Background#R##N#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.
['Michael R. Mehan', 'Juan Nunez-Iglesias', 'Chao Dai', 'Michael S. Waterman', 'Xianghong Jasmine Zhou']
An integrative modular approach to systematically predict gene-phenotype associations