An integrative modular approach to systematically predict gene-phenotype associations

Background: Complex human diseases are often caused by multiple mutations, each of which contributes only a minor effect to the disease phenotype. To study the basis for these complex phenotypes, we developed a network-based approach to identify coexpression modules specifically activated in particular phenotypes. We integrated these modules, protein-protein interaction data, Gene Ontology annotations, and our database of gene-phenotype associations derived from literature to predict novel human gene-phenotype associations. Our systematic predictions provide us with the opportunity to perform a global analysis of human gene pleiotropy and its underlying regulatory mechanisms. Results: We applied this method to 338 microarray datasets, covering 178 phenotype classes, and identified 193,145 phenotype-specific coexpression modules. We trained random forest classifiers for each phenotype and predicted a total of 6,558 gene-phenotype associations. We showed that 40.9% genes are pleiotropic...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Michael R. Mehan, Juan Nunez-Iglesias, Chao Dai, Michael S. Waterman, Xianghong Jasmine Zhou
• Keywords: BMCBI 2010, Coexpression Modules, Gene-phenotype Associations, Phenotype