Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR<5%) age-specific effects, of which 11 had larger effects in younger (<50y) than in older adults (≥50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may provide further insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.

Birth weight within the normal range is associated with a variety of adult-onset diseases, but the mechanisms behind these associations are poorly understood1. Previous genome-wide association studies identified a variant in the ADCY5 gene associated both with birth weight and type 2 diabetes, and a second variant, near CCNL1, with no obvious link to adult traits2. In an expanded genome-wide association meta-analysis and follow-up study (up to 69,308 individuals of European descent from 43 studies), we have now extended the number of genome-wide significant loci to seven, accounting for a similar proportion of variance to maternal smoking. Five of the loci are known to be associated with other phenotypes: ADCY5 and CDKAL1 with type 2 diabetes; ADRB1 with adult blood pressure; and HMGA2 and LCORL with adult height. Our findings highlight genetic links between fetal growth and postnatal growth and metabolism.

OBJECTIVE— In the present study, we aimed to validate the type 2 diabetes susceptibility alleles identified in six recent genome-wide association studies in the HHEX/KIF11/IDE (rs1111875), CDKN2A/B (rs10811661), and IGF2BP2 (rs4402960) loci, as well as the intergenic rs9300039 variant. Furthermore, we aimed to characterize quantitative metabolic risk phenotypes of the four variants. RESEARCH DESIGN AND METHODS— The variants were genotyped in the population-based Inter99 cohort (n = 5,970), the ADDITION Study (n = 1,626), a population-based sample of young healthy subjects (n = 377), and in additional type 2 diabetic case (n = 2,111) and glucose-tolerant (n = 521) subjects. The case-control studies involved a total of 4,089 type 2 diabetic patients and 5,043 glucose-tolerant control subjects. RESULTS— We validated association of variants near HHEX/KIF11/IDE, CDKN2A/B, and IGF2BP2 with type 2 diabetes. Interestingly, in middle-aged people, the rs1111875 C-allele of HHEX/KIF11/IDE strongly associated with lower acute insulin response during an oral glucose tolerance test (P = 6 × 10−7). In addition, decreased insulin release following intravenous tolbutamide injection was observed in young healthy subjects (P = 0.02). Also, a reduced insulin release was observed for the CDKN2A/B rs10811661 T-allele after both oral and intravenous glucose challenges (P = 0.001 and P = 0.009, respectively). CONCLUSIONS— We validate that variants in the proximity of the HHEX/KIF11/IDE, CDKN2A/B, and IFG2BP2 loci associate with type 2 diabetes. Importantly, variations within the HHEX/KIF11/IDE and CDKN2A/B loci confer impaired glucose- and tolbutamide-induced insulin release in middle-aged and young healthy subjects, suggesting a role for these variants in the pathogenesis of pancreatic β-cell dysfunction.

OBJECTIVE Genome-wide association studies have identified several variants within the MTNR1B locus that are associated with fasting plasma glucose (FPG) and type 2 diabetes. We refined the association signal by direct genotyping and examined for associations of the variant displaying the most independent effect on FPG with isolated impaired fasting glycemia (i-IFG), isolated impaired glucose tolerance (i-IGT), type 2 diabetes, and measures of insulin release and peripheral and hepatic insulin sensitivity. RESEARCH DESIGN AND METHODS We examined European-descent participants in the Inter99 study ( n = 5,553), in a sample of young healthy Danes ( n = 372), in Danish twins ( n = 77 elderly and n = 97 young), in additional Danish type 2 diabetic patients ( n = 1,626) and control subjects ( n = 505), in the Data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR) study ( n = 4,656), in the North Finland Birth Cohort 86 ( n = 5,258), and in the Haguenau study ( n = 1,461). RESULTS The MTNR1B intronic variant, rs10830963, carried most of the effect on FPG and showed the strongest association with FPG (combined P = 5.3 × 10 −31 ) and type 2 diabetes. The rs10830963 G-allele increased the risk of i-IFG (odds ratio [OR] 1.64, P = 5.5 × 10 −11 ) but not i-IGT. The G-allele was associated with a decreased insulin release after oral and intravenous glucose challenges ( P < 0.01) but not after injection of tolbutamide. In elderly twins, the G-allele associated with hepatic insulin resistance ( P = 0.017). CONCLUSIONS The G-allele of MTNR1B rs10830963 increases risk of type 2 diabetes through a state of i-IFG and not through i-IGT. The same allele associates with estimates of β-cell dysfunction and hepatic insulin resistance.

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662075, associated with a twofold increased risk for T2D in males. rs146662075 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches.

Aims/hypothesis An accurate molecular diagnosis of diabetes subtype confers clinical benefits; however, many individuals with monogenic diabetes remain undiagnosed.Biomarkers could help to prioritise patients for genetic investigation. We recently demonstrated that highsensitivity C-reactive protein (hsCRP) levels are lower in UK patients with hepatocyte nuclear factor 1 alpha Diabetologia (2011) 54:2801-2810 DOI 10.1007/s00125-011-2261 (HNF1A)-MODY than in other diabetes subtypes. In this large multi-centre study we aimed to assess the clinical validity of hsCRP as a diagnostic biomarker, examine the genotype-phenotype relationship and compare different hsCRP assays. Methods High-sensitivity CRP levels were analysed in individuals with HNF1A-MODY (n=457), glucokinase (GCK)-MODY (n=404), hepatocyte nuclear factor 4 alpha (HNF4A)-MODY (n=54) and type 2 diabetes (n=582) from seven European centres. Three common assays for hsCRP analysis were evaluated. We excluded 121 participants (8.1%) with hsCRP values >10 mg/l. The discriminative power of hsCRP with respect to diabetes aetiology was assessed by receiver operating characteristic curvederived C-statistic. Results In all centres and irrespective of the assay method, meta-analysis confirmed significantly lower hsCRP levels in those with HNF1A-MODY than in those with other aetiologies (z score −21.8, p<5×10 −105 ). HNF1A-MODY cases with missense mutations had lower hsCRP levels than those with truncating mutations (0.03 vs 0.08 mg/l, p<5× 10 −5 ). High-sensitivity CRP values between assays were strongly correlated (r 2 ≥0.91, p≤1×10 −5 ). Across the seven centres, the C-statistic for distinguishing HNF1A-MODY from young adult-onset type 2 diabetes ranged from 0.79 to 0.97, indicating high discriminative accuracy.

It has been hypothesized that, in aggregate, rare variants in coding regions of genes explain a substantial fraction of the heritability of common diseases. We sequenced the exomes of 1,000 Danish cases with common forms of type 2 diabetes (including body mass index > 27.5 kg/m(2) and hypertension) and 1,000 healthy controls to an average depth of 56×. Our simulations suggest that our study had the statistical power to detect at least one causal gene (a gene containing causal mutations) if the heritability of these common diseases was explained by rare variants in the coding regions of a limited number of genes. We applied a series of gene-based tests to detect such susceptibility genes. However, no gene showed a significant association with disease risk after we corrected for the number of genes analyzed. Thus, we could reject a model for the genetic architecture of type 2 diabetes where rare nonsynonymous variants clustered in a modest number of genes (fewer than 20) are responsible for the majority of disease risk.

In the original Supplemental Data available for download on November 27, 2013, the graphs in Figures S14-S16 and S18-S20 were unfortunately missing data because of a technical error during file conversion. The Supplemental Data file has been corrected online and is currently available for download. The authors regret the error.