Abstract
A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE's predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.
Highlights
Associating genes with diseases is a fundamental challenge in human health with applications to understanding disease mechanisms, diagnosis and therapy
When no causal genes are known, the prioritization is done by exploiting the modular view described above, comparing a candidate gene to other genes that were implicated in similar diseases
PRINCE is a powerful method for prioritizing genes and protein complexes for a disease of interest
Summary
Associating genes with diseases is a fundamental challenge in human health with applications to understanding disease mechanisms, diagnosis and therapy. Linkage studies are often used to infer genomic intervals that are associated with a disease of interest Prioritizing genes within these intervals is a formidable challenge and computational approaches are becoming the method of choice for such problems. When one or more genes were already implicated in a given disease, the prioritization task is often handled by computing the functional similarity between a given gene and the known disease genes. Such a similarity can be based on sequence [1], functional annotation [2], protein-protein interactions [3,4] and more (see [5] for a comprehensive review of these methods). When no causal genes are known, the prioritization is done by exploiting the modular view described above, comparing a candidate gene to other genes that were implicated in similar diseases
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