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A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

Genealogical Working Distributions for Bayesian Model Testing with Phylogenetic Uncertainty.

Guy Baele¹, Philippe Lemey², Marc A Suchard³

¹Department of Microbiology and Immunology, Rega Institute, KU Leuven-University of Leuven, Leuven, Belgium guy.baele@rega.kuleuven.be.

Systematic Biology

|November 4, 2015

Summary

This summary is machine-generated.

We developed new methods for Bayesian phylogenetic inference, improving model selection accuracy. Our generalized stepping-stone sampling approach handles complex models and diffuse priors, outperforming existing techniques.

Keywords:

Bayes factor Bayesian inference MCMC Working distribution coalescent model marginal likelihood phylogenetics

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Area of Science:

Computational evolutionary biology
Bayesian phylogenetics
Statistical modeling

Background:

Bayes factors and marginal likelihood estimates are crucial for comparing models in Bayesian phylogenetic inference.
Path sampling (PS) and stepping-stone sampling (SS) have improved model selection accuracy but face computational demands and numerical issues with diffuse priors.
Generalized stepping-stone sampling (GSS) was introduced to improve integration efficiency but was limited by fixed tree topologies.

Purpose of the Study:

To extend generalized stepping-stone sampling (GSS) by removing the fixed tree topology assumption.
To develop novel 'working' distributions for estimating marginal likelihoods while accounting for phylogenetic uncertainty.
To improve the accuracy and computational efficiency of model selection in Bayesian phylogenetics.

Main Methods:

Extended GSS by introducing a 'working' distribution on the space of genealogies, relaxing the fixed tree topology constraint.
Proposed two distinct 'working' distributions to facilitate the integration process in marginal likelihood estimation.
Implemented and tested the extended GSS methods within the BEAST software package.

Main Results:

The proposed GSS approaches demonstrated superior accuracy compared to PS and SS in comparing demographic and evolutionary models.
Extended GSS successfully estimated marginal likelihoods while accommodating phylogenetic uncertainty.
The new GSS methods accurately retrieved marginal likelihoods even with very diffuse priors, unlike PS and SS which showed overestimation.

Conclusions:

The extended GSS methods offer a more robust and accurate approach for model selection in Bayesian phylogenetics.
These advancements address computational demands and numerical instabilities associated with previous methods, particularly with diffuse priors.
The developed methods are available in BEAST, facilitating their application to complex evolutionary and population genetic models.