SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: 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).

Research over the last 10 years highlights the increasing importance of hybridization between species as a major force structuring the evolution of genomes and potentially providing raw material for adaptation by natural and/or sexual selection. Fueled by research in a few model systems where phenotypic hybrids are easily identified, research into hybridization and introgression (the flow of genes between species) has exploded with the advent of whole-genome sequencing and emerging methods to detect the signature of hybridization at the whole-genome or chromosome level. Amongst these are a general class of methods that utilize patterns of single-nucleotide polymorphisms (SNPs) across a tree as markers of hybridization. These methods have been applied to a variety of genomic systems ranging from butterflies to Neanderthals to detect introgression, however, when employed at a fine genomic scale these methods do not perform well to quantify introgression in small sample windows. We introduce a novel method to detect introgression by combining two widely used statistics: pairwise nucleotide diversity d xy and Patterson’s D . The resulting statistic, the distance fraction ( d f ), accounts for genetic distance across possible topologies and is designed to simultaneously detect and quantify introgression. We also relate our new method to the recently published f d and incorporate these statistics into the powerful genomics R-package PopGenome, freely available on GitHub ( pievos101/PopGenome ) and the Comprehensive R Archive Network (CRAN). The supplemental material contains a wide range of simulation studies and a detailed manual how to perform the statistics within the PopGenome framework. We present a new distance based statistic d f that avoids the pitfalls of Patterson’s D when applied to small genomic regions and accurately quantifies the fraction of introgression ( f ) for a wide range of simulation scenarios.