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Exact Bayesian inference for phylogenetic birth-death models.

Kris V Parag1, Oliver G Pybus1

  • 1Department of Zoology, University of Oxford, Oxford, UK.

Bioinformatics (Oxford, England)
|May 3, 2018
PubMed
Summary
This summary is machine-generated.

We introduce a deterministic Snyder filter (SF) method for inferring population change rates from phylogenies. This approach offers reproducible and provable birth-death process (BDP) estimates, serving as a reliable alternative to MCMC methods.

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Area of Science:

  • Macro-evolutionary biology
  • Epidemiology
  • Computational biology

Background:

  • Estimating population change rates from genetic phylogenies is crucial in evolutionary biology and epidemiology.
  • Current methods often use birth-death process (BDP) models with Markov chain Monte Carlo (MCMC) sampling, which can lack guaranteed convergence and reproducibility.

Purpose of the Study:

  • To present a novel, deterministic approach for parametric BDP inference using Snyder filter (SF) algorithms.
  • To provide a provable, reproducible, and computationally efficient alternative to existing MCMC-based inference methods.

Main Methods:

  • Implementation of flexible and easy-to-use Snyder filter (SF) algorithms for BDP inference.
  • Validation of the SF on constant and time-varying rate BDP models.
  • Direct computation of the posterior distribution of model parameters without approximations beyond numerical integration.

Main Results:

  • The SF method yields provable, guaranteed, and reproducible results for BDP likelihoods.
  • Estimates from the SF compare favorably with a recent parametric MCMC inference method for time-varying BDPs.
  • Model selection using SF on an Agamid species phylogeny produced results consistent with existing literature.

Conclusions:

  • The Snyder filter offers a promising, deterministic alternative for BDP inference, suitable as a standalone Bayesian estimator.
  • SF can serve as a valuable diagnostic tool for validating more complex MCMC strategies in phylogenetic analysis.
  • The SF method provides a robust framework for inferring population dynamics from phylogenetic data.