Codon-Substitution Models for Heterogeneous Selection Pressure at Amino Acid Sites

Corresponding author: Ziheng Yang, Department of Biology, 4 Stephenson Way, London NW1 2HE, United Kingdom., z.yang{at}ucl.ac.uk (E-mail)

Communicating editor: W. STEPHAN

Comparison of relative fixation rates of synonymous (silent) and nonsynonymous (amino acid-altering) mutations provides a means for understanding the mechanisms of molecular sequence evolution. The nonsynonymous/synonymous rate ratio ( = ) is an important indicator of selective pressure at the protein level, with = 1 meaning neutral mutations, < 1 purifying selection, and > 1 diversifying positive selection. Amino acid sites in a protein are expected to be under different selective pressures and have different underlying ratios. We develop models that account for heterogeneous ratios among amino acid sites and apply them to phylogenetic analyses of protein-coding DNA sequences. These models are useful for testing for adaptive molecular evolution and identifying amino acid sites under diversifying selection. Ten data sets of genes from nuclear, mitochondrial, and viral genomes are analyzed to estimate the distributions of among sites. In all data sets analyzed, the selective pressure indicated by the ratio is found to be highly heterogeneous among sites. Previously unsuspected Darwinian selection is detected in several genes in which the average ratio across sites is <1, but in which some sites are clearly under diversifying selection with > 1. Genes undergoing positive selection include the ß-globin gene from vertebrates, mitochondrial protein-coding genes from hominoids, the hemagglutinin (HA) gene from human influenza virus A, and HIV-1 env, vif, and pol genes. Tests for the presence of positively selected sites and their subsequent identification appear quite robust to the specific distributional form assumed for and can be achieved using any of several models we implement. However, we encountered difficulties in estimating the precise distribution of among sites from real data sets.

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