS Inst. Inc., Cary, NC). The percentage of cows with a class III follicle during the treatment period was analyzed using the GENMOD procedure for repeated measures over time. The main effect of CIDR administration and ovulation to GnRH1 on the average follicle diameter at GnRH1 and GnRH2, short-term follicle growth rates (from d −2 to 0), and serum concentrations of estradiol was analyzed by one-way ANOVA using the 2-sample t-test in SAS, whereas the interaction of the treatments was analyzed using a GLM with treatment as the independent variable. Long-term follicle growth (from d −5 to 0) was analyzed by a weighted ANOVA for repeated measures over time (PROC MIXED; Littell et al., 1998), in which time points were weighted according to the number of observations recorded at the time points. The correlation between concentrations of estradiol and ovulatory follicle diameter was analyzed with the CORR procedure in SAS. Additionally, a multiple regression model was used to analyze estrus and follicle size as the independent variables and serum concentrations of estradiol as the dependent variable (PROC GLM in SAS). The increase in serum concentrations of progesterone after GnRH2 between cows that ovulated a small vs. large follicle was analyzed using the MIXED procedure for repeated measures (Littell et al., 1998). RESULTS Ovulatory Response and Follicle Diameter Ovulatory responses to GnRH1 and GnRH2 were 36 and 71%, respectively. A larger proportion (P < 0.05) of cows with a follicle that was increasing in diameter at GnRH2 ovulated after GnRH2 than cows with a follicle that had reached a plateau in growth or cows with a decreasing follicle diameter at GnRH2 (20/22, 1/9, and 12/23 for cows with an increase, plateau, or decrease in follicle growth, respectively). Neither ovulation to GnRH1 nor CIDR administration affected the proportion of cows ovulating to GnRH2 (P = 0.27 and 0.73, respectively; Table 2). Table 2. Main treatment effects of ovulation to the first GnRH injection (GnRH1; Ov1+) or failure to ovulate (Ov1−) and presence (CIDR+) or absence (CIDR−) of a controlled internal drug-releasing (CIDR) insert1 on the proportion (%) ovulating after the second GnRH injection (GnRH2), size of the largest follicle (mm; mean ± SEM), and serum concentrations of estradiol (pg/mL; mean ± SEM) at GnRH2 Treatment  n  Ovulation after GnRH2  Dominant follicle diameter  Estradiol  GnRH1           Ov1+  20  16/20 (80)  12.3 ± 0.5a  3.4 ± 0.5a   Ov1−  35  23/35 (66)  11.0 ± 0.3b  2.3 ± 0.3b  CIDR           CIDR+  26  19/26 (73)  11.8 ± 0.4  2.8 ± 0.4   CIDR−  29  20/29 (69)  11.2 ± 0.4  2.5 ± 0.3  Treatment  n  Ovulation after GnRH2  Dominant follicle diameter  Estradiol  GnRH1           Ov1+  20  16/20 (80)  12.3 ± 0.5a  3.4 ± 0.5a   Ov1−  35  23/35 (66)  11.0 ± 0.3b  2.3 ± 0.3b  CIDR           CIDR+  26  19/26 (73)  11.8 ± 0.4  2.8 ± 0.4   CIDR−  29  20/29 (69)  11.2 ± 0.4  2.5 ± 0.3  a,bMeans within a column with different superscripts differed between treatments (P < 0.05). 1EAZI-Breed (Pfizer Animal Health, New York, NY). View Large Table 2. Main treatment effects of ovulation to the first GnRH injection (GnRH1; Ov1+) or failure to ovulate (Ov1−) and presence (CIDR+) or absence (CIDR−) of a controlled internal drug-releasing (CIDR) insert1 on the proportion (%) ovulating after the second GnRH injection (GnRH2), size of the largest follicle (mm; mean ± SEM), and serum concentrations of estradiol (pg/mL; mean ± SEM) at GnRH2 Treatment  n  Ovulation after GnRH2  Dominant follicle diameter  Estradiol  GnRH1           Ov1+  20  16/20 (80)  12.3 ± 0.5a  3.4 ± 0.5a   Ov1−  35  23/35 (66)  11.0 ± 0.3b  2.3 ± 0.3b  CIDR           CIDR+  26  19/26 (73)  11.8 ± 0.4  2.8 ± 0.4   CIDR−  29  20/29 (69)  11.2 ± 0.4  2.5 ± 0.3  Treatment  n  Ovulation after GnRH2  Dominant follicle diameter  Estradiol  GnRH1           Ov1+  20  16/20 (80)  12.3 ± 0.5a  3.4 ± 0.5a   Ov1−  35  23/35 (66)  11.0 ± 0.3b  2.3 ± 0.3b  CIDR           CIDR+  26  19/26 (73)  11.8 ± 0.4  2.8 ± 0.4   CIDR−  29  20/29 (69)  11.2 ± 0.4  2.5 ± 0.3  a,bMeans within a column with different superscripts differed between treatments (P < 0.05). 1EAZI-Breed (Pfizer Animal Health, New York, NY). View Large The range in ovulatory follicle diameter was 8.6 to 16.1 mm, with 41% of the follicles ≤11 mm. Ovulation to GnRH1, but not CIDR treatment, resulted in a larger follicle at GnRH2 (P = 0.04 and 0.3, respectively; Table 2; for each treatment group, see Table 3), and there was more variation (P < 0.05) in ovulatory follicle diameter at GnRH2 in the Ov1− cows compared with the OV1+ cows (variance was 4.0 vs. 2.9, respectively). When only cows that ovulated to GnRH2 were analyzed, the OV1+ treatment group had a decreased proportion of cows that ovulated small follicles in response to GnRH2 compared with the Ov1− treatment group (Ov1+ had 2/16 small ovulatory follicles and Ov1− had 14/23 small ovulatory follicles; P = 0.0025). Administration of CIDR did not affect the proportion of small ovulatory follicles at GnRH2 (CIDR+ had 7/19 small ovulatory follicles and CIDR− had 9/20 small ovulatory follicles; P = 0.61) or the variation in ovulatory follicle diameter (P > 0.10). Table 3. Proportion (%) ovulating, size of the largest follicle (mm; mean ± SEM), and serum concentrations of estradiol (pg/mL; mean ± SEM) at the second GnRH injection (GnRH2) in each individual treatment group Treatment1  n  Ovulation after GnRH2  Dominant follicle diameter  Estradiol  Ov1+CIDR+  9  7/9 (78)  12.3 ± 0.9  3.9 ± 0.8a  Ov1+CIDR−  11  9/11 (82)  12.3 ± 0.7  3.6 ± 0.6ab  Ov1−CIDR+  17  12/17 (71)  11.6 ± 0.4  2.4 ± 0.4ab  Ov1−CIDR−  18  11/18 (61)  10.5 ± 0.5  2.1 ± 0.3b  Treatment1  n  Ovulation after GnRH2  Dominant follicle diameter  Estradiol  Ov1+CIDR+  9  7/9 (78)  12.3 ± 0.9  3.9 ± 0.8a  Ov1+CIDR−  11  9/11 (82)  12.3 ± 0.7  3.6 ± 0.6ab  Ov1−CIDR+  17  12/17 (71)  11.6 ± 0.4  2.4 ± 0.4ab  Ov1−CIDR−  18  11/18 (61)  10.5 ± 0.5  2.1 ± 0.3b  a,bMeans within a column with different superscripts differ (P = 0.07). 1Ovulation (Ov1+) to the first GnRH injection (GnRH1) or failure to ovulate (Ov1−) and presence (CIDR+) or absence (CIDR−) of a controlled internal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY). View Large Table 3. Proportion (%) ovulating, size of the largest follicle (mm; mean ± SEM), and serum concentrations of estradiol (pg/mL; mean ± SEM) at the second GnRH injection (GnRH2) in each individual treatment group Treatment1  n  Ovulation after GnRH2  Dominant follicle diameter  Estradiol  Ov1+CIDR+  9  7/9 (78)  12.3 ± 0.9  3.9 ± 0.8a  Ov1+CIDR−  11  9/11 (82)  12.3 ± 0.7  3.6 ± 0.6ab  Ov1−CIDR+  17  12/17 (71)  11.6 ± 0.4  2.4 ± 0.4ab  Ov1−CIDR−  18  11/18 (61)  10.5 ± 0.5  2.1 ± 0.3b  Treatment1  n  Ovulation after GnRH2  Dominant follicle diameter  Estradiol  Ov1+CIDR+  9  7/9 (78)  12.3 ± 0.9  3.9 ± 0.8a  Ov1+CIDR−  11  9/11 (82)  12.3 ± 0.7  3.6 ± 0.6ab  Ov1−CIDR+  17  12/17 (71)  11.6 ± 0.4  2.4 ± 0.4ab  Ov1−CIDR−  18  11/18 (61)  10.5 ± 0.5  2.1 ± 0.3b  a,bMeans within a column with different superscripts differ (P = 0.07). 1Ovulation (Ov1+) to the first GnRH injection (GnRH1) or failure to ovulate (Ov1−) and presence (CIDR+) or absence (CIDR−) of a controlled internal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY). View Large The percentage of cows with a class III follicle (>9 mm) was affected by treatment day (P = 0.0005). Ovulation to GnRH1 and CIDR treatment did not affect the percentage of cows with a class III follicle during the treatment period (Figure 1a and 1b; P = 0.9) nor was there an interaction of these treatments (Figure 1c; P = 0.21). Figure 1. View largeDownload slide Percentage of cows with a class III follicle (>9 mm) during the treatment period. All cows were administered a first GnRH injection (GnRH1) on d −9, PGF2α on d −2, and a second GnRH injection (GnRH2) on d 0. There was no difference in percentage of cows with a class III follicle between cows that did (Ov1+) or did not ovulate (Ov1−) after GnRH1 (panel a; P = 0.09) during the treatment period. Similarly, the percentage of cows with a class III follicle during the treatment period did not differ between those administered a controlled internal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY; CIDR+) or not (CIDR−; panel b; P = 0.9). There was no interaction between ovulatory response and CIDR administration on the percentage of cows with a class III follicle over time (panel c; P = 0.21). Figure 1. View largeDownload slide Percentage of cows with a class III follicle (>9 mm) during the treatment period. All cows were administered a first GnRH injection (GnRH1) on d −9, PGF2α on d −2, and a second GnRH injection (GnRH2) on d 0. There was no difference in percentage of cows with a class III follicle between cows that did (Ov1+) or did not ovulate (Ov1−) after GnRH1 (panel a; P = 0.09) during the treatment period. Similarly, the percentage of cows with a class III follicle during the treatment period did not differ between those administered a controlled internal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY; CIDR+) or not (CIDR−; panel b; P = 0.9). There was no interaction between ovulatory response and CIDR administration on the percentage of cows with a class III follicle over time (panel c; P = 0.21). Follicle Growth The average long-term (d −5 to 0) and short-term (d −2 to 0) follicle growth rate across all cows was 1.1 and 0.89 mm/d, respectively. Among cows that ovulated to GnRH2, Ov1+ cows had similar follicle growth from d −5 to 0 compared with Ov1− cows (1.1 ± 0.06 and 1.1 ± 0.06 mm/d, respectively; Figure 2; P = 0.99) and from d −2 to 0 (0.77 ± 0.17 and 0.70 ± 0.15 mm/d, respectively; P = 0.9). The ovulatory follicle was already larger on d −5 in Ov1+ cows compared with Ov1− cows (Figure 2; P = 0.003). Among cows that ovulated to GnRH2, CIDR+ cows had a faster growth rate from d −2 to 0 (0.97 ± 0.15 and 0.50 ± 0.15 mm/d, respectively; P = 0.03) compared with CIDR− cows, but the growth rate was similar from d −5 to 0 (1.2 ± 0.07 and 1.1 mm/d ± 0.07, respectively; P = 0.99; Figure 2). There was no interaction between CIDR administration and ovulation after GnRH1 in either long-term (P = 0.97) or short-term (P = 0.17) follicle growth. Follicle growth was similar (P = 0.44) from d −5 to 0 in cows ovulating large (>11 mm) compared with small follicles at GnRH2 (1.1 ± 0.07 and 1.2 ± 0.07 mm/d, respectively; Figure 3), but cows that ovulated a large follicle at GnRH2 already had a larger follicle at d −5 compared with cows with a small ovulatory follicle (Figure 3; P < 0.001). The short-term follicle growth rate was positively correlated with the size of the follicle at GnRH2 (r = 0.44 and P = 0.005). Figure 2. View largeDownload slide The preovulatory follicle diameter leading up to the second GnRH injection (GnRH2) by treatment. All cows were administered the first GnRH injection (GnRH1) on d −9, PGF2α on d −2, and GnRH2 on d 0, and only cows that ovulated after GnRH2 were included. Cows that ovulated after GnRH1 (Ov1+; n = 16) had a similar follicular growth rate from d −5 to 0 (P = 0.99) and from d −2 to 0 (P = 0.9) compared with cows that did not ovulate after GnRH1 (Ov1−; n = 23). The ovulatory follicle was larger at d −5 in Ov1+ cows compared with Ov1− cows (P = 0.003). Cows that received a controlled internal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY; CIDR+; n = 19) had a similar follicular growth rate from d −5 to 0 (P = 0.76) compared with cows that did not receive a CIDR (CIDR−; n = 20) but had a faster follicular growth rate from d −2 to 0 (P = 0.03). Figure 2. View largeDownload slide The preovulatory follicle diameter leading up to the second GnRH injection (GnRH2) by treatment. All cows were administered the first GnRH injection (GnRH1) on d −9, PGF2α on d −2, and GnRH2 on d 0, and only cows that ovulated after GnRH2 were included. Cows that ovulated after GnRH1 (Ov1+; n = 16) had a similar follicular growth rate from d −5 to 0 (P = 0.99) and from d −2 to 0 (P = 0.9) compared with cows that did not ovulate after GnRH1 (Ov1−; n = 23). The ovulatory follicle was larger at d −5 in Ov1+ cows compared with Ov1− cows (P = 0.003). Cows that received a controlled internal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY; CIDR+; n = 19) had a similar follicular growth rate from d −5 to 0 (P = 0.76) compared with cows that did not receive a CIDR (CIDR−; n = 20) but had a faster follicular growth rate from d −2 to 0 (P = 0.03). Figure 3. View largeDownload slide The preovulatory follicle diameter leading up to the second GnRH injection (GnRH2) by ovulatory follicle size. All cows were administered a first GnRH injection (GnRH1) on d −9, PGF2α on d −2, and GnRH2 on d 0, and only cows that ovulated after GnRH2 were included. Cows with a large ovulatory follicle (>11 mm; n = 22) had a similar follicular growth rate from d −5 to 0 compared with cows that ovulated a small follicle (≤11 mm; P = 0.44; n = 17), but the follicle was already larger by d −5 in cows that ovulated a large follicle compared with cows that ovulated a small follicle (P < 0.001). DF = dominant follicle. Figure 3. View largeDownload slide The preovulatory follicle diameter leading up to the second GnRH injection (GnRH2) by ovulatory follicle size. All cows were administered a first GnRH injection (GnRH1) on d −9, PGF2α on d −2, and GnRH2 on d 0, and only cows that ovulated after GnRH2 were included. Cows with a large ovulatory follicle (>11 mm; n = 22) had a similar follicular growth rate from d −5 to 0 compared with cows that ovulated a small follicle (≤11 mm; P = 0.44; n = 17), but