Inferring Adaptive Introgression Using Hidden Markov Models

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 datasets for realistic population and selection parameters. We apply Ancestry_HMM-S to a dataset of an admixed Drosophila melanogaster population from South Africa and we identify 18 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 datasets 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/.

Rapid adaptation to malaria facilitated by admixture in the human population of Cabo Verde

“…Methods to detect adaptation driven by alleles introduced through gene flow in human populations have typically focused on ancient admixture between highly diverged populations, often with small contributions from one of the sources (Jagoda et al, 2018;Racimo et al, 2017;Setter et al, 2020). Recent advances leverage patterns of ancestry to consider recent admixture, though perform best for events at least hundreds of generations ago (Svedberg et al, 2020).…”

15 years of introgression studies: quantifying gene flow across Eukaryotes

“…The solution for comparative biology of introgression is to identify not only the proportion of introgression, but the timing as well. Several methods are making progress on this front Shchur, et al 2020;Martin and Amos 2021;Svedberg, et al 2021), while some f-statistics approaches can identify likely introgression timing given a tree topology (Malinsky, et al 2018). The future of the field thus may be better able to deal with some of the caveats we discuss next.…”

Rapid adaptation to malaria facilitated by admixture in the human population of Cabo Verde

“…Methods to detect adaptation driven by alleles introduced through gene flow in human populations have typically focused on ancient admixture between highly diverged populations, often with small contributions from one of the sources (Jagoda et al, 2018;Racimo et al, 2017;Setter et al, 2020). Recent advances leverage patterns of ancestry to consider recent admixture, though perform best for events at least hundreds of generations ago (Svedberg et al, 2020). Instead, we emphasize admixed populations as a model for adaptation on historical timescales, with selection dramatically changing genomic variation within tens of generations.…”