Whole‐genome resequencing uncovers molecular signatures of natural and sexual selection in wild bighorn sheep

Abstract

The identification of genes influencing fitness is central to our understanding of the genetic basis of adaptation and how it shapes phenotypic variation in wild populations. Here, we used whole-genome resequencing of wild Rocky Mountain bighorn sheep (Ovis canadensis) to >50-fold coverage to identify 2.8 million single nucleotide polymorphisms (SNPs) and genomic regions bearing signatures of directional selection (i.e. selective sweeps). A comparison of SNP diversity between the X chromosome and the autosomes indicated that bighorn males had a dramatically reduced long-term effective population size compared to females. This probably reflects a long history of intense sexual selection mediated by male-male competition for mates. Selective sweep scans based on heterozygosity and nucleotide diversity revealed evidence for a selective sweep shared across multiple populations at RXFP2, a gene that strongly affects horn size in domestic ungulates. The massive horns carried by bighorn rams appear to have evolved in part via strong positive selection at RXFP2. We identified evidence for selection within individual populations at genes affecting early body growth and cellular response to hypoxia; however, these must be interpreted more cautiously as genetic drift is strong within local populations and may have caused false positives. These results represent a rare example of strong genomic signatures of selection identified at genes with known function in wild populations of a nonmodel species. Our results also showcase the value of reference genome assemblies from agricultural or model species for studies of the genomic basis of adaptation in closely related wild taxa.

Population genomics of the eastern cottonwood (Populus deltoides)

“…Identification of loci under selection in natural populations provides insight into the genetic regulation of fitness and adaptation (Kardos et al., 2015). One widely used method to detect selection signatures is the Tajima's D statistic (Tajima, 1989), calculated based on the comparison between the number of segregating sites, in the form of θ W , and nucleotide diversity, π. Tajima's D was calculated by gene with PopGenome (Pfeifer et al., 2014) in each subpopulation using the 555,673 filtered SNPs.…”

The distribution of runs of homozygosity and selection signatures in six commercial meat sheep breeds

“…Several genomic regions under putative selection were identified in the present study across six commercial sheep breeds and these signatures provide an insight into the genes contributing to their diverse phenotypes. However, it is important to acknowledge that regions identified as putative selective sweeps should be interpreted cautiously as differences in demographic history such as genetic drift, effective population size, inbreeding and population bottlenecks can also result in false positive signatures of selection [23]. …”

“…Both hapFLK and F ST have been previously applied to varying sheep populations to identify regions of the genome under selection [1, 16, 18, 20–23]. These studies have successfully identified several genomic regions associated with morphological traits, reproductive performance, nematode resistance, body size and skeletal morphology, which have been targeted by both natural and artificial selection during domestication.…”

Recent advances in conservation and population genomics data analysis

“…GWAS of complex traits will therefore often fail to identify enough genotype–phenotype associations to explain a useful fraction of the heritability of traits of interest. This is particularly true of studies on populations with very large N e or high recombination rates where strong linkage disequilibrium (LD) extends only very short distances from the genotyped loci, or where relatively few loci are analyzed, thus resulting in low power to detect loci even with relatively large phenotypic effects (Kardos et al., 2015b). However, encouraging for studies in small or fragmented populations, the power to detect large effect quantitative trait loci (QTL) is expected to be higher in populations with small N e because strong LD extends over longer chromosomal distances in such populations.…”