21
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

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

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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 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.

          Related collections

          Author and article information

          Journal
          Mol Biol Evol
          Molecular biology and evolution
          Oxford University Press (OUP)
          1537-1719
          0737-4038
          Mar 2013
          : 30
          : 3
          Affiliations
          [1 ] Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, USA.
          Article
          mss265
          10.1093/molbev/mss265
          3563973
          23180580
          349086f1-9f9f-4cc3-aee7-c1822179c1a6
          History

          Comments

          Comment on this article