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• W2110994243 abstract "HomeCirculation: Cardiovascular GeneticsVol. 5, No. 6Large-Scale Association Analysis Provides Insights Into the Genetic Architecture and Pathophysiology of Type 2 Diabetes Mellitus Free AccessResearch ArticlePDF/EPUBAboutView PDFSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toFree AccessResearch ArticlePDF/EPUBLarge-Scale Association Analysis Provides Insights Into the Genetic Architecture and Pathophysiology of Type 2 Diabetes Mellitus Nehal N. MehtaMD, MSCE Nehal N. MehtaNehal N. Mehta From the Early Career Committee of the American Heart Association Functional Genomics and Translational Biology Council. Search for more papers by this author Originally published1 Dec 2012https://doi.org/10.1161/CIRCGENETICS.112.965350Circulation: Cardiovascular Genetics. 2012;5:708–710Study HypothesisWe are amidst an epidemic of cardiometabolic diseases. Despite aggressive lifestyle modifications, the incidence of type 2 diabetes mellitus (T2DM), a potent risk factor for cardiovascular diseases, continues to rise. Simple family histories in patients demonstrate that there is a clustering of the disease in families, and prior studies using genome-wide association (GWA) approaches have identified 56 genetic loci that are associated with T2DM. In a recently published herculean effort, the authors stated that prior GWA study results captured 10% of familial aggregation of the disease. Therefore, to extend understanding of the genetic architecture and molecular basis of T2DM, the authors conducted a meta-analysis (34 840 cases and 114 981 controls) of genetic variants on the Metabochip, a custom array of 196 725 variants designed to facilitate cost-effective follow-up of nominal associations for T2DM, and other metabolic and cardiovascular traits and to enhance fine mapping of established loci. The authors asked whether characterization of increasing numbers of risk loci may provide evidence, at the functional level, that susceptibility to T2DM involves a limited set of molecular processes.How Was the Hypothesis Tested?The authors conducted a 2-stage meta-analysis: stage-1 meta-analysis consisted of 12 171 T2DM cases and 56 862 controls across 12 GWA studies of individuals of European descent, and the stage-2 meta-analysis consisted of 21 491 T2DM cases and 55 647 controls across 25 studies of individuals of European descent, and 1178 T2DM cases and 2472 controls from 1 study of individuals of Pakistani descent (PROMIS). These participants all underwent genotyping on the Metabochip, in which the T2DM component was comprised of 21 774 variants: 5057 replication single-nucleotide polymorphisms (SNPs) that capture the strongest independent 13 autosomal association signals from the GWA meta-analysis conducted by the Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, and an additional 16 717 variants chosen from 1000 Genomes Project pilot data to fine-map 27 established susceptibility loci.The results of the stage-1 and stage-2 meta-analyses were combined for all Metabochip SNPs via fixed-effects inverse variance–weighted meta-analysis (34 840 cases and 114 981 controls) and was performed in 2 steps: (1) stage-1 meta-analysis with individuals of European descent was combined with stage-2 meta-analysis, and (2) the PROMIS study was added. After this, the stage-1, stage-2, and combined meta-analyses were repeated for men and women separately, with correction for population structure within each sex, and the authors conducted a sex-differentiated test of association and a test of heterogeneity in allelic effects.To better understand their findings, the authors next performed a series of analyses to better understand the loci: (1) physiological analyses of their findings with relationship to body mass index, waist-to-hip ratio, and insulin-resistance estimates; (2) expression analyses by interrogating public databases and unpublished resources for cis-expression quantitative trait locus expression with these SNPs in multiple tissues, along with examining 603 subcutaneous adipose tissue samples and 745 peripheral blood samples in a targeted population; and (3) pathway analysis using combined meta-analysis data with protein–protein interactions, semantic relationships within the published literature, and annotated pathways.Principal FindingsT2DM-susceptibility loci reaching genome-wide significance: Combining stage-1 and stage-2 meta-analyses, the authors identified 8 new T2DM-susceptibility loci at genome-wide significance (P<5×10−8). The strongest signals mapped to ZMIZ1 (P=1.0×10−10), ANK1 (P=2.5×10−10), KLHDC5 (P=6.1×10−10), HMG20A (P=4.6×10−9), and GRB14 (P=1.0×10−8). The lead SNPs from both meta-analyses were in strong linkage disequilibrium (HMG20A r2=0.89 and GRB14 r2=0.77 in European [CEU]) and likely represented the same association signals.Several of these signals mapped to loci previously implicated in T2DM-related