Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases.

ABSTRACT: Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the u

SUBMITTER: Mezlini AM 

PROVIDER: S-EPMC5638204 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature