Understanding Past Population Dynamics: Bayesian Coalescent-Based Modeling with Covariates
Mandev S. Gill, Philippe Lemey, Shannon N. Bennett, Roman Biek, and, Marc A. Suchard

TL;DR
This paper introduces a Bayesian coalescent-based framework that incorporates time-varying covariates to improve demographic history reconstruction from genetic data, demonstrated through four diverse case studies.
Contribution
It extends existing models by integrating covariates via Gaussian Markov random fields, enabling more accurate and interpretable population dynamic inferences.
Findings
Significant association between rabies spread and population size.
Cyclic patterns in DENV-4 effective population size.
Population dynamics of musk ox linked to climate change.
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…
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Taxonomy
TopicsRabies epidemiology and control · Virology and Viral Diseases · Wildlife Ecology and Conservation