the follicle was already larger by d −5 in cows that ovulated a large follicle compared with cows that ovulated a small follicle (P < 0.001). DF = dominant follicle. Serum Concentrations of Estradiol and Estrus Ovulation to GnRH1, but not CIDR treatment, resulted in increased serum concentrations of estradiol at GnRH2 (P = 0.004 and 0.59, respectively; Table 2). The serum concentrations of estradiol were positively correlated with size of the dominant follicle at GnRH2 (r = 0.62; P < 0.0001; Figure 4). Only 7 cows were in estrus at the time of GnRH2, and both estrus and follicle diameter had a significant positive relationship with serum concentrations of estradiol on the day of GnRH2 (P < 0.0001 and P = 0.003, respectively). Figure 4. View largeDownload slide The ovulatory follicle diameter and serum concentrations of estradiol at the second GnRH injection (GnRH2) were positively correlated (r = 0.622, P < 0.0001). Only cows that ovulated after GnRH2 were included in the analysis (n = 39). Figure 4. View largeDownload slide The ovulatory follicle diameter and serum concentrations of estradiol at the second GnRH injection (GnRH2) were positively correlated (r = 0.622, P < 0.0001). Only cows that ovulated after GnRH2 were included in the analysis (n = 39). Resumption of Cyclicity The proportion of cows that resumed cycling after GnRH2 was similar between Ov1+ and Ov1− cows (16/20 and 20/35, respectively; P = 0.11). Similarly, there was no effect of CIDR administration on the proportion of cows that resumed cycling (P = 0.33), nor was there an interaction among treatments (P > 0.10; Table 4). Of the cows that resumed cycling, there was a treatment interaction in the proportion of cows with a short cycle. Cows in the Ov1−CIDR− group had a smaller proportion of normal estrous cycle lengths (more cows with a short cycle) than OV1+ and CIDR+ cows (P < 0.05; Table 4). None of the cows classified as having normal estrous cycles had estrous cycles shorter than 19 d. Cows that ovulated a large follicle after GnRH2 had greater (P < 0.05) serum concentrations of progesterone by d 4 after GnRH2 than cows that ovulated a small follicle (Figure 5). Table 4. Proportion (%) of cows that were cycling after the second GnRH injection (GnRH2) and, of those cycling, proportion (%) having a normal-length estrous cycle per treatment group Treatment1  n  Cycling2  Normal cycle3  Ov1+CIDR+  9  7/9 (78)  7/7 (100)a  Ov1+CIDR−  11  9/11 (82)  8/9 (89)a  Ov1−CIDR+  17  12/17 (71)  10/12 (89)a  Ov1−CIDR−  18  8/18 (47)  1/8 (12)b  Treatment1  n  Cycling2  Normal cycle3  Ov1+CIDR+  9  7/9 (78)  7/7 (100)a  Ov1+CIDR−  11  9/11 (82)  8/9 (89)a  Ov1−CIDR+  17  12/17 (71)  10/12 (89)a  Ov1−CIDR−  18  8/18 (47)  1/8 (12)b  a,bMeans within a column with different superscripts differ among treatments (P < 0.05). 1Ovulation after the first GnRH injection (GnRH1; Ov1+) or failure to ovulate (Ov1−) and the presence (CIDR+) or absence (CIDR−) of a controlled internal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY). 2Resumption of cyclicity was based on the pattern of serum concentrations of progesterone after GnRH2. All cows that had either a short luteal cycle followed by a second increase in progesterone or a regular-length estrous cycle were consider to be cycling. Cows that continued to have serum concentrations of progesterone less than 1.0 ng/mL for the duration of the experiment were considered anestrus. 3Of the cows that resumed estrous cycling, cows that had a short increase in serum concentrations of progesterone (<16 d) were considered to have a short cycle. Cows that had increased concentrations of progesterone consistent with a regular estrous cycle (18 to 24 d) were considered to be normal cycling. View Large Table 4. Proportion (%) of cows that were cycling after the second GnRH injection (GnRH2) and, of those cycling, proportion (%) having a normal-length estrous cycle per treatment group Treatment1  n  Cycling2  Normal cycle3  Ov1+CIDR+  9  7/9 (78)  7/7 (100)a  Ov1+CIDR−  11  9/11 (82)  8/9 (89)a  Ov1−CIDR+  17  12/17 (71)  10/12 (89)a  Ov1−CIDR−  18  8/18 (47)  1/8 (12)b  Treatment1  n  Cycling2  Normal cycle3  Ov1+CIDR+  9  7/9 (78)  7/7 (100)a  Ov1+CIDR−  11  9/11 (82)  8/9 (89)a  Ov1−CIDR+  17  12/17 (71)  10/12 (89)a  Ov1−CIDR−  18  8/18 (47)  1/8 (12)b  a,bMeans within a column with different superscripts differ among treatments (P < 0.05). 1Ovulation after the first GnRH injection (GnRH1; Ov1+) or failure to ovulate (Ov1−) and the presence (CIDR+) or absence (CIDR−) of a controlled internal drug-releasing insert (CIDR; EAZI-Breed, Pfizer Animal Health, New York, NY). 2Resumption of cyclicity was based on the pattern of serum concentrations of progesterone after GnRH2. All cows that had either a short luteal cycle followed by a second increase in progesterone or a regular-length estrous cycle were consider to be cycling. Cows that continued to have serum concentrations of progesterone less than 1.0 ng/mL for the duration of the experiment were considered anestrus. 3Of the cows that resumed estrous cycling, cows that had a short increase in serum concentrations of progesterone (<16 d) were considered to have a short cycle. Cows that had increased concentrations of progesterone consistent with a regular estrous cycle (18 to 24 d) were considered to be normal cycling. View Large Figure 5. View largeDownload slide Serum concentration of progesterone for cows that ovulated a small (≤11 mm; n = 8) or large follicle (>11 mm; n = 18) after the second GnRH injection (GnRH2). There was no follicle size group × time interaction (P = 0.2), but differences between small and large follicle groups on specific days are indicated on the graph. Only cows that ovulated after GnRH2 and that had a normal luteal cycle were included. Figure 5. View largeDownload slide Serum concentration of progesterone for cows that ovulated a small (≤11 mm; n = 8) or large follicle (>11 mm; n = 18) after the second GnRH injection (GnRH2). There was no follicle size group × time interaction (P = 0.2), but differences between small and large follicle groups on specific days are indicated on the graph. Only cows that ovulated after GnRH2 and that had a normal luteal cycle were included. DISCUSSION In the current study, there was large variation in ovulatory follicle size at GnRH2. Perry et al. (2005) reported a range in ovulatory follicle diameter of 9 to 20 mm at the time of GnRH2 and AI, and Atkins et al. (2010) reported a range of 7.7 to 18.2 mm in cycling beef cows, with 43% of the follicles that ovulated being ≤11 mm. Ovulatory follicle size and physiological maturity of the follicle are implicated in contributing to the establishment and maintenance of pregnancy in beef (Lamb et al., 2001; Perry et al., 2005; Mussard et al., 2007) and dairy cattle (Vasconcelos et al., 2001). Lamb et al. (2001) reported that beef cows ovulating a follicle <12 mm in diameter had reduced pregnancy rates compared with cows that ovulated follicles ≥12 mm. Perry et al. (2005) also reported that cows induced to ovulate small dominant follicles had reduced serum concentrations of progesterone after ovulation, reduced initial pregnancy rates (d 28 after AI), and more late embryonic or fetal loss by d 60 to 68 of gestation. Cows that ovulated after GnRH1 did have larger follicles at GnRH2 compared with cows that did not ovulate after GnRH1. In both cycling (Atkins et al., 2010) and anestrous cows (current study), cows that ovulated after GnRH1 had a larger follicle by d −5 than cows that did not ovulate after GnRH1, and these cows continued to have a larger follicle up to GnRH2. The presence of the larger follicle at d −5 to 0 may indicate an improved control of the dominant follicle at GnRH2 in cows that ovulated after GnRH1 compared with cows that did not ovulate after GnRH1. Cows that failed to ovulate at GnRH1 likely had either a follicle that was too young (small) to ovulate in response to the GnRH-induced LH surge (Sartori et al., 2001) or an atretic follicle. Considering the former scenario, these cows would be expected to have a dominant follicle that was more advanced in age at GnRH2 that may have become atretic or undergone follicular turnover before the GnRH2 injection compared with cows that ovulated to GnRH1. However, another possibility is that the dominant follicle of that follicular wave had not yet reached a critical size or maturation such that it contained LH receptors and was capable of ovulating. Cows with an atretic follicle at GnRH1 would likely begin a new follicular wave around the same time as cows that ovulated to GnRH1 and may have a similarly synchronized follicular wave. Follicular growth rate in the current study was similar to the expected 1 to 2 mm/d (Fortune et al., 1988; Murphy et al., 1990) and to growth rates reported from the same herd in cyclic beef cows (0.89 mm/d; Atkins et al., 2010). There was no difference in long-term follicle growth (5 d leading up to GnRH2) between cows with a CIDR insert and cows without the CIDR insert. Garcia-Winder et al. (1986) reported that anestrous cows administered a progestogen supplement (norgestomet) had an increase in LH pulse frequency 5 d after the beginning of treatment compared with cows without the supplement. It is possible that CIDR administration did not affect the long-term growth of the ovulatory follicle because of timing of the increase in LH frequency. Progestogen supplementation increased follicular weight, concentrations of estradiol in follicular fluid, and LH receptors in the thecal and granulosal cells in postpartum beef cows compared with cows without supplementation (Inskeep et al., 1988), which may explain the increased short-term follicle growth rate in CIDR+ compared with CIDR− cows. Many reports indicate that progesterone supplementation can induce cyclicity in anestrous cows (Smith et al., 1987; Twagiramungu et al., 1995; Lucy et al., 2001). In the current study, neither CIDR administration nor ovulation to GnRH1 increased the number of cycling cows. This was unexpected, because both CIDR administration (Lucy et al., 2001) and GnRH-induced ovulation (Twagiramungu et al., 1995) were able to induce noncycling heifers and cows to cycle; we may not have had a sufficient number of cows in the current study to detect such a difference. The CL that forms after the first ovulation generally has a shortened lifespan compared with subsequent CL (Berardinelli et al., 1979; LaVoie et al., 1981) in suckled beef cows. Among cows that did return to estrous cycling, those that either received a CIDR or ovulated after GnRH1 (and therefore had a CL secreting progesterone) had a reduced incidence of short luteal phases compared with Ov1−CIDR− cows. This shortened luteal phase occurs because of an earlier release of PGF2α from the uterus (Copelin et al., 1989). Exposure to progesterone followed by estrogen is needed to correct the timing of the PGF2α release from the uterus (Cooper et al., 1991; Kieborz-Loos et al., 2003). Administration of exogenous progesterone to anestrous cows primes the uterus for appropriate timing of the PGF2α release, resulting in a normal CL lifespan and estrous cycle length. Similar to that of estrous cycling cows (Atkins et al., 2010), ovulation of a large follicle compared with ovulation of a small follicle after GnRH2 resulted in increased progesterone concentrations during the subsequent estrous cycle. In the current study, circulating concentrations of estradiol at GnRH2 increased as the ovulatory follicle diameter increased, which has been reported previously in dairy cows (Vasconcelos et al., 2001), beef heifers (Atkins et al., 2008), and cyclic beef cows (Atkins et al., 2010). Ireland et al. (1979) reported increased follicular fluid concentrations of estradiol in larger follicles compared with small follicles. The increase in serum concentrations of estradiol may be indicative of a more physiologically mature follicle. Mussard et al. (2007) also reported reduced serum concentrations of progesterone and pregnancy rates after GnRH-induced ovulation of immature follicles. Perry et al. (2005) reported increased pregnancy rates among cows with elevated serum estradiol concentrations at GnRH2 that were induced to ovulate large or small follicles. Concentrations of estradiol around the time of ovulation play a significant role in several events leading to the establishment of pregnancy, including sperm transport (Hawk, 1983), uterine pH (Perry and Perry, 2008a,b), follicular cell maturation (McNatty, 1979), and improved oviductal (Buhi, 2002) and uterine environment (Miller and Moore, 1976; Ing and Tornesi, 1997). Taken together, this evidence indicates that adequate serum concentrations of estradiol around ovulation can improve pregnancy by optimizing gamete transport, luteal function, and oviductal and uterine environment. Ovulation of small follicles may limit serum concentrations of estradiol and affect subsequent events associated with fertilization and pregnancy success. In summary, anestrous cows that ovulated after GnRH1 had larger follicles, and fewer of those cows ovulated a small follicle compared with cows that did not ovulate after GnRH1. Administration of a CIDR did not affect ovulatory follicle diameter. The long-term follicular growth rate was independent of the ovulatory response after GnRH1, CIDR administration, or size of the ovulatory follicle at GnRH2 in anestrous cows. Administration of a CIDR did increase the short-term follicle growth rate. Ovulatory follicle diameter was positively correlated with serum concentrations of estradiol on the day of GnRH2 in anestrous cows. We conclude that in anestrous beef cows, ovulation to GnRH1 increased the size of the ovulatory follicle at GnRH2 and decreased the proportion of cows ovulating a small follicle at GnRH2. Thus, increasing the proportion of cows ovulating to GnRH1 would likely increase the fertility of anestrous cows ovulating at GnRH2. LITERATURE CITED Atkins J. A. Busch D. C. Bader J. F. Keisler D. H. Patterson D. J. Lucy M. C. Smith M. F. 2008. Gonadotropin-releasing hormone-induced ovulation and luteinizing hormone release in beef heifers: Effect of day of the cycle. J. Anim. Sci.  86: 83– 93. https://doi.org/17911234 Google Scholar CrossRef Search ADS PubMed  Berardinelli J. G. Dailey R. A. Butcher R. L. Inskeep E. K. 1979. Source of progesterone prior to puberty in beef heifers. J. Anim. Sci.  49: 1276– 1280. https://doi.org/541292 Google Scholar CrossRef Search ADS PubMed  Buhi W. C. 2002. Characterization and biological roles of oviduct-specific, oestrogen-dependent glycoprotein. Reproduction  123: 355– 362. https://doi.org/11882012 Google Scholar CrossRef Search ADS PubMed  Cooper D. A. Carver D. A. Villeneuve P. Silvia W. J. Inskeep E. K. 1991. Effects of progestagen treatment on concentrations of prostaglandins and oxytocin in plasma from the posterior vena cava of post-partum beef cows. J. Reprod. Fertil.  