In recent years, genome‐scan methods have been extensively used to detect local signatures of selection and introgression. Most of these methods are either designed for one or the other case, which may impair the study of combined cases. Here, we introduce a series of versatile genome‐scan methods applicable for both cases, the detection of selection and introgression. The proposed approaches are based on nonparametric k‐nearest neighbour (kNN) techniques, while incorporating pairwise Fixation Index (F_ST) and pairwise nucleotide differences (d_xy) as features. We benchmark our methods using a wide range of simulation scenarios, with varying parameters, such as recombination rates, population background histories, selection strengths, the proportion of introgression and the time of gene flow. We find that kNN‐based methods perform remarkably well compared with the state‐of‐the‐art. Finally, we demonstrate how to perform kNN‐based genome scans on real‐world genomic data using the population genomics R‐package popgenome.

中文翻译：

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基于 k 最近邻技术的基因组扫描选择和基因渗入。

近年来，基因组扫描方法已被广泛用于检测选择和基因渗入的局部特征。这些方法大多是为一种或另一种情况设计的，这可能会影响对组合病例的研究。在这里，我们介绍了一系列适用于两种情况的通用基因组扫描方法，即选择和基因渗入的检测。所提出的方法基于非参数k-最近邻 (kNN) 技术，同时结合了成对固定指数 ( F _ST ) 和成对核苷酸差异 ( d _xy) 作为特征。我们使用各种模拟场景对我们的方法进行基准测试，这些参数具有不同的参数，例如重组率、种群背景历史、选择强度、基因渗入的比例和基因流动的时间。我们发现基于KNN-方法执行非常好与比较国家的最先进的。最后，我们演示了如何使用群体基因组学 R 包popgenome对真实世界的基因组数据执行基于 kNN 的基因组扫描。