Dataset Information

Identifying artificial selection signals in the chicken genome.

ABSTRACT: Identifying the signals of artificial selection can contribute to further shaping economically important traits. Here, a chicken 600k SNP-array was employed to detect the signals of artificial selection using 331 individuals from 9 breeds, including Jingfen (JF), Jinghong (JH), Araucanas (AR), White Leghorn (WL), Pekin-Bantam (PB), Shamo (SH), Gallus-Gallus-Spadiceus (GA), Rheinlander (RH) and Vorwerkhuhn (VO). Per the population genetic structure, 9 breeds were combined into 5 breed-pools, and a 'two-step' strategy was used to reveal the signals of artificial selection. GA, which has little artificial selection, was defined as the reference population, and a total of 204, 155, 305 and 323 potential artificial selection signals were identified in AR_VO, PB, RH_WL and JH_JF, respectively. We also found signals derived from standing and de-novo genetic variations have contributed to adaptive evolution during artificial selection. Further enrichment analysis suggests that the genomic regions of artificial selection signals harbour genes, including THSR, PTHLH and PMCH, responsible for economic traits, such as fertility, growth and immunization. Overall, this study found a series of genes that contribute to the improvement of chicken breeds and revealed the genetic mechanisms of adaptive evolution, which can be used as fundamental information in future chicken functional genomics study.

SUBMITTER: Ma Y

PROVIDER: S-EPMC5919632 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identifying artificial selection signals in the chicken genome.

Ma Yunlong Y Gu Lantao L Yang Liubin L Sun Chenghao C Xie Shengsong S Fang Chengchi C Gong Yangzhang Y Li Shijun S

PloS one 20180426 4

Identifying the signals of artificial selection can contribute to further shaping economically important traits. Here, a chicken 600k SNP-array was employed to detect the signals of artificial selection using 331 individuals from 9 breeds, including Jingfen (JF), Jinghong (JH), Araucanas (AR), White Leghorn (WL), Pekin-Bantam (PB), Shamo (SH), Gallus-Gallus-Spadiceus (GA), Rheinlander (RH) and Vorwerkhuhn (VO). Per the population genetic structure, 9 breeds were combined into 5 breed-pools, and ...[more]

PMID: 29698423

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Project description:Comparative genomics studies investigating the signals of positive selection among groups of closely related species are still rare and limited in taxonomic breadth. Such studies show great promise in advancing our knowledge about the proportion and the identity of genes experiencing diversifying selection. However, methodological challenges have led to high levels of false positives in past studies. Here, we use the well-annotated genome of the purple sea urchin, Strongylocentrotus purpuratus, as a reference to investigate the signals of positive selection at 6520 single-copy orthologs from nine sea urchin species belonging to the family Strongylocentrotidae paying careful attention to minimizing false positives.We identified 1008 (15.5%) candidate positive selection genes (PSGs). Tests for positive selection along the nine terminal branches of the phylogeny identified 824 genes that showed lineage-specific adaptive diversification (1.67% of branch-sites tests performed). Positively selected codons were not enriched at exon borders or near regions containing missing data, suggesting a limited contribution of false positives caused by alignment or annotation errors. Alignments were validated at 10 loci with re-sequencing using Sanger methods. No differences were observed in the rates of synonymous substitution (d S), GC content, and codon bias between the candidate PSGs and those not showing positive selection. However, the candidate PSGs had 68% higher rates of nonsynonymous substitution (d N) and 33% lower levels of heterozygosity, consistent with selective sweeps and opposite to that expected by a relaxation of selective constraint. Although positive selection was identified at reproductive proteins and innate immunity genes, the strongest signals of adaptive diversification were observed at extracellular matrix proteins, cell adhesion molecules, membrane receptors, and ion channels. Many candidate PSGs have been widely implicated as targets of pathogen binding, inactivation, mimicry, or exploitation in other groups (notably mammals).Our study confirmed the widespread action of positive selection across sea urchin genomes and allowed us to reject the possibility that annotation and alignment errors (including paralogs) were responsible for creating false signals of adaptive molecular divergence. The candidate PSGs identified in our study represent promising targets for future research into the selective agents responsible for their adaptive diversification and their contribution to speciation.

Project description:BackgroundAngiosperms employ an astonishing variety of visual and olfactory floral signals that are generally thought to evolve under natural selection. Those morphological and chemical traits can form highly correlated sets of traits. It is not always clear which of these are used by pollinators as primary targets of selection and which would be indirectly selected by being linked to those primary targets. Quantitative genetics tools for predicting multiple traits response to selection have been developed since long and have advanced our understanding of evolution of genetically correlated traits in various biological systems. We use these tools to predict the evolutionary trajectories of floral traits and understand the selection pressures acting on them.ResultsWe used data from an artificial selection and a pollinator (bumblebee, hoverfly) evolution experiment with fast cycling Brassica rapa plants to predict evolutionary changes of 12 floral volatiles and 4 morphological floral traits in response to selection. Using the observed selection gradients and the genetic variance-covariance matrix (G-matrix) of the traits, we showed that the observed responses of most floral traits including volatiles were predicted in the right direction in both artificial- and bumblebee-selection experiment. Genetic covariance had a mix of constraining and facilitating effects on evolutionary responses. We further revealed that G-matrices also evolved in the selection processes.ConclusionsOverall, our integrative study shows that floral signals, especially volatiles, evolve under selection in a mostly predictable way, at least during short term evolution. Evolutionary constraints stemming from genetic covariance affected traits evolutionary trajectories and thus it is important to include genetic covariance for predicting the evolutionary changes of a comprehensive suite of traits. Other processes such as resource limitation and selfing also need to be considered for a better understanding of floral trait evolution.

Dataset Information

Identifying artificial selection signals in the chicken genome.

Publications

Identifying artificial selection signals in the chicken genome.

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