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This study introduces a new Bayesian nonparametric framework to estimate effective population size, incorporating time-varying factors for more precise demographic inference. The model reveals associations between population dynamics and factors like disease spread and climate change.

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Area of Science:

  • Population genetics
  • Evolutionary biology
  • Computational biology

Background:

  • Effective population size (Ne) is crucial for understanding genetic variability and evolutionary trajectories.
  • Coalescent theory and Bayesian nonparametric models are established methods for inferring past population dynamics from molecular data.

Purpose of the Study:

  • To develop a flexible framework for estimating effective population size (Ne) over time, incorporating time-varying covariates.
  • To model associations between Ne and external factors while accounting for demographic uncertainty.
  • To improve the precision of population dynamic estimates.

Main Methods:

  • Developed a Bayesian nonparametric coalescent-based framework utilizing Gaussian Markov random fields for temporal smoothing of Ne.
  • Adapted efficient Markov chain Monte Carlo (MCMC) algorithms for approximating posterior distributions in complex Gaussian models.
  • Integrated time-varying covariates into demographic inference to explore associations with Ne.

Main Results:

  • The framework successfully reconstructed demographic histories for diverse examples, including raccoon rabies, DENV-4 virus, HIV-1, and musk ox populations.
  • Demonstrated significant associations between Ne and factors such as disease outbreak spread, viral isolate counts, HIV incidence, and climate change.
  • Showcased improved precision in population dynamic estimates through covariate incorporation.

Conclusions:

  • The proposed flexible framework enhances demographic inference by incorporating time-varying covariates, leading to more accurate effective population size (Ne) estimates.
  • This approach facilitates the identification of key drivers influencing population dynamics and their historical associations.
  • The model's applicability across various biological systems highlights its utility in phylodynamics and evolutionary studies.