Functional Divergence after Gene Duplication: Statistical Modeling

Many organisms have undergone genome-wide or local chromosome duplication events during their evolution. As a result, many genes are represented as several paralogs in the genome with related but distinct functions (gene families). Since gene duplication is thought to have provided the raw materials for functional innovations, it is desirable to identify amino acid sites that are responsible for functional divergence from the sequence analysis of a gene family. A series of statistical models has been developed, based on the principle that functional divergence between duplicate genes is...

Many organisms have undergone genome-wide or local chromosome duplication events during their evolution. As a result, many genes are represented as several paralogs in the genome with related but distinct functions (gene families). Since gene duplication is thought to have provided the raw materials for functional innovations, it is desirable to identify amino acid sites that are responsible for functional divergence from the sequence analysis of a gene family. A series of statistical models has been developed, based on the principle that functional divergence between duplicate genes is highly correlated with the change of evolutionary rate after the gene duplication. This chapter discusses these statistical and computational methods. These include the Poisson-gamma model for protein sequence evolution, the Markov chain model for type-I functional divergence, and statistical methods for type-II functional divergence.