Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation.

ABSTRACT: Recent studies have analyzed large-scale data sets of gene expression to identify genes associated with interindividual variation in phenotypes ranging from cancer subtypes to drug sensitivity, promising new avenues of research in personalized medicine. However, gene expression data alone is limited in its ability to reveal cis-regulatory mechanisms underlying phenotypic differences. In this study, we develop a new probabilistic model, called pGENMi, that integrates multi-omic data to investigate the transcriptional regulatory mechanisms underlying interindividual variation of a specific phenotype-that of cell line response to cytotoxic treatment. In particular, pGENMi simultaneously analyzes genotype, DNA methylation, gene expression, and transcription factor (TF)-DNA binding data,

SUBMITTER: Hanson C 

PROVIDER: S-EPMC6071639 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature