Controlling for p-value inflation in allele frequency change in experimental evolution and artificial selection experiments

Experimental evolution studies can be used to explore genomic response to artificial and natural selection. In such studies, loci that display larger allele frequency change than expected by genetic drift alone are assumed to be directly or indirectly associated with traits under selection. However, such studies report surprisingly many loci under selection, suggesting that current tests for allele frequency change may be subject to p-value inflation and hence be anti-conservative. One factor known from genome wide association (GWA) studies to cause p-value inflation is population stratification, such as relatedness among individuals. Here we suggest that by treating presence of an individual in a population after selection as a binary response variable, existing GWA methods can be used to account for relatedness when estimating allele frequency change. We show that accounting for relatedness like this effectively reduces false positives in tests for allele frequency change in simulated...

实验演化研究（Experimental evolution studies）可用于探索基因组对人工选择与自然选择的响应。在此类研究中，等位基因频率变化幅度超出仅由遗传漂变（genetic drift）所预期水平的基因座（locus，复数形式loci），会被认定为与受选择性状存在直接或间接关联。然而，此类研究报告了数量惊人的受选择基因座，这表明当前针对等位基因频率变化的检测方法可能存在p值通胀（p-value inflation）问题，因而呈现反保守性（anti-conservative）。全基因组关联研究（genome wide association studies, GWA）中已知会引发p值通胀的一个因素是群体分层（population stratification），例如个体间的亲缘关系。本文提出，可将选择后个体在群体中的存在状态视为二分类响应变量，从而在估算等位基因频率变化时，借助现有全基因组关联研究方法对亲缘关系进行校正。本研究表明，采用此类亲缘关系校正策略，可有效降低模拟实验中等位基因频率变化检测中的假阳性结果……