A Bayesian implementation of the multispecies coalescent model with introgression for comparative genomic analysis

Recent analyses suggest that cross-species gene flow or introgression is common in nature, especially during species divergences. Genomic sequence data can be used to infer introgression events and to estimate the timing and intensity of introgression, providing an important means to advance our understanding of the role of gene flow in speciation. Here we implement the multispecies-coalescent-with-introgression (MSci) model, an extension of the multispecies-coalescent (MSC) model to incorporate introgression, in our Bayesian Markov chain Monte Carlo (MCMC) program BPP. The MSci model accommodates deep coalescence (or incomplete lineage sorting) and introgression and provides a natural framework for inference using genomic sequence data. Computer simulation confirms the good statistical properties of the method, although hundreds or thousands of loci are typically needed to estimate introgression probabilities reliably. Re-analysis of datasets from the purple cone spruce confirms the hypothesis of homoploid hybrid speciation. We estimated the introgression probability using the genomic sequence data from six mosquito species in the Anopheles gambiae species complex, which varies considerably across the genome, likely driven by differential selection against introgressed alleles.