Improving Bayesian population dynamics inference: a coalescent-based model for multiple loci.

Journal article

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 present a generalization of the GMRF model that allows 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). We analyze a multilocus alignment of HIV-1 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 data set.

Authors: Gill, MS; Lemey, P; Faria, N; Rambaut, A; Shapiro, B

• Publication status: Published
• Journal: Molecular biology and evolution
• Volume: 30
• Issue: 3
• Pages: 713-724
• Publication date: 2013-03-01
• DOI: 10.1093/molbev/mss265
• EISSN: 1537-1719
• ISSN: 0737-4038
• Language: English
• Keywords: Monte Carlo Method; Models, Genetic; Markov Chains; HIV-1; Statistics, Nonparametric; Genetic Loci; Evolution, Molecular; Algorithms; Genes, Viral; Humans; Population Density; Computer Simulation; Bayes Theorem; Genetic Speciation; Mutation; Population Dynamics