Statistical methods for detecting molecular adaptation

TLDR

Recent statistical methods compare synonymous and nonsynonymous substitution rates to detect adaptive evolution, revealing positive selection at individual sites and lineages across diverse organisms. The study reviews recent statistical methods for detecting molecular adaptation and evaluates their limitations and potential improvements. The authors compile and analyze recent statistical techniques that assess substitution rate differences to identify adaptive evolution.

Abstract

The past few years have seen the development of powerful statistical methods for detecting adaptive molecular evolution. These methods compare synonymous and nonsynonymous substitution rates in protein-coding genes, and regard a nonsynonymous rate elevated above the synonymous rate as evidence for darwinian selection. Numerous cases of molecular adaptation are being identified in various systems from viruses to humans. Although previous analyses averaging rates over sites and time have little power, recent methods designed to detect positive selection at individual sites and lineages have been successful. Here, we summarize recent statistical methods for detecting molecular adaptation, and discuss their limitations and possible improvements.