Kapan, D. D. | bioRxiv Science

bioRxiv2022

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 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.

evolutionary biology↗

bioRxiv2017

Estimates of Introgression as a Function of Pairwise Distances

BackgroundResearch 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.\n\nResultsWe introduce a novel method to detect introgression by combining two widely used statistics: pairwise nucleotide diversity dxy and Pattersons D. The resulting statistic, the Basic distance fraction (Bdf), 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 fd and incorporate these statistics into the powerful genomics R-package PopGenome, freely available on CRAN. The supplemental material contains a wide range of simulation studies and a detailed manual how to perform the statistics within the PopGenome framework.\n\nConclusionWe present a new distance based statistic Bdf that avoids the pitfalls of Pattersons D when applied to small genomic regions and accurately quantifies the fraction of introgression (f) for a wide range of simulation scenarios.

evolutionary biology↗