Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models

Abstract

AbstractSites of positive selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect many interesting forms of selection. I describe a new approach that uses a null model based on high-throughput lab measurements of a gene's site-specific amino-acid preferences. This null model makes it possible to identify diversifying selection for amino-acid change and differential selection for mutations to amino acids that are unexpected given the measurements made in the lab. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than a comparable method that simply compares the rates of nonsynonymous and synonymous substitutions. As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual sites, approaches like the one here can improve our ability to identify many interesting forms of selection.