Complex Disease Networks of Trait-­‐Associated SNPs Unveiled by Information Theory

Objective: Thousands of complex disease SNPs have been discovered in Genome Wide Association Studies (GWAS). However, these intragenic SNPs have not been collectively mined to unveil the genetic architecture between complex clinical traits. We hypothesize that biological annotations of host genes of trait-associated SNPs may reveal the biomolecular modularity across complex disease traits and offer insights for drug repositioning. Methods: In this study, we used trait-to-polymorphism (SNPs) associations confirmed in GWAS. We developed a novel method to quantify trait-trait similarity anchored in Gene Ontology annotations of human proteins and information theory. We then validated these results with the shortest paths of physical protein interactions between biologically similar traits. Results: We constructed a network consisting of 280 significant intertrait similarities among 177 disease traits, which covered 1,438 well-validated disease-associated SNPs. 39% of intertrait connections were confirmed by curators and the following additional studies demonstrated the validity of a proportion of the remainder. On a phenotypic trait level, higher Gene Ontology similarity between proteins correlated with smaller "shortest distance" in protein interaction networks of complexly inherited diseases (Spearman p<2.2x10-16). Further, "cancer traits" were similar to one another, as were "metabolic syndrome traits"(FET p=0.001 and 3.5x10-7). Conclusion: We report an imputed disease network by information-anchored functional similarity from GWAS trait-associated SNPs. We also demonstrate that small shortest path of protein interactions correlates with complex disease function. Taken together, these findings provide the framework for investigating drug targets with unbiased functional biomolecular networks rather than worn-out single gene and subjective canonical pathway approaches.