Identifying and exploiting gene-pathway interactions from RNA-seq data for binary phenotype.

ABSTRACT:

Background

RNA sequencing (RNA-seq) technology has identified multiple differentially expressed (DE) genes associated to complex disease, however, these genes only explain a modest part of variance. Omnigenic model assumes that disease may be driven by genes with indirect relevance to disease and be propagated by functional pathways. Here, we focus on identifying the interactions between the external genes and functional pathways, referring to gene-pathway interactions (GPIs). Specifically, relying on the relationship between the garrote kernel machine (GKM) and variance component test and permutations for the empirical distributions of score statistics, we propose an efficient analysis procedure as Permutation based gEne-pAthway interaction identification in binary phenotype (PEA)

SUBMITTER: Shao F 

PROVIDER: S-EPMC6423879 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature