Inferring Adaptive Introgression Using Hidden Markov Models
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- Inferring Adaptive Introgression Using Hidden Markov Models
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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/.
Originalsprog | Engelsk |
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Tidsskrift | Molecular Biology and Evolution |
Vol/bind | 38 |
Udgave nummer | 5 |
Sider (fra-til) | 2152-2165 |
Antal sider | 14 |
ISSN | 0737-4038 |
DOI | |
Status | Udgivet - 2021 |
Bibliografisk note
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).
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