TY - JOUR

T1 - Inferring Adaptive Introgression Using Hidden Markov Models

AU - Svedberg, Jesper

AU - Shchur, Vladimir

AU - Reinman, Solomon

AU - Nielsen, Rasmus

AU - Corbett-Detig, Russell

N1 - Funding Information: This study was supported by the Institute of General Medical Sciences at the National Institutes (Grant No. R35GM128932) and an award from the Alfred P. Sloan Foundation to R.B.C. R.N. and R.C.D. were funded within the framework of the HSE University Basic Research Program. V.S. was supported by grant RFBR 19-07-00515. Publisher Copyright: © 2021 The Author(s).

PY - 2021

Y1 - 2021

N2 - 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/.

AB - 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/.

KW - adaptive

KW - adaptive introgression

KW - admixture

KW - evolution

KW - HMM

KW - hybridisation

KW - pesticide resistance

KW - population genomics

KW - selection

U2 - 10.1093/molbev/msab014

DO - 10.1093/molbev/msab014

M3 - Journal article

C2 - 33502512

AN - SCOPUS:85106068919

VL - 38

SP - 2152

EP - 2165

JO - Molecular Biology and Evolution

JF - Molecular Biology and Evolution

SN - 0737-4038

IS - 5

ER -