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

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 mos...

Full description

Saved in:
Bibliographic Details
Published in:Molecular biology and evolution 2013-03, Vol.30 (3), p.713-724
Main Authors: Gill, Mandev S, Lemey, Philippe, Faria, Nuno R, Rambaut, Andrew, Shapiro, Beth, Suchard, Marc A
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Bayesian analysis
Computer Simulation
Evolution, Molecular
Gene loci
Genes, Viral
Genetic diversity
Genetic Loci
Genetic Speciation
Genetics
HIV-1 - genetics
Humans
Markov analysis
Markov Chains
Mathematical models
Methods
Models, Genetic
Molecular biology
Monte Carlo Method
Mutation
Population Density
Population Dynamics
Population genetics
Population number
Statistics, Nonparametric
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0737-4038
1537-1719
DOI:10.1093/molbev/mss265