Inferring Adaptive Introgression Using Hidden Markov Models

Jesper Svedberg*, Vladimir Shchur, Solomon Reinman, Rasmus Nielsen, Russell Corbett-Detig

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

12 Citations (Scopus)
16 Downloads (Pure)

Abstract

Adaptive introgression - the flow of adaptive genetic variation between species or populations - has attracted significant interest in recent years and it has been implicated in a number of cases of adaptation, from pesticide resistance and immunity, to local adaptation. Despite this, methods for identification of adaptive introgression from population genomic data are lacking. Here, we present Ancestry-HMM-S, a hidden Markov model-based method for identifying genes undergoing adaptive introgression and quantifying the strength of selection acting on them. Through extensive validation, we show that this method performs well on moderately sized data sets for realistic population and selection parameters. We apply Ancestry-HMM-S to a data set of an admixed Drosophila melanogaster population from South Africa and we identify 17 loci which show signatures of adaptive introgression, four of which have previously been shown to confer resistance to insecticides. Ancestry-HMM-S provides a powerful method for inferring adaptive introgression in data sets that are typically collected when studying admixed populations. This method will enable powerful insights into the genetic consequences of admixture across diverse populations. Ancestry-HMM-S can be downloaded from https://github.com/jesvedberg/Ancestry-HMM-S/.

Original languageEnglish
JournalMolecular Biology and Evolution
Volume38
Issue number5
Pages (from-to)2152-2165
Number of pages14
ISSN0737-4038
DOIs
Publication statusPublished - 2021

Keywords

  • adaptive
  • adaptive introgression
  • admixture
  • evolution
  • HMM
  • hybridisation
  • pesticide resistance
  • population genomics
  • selection

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