Swedish Research Council, European Research Council, Vinnova, Academy of Finland, Novo Nordisk Foundation, Scania University Hospital, Sigrid Juselius Foundation, Innovative Medicines Initiative 2 Joint Undertaking, Vasa Hospital district, Jakobstadsnejden Heart Foundation, Folkhälsan Research Foundation, Ollqvist Foundation, and Swedish Foundation for Strategic Research.

To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS) including 26,488 cases and 83,964 controls of European, East Asian, South Asian, and Mexican and Mexican American ancestry. We observed significant excess in directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven novel T2D susceptibility loci. Furthermore, we observed considerable improvements in fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterisation of complex trait loci, and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.

Background Animal studies suggest that the arginine vasopressin (AVP) system may play a role in glucose metabolism, but data from humans are limited. Methods and Results We analysed plasma copeptin (copeptin), a stable C-terminal fragment of the AVP pro-hormone. Using baseline and longitudinal data from a Swedish population-based sample (n=4742, mean age 58 years, 60% women), we examined the association of increasing quartiles of copeptin (lowest quartile as reference) with prevalent diabetes at baseline, insulin resistance (top quartile of fasting plasma insulin among non-diabetic subjects), and incident diabetes on long-term follow up using multivariable logistic regression. New-onset diabetes was ascertained through 3 national and regional registers. All models were adjusted for clinical and anthropometric risk factors, cystatin C, and C-reactive protein. In cross-sectional analyses, increasing copeptin was associated with prevalent diabetes (P=0.04) and insulin resistance (P<0.001). During 12.6 years of follow up 174 subjects (4%) developed new-onset diabetes. The odds of developing diabetes increased across increasing quartiles of copeptin, even after additional adjustment for baseline fasting glucose and insulin (adjusted odds ratios 1.0, 1.37, 1.79, and 2.09; P for trend =0.004). The association with incident diabetes remained significant in analyses restricted to subjects with fasting whole blood glucose <5.4 mmol/L at baseline (adjusted odds ratios 1.0, 1.80, 1.92, and 3.48; P=0.001). Conclusions Elevated copeptin predicts increased risk for diabetes, independent of established clinical risk factors, including fasting glucose and insulin. These findings could have implications for risk assessment, novel anti-diabetic treatments, and metabolic side effects from AVP system modulation.

We performed fine-mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in/near KCNQ1. “Credible sets” of variants most likely to drive each distinct signal mapped predominantly to non-coding sequence, implying that T2D association is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine-mapping implicated rs10830963 as driving T2D association. We confirmed that this T2D-risk allele increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D-risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.

OBJECTIVE-Using the genome-wide association approach, we recently identified the glucokinase regulatory protein gene (GCKR, rs780094) region as a novel quantitative trait locus for plasma triglyceride concentration in Europeans. Here, we sought to study the association of GCKR variants with metabolic phenotypes, including measures of glucose homeostasis, to evaluate the GCKR locus in samples of non-European ancestry and to finemap across the associated genomic interval.RESEARCH DESIGN AND METHODS-We performed association studies in 12 independent cohorts comprising Ͼ45,000 individuals representing several ancestral groups (whites from Northern and Southern Europe, whites from the U.S., African Americans from the U.S., Hispanics of Caribbean origin, and Chinese, Malays, and Asian Indians from Singapore). We conducted genetic fine-mapping across the ϳ417-kb region of linkage disequilibrium spanning GCKR and 16 other genes on chromosome 2p23 by imputing untyped HapMap single nucleotide polymorphisms (SNPs) and genotyping 104 SNPs across the associated genomic interval.RESULTS-We provide comprehensive evidence that GCKR rs780094 is associated with opposite effects on fasting plasma triglyceride (P meta ϭ 3 ϫ 10 Ϫ56) and glucose (P meta ϭ 1 ϫ 10 Ϫ13 ) concentrations. In addition, we confirmed recent reports that the same SNP is associated with C-reactive protein (CRP) level (P ϭ 5 ϫ 10 Ϫ5). Both fine-mapping approaches revealed a common missense GCKR variant (rs1260326, Pro446Leu, 34% frequency, r 2 ϭ 0.93 with rs780094) as the strongest association signal in the region.CONCLUSIONS-These findings point to a molecular mechanism in humans by which higher triglycerides and CRP can be coupled with lower plasma glucose concentrations and position GCKR in central pathways regulating both hepatic triglyceride and glucose metabolism. Diabetes 57:3112-3121, 2008

OBJECTIVE—Latent autoimmune diabetes in adults (LADA) is often considered a slowly progressing subtype of type 1 diabetes, although the clinical picture more resembles type 2 diabetes. One way to improve classification is to study whether LADA shares genetic features with type 1 and/or type 2 diabetes. RESEARCH DESIGN AND METHODS—To accomplish this, we studied whether LADA shares variation in the HLA locus or INS VNTR and PTPN22 genes with type 1 diabetes or the TCF7L2 gene with type 2 diabetes in 361 LADA, 718 type 1 diabetic, and 1,676 type 2 diabetic patients, as well as 1,704 healthy control subjects from Sweden and Finland. RESULTS—LADA subjects showed, compared with type 2 diabetic patients, increased frequency of risk for the HLA-DQB1 *0201/*0302 genotype (27 vs. 6.9%; P &lt; 1 × 10−6), with similar frequency as with type 1 diabetes (36%). In addition, LADA subjects showed higher frequencies of protective HLA-DQB1 *0602(3)/X than type 1 diabetic patients (8.1 vs. 3.2%, P = 0.003). The AA genotype of rs689, referring to the class I allele in the INS VNTR, as well as the CT/TT genotypes of rs2476601 in the PTPN22 gene, were increased both in type 1 diabetic (P = 3 × 10−14 and P = 1 × 10−10, respectively) and LADA (P = 0.001 and P = 0.002) subjects compared with control subjects. Notably, the frequency of the type 2 diabetes–associated CT/TT genotypes of rs7903146 in the TCF7L2 were increased in LADA subjects (52.8%; P = 0.03), to the same extent as in type 2 diabetic subjects (54.1%, P = 3 × 10−7), compared with control subjects (44.8%) and type 1 diabetic subjects (43.3%). CONCLUSIONS—LADA shares genetic features with both type 1 (HLA, INS VNTR, and PTPN22) and type 2 (TCF7L2) diabetes, which justifies considering LADA as an admixture of the two major types of diabetes.

Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling.