Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks

Oded Magger, Yedael Y. Waldman, Eytan Ruppin, Roded Sharan

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article numbere1002690
JournalPLoS Computational Biology
Volume8
Issue number9
DOIs
StatePublished - Sep 2012

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