Learning gene networks underlying clinical phenotypes using SNP perturbation.

ABSTRACT: Availability of genome sequence, molecular, and clinical phenotype data for large patient cohorts generated by recent technological advances provides an opportunity to dissect the genetic architecture of complex diseases at system level. However, previous analyses of such data have largely focused on the co-localization of SNPs associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus mapping. Thus, their description of the molecular mechanisms behind the SNPs influencing clinical phenotypes was limited to the single gene linked to the co-localized SNP. Here we introduce PerturbNet, a statistical framework for learning gene networks that modulate the influence of genetic variants on phenotypes, using genetic

SUBMITTER: McCarter C 

PROVIDER: S-EPMC7584257 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature