Bayesian inference of the mode of speciation and gene flow using genomic data

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Genomic sequences from contemporary species contain rich information about the history of species divergence and the origin of species. By comparing DNA sequences from different species we can work out how the species are related, when they diverged from each other, and whether they have been exchanging genes. However, powerful statistical models and efficient computational algorithms are necessary to extract this information from genomic data. The multispecies coalescent model provides a natural framework for comparative analysis of genomic sequence data, as it accommodates the random fluctuations of biological reproduction when genetic materials are passed over generations, random accumulations of genetic mutations as well as possible hybridisation or introgression between species. We will extend our Bayesian inference program BPP to allow continuous gene flow between species, including isolation-with-migration, isolation-with-initial-migration, secondary contact, as well as complete isolation. Those models represent different biological hypotheses about the modes of speciation, and can be compared to further our understanding of the speciation process. The Bayesian methods to be developed in the project are efficient in extracting information in the genomic datasets, while accommodating important biological processes involved, such as the polymorphism in the ancestral species, uncertainties in the gene genealogical trees due to limited information in the sequence data from a short genomic segment. We will apply our newly developed methods to analyse genomic datasets from the big cats in the Panthera genus, Heliconius butterflies, Malagasy mouse lemurs, and North-American lizards, generated by our collaborators. We will infer the history of species divergences, the direction and timing of gene flow, as well as evolutionary parameters such as species divergence times and population sizes.