91: 411– 421. https://doi.org/2013870 Google Scholar CrossRef Search ADS PubMed  Copelin J. P. Smith M. F. Keisler D. H. Garverick H. A. 1989. Effect of active immunization of prepartum and post-partum cows against prostaglandin F-2α on lifespan and progesterone secretion of short-lived corpora lutea. J. Reprod. Fertil.  87: 199– 207. https://doi.org/2621696 Google Scholar CrossRef Search ADS PubMed  Fortune J. E. Sirois J. Quirk S. M. 1988. The growth and differentiation of ovarian follicles during the bovine estrous cycle. Theriogenology  29: 95– 109. Google Scholar CrossRef Search ADS   Garcia-Winder M. Lewis P. E. Deaver D. R. Smith V. G. Lewis G. S. Inskeep E. K. 1986. Endocrine profiles associated with life span of induced corpora lutea in postpartum beef cows. J. Anim. Sci.  62: 1353– 1362. https://doi.org/3087928 Google Scholar CrossRef Search ADS PubMed  Geary T. W. Whittier J. C. 1998. Effects of a timed insemination following synchronization of ovulation using the Ovsynch or CO-Synch protocol in beef cows. Prof. Anim. Sci.  14: 217– 220. Hawk H. W. 1983. Sperm survival and transport in the female reproductive tract. J. Dairy Sci.  66: 2645– 2660. https://doi.org/6365994 Google Scholar CrossRef Search ADS PubMed  Ing N. H. Tornesi M. B. 1997. Estradiol up-regulates estrogen receptor and progesterone receptor gene expression in specific ovine uterine cells. Biol. Reprod.  56: 1205– 1215. https://doi.org/9160720 Google Scholar CrossRef Search ADS PubMed  Inskeep E. K. Braden T. D. Lewis P. E. Garcia-Winder M. Niswender G. D. 1988. Receptors for luteinizing hormone and follicle-stimulating hormone in largest follicles of postpartum beef cows. Biol. Reprod.  38: 587– 591. https://doi.org/3378070 Google Scholar CrossRef Search ADS PubMed  Ireland J. J. Coulson P. B. Murphree R. L. 1979. Follicular development during four stages of the estrous cycle of beef cattle. J. Anim. Sci.  49: 1261– 1269. https://doi.org/575533 Google Scholar CrossRef Search ADS PubMed  Kieborz-Loos K. R. Garverick H. A. Keisler D. H. Hamilton S. A. Salfen B. E. Youngquist R. S. Smith M. F. 2003. Oxytocin-induced secretion of prostaglandin F2α in postpartum beef cows: Effects of progesterone and estradiol-17β treatment. J. Anim. Sci.  81: 1830– 1836. https://doi.org/12854821 Google Scholar CrossRef Search ADS PubMed  Kirby C. J. Smith M. F. Keisler D. H. Lucy M. C. 1997. Follicular function in lactating dairy cows treated with sustained-release bovine somatotropin. J. Dairy Sci.  80: 273– 285. https://doi.org/9058268 Google Scholar CrossRef Search ADS PubMed  Lamb G. C. Stevenson J. S. Kesler D. J. Garverick H. A. Brown D. R. Salfen B. E. 2001. Inclusion of an intravaginal progesterone insert plus GnRH and prostaglandin F2α for ovulation control in postpartum suckled beef cows. J. Anim. Sci.  79: 2253– 2259. https://doi.org/11583411 Google Scholar CrossRef Search ADS PubMed  LaVoie V. Han D. K. Foster D. B. Moody E. L. 1981. Suckling effect on estrus and blood plasma progesterone in postpartum beef cows. J. Anim. Sci.  52: 802– 812. https://doi.org/7196400 Google Scholar CrossRef Search ADS PubMed  Littell R. C. Henry P. R. Ammerman C. B. 1998. Statistical analysis of repeated measures data using SAS procedures. J. Anim. Sci.  76: 1216– 1231. https://doi.org/9581947 Google Scholar CrossRef Search ADS PubMed  Lucy M. C. Billings H. J. Butler W. R. Ehnis L. R. Fields M. J. Kesler D. J. Kinder J. E. Mattos R. C. Short R. E. Thatcher W. W. Wetteman R. P. Yelich J. V. Hafs H. D. 2001. Efficacy of an intravaginal progesterone insert and an injection of PGF2α for synchronizing estrus and shortening the interval to pregnancy in postpartum beef cows, peripubertal beef heifers, and dairy heifers. J. Anim. Sci.  79: 982– 995. https://doi.org/11325206 Google Scholar CrossRef Search ADS PubMed  McNatty K. P. 1979. Follicular determinants of corpus luteum function in the human ovary. Adv. Exp. Med. Biol.  112: 465– 481. https://doi.org/463623 Google Scholar CrossRef Search ADS PubMed  Miller B. G. Moore N. W. 1976. Effect of progesterone and oestradiol on endometrial metabolism and embryo survival in the ovariectomized ewe. Theriogenology  6: 636. https://doi.org/1029671 Google Scholar CrossRef Search ADS PubMed  Moreira F. de la Sota R. L. Diaz T. Thatcher W. W. 2000. Effect of day of the estrous cycle at the initiation of a timed artificial insemination protocol on reproductive responses in dairy heifers. J. Anim. Sci.  78: 1568– 1576. https://doi.org/10875641 Google Scholar CrossRef Search ADS PubMed  Murphy M. G. Boland M. P. Roche J. F. 1990. Pattern of follicular growth and resumption of ovarian activity in post-partum beef suckler cows. J. Reprod. Fertil.  90: 523– 533. https://doi.org/2250250 Google Scholar CrossRef Search ADS PubMed  Mussard M. L. Burke C. R. Behlke E. J. Gasser C. L. Day M. L. 2007. Influence of premature induction of a luteinizing hormone surge with gonadotropin-releasing hormone on ovulation, luteal function, and fertility in cattle. J. Anim. Sci.  85: 937– 943. https://doi.org/17145968 Google Scholar CrossRef Search ADS PubMed  Perry G. A. Perry B. L. 2008a. Effect of preovulatory concentrations of estradiol and initiation of standing estrus on uterine pH in beef cows. Domest. Anim. Endocrinol.  34: 333– 338. Google Scholar CrossRef Search ADS   Perry G. A. Perry B. L. 2008b. Effects of standing estrus and supplemental estradiol on changes in uterine pH during a fixed-timed artificial insemination protocol. J. Anim. Sci.  86: 2928– 2935. https://doi.org/18641170 Google Scholar CrossRef Search ADS   Perry G. A. Smith M. F. Lucy M. C. Green J. A. Parks T. E. MacNeil M. D. Roberts A. J. Geary T. W. 2005. Relationship between follicle size at insemination and pregnancy success. Proc. Natl. Acad. Sci. USA  102: 5268– 5273. https://doi.org/15795381 Google Scholar CrossRef Search ADS   Rantala M. H. Katila T. Taponen J. 2009. Effect of time interval between prostaglandin F2α and GnRH treatments on occurrence of short estrous cycles in cyclic dairy heifers and cows. Theriogenology  71: 930– 938. https://doi.org/19111892 Google Scholar CrossRef Search ADS PubMed  Sartori R. Fricke P. M. Ferreira J. C. P. Ginther O. J. Wiltbank M. C. 2001. Follicular deviation and acquisition of ovulatory capacity in bovine follicles. Biol. Reprod.  65: 1403– 1409. https://doi.org/11673256 Google Scholar CrossRef Search ADS PubMed  Smith V. G. Chenault J. R. McAllister J. F. Lauderdale J. W. 1987. Response of postpartum beef cows to exogenous progestogens and gonadotropin releasing hormone. J. Anim. Sci.  64: 540– 551. https://doi.org/3549660 Google Scholar CrossRef Search ADS PubMed  Twagiramungu H. Builbault L. A. Dufour J. J. 1995. Synchronization of ovarian follicular waves with gonadotropin-releasing hormone agonist to increase the precision of estrus in cattle: A review. J. Anim. Sci.  73: 3141– 3151. https://doi.org/8617687 Google Scholar CrossRef Search ADS PubMed  Vasconcelos J. L. M. Sartori R. Oliveira H. N. Guenther J. G. Wiltbank M. C. 2001. Reduction in size of the ovulatory follicle reduces subsequent luteal size and pregnancy rate. Theriogenology  56: 307– 314. https://doi.org/11480622 Google Scholar CrossRef Search ADS PubMed  Williams G. L. Talavera F. Petersen B. J. Kirsch J. D. Tilton J. E. 1983. Coincident secretion of follicle stimulating hormone and luteinizing hormone in early postpartum beef cows: Effects of suckling and low-level increases of systemic progesterone. Biol. Reprod.  29: 362– 373. https://doi.org/6416311 Google Scholar CrossRef Search ADS PubMed  Yavas Y. Walton J. S. 2000. Postpartum acyclicity in suckled beef cows: A review. Theriogenology  54: 25– 55. https://doi.org/10990346 Google Scholar CrossRef Search ADS PubMed  American Society of Animal Science

Scharf, B.;Carroll, J. A.;Riley, D. G.;Jr., C. C. Chase,;Coleman, S. W.;Keisler, D. H.;Weaber, R. L.;Spiers, D. E.

doi: 10.2527/jas.2009-2551pmid: 20190161

ABSTRACT A study was performed to evaluate differences in thermoregulatory ability of 2 Bos taurus breeds with known differences in heat tolerance. Nine Angus (AG; 304 ± 7 kg of BW) and 9 Romosinuano (RO; 285 ± 7.5 kg of BW) steers were transported to the Brody Environmental Center at the University of Missouri. Steers were housed for 18 d at thermoneutrality (TN; 21°C) before initiation of heat stress (HS), which consisted of daily cyclic air temperature (26°C, night; 36°C, day) for 14 d. Rectal temperature and respiration rate were measured 6 times daily throughout the study. Sweat rates at shaved skin sites were recorded on specific days. Blood samples were taken once per week. Angus steers maintained rectal temperature 0.5°C greater than RO at TN (P < 0.001). Likewise, respiration and sweat rates were greater (P < 0.001) in AG than RO at TN (P < 0.05). Rectal temperature increased during HS for both breeds with AG maintaining greater temperatures (P < 0.001). Both breeds increased respiration rate during HS, with AG steers exhibiting the greater rate (P < 0.001). Sweat rate increased more than 4-fold during HS (P < 0.001), followed by reduction after 7 d. Even after HS acclimation, AG exhibited the greater sweat rate (P < 0.001). Breed differences for serum leptin, creatinine, and cholesterol were found throughout the study with AG being greater than RO. Although there were no breed differences (P = 0.21) at TN, only AG steers exhibited a HS-induced increase (P < 0.05) in prolactin, creatinine, and cholesterol concentrations to suggest that an increase in rectal temperature is required for this effect. Use of rectal temperature along with endocrine markers, such as prolactin, may aid in the identification of B. taurus sensitivity to heat. INTRODUCTION Development of a breed of cattle that can tolerate heat stress (HS) and maintain productivity has been a long-term goal of researchers and cattle breeders. Historically, most research in this area has emphasized comparisons of heat-tolerant Bos indicus cattle (e.g., Brahman) vs. heat-intolerant Bos taurus cattle (Brody, 1956; Finch et al., 1982; Gaughan et al., 1999; Beatty et al., 2006). However, isolation of specific differences in thermoregulatory ability is challenging due to the many physical and genetic differences that could have secondary influences. Current interest has shifted toward using breeds of B. taurus cattle from tropical climates that might be heat tolerant compared with Angus (AG) and superior to B. indicus cattle in terms of reproduction, growth, and carcass quality (Spiers et al., 1994; Chase et al., 1997). The Romosinuano (RO) is a tropically adapted Criollo beef breed that is native to Colombia, South America (Chase et al., 1997). Previous research suggests that RO exhibit heat tolerance, as well as superior growth and fertility on tropical pastures compared with AG cattle (Vogt et al., 1991). Likewise, they are noted for longevity and docile temperament (Chase et al., 1997; Elzo et al., 1998). There have been few studies to compare the specific thermoregulatory abilities of RO under laboratory or field conditions (Vogt et al., 1991; Spiers et al., 1994; Hammond et al., 1996). The purpose of this study was to determine the thermoregulatory responses of heat-tolerant RO and heat-sensitive AG B. taurus breeds to a controlled heat challenge in order to derive potential physiological and biochemical indicators of heat tolerance. MATERIALS AND METHODS The experimental protocol and procedures were approved by the University of Missouri Animal Care and Use Committee. Animals Nine AG (304 ± 7.0 kg of BW) and 9 RO steers (285 ± 7.5 kg of BW) were obtained from the Subtropical Agricultural Research Station (USDA-ARS) in Brooksville, Florida. Romosinuano steers were derived from embryos imported from an upgraded herd at the Centro Agronomico Tropical de Investigacion y Ensenanza in Turrialba, Costa Rica (1991 and 1993) or from purebred RO herds in Venezuela (1997). Embryo calves were born in 1991, 1993, or 1997, forming the present-day herd in Brooksville (Riley et al., 2007). Animals in the current study were raised as part of the Brooksville herd before shipment to Oklahoma and finally being brought to the University of Missouri in late January. Steers were housed in covered feedlots at the University of Missouri South Farm