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Efficient Bayesian inference under the structured coalescent.

Timothy G Vaughan1, Denise Kühnert2, Alex Popinga3

  • 1Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North 4442, New Zealand, Institute of Integrative Biology, Swiss Federal Institute of Technology (ETH), Zurich 8092, Switzerland and Department of Computer Science, University of Auckland, Auckland 1142, New Zealand.

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Summary
This summary is machine-generated.

We developed a new Markov Chain Monte Carlo (MCMC) sampler for structured phylogenetic trees. This tool improves the inference of population structure and migration rates from genetic data.

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Area of Science:

  • Computational evolutionary biology
  • Phylogenetics
  • Population genetics

Background:

  • Population structure is crucial for evolutionary dynamics and phylogenetic inference.
  • The structured coalescent model aids in understanding gene flow but faces computational challenges.
  • Efficient Bayesian inference requires advanced Markov Chain Monte Carlo (MCMC) sampling algorithms.

Purpose of the Study:

  • To present a novel MCMC sampler for efficient Bayesian inference on structured phylogenetic trees.
  • To improve the exploration of posterior distributions in models with population structure.
  • To facilitate the estimation of demographic parameters like migration rates and subpopulation sizes.

Main Methods:

  • Developed a new MCMC sampler implemented as a BEAST 2 package.
  • Incorporated MCMC proposal functions for enhanced mixing efficiency.
  • Applied the sampler to infer H3N2 influenza migration patterns.

Main Results:

  • The new sampler significantly improves mixing over previous methods.
  • Demonstrated successful inference of migration rates and effective population sizes for H3N2 influenza.
  • The BEAST 2 package offers flexibility in model and prior specification.

Conclusions:

  • The developed MCMC sampler enhances phylogenetic inference under the structured coalescent model.
  • This tool facilitates a deeper understanding of population dynamics and evolutionary processes.
  • The sampler is publicly available, promoting wider application in evolutionary studies.