Efficiency of eDNA and iDNA in assessing vertebrate diversity and its abundanceCarvalho, Carolina S.; Oliveira, Marina Elisa; Rodriguez‐Castro, Karen Giselle; Saranholi, Bruno H.; Galetti, Pedro M.
doi: 10.1111/1755-0998.13543pmid: 34724330
Environmental DNA (eDNA) and invertebrate‐derived DNA (iDNA) have been increasingly recognized as powerful tools for biodiversity assessment and conservation management. However, eDNA/iDNA efficiency for vertebrate diversity assessment remains uncertain, and comparisons to conventional methods are still rare. Through a meta‐analysis of previously published vertebrate diversity surveys, we compared the efficiency of eDNA/iDNA against conventional methods across several types of samplers, vertebrate groups, and locations (tropical vs. temperate zones). We also assess eDNA/iDNA efficiency to estimate relative abundance or biomass over different molecular methods (qPCR and metabarcoding) and type of experiment (in the laboratory or in the field). We showed that for water sampler, fish as a target species, and studies achieved in temperate zones, eDNA presents lower risk of not detecting a species or a site with a target species than conventional methods. These results show that eDNA is an efficient tool to assess fish diversity. Moreover, eDNA data presents positive correlation with fish abundance or biomass. However, such correlation was higher in laboratory experiments than in the field. For the other samplers, vertebrate groups, and in tropical zones we were not able to draw general conclusion, highlighting the urgency of conducting more comparative studies.
Inferring the timing and strength of natural selection and gene migration in the evolution of chicken from ancient DNA dataLyu, Wenyang; Dai, Xiaoyang; Beaumont, Mark; Yu, Feng; He, Zhangyi
doi: 10.1111/1755-0998.13553pmid: 34783162
With the rapid growth of the number of sequenced ancient genomes, there has been increasing interest in using this new information to study past and present adaptation. Such an additional temporal component has the promise of providing improved power for the estimation of natural selection. Over the last decade, statistical approaches for the detection and quantification of natural selection from ancient DNA (aDNA) data have been developed. However, most of the existing methods do not allow us to estimate the timing of natural selection along with its strength, which is key to understanding the evolution and persistence of organismal diversity. Additionally, most methods ignore the fact that natural populations are almost always structured, which can result in an overestimation of the effect of natural selection. To address these issues, we introduce a novel Bayesian framework for the inference of natural selection and gene migration from aDNA data with Markov chain Monte Carlo techniques, co‐estimating both timing and strength of natural selection and gene migration. Such an advance enables us to infer drivers of natural selection and gene migration by correlating genetic evolution with potential causes such as the changes in the ecological context in which an organism has evolved. The performance of our procedure is evaluated through extensive simulations, with its utility shown with an application to ancient chicken samples.
f‐Statistics estimation and admixture graph construction with Pool‐Seq or allele count data using the R package poolfstatGautier, Mathieu; Vitalis, Renaud; Flori, Laurence; Estoup, Arnaud
doi: 10.1111/1755-0998.13557pmid: 34837462
By capturing various patterns of the structuring of genetic variation across populations, f‐statistics have proved highly effective for the inference of demographic history. Such statistics are defined as covariances of SNP allele frequency differences among sets of populations without requiring haplotype information and are hence particularly relevant for the analysis of pooled sequencing (Pool‐Seq) data. We here propose a reinterpretation of the F (and D) parameters in terms of probability of gene identity and derive from this unified definition unbiased estimators for both Pool‐Seq data and standard allele count data obtained from individual genotypes. We implemented these estimators in a new version of the R package poolfstat, which now includes a wide range of inference methods: (i) three‐population test of admixture; (ii) four‐population test of treeness; (iii) F4‐ratio estimation of admixture rates; and (iv) fitting, visualization and (semi‐automatic) construction of admixture graphs. A comprehensive evaluation of the methods implemented in poolfstat on both simulated Pool‐Seq (with various sequencing coverages and error rates) and allele count data confirmed the accuracy of these approaches, even for the most cost‐effective Pool‐Seq design involving relatively low sequencing coverages. We further analysed a real Pool‐Seq data made of 14 populations of the invasive species Drosophila suzukii, which allowed refining both the demographic history of native populations and the invasion routes followed by this emblematic pest. Our new package poolfstat provides the community with a user‐friendly and efficient all‐in‐one tool to unravel complex population genetic histories from large‐size Pool‐Seq or allele count SNP data.
Genome and gene evolution of seahorse species revealed by the chromosome‐level genome of Hippocampus abdominalisHe, Libin; Long, Xin; Qi, Jianfei; Wang, Zongji; Huang, Zhen; Wu, Shuiqing; Zhang, Xingtan; Luo, Huiyu; Chen, Xinxin; Lin, Jinbo; Yang, Qiuhua; Huang, Shiyu; Zhou, Qi; Zheng, Leyun
doi: 10.1111/1755-0998.13541pmid: 34698429
Seahorses belong to the teleost family Syngnathidae that evolved a distinct body plan and unique male pregnancy compared to other teleosts. As a classic model for studying evolution of viviparity and sexual selection of teleosts, seahorse species still lack a publicly available high‐quality reference genome. Here, we generated the genome assembly of the big‐belly seahorse, Hippocampus abdominalis with long‐read and Hi‐C technologies. We managed to place over 99% of the total length of 444.7 Mb of assembled genome into 21 linkage groups with almost no gaps. We reconstructed a phylogenomic tree with the big‐belly seahorse genome and other representative Syngnathidae and teleost species. We also reconstructed the historical population dynamics of four representative Syngnathidae species. We found the gene families that underwent expansion or contraction in the Syngnathidae ancestor were enriched for immune‐related or ion transporter gene ontology terms. Many of these genes were also reported to show a dynamic expression pattern during the pregnancy stages of H. abdominalis. We also identified putative positively selected genes in the Syngnathidae ancestor or in H. abdominalis, whose mouse mutants are enriched for abnormal craniofacial and limb morphological phenotypes. Overall, our study provides an important genome resource for evolutionary and developmental studies of seahorse species, and candidate genes for future experimental works.
Tools and applications for integrative analysis of DNA methylation in social insectsMorandin, Claire; Brendel, Volker P.
doi: 10.1111/1755-0998.13566pmid: 34861105
DNA methylation is a common epigenetic signalling tool and an important biological process which is widely studied in a large array of species. The presence, level and function of DNA methylation vary greatly across species. In some insects, DNA methylation systems are minimal, and overall methylation rates tend to be low in all studied insect species. Low methylation levels probed by whole‐genome bisulphite sequencing require great care with respect to data quality control and interpretation. Here, we introduce BWASP/R, a complete workflow that allows efficient, scalable and entirely reproducible analyses of raw DNA methylation sequencing data. Consistent application of quality control filters and analysis parameters provides fair comparisons among different studies and an integrated view of all experiments on one species. We describe the capabilities of the BWASP/R workflow by re‐analysing several publicly available social insect WGBS data sets, comprising 70 samples and cumulatively 147 replicates from four different species. We show that the CpG methylome comprises only about 1.5% of CpG sites in the honeybee genome and that the cumulative data are consistent with genetic signatures of site accessibility and physiological control of methylation levels.