Genetic interaction effects reveal lipid-metabolic and inflammatory pathways underlying common metabolic disease risks.
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BACKGROUND Common metabolic diseases including type 2 diabetes coronary artery disease and hypertension arise from disruptions of the body s metabolic homeostasis with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity Although genome wide association studies have revealed many genomic loci robustly associated with these diseases biological interpretation of such association is challenging because of the difficulty in mapping single nucleotide polymorphisms SNPs onto the underlying causal genes and pathways Furthermore common diseases are typically highly polygenic and conventional single variant based association testing does not adequately capture potentially important large scale interaction effects between multiple genetic factors METHODS We analyzed moderately sized case control data sets for type 2 diabetes coronary artery disease and hypertension to characterize the genetic risk factors arising from non additive collective interaction effects using a recently developed algorithm discrete discriminant analysis We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group RESULTS In contrast to non interacting SNP mapping which produced few genome wide significant loci our analysis revealed extensive arrays of pathways many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies They comprised cell stress and apoptotic pathways for insulin producing cells in type 2 diabetes processes covering different atherosclerotic stages in coronary artery disease and elements of both type 2 diabetes and coronary artery disease risk factors cell cycle apoptosis and hemostasis associated with hypertension CONCLUSIONS Our results support the view that non additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs