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

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 detecting and quantifying introgression, df-BF, that builds upon the recently published distance-based df statistic. Unlike df, df-BF accounts for the number of variant sites within a genomic region. The underlying model parameter of our df-BF method, here denoted as dfθ, accurately quantifies introgression, and the corresponding Bayes Factors (df-BF) enables weighing the strength of evidence for introgression. To ensure fast computation, we use conjugate priors with no need for computationally demanding MCMC iterations. We compare our method with other approaches including df, fd, Dp, and Patterson's D using a wide range of coalescent simulations. Furthermore, we showcase the applicability of df-BF and dfθ using whole-genome mosquito data. Finally, we integrate the new method into the powerful genomics R-package PopGenome.

The presented methods are implemented within the R-package PopGenome (https://github.com/pievos101/PopGenome) and the simulation as the application results can be reproduced from the source code available from a dedicated GitHub repository (https://github.com/pievos101/Introgression-Simulation).