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.
Highlights
A fundamental challenge in human health is elucidating the molecular basis of hereditary diseases
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
Focusing on the cases where the disease-related gene is expressed in the associated tissue, we show that integrating tissue specific expression information into a gene prioritization scheme markedly improves its prediction accuracy
Summary
A fundamental challenge in human health is elucidating the molecular basis of hereditary diseases. Contemporary methods for discovering disease-causing genes usually consist of two steps: first, genome-wide association studies identify genomic intervals that are linked to a disease of interest. A plethora of computational methods were developed to meet this challenge. These methods are often based on system-wide data such as protein interaction networks [4,5,6,7,8,9], gene expression [8,10,11,12], sequence similarity of genes [13,14], functional similarity and annotation [8,12,13] and more (for a review on these methods see [15,16])
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