Document Detail

Improving Bayesian population dynamics inference: a coalescent-based model for multiple loci.
MedLine Citation:
PMID:  23180580     Owner:  NLM     Status:  MEDLINE    
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.
Mandev S Gill; Philippe Lemey; Nuno R Faria; Andrew Rambaut; Beth Shapiro; Marc A Suchard
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2012-11-22
Journal Detail:
Title:  Molecular biology and evolution     Volume:  30     ISSN:  1537-1719     ISO Abbreviation:  Mol. Biol. Evol.     Publication Date:  2013 Mar 
Date Detail:
Created Date:  2013-02-06     Completed Date:  2013-07-16     Revised Date:  2014-03-07    
Medline Journal Info:
Nlm Unique ID:  8501455     Medline TA:  Mol Biol Evol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  713-24     Citation Subset:  IM    
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MeSH Terms
Bayes Theorem
Computer Simulation
Evolution, Molecular
Genes, Viral
Genetic Loci*
Genetic Speciation
HIV-1 / genetics
Markov Chains
Models, Genetic*
Monte Carlo Method
Population Density
Population Dynamics
Statistics, Nonparametric
Grant Support
260864//European Research Council; 5T32AI007370-22/AI/NIAID NIH HHS; R01 GM086887/GM/NIGMS NIH HHS; R01 HG006139/HG/NHGRI NIH HHS; R01 HG006139/HG/NHGRI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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