metabolic traits. For example, the lead SNP at MC4R was in strong linkage disequilibrium with variants associated with body mass index (CEU r2=0.80) and triglyceride concentration (CEU r2=0.84) and has been associated with waist circumference and insulin resistance. The lead SNP at GRB14 was highly correlated with variants associated with waist-to-hip ratio and high-density lipoprotein cholesterol (CEU r2=0.93). At CILP2, the lead SNP for T2DM was also associated with triglyceride, low-density lipoprotein, and total cholesterol concentrations. In contrast, the previously reported association signals for hemoglobin A1C concentrations near ANK1 were both independent (CEU r2<0.01) of the lead T2DM-associated SNP from their meta-analysis suggesting that variation at this locus also has direct effects on glucose homeostasis. Finally, the authors demonstrated that by including their 8 new loci with assumed population prevalence for T2DM of 8%, the total 63 loci together account for 5.7% of variance in disease susceptibility. The authors performed additional models to determine the extent to which additional common variant associations contributed to the overall variance explained.Sex differentiated analyses: Next, the authors performed sex-differentiated meta-analysis to test for association of each SNP with T2DM, allowing for heterogeneity in allelic effects between men (20 219 cases, 54 604 controls) and women (14 621 cases, 60 377 controls). They identified 2 additional loci achieving genome-wide significance mapping near CCND2 in men (men P=1.1×10−9, women P=0.036; heterogeneity P=0.013) and upstream of GIPR in women (women P=2.2×10−7, men P=0.0037; heterogeneity P=0.057).Understanding the biology of T2DM-susceptibility loci: Next, the authors applied a variety of approaches to the newly discovered and established T2DM-susceptibility loci to identify mechanisms involved in disease pathogenesis. Using physiological analyses, they studied the effect of SNPs on glycemic traits using fasting glucose concentration in 133 010 non-T2DM individuals. In addition to the 9 loci previously reported (MTNR1B, DGKB, ADCY5, PROX1, GCK, GCKR, TCF7L2, SLC30A8, and C2CD4A), 4 more T2DM-association signals reached genome-wide significance for fasting glucose: CDKN2A–CDKN2B (P=5.7×10−18), ARAP1 (P=1.2×10−10), IGF2BP2 (P=1.8×10−8), and CDKAL1 (P=2.0×10−8). The ZBED3 locus also attained genome-wide significance with fasting glucose concentration, after adjustment for body mass index (P=1.2×10−8). In contrast, lead T2DM-associated SNPs at 27 of the newly discovered and established loci showed no evidence of association with fasting glucose (P>0.05). Finally, lead T2DM-associated SNPs at the remaining 24 loci were nominally associated with fasting glucose concentrations (P<0.05), suggesting that the genetic landscapes of pathological and physiological variation in glycemia are only partially overlapping. They then examined how homeostatic assessment insulin resistance (HOMA-IR), an estimation of insulin resistance, and homeostatic assessment beta-cell function (HOMA-B), an estimate of pancreatic β-cell function, were related to SNPs. ANK1 was nominally significant in reduction of HOMA-B, indicating a primary effect on β-cell function, whereas those at GRB14 and AKNRD55 increased HOMA-IR. The only lead SNP to show convincing evidence of association (P<1×10−5) with adiposity was at MC4R.Mapping potential causal transcripts and variants: As the most comprehensive effort to date, the authors embarked on various approaches to understand functional variants. To identify promising regional transcripts, they examined expression quantitative trait locus data from a variety of tissues. At 6 of the newly discovered loci, the lead T2DM-associated SNP showed strong cis-expression quantitative trait locus associations and was highly correlated (CEU r2>0.8) with the lead cis-expression quantitative trait locus SNP that implicated GRB14 (omental fat), ANK1 (omental and subcutaneous fat, liver, and prefrontal cortex), KLHDC5 (blood, T–cells, and CD4+ lymphocytes), BCAR1 (blood), ATP13A1 (at the CILP2 locus; blood and monocytes), HMG20A (liver), and LINGO1 (also at the HMG20A locus; adipose tissue).Finally, to extend previous efforts to define pathways and networks involved in T2DM pathogenesis, they combined meta-analysis data with protein–protein interactions, semantic relationships within the published literature, and annotated pathways. All direct interactions and common interactors between direct connections were extracted from the larger network of 314 proteins defined in an established network analysis of 77 selected transcripts mapping nearest to lead SNPs at T2DM-susceptibility loci or implicated in monogenic diabetes. They detected an excess of physical interactions in the network, both direct and indirect. The transcriptional coactivator protein CREBBP, implicated in the coupling of chromatin remodeling to transcription