for training from January to March before entering the Brody Environmental Center. Steers were approximately 12 mo old at the start of the experiment. Three environmental chambers within the Brody Environmental Center were used. Each was divided into 6 stanchions for maintenance of 6 steers (3 RO and 3 AG per room). Animals were limit fed a high-concentrate diet (39% each of corn and soybean hulls, 20% dried corn distillers grains, and 2% mineral supplement; as-fed basis) at 1.6% of BW per day (AG: 5 kg; RO: 4.5 kg), with water available for ad libitum consumption. General Procedure Steers were housed for 18 d at thermoneutrality (TN; 19 to 21°C) before initiation of HS to allow acclimation to the chamber environment (acclimation is defined as “adaptive changes that occur within an organism in response to experimentally induced changes in particular climatic factors such as ambient temperature in a controlled environment”; Bligh and Johnson, 1973). Heat stress consisted of daily cyclic air temperature (26°C night: 36°C day) for 14 d (Figure 1). Transition from TN to cyclic HS conditions was accomplished over a 2-d period (d 1: 28°C; d 2: 33°C; Figure 1). Chambers at TN had a set point of 20°C and only slight fluctuations. Chambers during HS cycle increased as a step-up function with 3 set points throughout the rise phase, followed by a 4-h stable period (36°C; 1200 to 1600 h). The decline phase consisted of 2 set points to reach the stable low temperature (26°C; 0000 to 0600 h). Environmental conditions were measured using Hobo H8 data loggers (Onset, Bourne, MA) to record air temperature (Ta) and percentage relative humidity (RH) every 10 min. Relative humidity was maintained under 50% during the entire study (TN: 40 to 50%; HS: 35 to 45%). Animal measurements, including respiration rate (RR), skin temperature (Tskin), and rectal temperature (Tre), were taken 6 times daily (0600, 1100, 1300, 1600, 1900, and 2100 h). Determination of RR was made by counting flank movement over a 1-min interval and is presented in breaths per minute (bpm). Skin temperatures, at 5 different shaved sites (ear, shoulder, rump, tail head, and lower tail), were measured using an infrared thermometer (model C-1600M, Linear Laboratories, Fremont, CA; accuracy ± 0.1°C). Readings were taken less than 31 cm away from each skin site with a thermometer target ratio of 3:1. Rectal temperature was measured using a thermistor thermometer (model 8110–20, Cole-Parmer Instruments, Chicago, IL). This was accomplished by inserting a YSI probe (model 400, YSI Inc., Yellow Springs, OH; accuracy: ± 0.1°C) approximately 15 cm into the rectum for 2 min. Sweat rate was measured on specific days throughout the study (targeting TN, early heat, and late heat periods) at shaved shoulder and rump sites using a calibrated, digital moisture sensor (Vapometer; Delfin Technologies Ltd., Kuopio, Finland) that determines transepidermal water loss. All calibrations are certified and performed at the company laboratory using 3 different relative humidities. The Vapometer uses a closed system approach, free of ambient airflow, to measure ambient relative humidity and temperature. This is automatically performed by the instrument before skin application (open surface area of 1.0 cm2). The device is then held on the skin for 10 to 20 s before the evaporation rate is displayed in g/(m2·h) (accuracy = ±10%). The time between measurements is automatically controlled to allow the relative humidity in the chamber to return to the ambient baseline measured before skin contact. Only after this baseline is achieved will the Vapometer allow the next measurement. Other recent studies have used the same type of device to measure moisture loss in a variety of environments (Nuutinen et al., 2003; Gebremedhin et al., 2007; Scharf et al., 2008a). Figure 1. View largeDownload slide Mean room air temperature beginning on d 14 at thermoneutrality (19 to 21°C) and continuing through d 33, which was the last day of heat stress (night: 26°C; day: 36°C). Values were collected hourly for each day. Figure 1. View largeDownload slide Mean room air temperature beginning on d 14 at thermoneutrality (19 to 21°C) and continuing through d 33, which was the last day of heat stress (night: 26°C; day: 36°C). Values were collected hourly for each day. Blood (20 mL) was collected at 0900 h on d 16 (TN) and 31 (HS) via jugular venous puncture. Samples were collected into two 15-mL tubes and allowed to clot before centrifugation. Serum was separated by centrifugation (2,300 × g for 25 min; 4°C) before being removed and stored at −20°C for later analysis. Serum analyses used standard procedures. Most serum measurements were components of a larger biochemical profile produced by the Veterinary Medical Diagnostic Laboratory, University of Missouri–Columbia using an auto-analyzer (Olympus AV400, Olympus America Inc., Melville, NY). These include albumin, chloride, cholesterol, creatinine phosphokinase, creatinine, globulin, glucose, magnesium, potassium, sodium, total protein, triglyceride, and urea nitrogen. Serum leptin concentrations were determined by a sensitive ovine leptin RIA validated for bovine serum (Delavaud et al., 2000). Standards were assayed in quadruplicate and samples in triplicate 200-μL volumes. Assay sensitivity and intraassay CV were 0.03 nmol/L and 3.1%. Serum concentrations of prolactin were determined by RIA procedures previously validated at the University of Missouri (Lutz et al., 1991). Minimum detectable concentration of prolactin in serum was 1.2 ng/tube. Intraassay CV was 9.2%. Statistical Analyses Data was analyzed using a repeated measures ANOVA procedure in JMP statistical software (SAS Inst. Inc., Cary, NC). All evaluations at TN were performed using the last 5 d (i.e., d 14, 15, 16, 17, and 18) before the increase in Ta. The analysis included RR, Tskin, sweat rate, or Tre as the dependent variable. Skin temperatures were included as an average of all skin sites (Tskin), an average of the shoulder and rump sites (Ttrunk), or an average of ear, tail head, and lower tail (Tappendage). Breed, time, and breed × time were set as fixed effects, with animal nested within breed as a random effect. For ANOVA analyses present in text, experiment-wise type I error rate was controlled to α = 0.05 utilizing the Tukey-Kramer honestly significant difference adjustment procedures for multiple mean comparisons. For plotting purposes, Fisher's least significant difference (Steele and Torrie, 1980) was calculated utilizing the LSMEANS statement in PROC MIXED of SAS and is presented in the upper left portion of figures. Simple linear and polynomial regression procedures of JMP were utilized to establish relationships between animal variables (RR, Tskin, sweat rate, and Tre) and Ta. Regression coefficients for slope and model r, as well as P-values for the hypothesis test that the regression coefficients are significantly different from zero, are reported. Similarly, simple linear regressions were constructed to determine the relationship for early heat (d 21 to 24) and late heat exposure periods (d 29 to 32) using the regression procedures of JMP. Blood analyses were performed using the repeated measures ANOVA procedure in JMP with fixed and random effects as described above. RESULTS Steers were brought into the chambers with AG starting BW slightly larger than that of the RO (AG: 317.5 ± 7.2 kg of BW; RO: 288.0 ± 7.2 kg of BW; P < 0.05). While present in the chambers, animals were limit-fed at 1.6% BW daily, resulting in a slight drop in BW for both breeds over the study (AG: −13.2 ± 4.2 kg; RO: −4.6 ± 4.2 kg). Final BW at the end of the study did not differ between groups (AG: 304 ± 7.0 kg of BW; RO: 285 ± 7.5 kg of BW; P = 0.07). TN Respiration rate at TN was greater (P < 0.001) in AG than RO by ~12 bpm (Figure 2). There was a breed × day interaction (P < 0.01) but no breed × day × hour interaction (P = 0.08) at TN, with the RO showing no change and the AG slowly reducing RR over time (38 to 33 bpm) from d 14 to 18. After acclimation, AG steers continued to exhibit the greatest RR (P < 0.001). Angus steers also maintained a greater Tre (~0.21°C) than RO at TN (P < 0.01; Figure 3); however, no acclimation took place with Tre remaining constant over the last 3 d at TN (P > 0.05). There was a daily increase in Tre of approximately 0.25°C (P < 0.001) from 1100 to 1900 h at TN. Regional skin temperatures were less (P < 0.01) for RO than for AG steers, with the differences being 1.0°C at the extremities (Figure 4A) and 0.7°C at the trunk (Figure 4B). There was also a daily increase in skin temperature of both regions of approximately 0.7°C from 1300 to 2100 h (P < 0.001). The thermal circulation index was calculated using the equation [(Tskin − Ta)/(Tre − Tskin); Burton and Edholm, 1955] as an indicator of blood flow and heat transfer to the skin. Angus steers had greater values for appendage and trunk skin sites in comparison with RO steers (appendage: AG = 0.91, RO = 0.77, P = 0.036; trunk: AG = 1.15, RO = 1.0, P < 0.02). It is apparent that the thermoneutral temperature used in this study represents a cooler Ta for RO steers compared with AG steers, with a reduction in blood and heat flows to the periphery. Similar results were found for sweat rates with AG being ~10 g/m2·h greater that RO (Figure 5; P < 0.05). All variables (Tre, Tskin, and sweat rate) were stable with the exception of RR, which stabilized within 5 d at TN, resulting in a steady baseline before heat exposure. Figure 2. View largeDownload slide Mean respiration rate of Angus and Romosinuano (Romo) steers is shown as a function of time in days for the last 5 d at thermoneutrality followed by 15 d of heat stress exposure. All of the 6 sample times during a day are shown. The vertical dashed line separates thermoneutral and heat stress periods, and the small solid line is the LSD distance at P < 0.05. bpm = breaths per minute. Figure 2. View largeDownload slide Mean respiration rate of Angus and Romosinuano (Romo) steers is shown as a function of time in days for the last 5 d at thermoneutrality followed by 15 d of heat stress exposure. All of the 6 sample times during a day are shown. The vertical dashed line separates thermoneutral and heat stress periods, and the small solid line is the LSD distance at P < 0.05. bpm = breaths per minute. Figure 3. View largeDownload slide Mean rectal temperature of Angus and Romosinuano (Romo) steers is shown as a function of time in days for the last 5 d at thermoneutrality followed by 15 d of heat stress exposure. All of the 6 sample times during a day are shown. The vertical dashed line separates thermoneutral and heat stress periods, and the small solid line is the LSD distance at P < 0.05. Figure 3. View largeDownload slide Mean rectal temperature of Angus and Romosinuano (Romo) steers is shown as a function of time in days for the last 5 d at thermoneutrality followed by 15 d of heat stress exposure. All of the 6 sample times during a day are shown. The vertical dashed line separates thermoneutral and heat stress periods, and the small solid line is the LSD distance at P < 0.05. Figure 4. View largeDownload slide Mean appendage (A) and trunk (B) skin temperatures of Angus and Romosinuano (Romo) steers are shown as a function of time in days for the last 5 d at thermoneutrality followed by 15 d of heat stress exposure. All of the 6 sample times during a day are shown. The vertical dashed line separates thermoneutral and heat stress periods, and the small solid line is the LSD distance at P < 0.05. Figure 4. View largeDownload slide Mean appendage (A) and trunk (B) skin temperatures of Angus and Romosinuano (Romo) steers are shown as a function of time in days for the last 5 d at thermoneutrality followed by 15 d of heat stress exposure. All of the 6 sample times during a day are shown. The vertical dashed line separates thermoneutral and heat stress periods, and the small solid line is the LSD distance at P < 0.05. Figure 5. View largeDownload slide Mean skin sweat rate of shaved shoulder (A) and rump (B) skin sites for Angus and Romosinuano (Romo) cattle on selected days before heat exposure (i.e., pre d 20) and during heat stress (i.e., post d 20). The vertical line on top of each column is +1 SE, and the vertical LSD line is for P < 0.05. Figure 5. View largeDownload slide Mean skin sweat rate of shaved shoulder (A) and rump (B) skin sites for Angus and Romosinuano (Romo) cattle on selected days before heat exposure (i.e., pre d 20) and during heat stress (i.e., post d 20). The vertical line on top of each column is +1 SE, and the vertical LSD line is for P < 0.05. HS The transition from TN to HS environments produced a more rapid increase in RR for AG compared with RO, with a 9.7 bpm increase in AG steers on the first HS day (P = 0.05; Figure 2) and no increase for RO steers. By the second HS day, RR of AG and RO steers had increased 13.7 and 9.9 bpm, respectively, from the last TN day (P = 0.05). Both breeds had a RR that remained greater than the TN level during the first week of HS, with AG steers exhibiting the greater rate (60 bpm, AG; 38 bpm, RO; P < 0.001; Figure 2). Respiration rate showed no acclimation (P = 0.07) for AG, but increased 8.4 bpm for RO from d 21 to 28. The breed differences in RR persisted into wk 2 (61 bpm, AG; 42 bpm, RO; Figure 2). During HS, there was a daily increase in RR of 15.4 bpm from 0600 to 1300 h (P = 0.05), with the increase in AG (19.3 bpm) being greater (P < 0.05) than for RO (11.5 bpm). Both breeds exhibited an increase in Tre during transition TN