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Thursday, March 16 • 4:00pm - 5:30pm
Poster: Statistical Inference of Reticulate Phylogenies and Gene Trees from Multi-locus Data

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Evolutionary history explains how species diverged and how genes and traits evolved. Phylogenetic trees have been considered as the basic structure to represent evolutionary relationships. However, the evolutionary history of a set of genomes could be reticulate due to the occurrence of hybridization or horizontal gene transfer. My research focuses on developing novel statistical models and computational methods for inferring reticulate evolutionary histories and studying their mathematical properties. Here I report on the first Bayesian method for sampling the species phylogeny, gene trees, divergence times, and population sizes, from DNA sequences of multiple independent loci. As different numbers of reticulation events correspond to different dimensions in the searching space, I have devised reversible-jump Markov chain Monte Carlo (RJMCMC) techniques for sampling the posterior distribution from genome sequences directly. I have implemented these methods in the publicly available, open-source software package PhyloNet. I demonstrate the utility of the method by analyzing simulated data and reanalyzing three biological data sets. The results demonstrate the significance of not only co-estimating species phylogenies and gene trees, but also accounting for gene flow and ILS simultaneously. My research have provided a big step toward putting networks on equal footing with trees as the model of choice for biologists to use in genomic data analysis.


Thursday March 16, 2017 4:00pm - 5:30pm
Exhibit Hall BRC