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Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo.

John P Huelsenbeck1, Bret Larget, Michael E Alfaro

  • 1Section of Ecology, Behavior and Evolution, Division of Biological Sciences, University of California, San Diego, USA. johnh@biomail.ucsd.edu

Molecular Biology and Evolution
|March 23, 2004
PubMed
Summary

Selecting the best DNA substitution model for phylogenetic analysis is crucial. This study introduces a method using Bayes factors and reversible jump Markov chain Monte Carlo to evaluate all time-reversible models, improving phylogenetic inference accuracy.

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Area of Science:

  • Molecular Phylogenetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Choosing appropriate DNA substitution models is vital for accurate phylogenetic analysis.
  • Existing model selection methods (AIC, BIC) examine limited models and struggle with non-nested comparisons.
  • Superfluous parameters in substitution models can lead to inaccurate evolutionary inferences.

Purpose of the Study:

  • To expand the selection of DNA substitution models beyond commonly used ones.
  • To implement a method for comparing all possible time-reversible models.
  • To improve phylogenetic model parameter estimation by accounting for model uncertainty.

Main Methods:

  • Utilized reversible jump Markov chain Monte Carlo (rjMCMC) for Bayes factor calculation.

Related Experiment Videos

  • Expanded candidate models to include all 20 time-reversible substitution models.
  • Applied the method to 16 diverse DNA sequence alignments for model comparison.

    • Main Results:

    • Bayes factors identified optimal models, often favoring those with intermediate complexity.
    • Most selected models did not constrain transition and transversion rates equally.
    • The transition/transversion rate bias significantly influenced model selection outcomes.

    Conclusions:

    • The rjMCMC approach effectively compares a comprehensive set of time-reversible DNA substitution models.
    • Accounting for model uncertainty during phylogenetic analysis enhances accuracy.
    • This method provides a robust framework for selecting DNA substitution models in phylogenetics.