to HS (0.17°C; P = 0.05; Figure 3). The daily increase in Tre during the transition period was significant (P < 0.01) from 1100 h (38.18°C) to 1600 h (38.34°C) to 1900 h (38.52°C), with no breed differences. Interestingly, it was RO steers that showed the larger increase, rising to the same Tre level of AG animals during the first full day of heat (d 21). However, RO steers quickly acclimated, maintaining a 0.30°C less average daily Tre than AG steers throughout wk 1 of HS (P < 0.05; Figure 3; d 21 to 27). Angus steers displayed a more delayed Tre response with no significant increase (P = 0.50) until second day of full the heat (0.33°C; d 22). Rectal temperature during HS increased (P < 0.05) by 0.10°C for each period from 1100 h (38.35°C) to 1300 to 1600 h, with a peak at 1900 h (38.70°C). Less daily Tre at 1100 h was different (P < 0.05) for RO (38.21°C) and AG (38.49°C). The breed difference in Tre of 0.28°C coincided with peak daily Tre, which occurred at 1900 h. Rectal temperature of AG steers continued to increase during wk 2 of HS, whereas Tre for RO remained constant (P < 0.01). This occurred despite the fact that RR remained the same for this breed. The breed differences in Tskin that were observed at TN were not observed (P = 0.13) during the transition from TN to HS environments. There were daily increases (P < 0.05) in Tskin of trunk and appendage sites, beginning on the first day of Ta increase and continuing to full heat exposure (Figures 4A and 5B). The increases in trunk and appendage skin sites over this period were 4.91 and 6.26°C. Average daily skin temperature in both regions for both breeds increased (P < 0.05) from the first day of full heat to d 4 when peak skin temperature was observed. The increase for the trunk sites was 1.69 to 35.39°C followed by a 0.78°C reduction (P < 0.05) to 34.61°C on the last day (Figure 4B). Likewise, the appendage sites increased 1.63 to 34.98°C followed by a 0.63°C reduction (P < 0.05) to 34.35°C at the end of the study (Figure 4A). This reduction in Tskin was not due to a change in Ta because it decreased only 0.14°C from the day of peak Tskin to the last day of the study. Average skin temperature closely followed the cyclic Ta pattern among all skin sites. Daily increases in Tskin for trunk and appendage sites over the entire HS period were 3.00 and 3.25°C, respectively, from 0600 to 1300 h (P < 0.05; Figures 4A and 4B). Both breeds initially increased shoulder sweat rate (4-fold) during HS but with a greater increase in AG compared with the RO (292.6 vs. 175.23 g/m2h, respectively; P < 0.001; Figure 5A). This was followed by a reduction after 7 d for both breeds (P < 0.001; Figure 5A) to approximately one-half the level of the first 3 d of HS. However, AG retained a greater sweat rate even after the acclimation period compared with the RO (200.5 vs. 110.3 g/m2·h, respectively; P < 0.001). Reduction in sweat rate coincided with the increase in Tre that was noted for AG cattle. Romosinuano cattle also maintained a reduced sweat rate after acclimation, but unexpectedly Tre showed no increase. Sweat rate was greater (P < 0.001) at the shoulder vs. the rump regions during HS for both breeds (Figure 5B). However, sweat rates of shoulder and rump regions for both breeds paralleled each other during wk 1 of HS, with AG having the greater rate. Temperature Relationships All thermal variables were evaluated to determine the linear correlation coefficients and predictors of the thermoneutral and early HS responses, as well as the HS response alone for the 2 breeds using individual animal values. Use of TN, transition, and early HS response allowed for the determination of thermal responsiveness in the absence of acclimation (Table 1). Both RR and Tre are reliable and are often used as indicators of thermal status. Table 1 illustrates that all indicators of thermal input (i.e., Ta, Tskin, Ttrunk, and Tappendage) are reasonable predictors. In every case, the correlation coefficients were greater for AG compared with RO steers. More importantly, the responsiveness of AG steers during this transition period, as indicated by slope, was twice that of RO steers. The determinants of Tre again showed a greater (P < 0.05) correlation for AG compared with RO animals (Table 1). However, the slopes of the responses across breeds were identical and extremely small when compared with the RR responses. Both the correlation coefficients and slopes were small for the Tre response compared with the RR response. As expected, the relationships between Tskin and Ta, and Ttrunk and Tappendage were very large and not different (P = 0.81) across breeds (Table 1). Table 1. Linear regressions of variables from d 14 to 24 using individual values1 Variable  Breed  Correlationcoefficient  Slope  P-value  Dependent  Independent  RR  Ta  AG  0.73  1.74  0.001      RO  0.60  0.96  0.001  RR  Tskin  AG  0.74  3.92  0.001      RO  0.61  1.91  0.001  RR  Ttrunk  AG  0.73  4.14  0.001      RO  0.61  2.11  0.001  RR  Tappendage  AG  0.74  3.73  0.001      RO  0.60  1.76  0.001  Tskin  Ta  AG  0.93  0.42  0.001      RO  0.93  0.48  0.001  Tre  Ta  AG  0.33  0.01  0.001      RO  0.22  0.01  0.050  Tre  Tskin  AG  0.35  0.03  0.001      RO  0.27  0.03  0.010  Tre  Ttrunk  AG  0.35  0.03  0.001      RO  0.26  0.03  0.010  Tre  Tappendage  AG  0.34  0.03  0.001      RO  0.28  0.03  0.010  Ttrunk  Tappendage  AG  0.98  0.87  0.001      RO  0.97  0.81  0.001  Variable  Breed  Correlationcoefficient  Slope  P-value  Dependent  Independent  RR  Ta  AG  0.73  1.74  0.001      RO  0.60  0.96  0.001  RR  Tskin  AG  0.74  3.92  0.001      RO  0.61  1.91  0.001  RR  Ttrunk  AG  0.73  4.14  0.001      RO  0.61  2.11  0.001  RR  Tappendage  AG  0.74  3.73  0.001      RO  0.60  1.76  0.001  Tskin  Ta  AG  0.93  0.42  0.001      RO  0.93  0.48  0.001  Tre  Ta  AG  0.33  0.01  0.001      RO  0.22  0.01  0.050  Tre  Tskin  AG  0.35  0.03  0.001      RO  0.27  0.03  0.010  Tre  Ttrunk  AG  0.35  0.03  0.001      RO  0.26  0.03  0.010  Tre  Tappendage  AG  0.34  0.03  0.001      RO  0.28  0.03  0.010  Ttrunk  Tappendage  AG  0.98  0.87  0.001      RO  0.97  0.81  0.001  1Each incorporates both thermoneutral [air temperature (Ta): 19 to 21°C] and early heat stress (Ta: 26 to 36°C) responses into the analyses before heat acclimation. RR = respiration rate; Tskin = skin temperature; Tre = rectal temperature; Ttrunk = average of the shoulder and rump sites; Tappendage = average of ear, tail head, and lower tail. AG = Angus; RO = Romosinuano. View Large Table 1. Linear regressions of variables from d 14 to 24 using individual values1 Variable  Breed  Correlationcoefficient  Slope  P-value  Dependent  Independent  RR  Ta  AG  0.73  1.74  0.001      RO  0.60  0.96  0.001  RR  Tskin  AG  0.74  3.92  0.001      RO  0.61  1.91  0.001  RR  Ttrunk  AG  0.73  4.14  0.001      RO  0.61  2.11  0.001  RR  Tappendage  AG  0.74  3.73  0.001      RO  0.60  1.76  0.001  Tskin  Ta  AG  0.93  0.42  0.001      RO  0.93  0.48  0.001  Tre  Ta  AG  0.33  0.01  0.001      RO  0.22  0.01  0.050  Tre  Tskin  AG  0.35  0.03  0.001      RO  0.27  0.03  0.010  Tre  Ttrunk  AG  0.35  0.03  0.001      RO  0.26  0.03  0.010  Tre  Tappendage  AG  0.34  0.03  0.001      RO  0.28  0.03  0.010  Ttrunk  Tappendage  AG  0.98  0.87  0.001      RO  0.97  0.81  0.001  Variable  Breed  Correlationcoefficient  Slope  P-value  Dependent  Independent  RR  Ta  AG  0.73  1.74  0.001      RO  0.60  0.96  0.001  RR  Tskin  AG  0.74  3.92  0.001      RO  0.61  1.91  0.001  RR  Ttrunk  AG  0.73  4.14  0.001      RO  0.61  2.11  0.001  RR  Tappendage  AG  0.74  3.73  0.001      RO  0.60  1.76  0.001  Tskin  Ta  AG  0.93  0.42  0.001      RO  0.93  0.48  0.001  Tre  Ta  AG  0.33  0.01  0.001      RO  0.22  0.01  0.050  Tre  Tskin  AG  0.35  0.03  0.001      RO  0.27  0.03  0.010  Tre  Ttrunk  AG  0.35  0.03  0.001      RO  0.26  0.03  0.010  Tre  Tappendage  AG  0.34  0.03  0.001      RO  0.28  0.03  0.010  Ttrunk  Tappendage  AG  0.98  0.87  0.001      RO  0.97  0.81  0.001  1Each incorporates both thermoneutral [air temperature (Ta): 19 to 21°C] and early heat stress (Ta: 26 to 36°C) responses into the analyses before heat acclimation. RR = respiration rate; Tskin = skin temperature; Tre = rectal temperature; Ttrunk = average of the shoulder and rump sites; Tappendage = average of ear, tail head, and lower tail. AG = Angus; RO = Romosinuano. View Large Analysis of the thermal relationships during the stable HS period (i.e., d 21 to 33) revealed that the different skin temperatures were highly correlated with each other (r = 0.90 to 0.96) and with Ta (r = 0.74 to 0.80) when data from both breeds were combined. There was little improvement when the analysis was performed on data from each breed separately. Respiration rate (r = 0.37) also showed linear correlation (P < 0.001) with Ta using both breeds together. However, there was a breed difference when predictors of RR were analyzed separately. Prediction of RR for AG using Ta and each skin temperature was r = 0.54 and r = 0.46 to 0.51, respectively. In contrast, the same predictor relationships for Ta (r = 0.39) and skin temperatures (r = 0.38 to 0.39) were smaller (P < 0.05) for RO. This was expected because daily change in RR for RO was less than for AG. Rectal temperature showed a very poor correlation with Ta (r = −0.0002) and skin temperatures (r = 0.12 to 0.19) when both breeds were combined. Romosinuano steers regulated Tre almost independent of Ta and skin temperature. Sweat rates were highly correlated with Ta during initial 3 d of HS, with AG steers having the slightly greater correlation for shoulder (r = 0.68 vs. 0.65; Figure 6) and rump (r = 0.68 vs. 0.59, data not shown) sites using a second-order polynomial regression fit. A comparison of sweat rate across skin sites was also performed using skin temperature at the site as the thermal input, to determine if this is a better input for sudomotor activity than Ta. Once again, AG steers exhibited the greater correlation (P < 0.01) for shoulder (r = 0.69 vs. 0.63; Figure 6) and rump (r = 0.66 vs. 0.53, data not shown) sites. Using these correlations, it was evident that use of skin temperature did not improve sweat rate prediction and that AG steers were more responsive to early HS exposure. Figure 6. View largeDownload slide Shoulder sweat rate using mean group values for breed [i.e., Angus and Romosinuano (Romo)], day, and time of day for d 14 and 19 (preheat; Ta: 19 to 21°C), and d 21 and 24 (early heat; Ta: 26 to 36°C). Ta = air temperature. The fitted line is a second-order polynomial regression. Figure 6. View largeDownload slide Shoulder sweat rate using mean group values for breed [i.e., Angus and Romosinuano (Romo)], day, and time of day for d 14 and 19 (preheat; Ta: 19 to 21°C), and d 21 and 24 (early heat; Ta: 26 to 36°C). Ta = air temperature. The fitted line is a second-order polynomial regression. Thermoregulatory responses between the end of wk 1 (d 21 to 24) and end of wk 2 (d 29 to 32) of HS were compared with assess short-term acclimation to heat (Figure 7A to 7D). Linear regression was used to determine the change in response of sweat rate, RR, and Tre vs. Ta between the 2 wk. Comparison of the 2 breeds shoulder sweat rate during wk 1 of HS shows that, although starting at different levels, they exhibit a similar response to HS (i.e., both lines have a slope of 26.19; Figure 7C and 7D). As stated previously, both breeds showed a reduction in sweating rate after 7 d of HS. Comparing wk 1 vs. 2 of HS, all steers showed acclimation with a reduction (P < 0.05) in slope (AG = 26.19 to 17.02; RO = 26.19 to 8.98) of shoulder sweat rate vs. Ta (Figure 7C). Although AG maintained the greater rate, the breeds converged as Ta fell below 25°C. Rump sweat rate exhibited the same response as shoulder to a lesser degree (Figure 7D). Angus also maintained a greater sweat rate over RO during wk 1 and 2 of HS. Unlike sweat rate, RR showed little acclimation between weeks (AG slope = 1.92 to 1.13; RO slope = 0.65 to 0.65; Figure 7A) with AG maintaining the greater rate (P < 0.05). Rectal temperature also showed no acclimation over the HS period, and neither breed exhibited a good correlation with Ta (P > 0.10; Figure 7B). Figure 7. View largeDownload slide Heat stress-induced changes in respiration rate (A), rectal temperature (B), shoulder sweat rate (C), and rump sweat rate (D) are shown for Angus and Romosinuano (Romo) steers from early heat exposure (i.e., d 21 to 24) to late heat exposure (i.e., d 29 to 32). A linear fit is shown through points for each variable, breed, and set of days (air temperature: 26 to 36°C). Figure 7. View largeDownload slide Heat stress-induced changes in respiration rate (A), rectal temperature (B), shoulder sweat rate (C), and rump sweat rate (D) are shown for Angus and Romosinuano (Romo) steers from early heat exposure (i.e., d 21 to 24) to late heat exposure (i.e., d 29 to 32). A linear fit is shown through points for each variable, breed, and set of days (air temperature: 26 to 36°C). Blood Analyses Several blood analyses, including urea nitrogen, potassium, creatinine phosphokinase, chloride, magnesium, glucose, and globulin, showed no breed or thermal effects (Table 2). Other blood variables, including albumin and triglyceride, revealed no breed differences, but exhibited HS-induced increases in both breeds (P < 0.05; Table 2). Throughout the study, breed differences were observed among cholesterol, creatinine, and leptin concentrations (P < 0.05). The most interesting of these responses were those that showed thermal × breed