Bayesian implementation of the multispecies-coalescent-with-introgression (MSci) model for analysis of population genomic data

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Genomes from different species contain rich information about the evolutionary history of the species. By comparing DNA sequences from different species or different individuals of the same species we can work out how the species are related, when they diverged from each other, whether and when there was cross-species hybridisation. Nevertheless, to extract this information from our genomes, powerful statistical models and efficient computational algorithms are necessary. The multispecies-coalescent-with-introgression (MSci) model provides a natural framework for comparative analysis of genomic sequence data, accommodating the random fluctuations of biological reproduction when genetic materials are passed over generations, random accumulations of genetic mutations as well as possible cross-species hybridisation events. We will implement the MSci model in our Bayesian Markov chain Monte Carlo simulation program, so that it can be used to estimate species phylogenies and species divergence times, ancestral population sizes, and the time and rate of hybridisation. Those parameters will provide important insights into the origin of species. We will apply our newly developed methods to analyse genomic datasets from Heliconius butterflies, Malagasy mouse lemurs, and lizards, generated by our collaborators.