RT Journal Article SR Electronic T1 Genome Scans for Selection and Introgression based on k-nearest Neighbor Techniques JF bioRxiv FD Cold Spring Harbor Laboratory SP 752758 DO 10.1101/752758 A1 Bastian Pfeifer A1 Nikolaos Alachiotis A1 Pavlos Pavlidis A1 Michael G. Schimek YR 2019 UL http://biorxiv.org/content/early/2019/09/28/752758.abstract AB In recent years, genome-scan methods have been extensively used to detect local signatures of selection and introgression. Here, we introduce a series of versatile genome-scan methods that are based on non-parametric k-nearest neighbors (kNN) techniques, while incorporating pairwise Fixation Index (FST) estimates and pairwise nucleotide differences (dxy) as features. Simulations were performed for both positive directional selection and introgression, with varying parameters, such as recombination rates, population background histories, the proportion of introgression, and the time of gene flow. We find that kNN-based methods perform remarkably well while yielding stable results almost over the entire range of k. We provide a GitHub repository (pievos101/kNN-Genome-Scans) containing R source code to demonstrate how to apply the proposed methods to real-world genomic data using the population genomics R-package PopGenome.