interactions. These variables included creatinine, cholesterol, prolactin, sodium, and total protein (P < 0.05; Table 2). Angus steers demonstrated HS-induced increases in prolactin, creatinine, and cholesterol (P < 0.05), but showed no breed differences at TN. There were also no breed differences (P < 0.55 and 0.16, respectively) between sodium and total protein levels. However, there were breed × time interactions for sodium and total protein with AG steers showing an increase in serum concentrations and RO showing a decrease (P < 0.05; Table 2). Table 2. Breed averaged blood values during thermoneutral (TN; d 16; Ta: 19 to 21°C) and heat stress (HS; d 31; Ta: 26 to 36°C) periods with ±1 SE1 Item  Breed  TN  HS  ±SE  P-value  Breed  Time  Interaction  Albumin, g/dL  AG  3.15  3.45  0.04  0.47  0.001  0.12    RO  3.28  3.44          Chloride, mEq/L  AG  101.78  101.56  1.57  0.13  0.22  0.28    RO  101.28  97.78          Cholesterol, mg/dL  AG  64.11  91.55  6.17  0.01  0.001  0.001    RO  58.01  64.88          CPK, units/L  AG  126.44  114.44  20.90  0.80  0.53  0.98    RO  131.00  118.00          Creatinine, mg/dL  AG  1.16  1.51  0.07  0.01  0.01  0.05    RO  1.07  1.11          Globulin, g/dL  AG  3.08  3.21  0.14  0.90  0.75  0.12    RO  3.26  3.06          Glucose, mg/dL  AG  77.67  71.78  4.58  0.89  0.26  0.74    RO  78.13  73.44          Leptin, ng/mL  AG  4.45  5.47  0.38  0.01  0.05  0.56    RO  3.63  4.22          Magnesium, mg/dL  AG  2.24  2.41  0.06  0.84  0.06  0.32    RO  2.31  2.36          Potassium, mEq/L  AG  4.31  4.50  0.12  0.28  0.48  0.20    RO  4.27  4.22          Prolactin, ng/mL  AG  34.11  42.39  1.59  0.10  0.01  0.05    RO  34.47  36.46          Sodium, mg/dL  AG  141.11  144.44  1.31  0.55  0.95  0.05    RO  143.63  140.44          Total protein, g/dL  AG  6.23  6.67  0.16  0.66  0.08  0.05    RO  6.55  6.51          Triglyceride, mg/dL  AG  15.22  19.22  1.88  0.80  0.05  0.94    RO  15.64  19.88          Urea N, mg/dL  AG  7.00  6.89  0.67  0.08  0.25  0.32    RO  6.38  4.89          Item  Breed  TN  HS  ±SE  P-value  Breed  Time  Interaction  Albumin, g/dL  AG  3.15  3.45  0.04  0.47  0.001  0.12    RO  3.28  3.44          Chloride, mEq/L  AG  101.78  101.56  1.57  0.13  0.22  0.28    RO  101.28  97.78          Cholesterol, mg/dL  AG  64.11  91.55  6.17  0.01  0.001  0.001    RO  58.01  64.88          CPK, units/L  AG  126.44  114.44  20.90  0.80  0.53  0.98    RO  131.00  118.00          Creatinine, mg/dL  AG  1.16  1.51  0.07  0.01  0.01  0.05    RO  1.07  1.11          Globulin, g/dL  AG  3.08  3.21  0.14  0.90  0.75  0.12    RO  3.26  3.06          Glucose, mg/dL  AG  77.67  71.78  4.58  0.89  0.26  0.74    RO  78.13  73.44          Leptin, ng/mL  AG  4.45  5.47  0.38  0.01  0.05  0.56    RO  3.63  4.22          Magnesium, mg/dL  AG  2.24  2.41  0.06  0.84  0.06  0.32    RO  2.31  2.36          Potassium, mEq/L  AG  4.31  4.50  0.12  0.28  0.48  0.20    RO  4.27  4.22          Prolactin, ng/mL  AG  34.11  42.39  1.59  0.10  0.01  0.05    RO  34.47  36.46          Sodium, mg/dL  AG  141.11  144.44  1.31  0.55  0.95  0.05    RO  143.63  140.44          Total protein, g/dL  AG  6.23  6.67  0.16  0.66  0.08  0.05    RO  6.55  6.51          Triglyceride, mg/dL  AG  15.22  19.22  1.88  0.80  0.05  0.94    RO  15.64  19.88          Urea N, mg/dL  AG  7.00  6.89  0.67  0.08  0.25  0.32    RO  6.38  4.89          1Ta = air temperature; AG = Angus; RO = Romosinuano. CPK = creatine phosphokinase. View Large Table 2. Breed averaged blood values during thermoneutral (TN; d 16; Ta: 19 to 21°C) and heat stress (HS; d 31; Ta: 26 to 36°C) periods with ±1 SE1 Item  Breed  TN  HS  ±SE  P-value  Breed  Time  Interaction  Albumin, g/dL  AG  3.15  3.45  0.04  0.47  0.001  0.12    RO  3.28  3.44          Chloride, mEq/L  AG  101.78  101.56  1.57  0.13  0.22  0.28    RO  101.28  97.78          Cholesterol, mg/dL  AG  64.11  91.55  6.17  0.01  0.001  0.001    RO  58.01  64.88          CPK, units/L  AG  126.44  114.44  20.90  0.80  0.53  0.98    RO  131.00  118.00          Creatinine, mg/dL  AG  1.16  1.51  0.07  0.01  0.01  0.05    RO  1.07  1.11          Globulin, g/dL  AG  3.08  3.21  0.14  0.90  0.75  0.12    RO  3.26  3.06          Glucose, mg/dL  AG  77.67  71.78  4.58  0.89  0.26  0.74    RO  78.13  73.44          Leptin, ng/mL  AG  4.45  5.47  0.38  0.01  0.05  0.56    RO  3.63  4.22          Magnesium, mg/dL  AG  2.24  2.41  0.06  0.84  0.06  0.32    RO  2.31  2.36          Potassium, mEq/L  AG  4.31  4.50  0.12  0.28  0.48  0.20    RO  4.27  4.22          Prolactin, ng/mL  AG  34.11  42.39  1.59  0.10  0.01  0.05    RO  34.47  36.46          Sodium, mg/dL  AG  141.11  144.44  1.31  0.55  0.95  0.05    RO  143.63  140.44          Total protein, g/dL  AG  6.23  6.67  0.16  0.66  0.08  0.05    RO  6.55  6.51          Triglyceride, mg/dL  AG  15.22  19.22  1.88  0.80  0.05  0.94    RO  15.64  19.88          Urea N, mg/dL  AG  7.00  6.89  0.67  0.08  0.25  0.32    RO  6.38  4.89          Item  Breed  TN  HS  ±SE  P-value  Breed  Time  Interaction  Albumin, g/dL  AG  3.15  3.45  0.04  0.47  0.001  0.12    RO  3.28  3.44          Chloride, mEq/L  AG  101.78  101.56  1.57  0.13  0.22  0.28    RO  101.28  97.78          Cholesterol, mg/dL  AG  64.11  91.55  6.17  0.01  0.001  0.001    RO  58.01  64.88          CPK, units/L  AG  126.44  114.44  20.90  0.80  0.53  0.98    RO  131.00  118.00          Creatinine, mg/dL  AG  1.16  1.51  0.07  0.01  0.01  0.05    RO  1.07  1.11          Globulin, g/dL  AG  3.08  3.21  0.14  0.90  0.75  0.12    RO  3.26  3.06          Glucose, mg/dL  AG  77.67  71.78  4.58  0.89  0.26  0.74    RO  78.13  73.44          Leptin, ng/mL  AG  4.45  5.47  0.38  0.01  0.05  0.56    RO  3.63  4.22          Magnesium, mg/dL  AG  2.24  2.41  0.06  0.84  0.06  0.32    RO  2.31  2.36          Potassium, mEq/L  AG  4.31  4.50  0.12  0.28  0.48  0.20    RO  4.27  4.22          Prolactin, ng/mL  AG  34.11  42.39  1.59  0.10  0.01  0.05    RO  34.47  36.46          Sodium, mg/dL  AG  141.11  144.44  1.31  0.55  0.95  0.05    RO  143.63  140.44          Total protein, g/dL  AG  6.23  6.67  0.16  0.66  0.08  0.05    RO  6.55  6.51          Triglyceride, mg/dL  AG  15.22  19.22  1.88  0.80  0.05  0.94    RO  15.64  19.88          Urea N, mg/dL  AG  7.00  6.89  0.67  0.08  0.25  0.32    RO  6.38  4.89          1Ta = air temperature; AG = Angus; RO = Romosinuano. CPK = creatine phosphokinase. View Large DISCUSSION Previous research has provided evidence that RO steers have a superior thermoregulatory ability compared with AG cattle (Spiers et al., 1994; Hammond et al., 1996). This ability must be the result of a reduction in heat production, increased capacity for heat loss to the environment, or a combination of both. There is ample evidence that basal metabolic rate of heat-tolerant B. indicus cattle is less than for heat-intolerant B. taurus cattle (Hansen, 2004). A reduced metabolic rate usually results in a reduced growth rate or reduced milk production (Hansen, 2004). Results from Chase et al. (1997) and Riley et al. (2007) have demonstrated that RO steers have a slower growth rate than other B. taurus breeds, again indicating that reduced metabolic rate may be a contributing factor to their thermotolerance. Steers in the present study were limit-fed and metabolic heat production was not measured; therefore, we did not quantify growth rate or feed intake differences. However, it has been documented that feed-restricted steers show a reduced rectal temperature (0.3 to 0.5°C) under hot environmental conditions (Davis et al., 2003). Because of this, animals from the current study might have been slightly less susceptible to the heat challenge. However, heat strain was demonstrated in both breeds used in this study, which allowed us to address the initial objective of determining if superior heat dissipation contributes to the underlying mechanism for heat tolerance of RO steers. The most commonly used variable to assess heat tolerance is Tre. Because Tre is easy to measure, well documented in the literature, and heritability is moderate to low (i.e., 0.25, Turner, 1982; 0.33, Turner, 1984), it makes a reliable index. In a study by Hammond et al. (1996), AG heifers were contrasted with Brahman, RO, and Senepol heifers. Results showed that Tre in AG was greater than the 3 other breeds on the hottest day of summer (~1.0°C). Even when Ta was much cooler in the winter, Tre of the AG remained greater than in RO or Senepol breeds, although the differences in winter were no greater than 0.5°C (Hammond et al., 1996). These results are consistent with those found in the present study. Whether under TN conditions or during HS, the Tre of AG cattle was maintained at 0.5°C greater than RO cattle, with the exception of the first 3 d of heat. During the first few days after initiation of heat, Tre of both breeds overlapped before the Tre of RO returned to ~0.5°C less than AG. This is a common phenomenon during acute HS, with animals having to increase heat loss mechanisms to dissipate heat, followed by a reduction in feed intake and a reduction in metabolic heat production. Alterations in heat dissipation can occur rapidly, whereas a change in metabolism takes longer. Typically, 3 to 4 d are necessary for an animal to begin acclimation and for rectal temperatures to be reduced to a new set point (Hahn, 1999). Even though they possessed the greater Tre, AG cattle maintained an ability to regulate core body temperature below 40°C. Use of rectal or core temperature is not the only assessment of heat tolerance; respiratory dynamics are also a reliable indicator (Gaughan et al., 2009). It is known that RR or panting in cattle varies among individuals within a single breed (Bianca, 1963). However, RR during heat exposure is known to increase more rapidly than other responses and often occurs at a lesser critical Ta than other responses such as Tre or changes in feed intake (Hahn, 1999). In addition, RR (i.e., an indicator of respiratory evaporative heat loss) is one of several effector responses, including sweat rate and peripheral vasodilation, that determine the internal body temperature response to HS. In theory, it is only when the avenues for heat loss are compromised, or limits of effectiveness are reached, that there would be an increase in internal body temperature. Respiration rates in the present study for AG during wk 1 at TN were increased above normal, which is likely due to a change in environment or other stressors. However, by the end of wk 1, RR decreased to a similar level as others have reported at TN (Gaughan et al., 1999; Brown-Brandl et al., 2003; Beatty et al., 2006). The RO steers showed no such reduction, consistent with the breed being known as a docile breed (Chase et al., 1997). They maintained a RR less than AG steers and similar to what others have reported for Brahman steers (Hammond et al., 1996; Gaughan et al., 1999; Beatty et al., 2006). Both breeds in the present study increased RR during heat exposure. Similarly to TN conditions, AG cattle had a greater RR under HS conditions than the RO cattle. This is consistent with the results reported in other studies (Spiers et al., 1994; Hammond et al., 1996). It is known that B. indicus breeds, specifically Brahman, have less RR compared with most B. taurus cattle. It is thought that heat-tolerant cattle rely on several physiological mechanisms to improve heat dissipation. Because the appearance of RO cattle and their heat tolerance ability are similar to Brahman cattle, they may have other characteristics in common. Most of these differences allow for greater heat dissipation through the skin, including greater blood flow to the skin and shorter hair coats (Finch, 1986). This could offer some explanation for the differences in RR between the 2 breeds in this study. All steers in the present study showed a linear increase in Tskin with Ta. However, in RO steers, which maintained a lesser Tskin than AG steers at TN, the Tskin was not different from AG steers during HS. This phenomenon has been reported by others for B. indicus-type cattle. Allen (1962) compared Brahman and Jersey cattle Tskin at Ta from 24 to 35°C. He reported that B. indicus-type cattle had the lesser Tskin below Ta 24°C and the greater mean Tskin above Ta 35°C. An increase in the Tskin to Ta gradient enhances heat loss by radiation, conduction, and convection. Therefore, the increase in Tskin in the present study is advantageous to both breeds, as long as Tskin is below core temperature to maintain the outward flow of heat. The decreased Tskin of RO at TN suggests that the TN Ta represented a cool temperature for the RO steers, which would result in vasoconstriction. Only 1 known study has examined sweating rate of RO cattle (Spiers et al., 1994). This study found that RO cattle exhibited a superior ability to sweat compared with AG cattle. Finch (1985) noted that when internal body temperature increases, sweating rate is greater and increases more rapidly in tropically adapted cattle than in temperate-zone B. taurus cattle. Therefore, expectations in the present study were that RO cattle would have a greater sweating rate than AG cattle. Surprisingly, results of the present study contradicted the results of Finch (1985) and Spiers et al. (1994). However, there are some major differences between the studies that may explain these results. Spiers et al. (1994) only measured sweating rate twice during their experiment. Spiers et al. (1994) also placed the animals in a constant HS compared with the cyclic method used in the present experiment. Although not many researchers