Enriching human interactome with functional mutations to detect high-impact network modules underlying complex diseases

Abstract

Progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, DIMSUM, which enables the integration of genome-wide association studies (GWAS), functional effects of mutations, and protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for disease module analysis, facilitating discovery of new disease markers.Unlike the driver mutations, which induce the clonal expansion, the passenger mutations do not provide any functional advantage to the development of cancer cells [9,10]. Thus, distinguishing between the functional and non-functional mutations is usually the first step in genetics studies. Additionally, computational approaches for functional annotation are increasingly important, since many variants are not previously described in the literature [6,11]. There are a plethora of functional annotation tools for genetic variants [6]. Many tools focus on the annotation of single nucleotide variants (SNVs), the most common type of genetic variation, which is easier to capture and analyze. Recently, we have developed a new computational method, SNP-IN tool [12], which predicts the effects of non-synonymous SNVs on protein-protein interaction (PPI), provided the interaction's structure or structural model. The method leverages supervised and semi-supervised feature-based classifiers, including a new random forest self-learning protocol. The accurate and balanced performance of SNP-IN tool makes it apt for functional annotation of non-synonymous single nucleotide polymorphisms (nsSNPs). SNP-IN tool could also be helpful system-wide variation analysis to discover pathways shared by associated alleles and reveal disease-related biological process.The need of integrating GWAS studies and the functional impact of the disease-associated mutations with the systems data is supported by the increasing body of evidence that large-scal...

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