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A major challenge in biomedical data science is to identify the causal genes underlying complex genetic diseases. Despite the massive influx of genome sequencing data, identifying disease-relevant genes remains difficult as individuals with the same disease may share very few, if any, genetic variants. Protein-protein interaction networks provide a means to tackle this heterogeneity, as genes causing the same disease tend to be proximal within networks. Previously, network propagation approaches have spread signal across the network from either known disease genes or genes that are newly putatively implicated in the disease (e.g., found to be mutated in exome studies or linked via genome-wide association studies). Here we introduce a general framework that considers both sources of data within a network context. Specifically, we use prior knowledge of disease-associated genes to guide random walks initiated from genes that are newly identified as perhaps disease-relevant. In large-scale testing across 24 cancer types, we demonstrate that our approach for integrating both prior and new information not only better identifies cancer driver genes than using either source of information alone but also readily outperforms other state-of-the-art network-based approaches. To demonstrate the versatility of our approach, we also apply it to genome-wide association data to identify genes functionally relevant for several complex diseases. Overall, our work suggests that guided network propagation approaches that utilize both prior and new data are a powerful means to identify disease genes.

A Guided Network Propagation Approach to Identify Disease Genes that Combines Prior and New Information