have looked at sweat rate in heat-tolerant B. taurus cattle, research with B. indicus cattle has been conducted since the 1930s. Bos indicus cattle have been shown to have reduced sweating rates compared with B. taurus cattle under mild conditions, but greater rates at greater stress under acute conditions (Rhoad, 1940; Yeck and Kibler, 1956; Allen et al., 1963). With no nighttime cooling in the study conducted by Spiers et al. (1994), it is fair to say the cattle may have been under a greater heat strain. Variations in factors such as diet, coat cover, and acclimatization complicate comparisons and may also be responsible for some of the noted differences. Another surprising result of the present study was a reduction in sweating rate of animals of both breeds after 7 d of cyclic HS. Studies measuring sweat rate have concentrated on either short-term controlled studies or seasonal differences in sweating (Allen, 1962; McLean, 1963; Schleger and Turner, 1965; Robertshaw and Taylor, 1969). This reduction has never been reported in cattle. A reduction in sweat rate after several hours of heat strain has been reported in sheep, goats, and humans (Collins and Weiner, 1962; Jenkinson et al., 1971). However, this reduction occurs when sweat rate is continuously monitored for secretion rates over minutes. This phenomenon, known as sweat gland fatigue, is believed to be due to the rate of expulsion exceeding the rate of sweat production (Jenkinson et al., 1971; Johnson, 1973). Although this may play a role in the reduction of sweat rate, it is unclear why steers from both breeds would not utilize this mechanism to regulate core body temperature. Interestingly, this reduction in sweat rate coincided with the increase in maximum Tre for AG steers beginning on d 10 of HS. Unexpectedly, RO cattle also maintained a reduced sweat rate during the second week of HS, but demonstrated no change in Tre at this time. It is unclear why this late increase in Tre occurred. Finch (1986) reported that a widening of the daily body temperature cycle in cattle arises from changes in energy and water metabolism. There is a possibility that water or mineral balance may have played a role in this delayed increase in core body temperature of AG cattle. Because water was available ad libitum, it is unlikely that water balance was a contributor. In the present study, Ta showed the greatest correlation with all measured animal variables. These results are consistent with others (Scharf et al., 2008a), demonstrating that Tskin and RR exhibit good correlations with Ta, whereas Tre (i.e., the end result of thermoregulatory mechanisms) shows a poor correlation. Shoulder sweat rate vs. Ta and Tskin showed that Ta had a slightly greater correlation. The relationships between sweat rate and Ta and Tskin show these thermal variables could be the driving stimuli for sweating in cattle. The driving stimulus of sudomotor activity is currently unknown (Scharf et al., 2008a). Interestingly, AG steers showed greater correlation coefficients across the board. This again illustrates that RO steers may have a superior ability to regulate internal body temperature than AG steers. Several blood variables were measured in the present study to identify potential markers of heat sensitivity in cattle. Several of the measured blood variables did not change with HS and were not different between breeds. These included urea nitrogen, creatinine phosphokinase, magnesium, and chloride. Urea nitrogen can be indicative of dehydration (Schmidt-Nielsen and Schmidt-Nielsen, 1952) and protein catabolism (Srikandakumar et al., 2003). Creatinine phosphokinase is indicative of muscle injury or other pathologies (Spears et al., 1986). No change in these variables in the present study suggests that these conditions (i.e., muscle pathology, energy balance, or dehydration) did not exist. Both magnesium and chloride blood concentrations remained stable throughout the experiment. Because both breeds were fed a maintenance diet, the results demonstrated that the concentration of magnesium and chloride in the diet met the necessary requirements for the animal. Blood glucose concentrations also were not different between breeds or with HS, lending additional support for caloric balance in the present study. Total protein represents a portion of the AA pool of the body and is believed to be indicative of the nutritional status of the animal (Doornenbal et al., 1988). Total protein consists mostly of albumin and globulin. In the present study, total blood protein content increased with HS, which has been reported by others (Shaffer et al., 1981). However, albumin showed an HS-related increase in both breeds, whereas globulin did not, indicating that the increase in total protein was primarily due to an increase in albumin. Like many blood variables, albumin is not well studied with regard to HS, but may be involved in some component of water balance (Parker et al., 2003). No breed or HS differences were seen in serum sodium concentrations. However, there was a time × breed interaction with RO steers having a reduction in sodium concentration, whereas AG steers showed an increase. A reduction in serum sodium concentration is expected during HS and has been documented by El-Nouty et al. (1980). This reduction may be due to an increase in urinary sodium excretion due to increased total urinary output or expanded blood volume due to an increase in water intake. Normally, an increase in serum sodium could signify that an animal is becoming dehydrated. However, it is unlikely that dehydration resulted in any of the heat-induced changes or breed differences in sodium. Although water intake and drinking behavior were not measured in the present study, animals had ad libitum access to water. A commonly used variable as an indicator of dehydration is hematocrit or packed cell volume. However, it has been shown that hematocrit is not a reliable indicator of dehydration because it is highly variable (Parker et al., 2004; Scharf et al., 2008b). One rapid response to dehydration is a significant reduction in feed intake (Bianca et al., 1965; Olsson, 2005; Scharf et al., 2008b). Even though animals in the current study were limit-fed, dehydration should have resulted in some reduction in feed consumption. Another indication of dehydration is an increase in plasma urea nitrogen. Previous research has shown that water restriction increases N retention, increasing serum concentrations in camels, sheep, and cattle (Schmidt-Nielsen and Schmidt-Nielsen, 1952; Goodall and Kay, 1968; Utley et al., 1970). Serum urea N in the current study did not change, further supporting the conclusion that water restriction was not an issue in the present study. Unlike serum sodium, serum potassium showed no changes. This is expected as potassium is well maintained throughout the body. Some studies, including El-Nouty et al. (1980), found that potassium concentrations were reduced in cows during prolonged HS. El-Nouty et al. (1980) suggested that reductions in serum potassium were due to loss of potassium in sweat. Because cattle possess apocrine glands, secretions from the skin of cattle can contain 4 to 5 times the amount of potassium compared with sodium (Johnson, 1970). Because the HS blood sample was taken during wk 2 of HS, sweat rate had already acclimated to a reduced amount. Therefore, it may be that the dietary potassium in this experiment was enough to offset the losses from the skin. Very little is known about breed differences in serum leptin concentrations. In the present study, AG steers had greater serum leptin concentration compared with RO steers throughout the study. This is consistent with Thomas et al. (2002) in which Angus bulls showed greater concentration of serum leptin in comparison with Brangus and Brahman bulls of similar age. It has been shown that plasma leptin is strongly related to adipose cell size and number in cattle (Delavaud et al., 2002). Because RO steers in this study were lighter and leaner than AG steers, it may be that adipose cells played a role in the differences in leptin concentration. To our knowledge, no one has examined leptin in regard to HS. In the present study, leptin increased in both breeds with HS. However, leptin has been implicated in reducing feed intake and alteration of heat shock protein 70, which can alter the HS response (Figueiredo et al., 2007). Similar to leptin, serum creatinine showed breed differences throughout the study. However, only AG steers showed heat-induced increases. Romosinuano steers began the experiment with reduced concentrations than AG steers and showed no increase in serum creatinine concentrations with time. Increases in plasma creatinine during HS have been documented in sheep and cattle (Koubkova et al., 2002; Srikandakumar et al., 2003). Heat stress increases peripheral vasodilation to increase heat loss and reduces blood flow to the internal organs (Srikandakumar et al., 2003). The rate of excretion of creatinine is influenced by renal perfusion and glomerular filtration rate. A reduction in renal blood flow during HS might then raise plasma creatinine concentration. With a large increase in Tskin (i.e., blood flow to the skin) with HS and no increase in serum creatinine in the RO steers, it may be that they have a greater ability to regulate blood flow to internal organs during HS or provide may provide additional support to the idea that RO steers were not significantly heat stressed in the present study. Cholesterol was similar between the 2 breeds at TN. However, serum cholesterol in AG steers showed an increase during HS, whereas it remained unchanged in RO steers. Although not extensively studied in cattle, it has been reported that cholesterol increases during HS (Brody, 1956). Shaffer et al. (1981) reported that circulating cholesterol is influenced by the degree of stress. This is consistent with the hypothesis that the RO steers were not heat stressed to the same level as AG steers. The results of the present study were interesting given that it has been reported that other heat-tolerant breeds have greater cholesterol levels than B. taurus breeds (O'Kelly, 1968; Olbrich et al., 1971). Olbrich et al. (1971) found that the mean serum cholesterol concentrations were greater for Zebu than Scotch Highland heifers. O'Kelly (1968) also found that cholesterol, phospholipid, and total lipid concentrations were all significantly greater in Zebu than British breeds. It is unclear why RO steers would maintain reduced serum cholesterol in comparison with AG steers throughout the study. Changes in serum prolactin concentrations in response to increases in Ta have been extensively studied and are known to be positively correlated (Schams, 1972; Wetteman and Tucker, 1974; Head et al., 1976; Johnson, 1985). Although plasma prolactin is known to increase during thermal stress, the mechanism is not well understood (Johnson, 1985). In the present study, there were no breed differences in serum prolactin concentration at TN, which is consistent with other reports between breeds (Ohlson et al., 1981; Wettemann et al., 1982). Serum prolactin concentrations in the present study increased with HS for the AG steers. However, no increase was seen for RO steers. Wettemann et al. (1982) reported that concentrations of serum prolactin in B. indicus and B. taurus heifers that were acutely and chronically exposed to various Ta responded similarly, suggesting that the mechanisms responsible for the control of prolactin in serum are not different between breeds. If this assumption is correct, it would again provide evidence that the RO steers were not heat stressed to the extent of AG steers in the current study. Although it is not understood why prolactin increases with Ta, prolactin shows potential for being an indicator of heat tolerance. Angus and RO steers were tested in TN and under HS environments to determine differences between heat-sensitive and heat-tolerant B. taurus breeds. In both environments, AG cattle exhibited a greater heat loss (greater RR and sweat rate and similar Tskin) than the heat-tolerant breed. Surprisingly, the AG also exhibited the greater heat load as indicated by Tre. Romosinuano steers appeared to be only minimally heat stressed, showing an increase in RR but only a moderate rise in Tre. Because not all heat loss mechanisms were measured in the present experiment, it is impossible to definitively state the reason for the decreased Tre in RO. From these results, decreased heat production or a greater ability to vasodilate are the most likely candidates for the superior heat tolerance in RO steers. A final objective of this study was to identify additional markers of heat tolerance. Rectal temperature is still a good indicator of heat tolerance. This study identified prolactin, cholesterol, and creatinine as additional markers of HS that require further research. Using these other variables, in addition to Tre, could allow for improved identification of heat-tolerant animals. Once animals are identified, selection pressure on heat tolerance and growth rate can be applied to increase animal productivity. LITERATURE CITED Allen T. E. 1962. Responses of Zebu, Jersey and Zebu × Jersey crossbred heifers to rising temperature, with particular reference to sweating. Aust. J. Agric. Res.  13: 165– 179. Google Scholar CrossRef Search ADS   Allen T. E. Pan Y. S. Hayman R. 1963. The effect of feeding on the evaporative heat loss and body temperature in Zebu and Jersey heifers. Aust. J. Agric. Res.  14: 580– 593. Google Scholar CrossRef Search ADS   Beatty D. T. Barnes A. Taylor E. Pethick D. McCarthy M. Maloney S. K. 2006. Physiological responses of Bos taurus and Bos indicus cattle to prolonged, continuous heat and humidity. J. Anim. Sci.  84: 972– 985. https://doi.org/16543576 Google Scholar CrossRef Search ADS PubMed  Bianca W. 1963. Rectal temperature and respiratory rate as indicators of heat tolerance in cattle. J. Agric. Sci.  60: 113– 120. Google Scholar CrossRef Search ADS   Bianca W. Findlay J. D. McLean J. A. 1965. Responses of steers to water restriction. Res. Vet. Sci.  6: 38– 55. https://doi.org/14281667 Google Scholar PubMed  Bligh J. Johnson K. G. 1973. Glossary of terms for thermal physiology. J. Appl. Physiol.  