Detection and quantification of introgression using Bayesian inference based on conjugate priors

Abstract

Introgression (the flow of genes between species) is a major force structuring the evolution of genomes, potentially providing raw material for adaptation. Here, we present a versatile Bayesian model selection approach for the detection and quantification of introgression. The proposed df-BF approach builds upon the recently published distance-based df statistic. Unlike df, df-BF takes into account the number of variant sites within a genomic region. The df-BF method quantifies introgression with the inferred{theta} parameter, and at the same time enables weighing the strength of evidence for introgression based on Bayes Factors. To ensure fast computation we make use of conjugate priors with no need for computational demanding MCMC iterations. We compare our method with other approaches including df, fd, and Pattersons D using a wide range of coalescent simulations. Furthermore, we showcase the applicability of the df-BF approach using whole genome mosquito data. Finally, we integrate the new method into the powerful genomics R-package PopGenome.