Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility.

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 a significant excess in the 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 new T2D susceptibility loci. Furthermore, we observed considerable improvements in the 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 characterization 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

Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility

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 a significant excess in the 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 new T2D susceptibility loci. Furthermore, we observed considerable improvements in the 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 characterization 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 ancestr

Trans-ethnic study design approaches for fine-mapping.

Studies that traverse ancestrally diverse populations may increase power to detect novel loci and improve fine-mapping resolution of causal variants by leveraging linkage disequilibrium differences between ethnic groups. The inclusion of African ancestry samples may yield further improvements because of low linkage disequilibrium and high genetic heterogeneity. We investigate the fine-mapping resolution of trans-ethnic fixed-effects meta-analysis for five type II diabetes loci, under various settings of ancestral composition (European, East Asian, African), allelic heterogeneity, and causal variant minor allele frequency. In particular, three settings of ancestral composition were compared: (1) single ancestry (European), (2) moderate ancestral diversity (European and East Asian), and (3) high ancestral diversity (European, East Asian, and African). Our simulations suggest that the European/Asian and European ancestry-only meta-analyses consistently attain similar fine-mapping resolution. The inclusion of African ancestry samples in the meta-analysis leads to a marked improvement in fine-mapping resolution

Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility.

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 a significant excess in the 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 new T2D susceptibility loci. Furthermore, we observed considerable improvements in the 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 characterization 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

Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes

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

Circadian gene variants and susceptibility to type 2 diabetes: a pilot study.

BACKGROUND: Disruption of endogenous circadian rhythms has been shown to increase the risk of developing type 2 diabetes, suggesting that circadian genes might play a role in determining disease susceptibility. We present the results of a pilot study investigating the association between type 2 diabetes and selected single nucleotide polymorphisms (SNPs) in/near nine circadian genes. The variants were chosen based on their previously reported association with prostate cancer, a disease that has been suggested to have a genetic link with type 2 diabetes through a number of shared inherited risk determinants. METHODOLOGY/PRINCIPAL FINDINGS: The pilot study was performed using two genetically homogeneous Punjabi cohorts, one resident in the United Kingdom and one indigenous to Pakistan. Subjects with (N = 1732) and without (N = 1780) type 2 diabetes were genotyped for thirteen circadian variants using a competitive allele-specific polymerase chain reaction method. Associations between the SNPs and type 2 diabetes were investigated using logistic regression. The results were also combined with in silico data from other South Asian datasets (SAT2D consortium) and white European cohorts (DIAGRAM+) using meta-analysis. The rs7602358G allele near PER2 was negatively associated with type 2 diabetes in our Punjabi cohorts (combined odds ratio [OR] = 0.75 [0.66-0.86], p = 3.18 × 10(-5)), while the BMAL1 rs11022775T allele was associated with an increased risk of the disease (combined OR = 1.22 [1.07-1.39], p = 0.003). Neither of these associations was replicated in the SAT2D or DIAGRAM+ datasets, however. Meta-analysis of all the cohorts identified disease associations with two variants, rs2292912 in CRY2 and rs12315175 near CRY1, although statistical significance was nominal (combined OR = 1.05 [1.01-1.08], p = 0.008 and OR = 0.95 [0.91-0.99], p = 0.015 respectively). CONCLUSIONS/SIGNIFICANCE: None of the selected circadian gene variants was associated with type 2 diabetes with study-wide significance after meta-analysis. The nominal association observed with the CRY2 SNP, however, complements previous findings and confirms a role for this locus in disease susceptibility

Associations of genetic variants in/near body mass index-associated genes with type 2 diabetes: A systematic meta-analysis.

ObjectiveGenome-wide association studies have identified many obesity/body mass index (BMI)-associated loci in Europeans and East Asians. Since then, a large number of studies have investigated the role of BMI-associated loci in the development of type 2 diabetes (T2D). However, the results have been inconsistent. The objective of this study was to investigate the associations of eleven obesity/BMI loci with T2D risk and explore how BMI influences this risk. MethodsWe retrieved published literature from PubMed and Embase. The pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated using fixed- or random-effect models. ResultsIn the meta-analysis of 42 studies for 11 obesity/BMI-associated loci, we observed a statistically significant association of the FTO rs9939609 polymorphism (66425 T2D cases/239689 normoglycaemic subjects; P=100x10(-41)) and six other variants with T2D risk (17915 T2D cases/27531 normoglycaemic individuals: n=40629-130001; all P<0001 for SH2B1 rs7498665, FAIM2 rs7138803, TMEM18 rs7561317, GNPDA2 rs10938397, BDNF rs925946 and NEGR1 rs2568958). After adjustment for BMI, the association remained statistically significant for four of the seven variants (all P<005 for FTO rs9939609, SH2B1 rs7498665, FAIM2 rs7138803, GNPDA2 rs10938397). Subgroup analysis by ethnicity demonstrated similar results. ConclusionsThis meta-analysis indicates that several BMI-associated variants are significantly associated with T2D risk. Some variants increase the T2D risk independent of obesity, while others mediate this risk through obesity

The power of numbers

The technical and methodological advancements, as well as the knowledge accrued over the past decade on the haplotype block structure of the human genome, have enabled investigators to tackle the complexity of the genetic architecture of type 2 diabetes in populations of European and non-European descent by performing large-scale genome-wide association studies (GWAS) for both common and rare genetic variants. Interestingly, while interpreting the GWAS results one may observe that as the number of identified type 2 diabetes risk variants has increased over time, and the loci uncovered by earlier GWAS have been further replicated in larger association studies, the individual (per-allele) effect estimate has become smaller than the one originally detected in the discovery GWAS. This may be due to the non-mutually exclusive occurrence of two statistical phenomena, usually dubbed as "winner's curse" and "spectrum bias" effects. The present commentary discusses the work of the China Kadoorie Biobank Collaborative Group, which sought to provide a demonstration of the calculation of (relatively) unbiased allelic effect sizes for a set of 56 established type 2 diabetes risk variants in a large population-based cohort study of Chinese adult individuals. In particular we critically discuss whether theGWAS approach should remain a matter of statistical constraints only, or whether its integration with functional maps may highlight some sub-threshold loci as informative as those that reach genome-wide significance. The complementary information that could arise from the full integration of the genetic and functional maps holds the promise of potentially uncovering clinically relevant mechanistic insights and might expand the regulatory framework in which to interpret the functional follow-up and fine-mapping currently ongoing at established type 2 diabetes risk loci