Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F-model, which measures differences in allele frequencies by population specific F_ST coefficients. This model readily accounts for multiple evolutionary forces by partitioning F_ST coefficients into locus and population specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM), which characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations we show that the statistical power of our method is up to two-fold that of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP).

中文翻译：

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使用 F 模型检测链接站点的选择。

即使存在迁移，等位基因频率也会因人群和基因座的不同而变化。虽然大多数差异可能是由于遗传漂变，但发散选择将进一步增加某些基因座的分化。识别这些是研究当地适应的关键，但在统计上仍然具有挑战性。描述通过迁移连接的群体之间等位基因频率差异的一种特别优雅的方法是F模型，它通过群体特定的F _ST系数来测量等位基因频率的差异。该模型通过将F _ST系数划分为分别反映选择和漂移的基因座和群体特定成分，很容易解释多种进化力量。在这里，我们通过隐马尔可夫模型（HMM）将该模型扩展到连锁基因座，该模型通过基因组中基因座特异性成分的相关性来表征选择对连锁标记的影响。通过广泛的模拟，我们表明我们的方法的统计能力是之前假设站点独立的实现的两倍。我们最终通过将我们的方法应用于人类基因组多样性计划（HGDP）的数据来证明人类基因组中的选择。