Phylogeographic inference using genomic sequence data under the multispecies coalescent model

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Our evolutionary history is written in our genomes. By comparing DNA sequences from different species or multiple individuals of the same species we can work out how the species are related, when they diverged from each other, whether there was introgression between the species, and whether the population size of a species went through a bottleneck or other demographic changes. DNA sequences can also be used to identify species and delineate species boundaries. To address such exciting questions, powerful statistical methods and computational algorithms are necessary. In this project we will develop new statistical models and computer algorithms for efficient analysis of genomic sequence data within two well-established statistical frameworks: maximum likelihood and Bayesian inference. We will develop a maximum likelihood method for estimating the species tree that accommodates the random process of biological reproduction and genetic sequence evolution, as well as introgression or hybridisation that may be common between closely related species, especially during radiative speciations. We will introduce significant improvements and extensions to our Bayesian model-comparison approach to delimiting species using genomic sequence data. We will implement sophisticated models to describe the evolutionary process of DNA sequences and to allow changes in the evolutionary rate among lineages so that the program can be applied to estimate species phylogenies for distantly related species, such as different orders of mammals. We will parallelize the program to improve the computational efficiency.