SRA

Characterizing the evolutionary history of a gene's expression profile is a critical component for understanding the relationship between genotype, expression, and phenotype. However, it is not well-established how best to distinguish the different evolutionary forces acting on gene expression. Here, we use RNA-seq across 7 tissues from 17 mammalian species to show that expression evolution across mammals is accurately modeled by the Ornstein-Uhlenbeck (OU) process. This stochastic process models expression trajectories across time as Gaussian distributions whose variance is parameterized by the rate of genetic drift and strength of stabilizing selection. We use these mathematical properties to identify expression pathways under neutral, stabilizing, and directional selection, quantify the extent of selective pressure on a gene's expression, and detect deleterious expression levels outside expected evolutionary distributions in single patients. Our work provides a statistical framework for interpreting expression data across species and in disease. Overall design: RNA-sequencing of 7 tissues (brain, heart, kidney, liver, lung, skeletal muscle, testis) from rabbit, dog, ferret, cow, armadillo, and opposum