title: Identifying signatures of sexual selection using genomewide selection components analysis.
abstract: Sexual selection must affect the genome for it to have an evolutionary impact, yet signatures of selection remain elusive. Here we use an individual-based model to investigate the utility of genome-wide selection components analysis, which compares allele frequencies of individuals at different life history stages within a single population to detect selection without requiring a priori knowledge of traits under selection. We modeled a diploid, sexually reproducing population and introduced strong mate choice on a quantitative trait to simulate sexual selection. Genome-wide allele frequencies in adults and offspring were compared using weighted F ST values. The average number of outlier peaks (i.e., those with significantly large F ST values) with a quantitative trait locus in close proximity ("real" peaks) represented correct diagnoses of loci under selection, whereas peaks above the F ST significance threshold without a quantitative trait locus reflected spurious peaks. We found that, even with moderate sample sizes, signatures of strong sexual selection were detectable, but larger sample sizes improved detection rates. The model was better able to detect selection with more neutral markers, and when quantitative trait loci and neutral markers were distributed across multiple chromosomes. Although environmental variation decreased detection rates, the identification of real peaks nevertheless remained feasible. We also found that detection rates can be improved by sampling multiple populations experiencing similar selection regimes. In short, genome-wide selection components analysis is a challenging but feasible approach for the identification of regions of the genome under selection.
authors.affiliation: Biology Department, Texas A&M University 3258 TAMU, College Station, Texas, 77843.
authors.forename: Sarah P
authors.initials: SP
authors.lastname: Flanagan
authors.affiliation: Biology Department, Texas A&M University 3258 TAMU, College Station, Texas, 77843.
authors.forename: Adam G
authors.initials: AG
authors.lastname: Jones
keywords: Adaptation; RAD-seq; population genetics; population genomics; reproductive success; simulation model