Data from: Evaluation of demographic history and neutral parameterization on the performance of Fst outlier tests

FST outlier tests are a potentially powerful way to detect genetic loci under spatially divergent selection. Unfortunately, the extent to which these tests are robust to non-equilibrium demographic histories has been under-studied. We developed a landscape-genetics simulator to test the effects of isolation by distance (IBD) and range expansion on FST outlier methods. We evaluated the two most commonly used methods for the identification of FST outliers (FDIST2 and BayeScan, which assume samples are evolutionarily independent) and two recent methods (FLK and Bayenv2, which estimate and account for evolutionary non-independence). Parameterization with a set of neutral loci (“neutral parameterization”) always improved the performance of FLK and Bayenv2, while neutral parameterization caused FDIST2 to actually perform worse in the cases of IBD or range expansion. BayeScan was improved when the prior odds on neutrality was increased, regardless of the true odds in the data. On their best performance, however, the widely-used methods had high false-positive rates for IBD and range expansion and were outperformed by methods that accounted for evolutionary non-independence. In addition, default settings in FDIST2 and BayeScan resulted in many false positives under balancing selection. However, all methods did very well if a large set of neutral loci is available to create empirical p-values. We conclude that in species that exhibit IBD or have undergone range expansion, many of the published FST outliers based on FDIST2 and BayeScan are probably false positives, but FLK and Bayenv2 show great promise for accurately identifying loci under spatially-divergent selection.

FST异常值检验（FST outlier tests）是检测受空间异向选择作用的遗传位点的极具潜力的手段。遗憾的是，此类检验对非平衡种群历史的鲁棒性尚未得到充分研究。我们开发了一款景观遗传学模拟器（landscape-genetics simulator），用以探究距离隔离（isolation by distance, IBD）与种群范围扩张对FST异常值检验方法的影响。我们评估了两类最常用于识别FST异常值的方法：FDIST2与BayeScan，二者均假设样本具有进化独立性；以及两种近年提出的方法FLK与Bayenv2，二者可估算并校正进化非独立性。以中性位点集进行参数化（下称“中性参数化”）始终能提升FLK与Bayenv2的性能；但在距离隔离或种群范围扩张场景下，该参数化方式反而会导致FDIST2的表现劣化。当提高中性假设的先验优势比时，BayeScan的性能会得到改善，且不受数据中真实先验优势比的影响。然而，即便达到最佳性能，主流使用的方法在距离隔离与种群范围扩张场景下仍存在较高假阳性率，且性能不及可校正进化非独立性的方法。此外，FDIST2与BayeScan的默认参数设置会在平衡选择场景下产生大量假阳性结果。但当可获取足量中性位点以构建经验p值（empirical p-values）时，所有方法的表现均十分优异。我们得出结论：对于存在距离隔离现象或经历过种群范围扩张的物种，诸多基于FDIST2与BayeScan的已发表FST异常值结果大概率为假阳性；但FLK与Bayenv2在精准识别受空间异向选择作用的位点方面展现出巨大应用潜力。