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Bayesian estimation of positively selected sites.

John P Huelsenbeck1, Kelly A Dyer

  • 1Section of Ecology, Behavior and Evolution, Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093-0116, USA. johnh@biomail.ucsd.edu

Journal of Molecular Evolution
|October 6, 2004
PubMed
Summary

This study introduces a fully Bayesian method to identify protein-coding DNA sites under diversifying selection. The approach accounts for evolutionary uncertainties, improving the detection of positive selection compared to previous methods.

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Area of Science:

  • Evolutionary biology
  • Molecular evolution
  • Genomics

Background:

  • Amino acid substitution patterns in protein-coding DNA reveal historical selection pressures.
  • Nonsynonymous to synonymous substitution rates (dN/dS) indicate positive selection.
  • Existing methods use empirical Bayesian frameworks but have limitations.

Purpose of the Study:

  • To develop a fully Bayesian framework for identifying sites under diversifying selection.
  • To account for uncertainties in phylogenetic and codon models of DNA evolution.
  • To compare the new method with existing approaches.

Main Methods:

  • A fully Bayesian approach was implemented to analyze DNA sequences.
  • The model allows for site-specific variation in nonsynonymous to synonymous substitution rates.

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  • Uncertainties in tree topology, branch lengths, and codon substitution models were incorporated.

    • Main Results:

    • The fully Bayesian method identified sites under positive selection in the vertebrate beta-globin gene.
    • The hierarchical model detected similar sites to previous analyses.
    • Notable differences were observed, highlighting the impact of accounting for parameter uncertainty.

    Conclusions:

    • A fully Bayesian framework offers a robust method for detecting positive selection in protein-coding DNA.
    • Accounting for phylogenetic and model uncertainties enhances the accuracy of selection site identification.
    • This approach provides a more comprehensive understanding of evolutionary pressures shaping genes.