TY - JOUR T1 - Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks A1 - Magger, Oded A1 - Waldman, Yedael Y. A1 - Ruppin, Eytan A1 - Sharan, Roded Y1 - 2012/09/27 N2 - Author Summary 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. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 8 IS - 9 UR - https://doi.org/10.1371/journal.pcbi.1002690 SP - e1002690 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002690 ER -