Dissecting Genomic Determinants of Positive Selection with an Evolution-Guided Regression Model.

ABSTRACT: In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as gene expression level, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald-Kreitman (MK) test, are available to examine the association between genomic features and the rate of adaptation, we currently lack a statistical approach to disentangle the independent effect of a genomic feature from the effects of other correlated genomic features. To address this problem, I present a novel statistical model, the MK regression, which augments the MK test with a generalized linear model. Analogous to the classical multiple regression model, the MK regression can analyze multiple genomic features simultaneously to infer the independen

SUBMITTER: Huang YF 

PROVIDER: S-EPMC8763110 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature