Statistical Methods for Genomic Analysis of Species Divergences

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Our evolutionary history is written in our genomes. By comparing DNA sequences from different species we can work out how the species are related. By comparing the DNA sequences of multiple individuals from the same species, we can estimate the population size and infer demographic changes (such as population bottleneck) of the species. Such studies fall into the domains of phylogenetics and population genetics. Genomic sequence data from multiple individuals of several closely related species allow powerful inference at the interface of phylogenetics and population genetics. One can use such data to estimate species divergence times and ancestral population sizes, accounting for lineage sorting, and to detect gene flow at the time speciation or to test different models of speciation Such data also allow delimitation of species (for example, to decide whether the sampled individuals belong to one or two species).To achieve those goals, powerful statistical methods and computational algorithms are necessary. In this project we will implement such methods within two well-established statistical frameworks: maximum likelihood and Bayesian inference. We will develop maximum likelihood methods for estimating migration rates between populations, and design likelihood ratio tests to test whether there is gene flow at the time of speciation (that is, whether speciation is clean). We will implement models that allow the migration rate to decrease over time since species divergence. Those methods will be useful for testing different speciation models such as allopatric and parapatric speciation. Computational difficulties will limit our likelihood methods to 2 or 3 sequences at each sampled locus. However the methods can accommodate a huge number of loci (indeed the whole genome), and with population data at some loci and species data at other loci, powerful inference is feasible. We will use computer simulations to examine the statistical properties of the new methods, and apply the methods to genomic datasets from the hominoids.We will introduce significant improvements and extensions to a Bayesian model-comparison approach to delimiting species using genomic sequence data. Published a year ago (Yang and Rannala 2010 Proc Natl Acad Sci USA 107:9264-9269), this method has attracted much attention among evolutionary biologists. This uses an algorithm called reversible-jump Markov chain Monto Carlo (rjMCMC) to sample different species-delimitation models, such as the one-species model (which assumes that all sampled individuals are from one single species) and the two species model (which assumes that the sampled individuals are from two distinct species). However, our current implementation in the computer program BPP has serious limitations and is inefficient in intermediate or large datasets. A major objective of this project is to improve the rjMCMC algorithm so that the program becomes feasible for analysis of large genomic-scale datasets. We will also parallelize the programs to improve the computational efficiency.