Genome scans can potentially identify genetic loci involved in evolutionary processes such as local adaptation and gene flow. Here, we show that recombination rate variation across a neutrally evolving genome gives rise to mixed sampling distributions of mean F_ST (), a common population genetic summary statistic. In particular, we show that in regions of low recombination the distribution of estimates has more variance and a longer tail than in more highly recombining regions. Determining outliers from the genome‐wide distribution without taking local recombination rate into consideration may therefore increase the frequency of false positives in low recombination regions and be overly conservative in more highly recombining ones. We perform genome scans on simulated and empirical Drosophila melanogaster data sets and, in both cases, find patterns consistent with this neutral model. Similar patterns are observed for other summary statistics used to capture variation in the coalescent process. Linked selection, particularly background selection, is often invoked to explain heterogeneity in across the genome, but here we point out that even under neutrality, statistical artefacts can arise due to variation in recombination rate. Our results highlight a flaw in the design of genome‐scan studies and suggest that without estimates of local recombination rate, interpreting the genomic landscape of any summary statistic that captures variation in the coalescent process will be very difficult.

中文翻译：

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重组率的变化会影响在中性条件下基因组扫描中异常值的检测。

基因组扫描可以潜在地识别参与进化过程（例如局部适应和基因流）的遗传基因座。在这里，我们显示了在一个中性进化的基因组上的重组速率变化引起了平均F _ST（）的混合采样分布，这是一种常见的群体遗传总结统计数据。特别是，我们表明在低重组区域中，与更高重组区域相比，估计值具有更大的方差和更长的尾巴。因此，在不考虑局部重组率的情况下从全基因组分布确定异常值可能会增加低重组区域中假阳性的发生率，而在重组程度较高的区域中过于保守。我们对模拟的和经验的果蝇果蝇数据集进行基因组扫描，在两种情况下，我们都找到与该中性模型一致的模式。对于用于捕获合并过程中的变化的其他摘要统计信息，也观察到类似的模式。链接选择，尤其是背景选择通常被用来解释异质性。跨基因组，但在这里我们指出，即使在中性条件下，由于重组率的变化也会产生统计伪像。我们的结果凸显了基因组扫描研究设计中的一个缺陷，并表明，如果没有对本地重组率的估计，解释捕获聚结过程中变异的任何汇总统计数据的基因组格局将非常困难。