4.6 Article

Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates

期刊

SYSTEMATIC BIOLOGY
卷 65, 期 6, 页码 1041-1056

出版社

OXFORD UNIV PRESS
DOI: 10.1093/sysbio/syw050

关键词

Coalescent; effective population size; Gaussian Markov random fields; phylodynamics; phylogenetics; population genetics

资金

  1. European Research Council under European Community [278433-PREDEMICS]
  2. ERC [260864]
  3. National Institutes of Health [R01 AI107034, R01 HG006139, R01 LM011827, 5T32AI007370-24]
  4. National Science Foundation [DMS 1264153]
  5. NIH [RO1 AI047498]
  6. RAPIDD program of the Science and Technology Directorate of the Department of Homeland Security
  7. NIH Fogarty International Centre
  8. Direct For Mathematical & Physical Scien [1264153] Funding Source: National Science Foundation
  9. Division Of Mathematical Sciences [1264153] Funding Source: National Science Foundation

向作者/读者索取更多资源

Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics and evolutionary biology. 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. Major goals of demographic reconstruction include identifying driving factors of effective population size, and understanding the association between the effective population size and such factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates that exploit Gaussian Markov random fields to achieve temporal smoothing of effective population size trajectories. 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 inNorthAmerica 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. [Coalescent; effective population size; Gaussian Markov random fields; phylodynamics; phylogenetics; population genetics.]

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