35: 941– 961. https://doi.org/4765838 Google Scholar CrossRef Search ADS PubMed  Brody S. 1956. Climatic physiology of cattle. J. Dairy Sci.  39: 715– 725. Google Scholar CrossRef Search ADS   Brown-Brandl T. M. Nienaber J. A. Eigenberg R. A. Hahn G. L. Freetly H. 2003. Thermoregulatory responses of feeder cattle. J. Therm. Biol.  28: 149– 157. Google Scholar CrossRef Search ADS   Burton, A. C., and O. G. Edholm 1955. Man in a Cold Environment.  Edward Arnold Ltd., London, UK. Chase C. C.Jr. Hammond A. C. Olson T. A. Murphy C. N. Tewolde A. J. Griffin L. 1997. Introduction of Romosinuano in the U.S.A. Arch. Latinoam. Prod. Anim  5: 57– 71. Collins K. J. Weiner J. S. 1962. Observations on arm-bag suppression of sweating and its relationship to thermal sweat-gland ‘fatigue’. J. Physiol.  161: 538– 556. https://doi.org/13849429 Google Scholar CrossRef Search ADS PubMed  Davis M. S. Mader T. L. Holt S. M. Parkhurst A. M. 2003. Strategies to reduce feedlot cattle heat stress: Effects on tympanic temperature. J. Anim. Sci.  81: 649– 661. https://doi.org/12350014 Google Scholar CrossRef Search ADS PubMed  Delavaud C. Bocquier F. Chilliard Y. Keisler D. H. Gertler A. Kann G. 2000. Plasma leptin determination in ruminants: Effect of nutritional status and body fatness on plasma leptin concentration assessed by a specific RIA in sheep. J. Endocrinol.  165: 519– 526. Google Scholar CrossRef Search ADS PubMed  Delavaud C. Ferlay A. Faulconnier Y. Bocquier F. Kann G. Chilliard Y. 2002. Plasma leptin concentration in adult cattle: Effects of breed, adiposity, feeding level and meal intake. J. Anim. Sci.  80: 1317– 1328. https://doi.org/12019621 Google Scholar CrossRef Search ADS PubMed  Doornenbal H. Tong A. K. W. Murray N. L. 1988. Reference values of blood parameters in beef cattle of different ages and stages of lactation. Can. J. Vet. Res.  52: 99– 105. https://doi.org/3349406 Google Scholar PubMed  El-Nouty F. D. Elbanna I. M. Davis T. P. Johnson H. D. 1980. Aldosterone and ADH response to heat and dehydration in cattle. J. Appl. Physiol.  48: 249– 255. https://doi.org/7364609 Google Scholar CrossRef Search ADS   Elzo M. A. Manrique C. Ossa G. Acosta O. 1998. Additive and nonadditive genetic variability for growth traits in the Turipaná Romosinuano-Zebu multibreed herd. J. Anim. Sci.  76: 1539– 1549. https://doi.org/9655573 Google Scholar CrossRef Search ADS PubMed  Figueiredo D. Gertler A. Cabello G. Decupere E. Buyse J. Dridi S. 2007. Leptin down regulates heat shock protein-70 (HSP-70) gene expression in chicken liver and hypothalamus. Cell Tissue Res.  329: 91– 101. https://doi.org/17406896 Google Scholar CrossRef Search ADS PubMed  Finch V. A. 1985. Comparison of non-evaporative heat transfer in different cattle breeds. Aust. J. Agric. Res.  36: 497– 508. Google Scholar CrossRef Search ADS   Finch V. A. 1986. Body temperature in cattle: Its control and relevance to production in the tropics. J. Anim. Sci.  62: 531– 542. Google Scholar CrossRef Search ADS   Finch V. A. Bennet I. L. Holmes C. R. 1982. Sweating response in cattle and its relation to rectal temperature, tolerance of sun and metabolic rate. J. Agric. Sci.  99: 479– 487. Google Scholar CrossRef Search ADS   Gaughan J. B. Mader T. L. Holt S. M. Josey M. J. Rowan K. J. 1999. Heat tolerance of Boran and Tuli crossbred steers. J. Anim. Sci.  77: 2398– 2405. https://doi.org/10492446 Google Scholar CrossRef Search ADS PubMed  Gaughan J. B. Mader T. L. Holt S. M. Sullivan M. L. Hahn G. L. 2009. Assessing the heat tolerance of 17 beef cattle genotypes. Int. J. Biometeorol.  https://doi.org/10.1007/s00484-009-0233-4 Gebremedhin, K., P. Hillman, C. Lee, and R. Collier 2007. Sweating rate of dairy cows under shade and sunny environments.  2007 ASAE Annual Meeting, Minneapolis, MN. Paper No. 074083. Goodall E. D. Kay R. N. B. 1968. Water intake and the cycling of nitrogen to the stomach in sheep. J. Physiol.  194: 38P– 39P. https://doi.org/5639783 Google Scholar PubMed  Hahn G. L. 1999. Dynamic responses of cattle to thermal heat loads. J. Anim. Sci.  77: 10– 20. https://doi.org/15526777 Google Scholar CrossRef Search ADS PubMed  Hammond A. C. Chase C. C.Jr. Bowers E. J. Olson T. A. Randel R. D. 1996. Heat tolerance in Tuli-, Senepol-, and Brahman-sired F1 Angus heifers in Florida. J. Anim. Sci.  76: 1568– 1577. Google Scholar CrossRef Search ADS   Hansen P. J. 2004. Physiological and cellular adaptations of Zebu cattle to thermal stress. Anim. Reprod. Sci.  82–83: 349– 360. Google Scholar CrossRef Search ADS PubMed  Head H. H. Thatcher W. W. Wilcox C. J. Bachman K. C. 1976. Effect of a synthetic corticoid on milk yield and composition and on blood metabolites and hormones in dairy cows. J. Dairy Sci.  59: 880– 888. https://doi.org/1270650 Google Scholar CrossRef Search ADS PubMed  Jenkinson D. M. E. McEwan D. Robertshaw D. 1971. Studies on the nature of sweat gland 'fatigue' in the goat. J. Physiol.  212: 455– 465. https://doi.org/4100840 Google Scholar CrossRef Search ADS PubMed  Johnson, H. D. 1985. Physiological responses and productivity of cattle. Pages 3– 24 in Volume 1: Stress Physiology in Livestock. Basic Principles.  M. K. Yousef ed. CRC Press, Boca Raton, FL. Johnson K. G. 1970. Sweating rate and the electrolyte content of skin secretions of Bos taurus and Bos indicus cross-bred cows. J. Agric. Sci.  75: 397– 402. Google Scholar CrossRef Search ADS   Johnson K. G. 1973. Sweat storage as a factor influencing sweat discharge in sheep. J. Physiol.  235: 523– 534. https://doi.org/4764002 Google Scholar CrossRef Search ADS PubMed  Koubkova M. Knizkova I. Kunc P. Hartlova H. Flusser J. Dolezal O. 2002. Influence of high environmental temperatures and evaporative cooling on some physiological, hematological and biochemical parameters in high-yielding dairy cows. Czech J. Anim. Sci.  47: 309– 318. Lutz S. L. Smith M. F. Keisler D. H. Garverick H. A. 1991. Effect of constant infusion of oxytocin on luteal lifespan and oxytocin-induced release of prostaglandin F2α in heifers. Domest. Anim. Endocrinol.  8: 573– 585. https://doi.org/1786704 Google Scholar CrossRef Search ADS PubMed  McLean J. A. 1963. The regional distribution of cutaneous moisture vaporization in the Ayrshire calf. J. Agric. Sci. Camb.  61: 275– 280. Google Scholar CrossRef Search ADS   Nuutinen J. Alanen E. Autio P. Lahtinen M. Harvima I. Lahtinen T. 2003. A closed unventilated chamber for the measurement of transepidermal water loss. Skin Res. Technol.  9: 85– 89. https://doi.org/12709124 Google Scholar CrossRef Search ADS PubMed  Ohlson D. L. Davis S. L. Ferrell C. L. Jenkins T. G. 1981. Plasma growth hormone, prolactin and thyrotropic secretory patterns in Hereford and Simmental calves. J. Anim. Sci.  53: 371– 375. https://doi.org/7319944 Google Scholar CrossRef Search ADS PubMed  O'Kelly J. C. 1968. Comparative studies of lipid metabolism in Zebu and British cattle in a tropical environment. I. Plasma lipid levels of grazing cattle. Aust. J. Biol. Sci.  21: 1013– 1024. https://doi.org/5701182 Google Scholar CrossRef Search ADS PubMed  Olbrich S. E. Martz F. A. Tumbleson M. E. Johnson H. D. Hilderbrand E. S. 1971. Serum biochemical and hematological measurements of heat tolerant (Zebu) and cold tolerant (Scotch Highland) heifers. J. Anim. Sci.  33: 655– 658. https://doi.org/5122304 Google Scholar CrossRef Search ADS PubMed  Olsson K. 2005. Fluid balance in ruminants: Adaptation to external and internal challenges. Ann. N. Y. Acad. Sci.  1040: 156– 161. https://doi.org/15891020 Google Scholar CrossRef Search ADS PubMed  Parker A. J. Hamlin G. P. Coleman C. J. Fitzpatrick L. A. 2003. Dehydration in stressed ruminants may be the result of acortisol-induced diuresis. J. Anim. Sci.  81: 512– 519. https://doi.org/12643496 Google Scholar CrossRef Search ADS PubMed  Parker A. J. Hamlin G. P. Coleman C. J. Fitzpatrick L. A. 2004. Excess cortisol interferes with a principal mechanism of resistance to dehydration in Bos indicus steers. J. Anim. Sci.  82: 1037– 1045. https://doi.org/15080325 Google Scholar CrossRef Search ADS PubMed  Rhoad A. O. 1940. Absorption and reflection of solar radiation in relation to coat color in cattle. Am. Soc. Anim. Prod.  1940: 291– 293. Riley D. G. Chase C. C.Jr. Coleman S. W. Olson T. A. 2007. Evaluation of birth and weaning traits of Romosinuano calves as purebreds and crosses with Brahman and Angus. J. Anim. Sci.  85: 289– 298. https://doi.org/17235015 Google Scholar CrossRef Search ADS PubMed  Robertshaw D. Taylor C. R. 1969. A comparison of sweat gland activity in eight species of East African bovids. J. Physiol.  203: 135– 143. https://doi.org/5821863 Google Scholar CrossRef Search ADS PubMed  Schams D. 1972. Prolactin levels in bovine blood, influenced by milking manipulation, genital stimulation and oxytocin administration with specific consideration of the seasonal variations. Acta Endocrinol. (Copenh.)  71: 684– 696. https://doi.org/4678206 Google Scholar PubMed  Scharf B. Wax L. E. Aiken G. E. Spiers D. E. 2008a. Regional differences in sweat rate response of steers to short-term heat stress. Int. J. Biometeorol.  52: 725– 732. https://doi.org/18612663 Google Scholar CrossRef Search ADS   Scharf, B., L. E. Wax, T. J. Evans, and D. E. Spiers 2008b. Impact of dehydration on production and thermoregulation of Angus steers at thermoneutrality.  Pages 465– 471 in Proc. 8th Int. Livest. Environ. August 31 to September 4, 2008, Iguassu Falls, Brazil. Am. Soc. Agric. Biol. Eng., St. Joseph, MI. Schleger A. V. Turner H. G. 1965. Sweating rates of cattle in the field and their reaction to diurnal and seasonal changes. Aust. J. Agric. Res.  16: 92– 106. Google Scholar CrossRef Search ADS   Schmidt-Nielsen K. Schmidt-Nielsen B. 1952. Water metabolism of desert mammals. Physiol. Rev.  32: 135– 166. https://doi.org/14929697 Google Scholar CrossRef Search ADS PubMed  Shaffer L. Roussel J. D. Koonce K. L. 1981. Effects of age, temperature-season, and breed on blood characteristics of dairy cattle. J. Dairy Sci.  64: 62– 70. https://doi.org/7264021 Google Scholar CrossRef Search ADS PubMed  Spears J. W. Harvey R. W. Segerson E. C. 1986. Effects of marginal selenium deficiency and winter protein supplementation on growth, reproduction and selenium status of beef cattle. J. Anim. Sci.  63: 586– 594. https://doi.org/3759693 Google Scholar CrossRef Search ADS PubMed  Spiers D. E. Vogt D. W. Johnson H. D. Garner G. B. Murphy C. N. 1994. Heat-stress responses of temperate and tropical breeds of Bos taurus cattle. Arch. Latinoam. Prod. Anim.  2: 41– 52. Srikandakumar A. Johnson E. H. Mahgoub O. 2003. Effect of heat stress on respiratory rate, rectal temperature and blood chemistry in Omani and Australian Merino sheep. Small Rumin. Res.  49: 193– 198. Google Scholar CrossRef Search ADS   Steele, R. G. D., and J. H. Torrie 1980. Principles and Procedures of Statistics: A Biometrical Approach.  McGraw-Hill Publishing Co, New York, NY. Thomas M. G. Enns R. M. Hallford D. M. Keisler D. H. Obeidat B. S. Morrison C. D. Hernandez J. A. Bryant W. D. Flores R. Lopez R. Narro L. 2002. Relationships of metabolic hormones and serum glucose to growth and reproductive development in performance-tested Angus, Brangus, and Brahman bulls. J. Anim. Sci.  80: 757– 767. https://doi.org/11890413 Google Scholar CrossRef Search ADS PubMed  Turner H. G. 1982. Genetic variation of rectal temperature in cows and its relationship to fertility. Anim. Prod.  35: 401– 412. Google Scholar CrossRef Search ADS   Turner H. G. 1984. Variation in rectal temperature of cattle in a tropical environment and its relation to growth rate. Anim. Prod.  38: 417– 427. Google Scholar CrossRef Search ADS   Utley P. R. Bradley N. W. Boling J. A. 1970. Effect of restricted water intake on feed intake, nutrient digestibility and nitrogen metabolism in steers. J. Anim. Sci.  31: 130– 135. https://doi.org/5451024 Google Scholar CrossRef Search ADS PubMed  Vogt, D. C., C. Murphy, R. Crawford, A. Clarke, E. Cole, G. Garner, and A. Tewolde 1991. Comparison of growth, carcass and meat characteristics of straightbred Angus and Romo Sinuano × Angus cross cattle managed under a forage system.  Mo. Agric. Exp. Sta. Southwest Center Research Report, Vernon, MO. Wettemann R. P. Tucker H. A. 1974. Relationship of ambient temperature to serum prolactin in heifers. Proc. Soc. Exp. Biol. Med.  146: 908– 911. https://doi.org/4210174 Google Scholar CrossRef Search ADS PubMed  Wettemann R. P. Tucker H. A. Beck T. W. Meyerhoeffer D. C. 1982. Influence of ambient temperature on prolactin concentrations in serum of Holstein and Brahman × Hereford heifers. J. Anim. Sci.  55: 391– 394. https://doi.org/6815150 Google Scholar CrossRef Search ADS PubMed  Yeck, R. G., and H. H. Kibler 1956. Moisture vaporization by Jersey and Holstein cows during diurnal temperature cycles as measured with a hygrometric tent. Mo. Agric. Exp. Sta. Res. Bull. 600.  American Society of Animal Science