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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Identifying sites under positive selection with uncertain parameter estimates.

Stéphane Aris-Brosou1

  • 1Department of Biology, University of Ottawa, Ottawa, ON, Canada. sarisbro@uottawa.ca

Genome
|August 29, 2006
PubMed
Summary

Full-Bayes methods offer improved detection of positive selection in protein-coding genes compared to empirical Bayes approaches, especially for smaller datasets. Careful codon model selection is crucial for accurate inference of sites under selection.

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Area of Science:

  • Evolutionary biology
  • Molecular evolution
  • Bioinformatics

Background:

  • Codon-based substitution models are standard for assessing selective pressures on protein-coding genes.
  • The nonsynonymous to synonymous rate ratio (dN/dS or omega) quantifies selection at amino acid sites.
  • Inferring sites under positive selection (omega > 1) typically involves empirical Bayes or Bayes empirical Bayes methods.

Purpose of the Study:

  • To extend a full-Bayes approach for enhanced power and reduced false positives in detecting sites under positive selection.
  • To investigate the performance of full-Bayes methods against empirical Bayes and Bayes empirical Bayes, particularly with limited data.
  • To assess the sensitivity of Bayesian methods to model misspecification in evolutionary processes.

Main Methods:

  • Extension of a previous full-Bayes framework for codon-based substitution models.
  • Development of heuristics to reduce computational demands.
  • Comparative analysis of full-Bayes, empirical Bayes, and Bayes empirical Bayes approaches using simulated and small test datasets.

Main Results:

  • Full-Bayes methods demonstrated superiority over empirical Bayes for small datasets.
  • A marginal advantage of full-Bayes over Bayes empirical Bayes was observed with the specific small test data used.
  • Bayesian methods showed robustness to mild model misspecifications but sensitivity to significant ones in simulations.

Conclusions:

  • Full-Bayes approaches provide a powerful tool for identifying amino acid sites under positive selection.
  • The choice of codon model is critical for reliable inference and should be carefully selected, e.g., using the Akaike information criterion (AIC).
  • Computational efficiency can be improved through proposed heuristics, making full-Bayes methods more accessible.