Inferring non-additive multi-locus selection in introgressed populations using hidden markov models
Authors: Nicolas Ayala 1, 2; Russell Corbett-Detig 1, 2
Affiliations: 1) Department of Biomolecular Engineering, University of California, Santa Cruz; Santa Cruz, CA 95064, USA; 2) Genomics Institute, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
Keywords: Genomic selection/prediction
Admixture combines genetic material from potentially disparate populations and is thought to be a major source of adaptive novelty. As such, multi-locus and non-additive selection on introgressing mutations is potentially common in natural populations. However, existing tools for inferring adaptive introgression only account for additive selection at a single site, overlooking phenomena such as interference among selected loci and dominance. To meet this important need, we present AHMM-GLS, a hidden markov model based tool for inferring and identifying multiple selected sites on a chromosome. This tool numerically calculates local ancestry landscapes for a given MLS model, and then optimizes the model to fit the data. It uses read pileup data in an introgressed population to identify selected sites and estimate a multi-locus selection model. In applying our method to a suite of admixed populations, we find that the estimated strength of selection can be affected by ignoring the contributions of other sites. This method will enable more accurate and detailed analyses of selection in admixed populations than has been possible previously.