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

Abstract

Positive selection in genes and genomes can point to the evolutionary basis for differences among species and among races within a species. The detection of positive selection can also help identify functionally important protein regions and thus guide protein engineering. Many existing tests for positive selection are excessively conservative, vulnerable to artifacts caused by demographic population history, or computationally very intensive. I here propose a simple and rapid test that is complementary to existing tests and that overcomes some of these problems. It relies on the null hypothesis that neutrally evolving DNA regions should show a Poisson distribution of nucleotide substitutions. The test detects significant deviations from this expectation in the form of variation clusters, highly localized groups of amino acid changes in a coding region. In applying this test to several thousand human-chimpanzee gene orthologs, I show that such variation clusters are not generally caused by relaxed selection. They occur in well-defined domains of a protein's tertiary structure and show a large excess of amino acid replacement over silent substitutions. I also identify multiple new human-chimpanzee orthologs subject to positive selection, among them genes that are involved in reproductive functions, immune defense, and the nervous system.