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Bayesian model adequacy and choice in phylogenetics.

Jonathan P Bollback1

  • 1Department of Biology, University of Rochester, NY 14627, USA. bollback@brahms.biology.rochester.edu

Molecular Biology and Evolution
|June 26, 2002
PubMed
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Bayesian phylogenetic inference uses models of evolution. This study introduces a method using posterior predictive distributions to evaluate evolutionary model adequacy, ensuring reliable phylogenetic estimations.

Area of Science:

  • Evolutionary Biology
  • Computational Biology
  • Statistical Phylogenetics

Background:

  • Bayesian inference is a prevalent statistical method for phylogenetic estimation, enabling efficient analysis of large datasets and complex evolutionary models.
  • Current Bayesian phylogenetic methods rely on stochastic models of sequence evolution, but their accuracy is contingent upon model adequacy.
  • Inadequate evolutionary models can lead to erroneous phylogenetic inferences, highlighting the need for robust model evaluation.

Purpose of the Study:

  • To introduce a Bayesian phylogenetic method for evaluating the adequacy of evolutionary models.
  • To enable the selection of appropriate evolutionary models for Bayesian phylogenetic studies.
  • To provide a framework for assessing both global and local performance of phylogenetic models.

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Main Methods:

  • Utilizes posterior predictive distributions to assess the adequacy of evolutionary models.
  • Employs a test statistic to evaluate the overall (global) performance of phylogenetic models.
  • Offers the flexibility to tailor various test statistics for evaluating specific (local) features of evolutionary models.

Main Results:

  • The presented method allows for the selection of adequate evolutionary models based on their posterior predictive performance.
  • It provides a means to identify specific sources of model failure by evaluating local performance.
  • The approach accounts for uncertainty in both the phylogeny and model parameters, unlike traditional methods.

Conclusions:

  • The posterior predictive approach offers a robust framework for evolutionary model selection in Bayesian phylogenetics.
  • This method enhances the reliability of phylogenetic estimations by ensuring the use of adequate evolutionary models.
  • It addresses limitations of existing methods like likelihood-ratio tests and parametric bootstrapping by incorporating phylogenetic and parameter uncertainty.