Data from: 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 data with varying levels of population structure. However, once relatedness has been accounted for, the power to detect causal loci under selection is low. Finally, we demonstrate the presence of p-value inflation in allele frequency change in empirical data spanning multiple generations from an artificial selection experiment on tarsus length in two wild populations of house sparrow, and correct for this using genomic control. Our results indicate that since allele frequencies in large parts of the genome may change when selection acts on a heritable trait, such selection is likely to have considerable and immediate consequences for the eco-evolutionary dynamics of the affected populations.

实验进化研究可用于探究基因组对人工选择与自然选择的响应。在这类研究中，等位基因频率变化幅度超出仅由遗传漂变预期水平的基因座（loci），会被认为与受选择的性状直接或间接相关。然而，这类研究报告的受选择基因座数量出乎意料地多，这表明当前针对等位基因频率变化的检验可能存在p值膨胀问题，进而导致检验保守性不足。从全基因组关联研究（Genome Wide Association Study, GWA）中已知，引发p值膨胀的一个因素是群体分层，例如个体间的亲缘关系。在此，我们提出可将选择后个体在种群中的存在与否视为二分类响应变量，从而在估算等位基因频率变化时，利用现有的全基因组关联分析方法校正亲缘关系。我们证实，通过这种方式校正亲缘关系，可有效降低在具有不同程度群体结构的模拟数据中，等位基因频率变化检验的假阳性率。不过，在校正亲缘关系后，检测受选择因果基因座的统计功效有所降低。最终，我们在两项基于家麻雀野生种群的跗跖长度人工选择实验的多世代实证数据中，证实了等位基因频率变化存在p值膨胀问题，并通过基因组校正法对此进行了修正。我们的研究结果表明，由于当选择作用于可遗传性状时，基因组大片区域的等位基因频率可能发生改变，这类选择很可能对受影响种群的生态进化动力学产生显著且直接的影响。