New Methods for Detecting Positive Selection at Single Amino Acid Sites

Abstract

Inferring positive selection at single amino acid sites is of particular importance for studying evolutionary mechanisms of a protein. For this purpose, Suzuki and Gojobori (1999) developed a method (SG method) for comparing the rates of synonymous and nonsynonymous substitutions at each codon site in a protein-coding nucleotide sequence, using ancestral codons at interior nodes of the phylogenetic tree as inferred by the maximum parsimony method. In the SG method, however, selective neutrality of nucleotide substitutions cannot be tested at codon sites, where only termination codons are inferred at any interior node or the number of equally parsimonious inferences of ancestral codons at all interior nodes exceeds 10,000. Here I present a modified SG method which is free from these problems. Specifically, I use the distance-based Bayesian method for inferring the single most likely ancestral codon from 61 sense codons at each interior node. In the computer simulation and real data analysis, the modified SG method showed a higher overall efficiency of detecting positive selection than the original SG method, particularly at highly polymorphic codon sites. These results indicate that the modified SG method is useful for inferring positive selection at codon sites where neutrality cannot be tested by the original SG method. I also discuss that the p-distance is preferable to the number of synonymous substitutions for inferring the phylogenetic tree in the SG method, and present a maximum likelihood method for detecting positive selection at single amino acid sites, which produced reasonable results in the real data analysis.

Positive selection for gains of N-linked glycosylation sites in hemagglutinin during evolution of H3N2 human influenza A virus

“…Therefore, κ was assumed to be 4 in the computation. For each codon site, the numbers of synonymous and nonsynonymous differences were summed and the numbers of synonymous and nonsynonymous sites were averaged with the weight proportional to the branch length over all branches of the phylogenetic tree, to obtain the total numbers of synonymous (c S ) and nonsynonymous (c N ) differences and the average numbers of synonymous (s S ) and nonsynonymous (s N ) sites (Suzuki and Gojobori, 1999;Suzuki, 2004a). Although multiple substitutions were not corrected in this method, the degree of underestimation for c S and c N appeared to be negligible for the data set analyzed in the present study, because the branch lengths were generally very small (Saitou, 1989).…”

“…Although the single-substitution analysis is conceptually different from the single-site analysis, they are methodologically similar to each other. It has been shown that the latter analysis is generally conservative and reliable in the computer simulation and real data analysis (Suzuki and Gojobori, 1999;Suzuki, 2004aSuzuki, , 2007. It should be noted that in the single-substitution analysis, positive selection for original substitutions can be detected even when the number of (reverse) nonsynonymous substitutions is 0.…”

The evolutionary ecology of the major histocompatibility complex

“…Therefore, if balancing selection favours diversity at the PBR of MHC genes, advantageous nonsynonymous mutations will be retained and a high ratio of nonsynonymous to synonymous substitutions will be observed. Tests of deviation of dN:dS from parity are becoming increasingly sophisticated (Nielsen, 1997;Bierne and Eyre-Walker, 2003), and can now explicitly examine which amino-acid sites show the most potent signatures of selection (Nielsen and Yang, 1998;Suzuki, 2004;Massingham and Goldman, 2005). Such approaches will prove useful in studies of the MHC, as the effects of selection on specific peptide-binding codons can be ascertained.…”

Rapid Detection of Positive Selection in Genes and Genomes Through Variation Clusters

“…Many variations of this class of test exist. They differ in the amount of sequence data and computational resources required (Suzuki and Gojobori 1999;Suzuki 2004;Massingham and Goldman 2005;Pond and Frost 2005;Zhang et al 2005). The second class of tests relies on predictions made by the neutral theory for allele or haplotype frequencies (Kreitman 2000;Bamshad and Wooding 2003) within and among populations.…”