Constraints on the FST–heterozygosity outlier approach

The FST-heterozygosity outlier approach has been a popular method for identifying loci under balancing and positive selection since Beaumont and Nichols first proposed it in 1996 and recommended its use for studies sampling a large number of independent populations (at least 10). Since then, their program FDIST2 and a user-friendly program optimized for large datasets, LOSITAN, have been used widely in the population genetics literature, often without the requisite number of samples. We observed empirical datasets whose distributions could not be reconciled with the confidence intervals generated by the null coalescent island model. Here, we use forward-in-time simulations to investigate circumstances under which the FST-heterozygosity outlier approach performs poorly for next-generation single-nucleotide polymorphism (SNP) datasets. Our results show that samples involving few independent populations, particularly when migration rates are low, result in distributions of the FST-heterozy...自Beaumont与Nichols于1996年首次提出FST-杂合性离群值方法（FST-heterozygosity outlier approach），并推荐其应用于采集至少10个独立种群的研究以来，该方法已成为识别平衡选择与正向选择位点的常用手段。自此之后，他们开发的FDIST2程序，以及针对大型数据集优化的易用型程序LOSITAN，已在群体遗传学相关研究文献中得到广泛应用，但这类应用往往未达到研究所需的必要样本量标准。我们发现部分实证数据集的分布无法与零假设溯祖岛模型（null coalescent island model）生成的置信区间相匹配。本文采用正向时间模拟（forward-in-time simulations）方法，探究FST-杂合性离群值方法在处理下一代单核苷酸多态性（single-nucleotide polymorphism, SNP）数据集时表现欠佳的场景。研究结果显示，当独立种群样本量较少、尤其是迁移率较低时，FST-杂合性...