Inferring Adaptive Introgression Using Hidden Markov Models

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.

Automated summary

Adaptive introgression, the flow of adaptive genetic variation between species or populations, has attracted significant interest and has been implicated in cases of adaptation, but methods for identifying it from population genomic data are lacking. Ancestry_HMM-S is a hidden Markov model-based method for identifying genes undergoing adaptive introgression and quantifying selection strength, showing good performance on moderately sized data sets. It was applied to an admixed Drosophila melanogaster population from South Africa, identifying loci with signatures of adaptive introgression and providing powerful insights into genetic consequences of admixture across diverse populations.