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

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. Identifying the genes causing genetic disease is a key challenge in human health, and a crucial step on the road for developing novel diagnostics and treatments. Modern discovery methods involve genome-wide association studies that reveal regions of the genome where the causal gene is likely to reside, and then prioritizing the candidate genes within these regions and experimentally examining the most promising candidates' potential influence on the disease. Many computational methods were developed to automatically prioritize candidate genes. Some of the most successful methods use a biological network of interacting genes or proteins as an input. However, these networks – and subsequently, these methods – do not take into account the differences between tissues. In other words, a heart disease is analyzed using the same network as a skin disease. We constructed tissue-specific protein interaction networks and explored their effect on an existing prioritization algorithm by comparing the algorithm's performance on the tissue-specific networks and the generic network. We find that integrating tissue-specific data indeed leads to better prioritization. We also used the prioritization results of different tissues in order to suggest new disease-tissue associations.