Inferring Adaptive Introgression Using Hidden Markov Models |
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Authors: | Jesper Svedberg Vladimir Shchur Solomon Reinman Rasmus Nielsen Russell Corbett-Detig |
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Affiliation: | 1. Department of Biomolecular Engineering, Genomics Institute, UC Santa Cruz, Santa Cruz, CA, USA;2. National , Research University Higher School of Economics, Moscow, Russian Federation ;3. Department of Integrative Biology and Department of Statistics, UC Berkeley, Berkeley, CA, USA;4. Center for GeoGenetics, Globe Institute, University of Copenhagen, Copenhagen, Denmark |
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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/. |
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Keywords: | adaptive evolution adaptive introgression selection admixture hybridisation HMM population genomics pesticide resistance |
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