Genome scans for selection and introgression based on k-nearest neighbour techniques

Bastian Pfeifer*, Nikolaos Alachiotis, Pavlos Pavlidis, Michael G. Schimek

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

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 (FST) and pairwise nucleotide differences (dxy) 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.
Original languageEnglish
Pages (from-to)1597-1609
Number of pages13
JournalMolecular Ecology Resources
Volume20
Issue number6
DOIs
Publication statusPublished - 2020

Keywords

  • Adaptation
  • Genome scans
  • Introgression
  • k-nearest neighbours

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