Dataset Information

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

ABSTRACT: 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 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. [Coalescent; effective population size; Gaussian Markov random fields; phylodynamics; phylogenetics; population genetics.

SUBMITTER: Gill MS

PROVIDER: S-EPMC5066065 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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

Gill Mandev S MS Lemey Philippe P Bennett Shannon N SN Biek Roman R Suchard Marc A MA

Systematic biology 20160701 6

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 in ...[more]

PMID: 27368344

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Project description:The ongoing large-scale increase in the total amount of genetic data for viruses and other pathogens has led to a situation in which it is often not possible to include every available sequence in a phylogenetic analysis and expect the procedure to complete in reasonable computational time. This raises questions about how a set of sequences should be selected for analysis, particularly if the data are used to infer more than just the phylogenetic tree itself. The design of sampling strategies for molecular epidemiology has been a neglected field of research. This article describes a large-scale simulation exercise that was undertaken to select an appropriate strategy when using the GMRF skygrid, one of the Bayesian skyline family of coalescent methods, in order to reconstruct past population dynamics. The simulated scenarios were intended to represent sampling for the population of an endemic virus across multiple geographical locations. Large phylogenies were simulated under a coalescent or structured coalescent model and sequences simulated from these trees; the resulting datasets were then downsampled for analyses according to a variety of schemes. Variation in results between different replicates of the same scheme was not insignificant, and as a result, we recommend that where possible analyses are repeated with different datasets in order to establish that elements of a reconstruction are not simply the result of the particular set of samples selected. We show that an individual stochastic choice of sequences can introduce spurious behaviour in the median line of the skygrid plot and that even marginal likelihood estimation can suggest complicated dynamics that were not in fact present. We recommend that the median line should not be used to infer historical events on its own. Sampling sequences with uniform probability with respect to both time and spatial location (deme) never performed worse than sampling with probability proportional to the effective population size at that time and in that location and frequently was superior. As a result, we recommend this approach in the design of future studies. We also confirm that the inclusion of many recent sequences from a single geographical location in an analysis tends to result in a spurious bottleneck effect in the reconstruction and caution against interpreting this as genuine.

Dataset Information

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

Publications

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

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