Estimating Synonymous and Nonsynonymous Substitution Rates Under Realistic Evolutionary Models

TLDR

Approximate methods for estimating synonymous and nonsynonymous substitution rates between two DNA sequences involve counting sites, counting differences, and correcting for multiple substitutions at the same site. This study examines the complexities of these steps and proposes a new approximate method that incorporates transition/transversion bias and base/codon frequency bias. The authors compare the new method with maximum likelihood and other approximate approaches using infinitely long sequences, computer simulations, and a real data set. The results indicate that existing approximate methods can be seriously biased when such biases exist, whereas the new method is generally superior, though maximum likelihood remains the method of choice.

Abstract

Approximate methods for estimating the numbers of synonymous and nonsynonymous substitutions between two DNA sequences involve three steps: counting of synonymous and nonsynonymous sites in the two sequences, counting of synonymous and nonsynonymous differences between the two sequences, and correcting for multiple substitutions at the same site. We examine complexities involved in those steps and propose a new approximate method that takes into account two major features of DNA sequence evolution: transition/transversion rate bias and base/codon frequency bias. We compare the new method with maximum likelihood, as well as several other approximate methods, by examining infinitely long sequences, performing computer simulations, and analyzing a real data set. The results suggest that when there are transition/transversion rate biases and base/codon frequency biases, previously described approximate methods for estimating the nonsynonymous/synonymous rate ratio may involve serious biases, and the bias can be both positive and negative. The new method is, in general, superior to earlier approximate methods and may be useful for analyzing large data sets, although maximum likelihood appears to always be the method of choice.