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. 2018 Jun 20;11(1):54.
doi: 10.1186/s12920-018-0373-7.

Genetic Interaction Effects Reveal Lipid-Metabolic and Inflammatory Pathways Underlying Common Metabolic Disease Risks

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Free PMC article

Genetic Interaction Effects Reveal Lipid-Metabolic and Inflammatory Pathways Underlying Common Metabolic Disease Risks

Hyung Jun Woo et al. BMC Med Genomics. .
Free PMC article

Abstract

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 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.

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.

Keywords: Coronary artery disease; Epistasis; Genome-wide association studies; Hypertension; Metabolic syndrome; Type 2 diabetes.

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The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Association test results for type 2 diabetes. Dotted horizontal lines represent the Bonferroni threshold. a Non-interacting single-nucleotide polymorphisms (SNPs). b Pathways with (collective loci inference; CL) and without interaction effects (independent loci; IL) with varying numbers of SNPs in each group, scored with the cross-validation prediction measure, the area under the curve (AUC). Red symbols represent those with a false discovery rate (FDR) below 0.05. c Regression of p-values against the AUC (dotted line: fit to quadratic orthogonal polynomial). d, e Top-ranked pathways organized into the Reactome hierarchy (dendrograms). activ., activates/activation/activated; biosynth., biosynthesis; cont., containing; cytoch., cytochrome; deactiv., deactivation; degrad., degradation; dep., dependent; downstr., downstream; DSB, double-strand break; elong., elongation; eukar., eukaryotic; expr., expression; FGFR, fibroblast growth factor R.; form., formation; GLP, glucagon-like peptide; GPCR, G protein-coupled R.; HDR, homology-directed repair; HR, homologous recombination; HRR, HR repair; hydrol., hydrolysis; inactiv., inactivation; ind., induced; init., initiation; inter., interaction; K+, potassium; med., mediated; metab., metabolism; mito., mitochondria; path., pathway; PCD, programmed cell death; phosphoryl., phosphorylation; pol, polymerase; proc., processing; prot., protein; R., receptor; recog., recognition; reg., regulates; repl., replicative; repress., repression; resp., response; SALM, synaptic adhesion-like molecule; secret., secretion; sig., signaling; sol., soluble; SSA, single-strand annealing; synth., synthesis; term., termination; thru, through; transcr., transcription; transl., translation; transloc., translocation; transp., transport; UPR, unfolded protein response
Fig. 2
Fig. 2
Comparison of inference scores and p-values of all pathways in association with T2D (ac), CAD (df), and HT (gi). The left column compares the DDA score in AUC without (IL) and with interaction effects (CL). The middle and right columns compare p-values of pathways from Pascal (mean p-value option) with those from DDA CL and IL, respectively. Dotted lines represent the Bonferroni-corrected thresholds
Fig. 3
Fig. 3
Gene-based scan results under DDA CL for T2D (a), CAD (b), and HT (c). The horizontal lines show the Bonferroni-corrected threshold and the black symbols the genes with an estimated FDR below 0.05
Fig. 4
Fig. 4
Quantile-quantile plots of independent-SNP and gene-based scores for T2D (a), CAD (b), and HT (c). The horizontal lines represent the Bonferroni-corrected thresholds
Fig. 5
Fig. 5
Association test results for coronary artery disease. Horizontal lines represent the Bonferroni threshold. a Non-interacting SNPs. b Pathways with (CL) and without (IL) interaction effects. Red symbols indicate pathways with an FDR below 0.05. c Regression of p-values against the AUC. df Top-ranked pathways with AUC > 0.53. Ag., antigen; biogen., biogenesis; biol., biology; cell., cellular; comm., communication; conv., conversion; devel., developmental; ECM, extracellular matrix; ER, endoplasmic reticulum; FA, fatty acid; IFN, interferon; IL, interleukin; maint., maintenance; MHC, major histocompatibility complex; NCAM, neural cell adhesion molecule; NMDAR, n-methyl d-aspartate receptor; ox., oxidative; PPARA, peroxisome proliferator-activated receptor alpha; present., presentation; rxn., reaction; SCF, stem cell factor; surf., surface; TAR, triacylglyceride; TF, transcription factor; TLR, Toll-like receptors; transl., translation; VEGF, vascular endothelial growth factor
Fig. 6
Fig. 6
Association test results for hypertension. a Non-interacting SNPs. b Pathways with (CL) and without (IL) interaction effects. Red symbols indicate pathways with an FDR below 0.05. c Regression of p-values against the AUC. d, e Top-ranked pathways with AUC > 0.53. 5-HETE, 5-hydroxy-eicosatetraenoic acid; AMPK, AMP-activated kinase; assoc., association; checkpt., checkpoint; conv., conversion; COPII, coat protein 2; degranul., degranulation; downreg., downregulation; EGFR, epidermal growth factor receptor; fragment., fragmentation; func., function; GABA, gamma-aminobutyric acid; GIP, glucose-dependent insulinotropic polypeptide; indep., independent; inhib., inhibition; mod., modification; mTOR, target of rapamycin; org., organization; presynap., presynaptic; proteol., proteolytic; recomb., recombination; reorg., reorganization; transactiv., transactivation; TRiC/CCT, TCP1-ring complex or chaperonin containing TCP1
Fig. 7
Fig. 7
Association test results for type 2 diabetes in mice. a Non-interacting SNPs. b Distribution of pathway scores with interaction effects. c Regression of p-values against the AUC. d Top-ranked pathways with AUC > 0.63. Aggreg., aggregation; GPVI, glycoprotein VI; induct., induction; MDA5, melanoma differentiation-associated protein 5; mitoch., mitochondrial; RIG-I, retinoic acid-inducible gene I

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References

    1. O'Rahilly S. Human genetics illuminates the paths to metabolic disease. Nature. 2009;462(7271):307–314. doi: 10.1038/nature08532. - DOI - PubMed
    1. Rhodes CJ. Type 2 diabetes-a matter of beta-cell life and death? Science. 2005;307(5708):380–384. doi: 10.1126/science.1104345. - DOI - PubMed
    1. Ashcroft FM, Rorsman P. Diabetes mellitus and the beta cell: the last ten years. Cell. 2012;148(6):1160–1171. doi: 10.1016/j.cell.2012.02.010. - DOI - PMC - PubMed
    1. Rorsman P, Braun M. Regulation of insulin secretion in human pancreatic islets. Annu Rev Physiol. 2013;75:155–179. doi: 10.1146/annurev-physiol-030212-183754. - DOI - PubMed
    1. Kathiresan S, Srivastava D. Genetics of human cardiovascular disease. Cell. 2012;148(6):1242–1257. doi: 10.1016/j.cell.2012.03.001. - DOI - PMC - PubMed

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