title: Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks.
abstract: The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.
authors.affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. OdedMagger@gmail.com
authors.forename: Oded
authors.initials: O
authors.lastname: Magger
authors.forename: Yedael Y
authors.initials: YY
authors.lastname: Waldman
authors.forename: Eytan
authors.initials: E
authors.lastname: Ruppin
authors.forename: Roded
authors.initials: R
authors.lastname: Sharan
mesh_terms: D003198:Computer Simulation; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000235:genetics* / Q000378:metabolism*; D015398:Signal Transduction / Q000235:genetics*; D014018:Tissue Distribution
chemical_list: D020543:Proteome