Abstract
BackgroundCommon 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.MethodsWe 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.ResultsIn 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 β-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.ConclusionsOur 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.
Highlights
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
A growing proportion of the world population suffers from metabolic diseases, including type 2 diabetes (T2D), coronary artery disease (CAD), and hypertension (HT), many of which co-occur with obesity [1]
The overall level of association of each variant group was inferred by estimating the cross-validation prediction score of disease status (80% of sample individuals were used for inference and prediction was assessed for 20% of individuals) represented by the area under the curve (AUC) of the receiver operating characteristic
Summary
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. Deficient insulin secretion by pancreatic β-cells and, to a lesser extent, insulin resistance in peripheral tissues with the resulting burden on normal β-cell function, underlie T2D pathogenesis, which has strong genetic risk factors [2,3,4]. Cardiovascular diseases, such as CAD and HT, have significant genetic risk components [5]. High blood pressure (or HT) is believed to be closely related to abnormalities in renal salt excretion and vascular tone, affecting body fluid volume and resistance to blood flow, respectively [7]
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