Effects of gene action, marker density, and time since selection on the performance of landscape genomic scans of local adaptation

Genomic “scans” to identify loci that contribute to local adaptation are becoming increasingly common. Many methods used for such studies have assumed that local adaptation is created by loci experiencing antagonistic pleiotropy and that the selected locus itself is assayed, and few consider how signals of selection change through time. However, most empirical data sets have marker density too low to assume that a selected locus itself is assayed, researchers seldom know when selection was first imposed, and many locally adapted loci likely experience not antagonistic pleiotropy but conditional neutrality. We simulated data to evaluate how these factors affect the performance of tests for genotype-environment association. We found that three types of regression-based analyses (linear models, mixed linear models, and latent factor mixed models) and an implementation of BayEnv all performed well, with high rates of true positives and low rates of false positives, when the selected locus e...

旨在鉴定介导局部适应性（local adaptation）的基因座（loci）的基因组扫描（genomic scans）正愈发常见。此类研究中使用的多数方法均假定，局部适应性由存在拮抗多效性（antagonistic pleiotropy）的基因座所介导，且所检测的正是受选择的基因座本身，而极少有研究探讨选择信号如何随时间发生动态变化。然而，多数实证数据集的标记密度过低，无法保证所检测的即为受选择的基因座；研究者也极少能得知选择首次施加的时间；且诸多介导局部适应性的基因座，其作用机制大概率并非拮抗多效性，而是条件中性（conditional neutrality）。本研究通过模拟数据，评估上述因素对基因型-环境关联（genotype-environment association）检验效能的影响。研究发现，当受选择的基因座……时，三类基于回归的分析方法（线性模型、混合线性模型与潜在因子混合模型）以及一款BayEnv工具均表现优异，兼具较高的真阳性率与较低的假阳性率。