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A Practical Guide to Phylogenetics for Nonexperts

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Cross-validation to select Bayesian hierarchical models in phylogenetics.

Sebastián Duchêne^1,2, David A Duchêne³, Francesca Di Giallonardo^4,3

¹Marie Bashir Institute of Infectious Diseases and Biosecurity, Charles Perkins Centre, Sydney Medical School, University of Sydney, Sydney, NSW, 2006, Australia. sebastian.duchene@sydney.edu.au.

BMC Evolutionary Biology

|May 28, 2016

Summary

Cross-validation offers a robust alternative for Bayesian phylogenetic model selection, effectively comparing evolutionary models based on predictive performance. This method proves reliable for analyzing molecular sequence data, even with complex models.

Keywords:

Bayesian phylogenetics Cross-validation Demographic models Marginal likelihood Model selection Molecular clock

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Area of Science:

Phylogenetics
Computational Biology
Evolutionary Biology

Background:

Bayesian phylogenetic models enable advanced inferences from molecular data.
Model selection is crucial in phylogenetic analysis, often relying on marginal likelihood estimation.
Marginal likelihood estimation can be sensitive to improper priors.

Purpose of the Study:

To explore cross-validation as an alternative to marginal likelihood estimation for Bayesian phylogenetic model selection.
To assess the effectiveness of cross-validation in distinguishing between different molecular clock and demographic models.

Main Methods:

Utilized a cross-validation approach to compare phylogenetic models.
Applied the method to simulated data and empirical viral and bacterial datasets.
Compared cross-validation performance against marginal likelihood estimation.

Main Results:

Cross-validation effectively distinguished between strict- and relaxed-clock models.
Identified demographic models supporting population growth over time.
Cross-validation selected models consistent with marginal likelihood estimation in most empirical analyses.
Accuracy improved with longer sequence data, especially for relaxed-clock models.

Conclusions:

Cross-validation is a valuable and practical method for Bayesian phylogenetic model selection.
This approach simplifies model selection for complex phylogenetic models where prior specification is challenging.