Causal inference of gene regulation with subnetwork assembly from genetical genomics data.

ABSTRACT: Deciphering the causal networks of gene interactions is critical for identifying disease pathways and disease-causing genes. We introduce a method to reconstruct causal networks based on exploring phenotype-specific modules in the human interactome and including the expression quantitative trait loci (eQTLs) that underlie the joint expression variation of each module. Closely associated eQTLs help anchor the orientation of the network. To overcome the inherent computational complexity of causal network reconstruction, we first deduce the local causality of individual subnetworks using the selected eQTLs and module transcripts. These subnetworks are then integrated to infer a global causal network using a random-field ranking method, which was motivated by animal sociology. We demonstrate h

SUBMITTER: Peng CH 

PROVIDER: S-EPMC3950678 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature