Understanding past population dynamics: Bayesian coalescent-based modeling with covariates

Gill, M. S., Lemey, P., Bennett, S. N., Biek, R. and Suchard, M. A. (2016) Understanding past population dynamics: Bayesian coalescent-based modeling with covariates. Systematic Biology, 65(6), pp. 1041-1056. (doi: 10.1093/sysbio/syw050) (PMID:27368344)

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Abstract

Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics. Kingman's coalescent process enables inference of past population dynamics directly from molecular sequence data, and researchers have developed a number of flexible coalescent-based models for Bayesian nonparametric estimation of the effective population size as a function of time. A major goal of demographic reconstruction is understanding the association between the effective population size and potential explanatory factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates through Gaussian Markov random fields. To approximate the posterior distribution, we adapt efficient Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. Incorporating covariates into the demographic inference framework enables the modeling of associations between the effective population size and covariates while accounting for uncertainty in population histories. Furthermore, it can lead to more precise estimates of population dynamics. We apply our model to four examples. We reconstruct the demographic history of raccoon rabies in North America and find a significant association with the spatiotemporal spread of the outbreak. Next, we examine the effective population size trajectory of the DENV-4 virus in Puerto Rico along with viral isolate count data and find similar cyclic patterns. We compare the population history of the HIV-1 CRF02_AG clade in Cameroon with HIV incidence and prevalence data and find that the effective population size is more reflective of incidence rate. Finally, we explore the hypothesis that the population dynamics of musk ox during the Late Quaternary period were related to climate change.

Item Type:Articles
Additional Information:The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013) [grant agreement no. 278433-PREDEMICS and ERC grant agreement no. 260864]; and the National Institutes of Health [R01 AI107034, R01 HG006139, R01 LM011827, and 5T32AI007370-24]; and the National Science Foundation [DMS 1264153]. R.B. was supported by NIH [grant RO1 AI047498] and the RAPIDD program of the Science and Technology Directorate of the Department of Homeland Security, and NIH Fogarty International Centre.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Biek, Professor Roman
Authors: Gill, M. S., Lemey, P., Bennett, S. N., Biek, R., and Suchard, M. A.
College/School:College of Medical Veterinary and Life Sciences > Institute of Biodiversity Animal Health and Comparative Medicine
Journal Name:Systematic Biology
Publisher:Oxford University Press
ISSN:1063-5157
ISSN (Online):1076-836X
Published Online:01 July 2016
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in Systematic Biology 65(6):1041-1056
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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