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Activity Number: 121
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305886
Title: Bayesian Coalescent-Based Inference of Population Dynamics from Multiple Loci
Author(s): Mandev Gill*+ and Beth Shapiro and Philippe Lemey and Andrew Rambaut and Marc A Suchard
Companies: University of California at Los Angeles and Penn State University and Katholieke Universiteit Leuven and University of Edinburgh and University of California at Los Angeles
Address: 959 Gayley Ave, Los Angeles, CA, 90024, United States
Keywords: Bayesian Statistics ; Phylogenetics ; Molecular Evolution ; Statistical Genetics ; Biostatistics
Abstract:

Effective population size is fundamental in population genetics and characterizes genetic diversity. To infer past population dynamics from molecular sequence data, coalescent-based models have been developed for Bayesian nonparametric estimation of effective population size over time. Among the most successful is a Gaussian Markov random field (GMRF) model for a single gene locus. Here, we extend the GMRF model to allow for the analysis of multilocus sequence data. Using simulated data, we demonstrate the improved performance of our method to recover true population trajectories and the time to the most recent common ancestor (TMRCA) by incorporating multilocus data. We analyze a multilocus alignment of CRF02_AG gene sequences sampled from Cameroon. Our results are consistent with HIV prevalence data and uncover some aspects of the population history that go undetected in Bayesian parametric estimation. Finally, we recover an older and more reconcilable TMRCA for a classic ancient DNA dataset.


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