Abstract
Much effort has been devoted to assess disease risk based on large-scale protein-protein network and genotype-phenotype associations. However, the challenge of risk prediction for complex diseases remains unaddressed. Here, we propose a framework to quantify the risk based on a Voronoi tessellation network analysis, taking into account the disease association scores of both genes and variants. By integrating ClinVar, SNPnexus, and DISEASES databases, we introduce a gene-variant map that is based on the pairwise disease-associated gene-variant scores. This map is clustered using Voronoi tessellation and network analysis with a threshold obtained from fitting the background Voronoi cell density distribution. We define the relative risk of disease that is inferred from the scores of the data points within the related clusters on the gene-variant map. We identify autoimmune-associated clusters that may interact at the system-level. The proposed framework can be used to determine the clusters that are specific to a subtype or contribute to multiple subtypes of complex diseases.
Highlights
Rapid advances in exome sequencing technology combined with the development of novel genomic annotation approaches in the last two decades have provided important vistas for assessing the risk of complex disorders (McCarthy et al, 2008; Majewski and Pastinen, 2011)
genome-wide association studies (GWAS) studies, which mainly focused on common variants, suffer from the shortcomings of missing causal rare variants with low allele frequencies and moderate effects in complex diseases (Bodmer and Bonilla, 2008; Mitchell, 2012)
The method of Voronoi tessellation proposed here does not apply any filtering on the frequency of variants available in the databases, and includes both common and rare variants in the score-based clustering
Summary
Rapid advances in exome sequencing technology combined with the development of novel genomic annotation approaches in the last two decades have provided important vistas for assessing the risk of complex disorders (McCarthy et al, 2008; Majewski and Pastinen, 2011). Such databases include Kyoto Encyclopaedia of Genes and Genome Elements (KEGG) (Ogata et al, 1999; Kanehisa and Goto, 2000) and Gene Ontology (GO) (Ashburner et al, 2000) These approaches mainly deal with network analysis at the gene level without considering the disease-associated scores of variants in clustering or risk assessment. In an attempt to develop such an approach, we integrated databases of disease-associated genes and variants scores and applied the well-established method of Voronoi tessellation in the Euclidean coordinate for clustering and network analysis (Ebeling and Wiedenmann, 1993; Ramella et al, 2001; Edla and Jana, 2011). Building on the previous literature, in this study, we propose a framework to quantitatively infer disease risk based on the clusters identified by Voronoi tessellation and network analysis of a score-based gene-variant map
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