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MBE Advance Access published online on August 24, 2005
Molecular Biology and Evolution, doi:10.1093/molbev/msi250
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© The Author 2005. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org
Accepted August 15, 2005

Research Article

A Bayesian Model Comparison Approach to Inferring Positive Selection

K. Scheffler 1* and C. Seoighe 1

1 Computational Biology Group, Institute for Infectious Diseases and Molecular Medicine, University of Cape Town, Private Bag, Rondebosch 7701, South Africa Tel: +27 72 1579 600; Fax: +27 21 689 7573

* To whom correspondence should be addressed.
K. Scheffler, E-mail: konrad{at}cbio.uct.ac.za


  Abstract

A popular approach to detecting positive selection is to estimate the parameters of a probabilistic model of codon evolution and perform inference based on its maximum likelihood parameter values. This approach has been evaluated intensively in a number of simulation studies and found to be robust when the available data set is large. However, uncertainties in the estimated parameter values can lead to errors in the inference, especially when the data set is small or there is insufficient divergence between the sequences. We introduce a Bayesian model comparison approach to infer whether the sequence as a whole contains sites at which the rate of non-synonymous substitution is greater than the rate of synonymous substitution. We incorporated this probabilistic model comparison into a Bayesian approach to site-specific inference of positive selection. Using simulated sequences, we compared this approach to the commonly used empirical Bayes approach and investigated the effect of tree length on the performance of both methods. We found that the Bayesian approach outperforms the empirical Bayes method when the amount of sequence divergence is small and is less prone to false positive inference when the sequences are saturated, while the results are indistinguishable for intermediate levels of sequence divergence.

Keywords: positive selection; Bayesian inference; model comparison; phylogenetic simulation.
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