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Large-scale analyses of synonymous substitution rates can be sensitive to assumptions about the process of mutation
 

Summary: Large-scale analyses of synonymous substitution rates can be sensitive
to assumptions about the process of mutation
Stéphane Aris-Brosou a,, Joseph P. Bielawski b
a
Department of Biology, University of Ottawa, 30 Marie Curie, Ottawa, ON, Canada K1N 6N5
b
Department of Biology and Department of Mathematics and Statistics, Dalhousie University, Halifax, NS Canada
Received 11 February 2006; received in revised form 20 April 2006; accepted 26 April 2006
Available online 22 May 2006
Abstract
A popular approach to examine the roles of mutation and selection in the evolution of genomes has been to consider the relationship between
codon bias and synonymous rates of molecular evolution. A significant relationship between these two quantities is taken to indicate the action of
weak selection on substitutions among synonymous codons. The neutral theory predicts that the rate of evolution is inversely related to the level of
functional constraint. Therefore, selection against the use of non-preferred codons among those coding for the same amino acid should result in
lower rates of synonymous substitution as compared with sites not subject to such selection pressures. However, reliably measuring the extent of
such a relationship is problematic, as estimates of synonymous rates are sensitive to our assumptions about the process of molecular evolution.
Previous studies showed the importance of accounting for unequal codon frequencies, in particular when synonymous codon usage is highly
biased. Yet, unequal codon frequencies can be modeled in different ways, making different assumptions about the mutation process. Here we
conduct a simulation study to evaluate two different ways of modeling uneven codon frequencies and show that both model parameterizations can
have a dramatic impact on rate estimates and affect biological conclusions about genome evolution. We reanalyze three large data sets to

  

Source: Aris-Brosou, Stéphane - Department of Biology, University of Ottawa

 

Collections: Biology and Medicine