Data from: Identifying signatures of sexual selection using genomewide selection components analysis

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 FST values. The average number of outlier peaks (i.e., those with significantly large FST values) with a quantitative trait locus in close proximity (“real” peaks) represented correct diagnoses of loci under selection, whereas peaks above the FST 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.

性选择必须对基因组产生影响，方能带来进化效应，然而选择的分子信号至今仍难以捕捉。在此，我们采用基于个体的模型，探究全基因组选择成分分析（Genome-wide Selection Components Analysis）的应用潜力——该方法通过比较单一种群内不同生活史阶段个体的等位基因频率，无需预先知晓受选择作用的性状，即可检测选择信号。我们构建了二倍体有性繁殖种群模型，并针对某一数量性状引入强烈的配偶选择，以模拟性选择过程。通过加权FST值，比较成年个体与子代的全基因组等位基因频率。我们将邻近数量性状位点（Quantitative Trait Locus）的异常峰值（即FST值显著偏高的位点）定义为“真实峰值”，代表对受选择位点的正确识别；而那些超过FST显著性阈值却未邻近数量性状位点的峰值，则为虚假峰值。研究发现，即便样本量适中，仍可检测到强烈性选择的信号，但更大的样本量能够提升检测效率。该模型在中性标记更多、且数量性状位点与中性标记分布于多条染色体时，对选择的检测效果更佳。尽管环境变异会降低检测率，但真实峰值的识别依然具备可行性。我们还发现，对经历相似选择机制的多个种群进行采样，可提升检测效率。简言之，全基因组选择成分分析是一项兼具挑战性与可行性的方法，可用于识别基因组中受选择作用的区域。