factor recognition, did not map to any T2DM-susceptibility locus. However, it was the most connected gene for protein-level interactions (P<0.005) in the protein–protein interaction network, interacting with 9 primary transcripts, 8 implicated in monogenic diabetes, or mapping to established T2DM-susceptibility loci (HNF1A, HNF1B, HNF4A, PLAGL1, TCF7L2, PPARG, PROX1, and NOTCH2), suggesting that modulation of CREBBP-binding transcription factors has an important role in T2DM susceptibility. When the authors used this set of 77 genes as a seed to query a list of 77 secondary transcripts, they found significant connections between the primary associated transcripts and 4 other genes: LEPR (leptin obesity pathways), MYC (cell cycle pathway), GATA6 (pancreas development pathway), and DLL4 (Notch signaling target).They tested for enrichment of GWA-associated transcripts in pathway data in 16 biological hypotheses chosen for assumed relevance to T2DM pathogenesis, and 2 showed reproducible enrichment of T2DM associations. The strongest enrichment was observed for a broader set of primary and secondary transcripts mapping to T2DM-associated loci in the adipocytokine signaling that includes the adiponectin, leptin, and tumor necrosis factor-α signaling. This analysis highlighted 8 genes in this pathway most likely to be the cause for T2DM susceptibility: IRS1, LEPR, RELA, RXRG, ACSL1, NFKB1, CAMKK1, and a monogenic diabetes gene, AKT2. Lastly, they found modest but robust enrichment observed for genes influencing cell cycle, in particular, regulators of the G1 phase, such as cyclin-dependent kinase inhibitors (CDKN2A-CDKN2B, CDKN1C, and CDKN2C) and cyclins that activate cyclin-dependent kinases (CCNE2, CCND2, and CCNA2). Many of these regulate CDK4 or CDK6, which are known to have a role in pancreatic β-cell proliferation. They did not observe evidence of enrichment for other processes implicated in T2DM pathogenesis, including amyloid formation, endoplasmic reticulum stress, and insulin signaling.ImplicationsThese findings further our understanding of the potential genetic basis of T2DM in >150 000 individuals by adding another 10 loci to the list of confirmed common variant signals. These data support the notion of a large number of causal variants of modest effect impacting susceptibility to T2DM moreso than the contribution of rare and low-frequency risk variants. This study elegantly demonstrates how using clinical research, several large datasets along with simultaneous application of in silico, biomarker and genomic technologies can effectively advance steps to underpinning biology. Utilization of outcomes related to cardiometabolic diseases, such as insulin resistance, lipids, and anthropometrics, support observations within the epidemiology of T2DM, metabolic syndrome, and obesity. Variants biologically expected to be involved in the pathogenesis of T2DM, such as those involved in cell cycle regulation and adipocytokine signaling, not only confirm the established reports in these areas, but also demonstrate the critical need to perform targeted, tissue-specific experiments to further delineate mechanisms of these associations. However, notably missing were variants related to inflammation. This finding may reject the hypothesis that inflammation is causal in these disease states, despite recent evidence demonstrating evoked inflammation resembling a diabetes mellitus-like state1 and inflammatory diseases such as psoriasis being associated with T2DM.2 Indeed, ongoing resequencing studies will address the contribution of rare and low-frequency variants, and it will be important to determine whether further discovered loci, in fact, coalesce around a limited set of core pathways and networks.AcknowledgmentsThe author is a member of the Early Career Committee of the American Heart Association Functional Genomics and Translational Biology Council.DisclosuresDr Mehta is an employee of the National Heart, Lung and Blood Institute.FootnotesCorrespondence to Nehal N. Mehta, MD, MSCE, National Heart, Lung and Blood Institute, 10 Center Drive, Bethesda, MD. E-mail [email protected]REFERENCE1. Mehta NN, Heffron SP, Patel PN, Ferguson J, Shah RD, Hinkle CC, , et al.. A human model of inflammatory cardio-metabolic dysfunction; a double blind placebo-controlled crossover trial.J Transl Med. 2012; 10:124.CrossrefMedlineGoogle Scholar2. Armstrong AW, Harskamp CT, Armstrong EJ. Psoriasis and the risk of diabetes mellitus: a systematic review and meta-analysis.Arch Dermatol. 2012;Epub ahead of print:1–8.Google Scholar Previous Back to top Next FiguresReferencesRelatedDetails December 2012Vol 5, Issue 6Article InformationMetrics Download: 146 © 2012 American Heart Association, Inc.https://doi.org/10.1161/CIRCGENETICS.112.965350 Originally publishedDecember 1, 2012 Keywordsgenomicsdiabetes mellitusPDF download SubjectsDiabetes, Type 2" @default.
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