Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.

DNA methylation of the Klf14 gene region in whole blood cells provides prediction for the chronic inflammation in the adipose tissue.

Krüppel-Like Factor 14 (KLF14) gene, which appears to be a master regulator of gene expression in the adipose tissue and have previously been associated with BMI and Type 2 diabetes (T2D) by large genome-wide association studies. In order to find predictive biomarkers for the development of T2D, it is necessary to take epigenomic changes affected by environmental factors into account. This study focuses on ageing and obesity, which are T2D risk factors, and examines epigenetic changes and inflammatory changes. We investigated DNA methylation changes in the Klf14 promoter region in different organs of mice for comparing aging and weight. We found that methylation levels of these sites were increased with aging and weight in the spleen, the adipose tissue, the kidney, the lung, the colon and the whole blood cells. In addition, in the spleen, the adipose tissue and the whole blood, these epigenetic changes were also significantly associated with inflammatory levels. Moreover, not only Klf14, but also expression levels of some downstream genes were decreased with methylation in the spleen, the adipose tissue and the whole blood cells. Taken together, our results suggest that methylation changes of Klf14 in those tissues may be associated with changes in gene expression and inflammation on the adipose tissue of obesity and T2D. In addition, the methylation changes in the whole blood cells may serve as a predictive epigenetic biomarker for the development of T2D.

Bone mineral density and risk of type 2 diabetes and coronary heart disease: A Mendelian randomization study.

Background: Observational studies have demonstrated that increased bone mineral density is associated with a higher risk of type 2 diabetes (T2D), but the relationship with risk of coronary heart disease (CHD) is less clear. Moreover, substantial uncertainty remains about the causal relevance of increased bone mineral density for T2D and CHD, which can be assessed by Mendelian randomisation studies.  Methods: We identified 235 independent single nucleotide polymorphisms (SNPs) associated at p<5×10 -8 with estimated heel bone mineral density (eBMD) in 116,501 individuals from the UK Biobank study, accounting for 13.9% of eBMD variance. For each eBMD-associated SNP, we extracted effect estimates from the largest available GWAS studies for T2D (DIAGRAM: n=26,676 T2D cases and 132,532 controls) and CHD (CARDIoGRAMplusC4D: n=60,801 CHD cases and 123,504 controls). A two-sample design using several Mendelian randomization approaches was used to investigate the causal relevance of eBMD for risk of T2D and CHD. In addition, we explored the relationship of eBMD, instrumented by the 235 SNPs, on 12 cardiovascular and metabolic risk factors. Finally, we conducted Mendelian randomization analysis in the reverse direction to investigate reverse causality. Results: Each one standard deviation increase in genetically instrumented eBMD (equivalent to 0.14 g/cm 2) was associated with an 8% higher risk of T2D (odds ratio [OR] 1.08; 95% confidence interval [CI]: 1.02 to 1.14; p=0.012) and 5% higher risk of CHD (OR 1.05; 95%CI: 1.00 to 1.10; p=0.034). Consistent results were obtained in sensitivity analyses using several different Mendelian randomization approaches. Equivalent increases in eBMD were also associated with lower plasma levels of HDL-cholesterol and increased insulin resistance. Mendelian randomization in the reverse direction using 94 T2D SNPs or 52 CHD SNPs showed no evidence of reverse causality with eBMD. Conclusions: These findings suggest a causal relationship between elevated bone mineral density with risks of both T2D and CHD.

Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution.

Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals.

Identification of a glutamic acid repeat polymorphism of ALMS1 as a novel genetic risk marker for early-onset myocardial infarction by genome-wide linkage analysis.

BACKGROUND: Myocardial infarction (MI) is a leading cause of death worldwide. Given that a family history is an independent risk factor for coronary artery disease, genetic variants are thought to contribute directly to the development of this condition. The identification of susceptibility genes for coronary artery disease or MI may thus help to identify high-risk individuals and offer the opportunity for disease prevention. METHODS AND RESULTS: We designed a 5-step protocol, consisting of a genome-wide linkage study followed by association analysis, to identify novel genetic variants that confer susceptibility to coronary artery disease or MI. A genome-wide affected sib-pair linkage study with 221 Japanese families with coronary artery disease yielded a statistically significant logarithm of the odds score of 3.44 for chromosome 2p13 and MI. Further association analysis implicated Alström syndrome 1 gene (ALMS1) as a candidate gene within the linkage region. Validation association analysis revealed that representative single-nucleotide polymorphisms of the ALMS1 promoter region were significantly associated with early-onset MI in both Japanese and Korean populations. Moreover, direct sequencing of the ALMS1 coding region identified a glutamic acid repeat polymorphism in exon 1, which was subsequently found to be associated with early-onset MI. CONCLUSIONS: The glutamic acid repeat polymorphism of ALMS1 identified in the present study may provide insight into the pathogenesis of early-onset MI.

A genome-wide association study of a coronary artery disease risk variant.

Although over 30 common genetic susceptibility loci have been identified to be independently associated with coronary artery disease (CAD) risk through genome-wide association studies (GWAS), genetic risk variants reported to date explain only a small fraction of heritability. To identify novel susceptibility variants for CAD and confirm those previously identified in European population, GWAS and a replication study were performed in the Koreans and Japanese. In the discovery stage, we genotyped 2123 cases and 3591 controls with 521 786 SNPs using the Affymetrix SNP Array 6.0 chips in Korean. In the replication, direct genotyping was performed using 3052 cases and 4976 controls from the KItaNagoya Genome study of Japan with 14 selected SNPs. To maximize the coverage of the genome, imputation was performed based on 1000 Genome JPT+CHB and 5.1 million SNPs were retained. CAD association was replicated for three GWAS-identified loci (1p13.3/SORT1 (rs599839), 9p21.3/CDKN2A/2B (rs4977574), and 11q22.3/ PDGFD (rs974819)) in Koreans. From GWAS and a replication, SNP rs3782889 showed a strong association (combined P=3.95 × 10(-14)), although the association of SNP rs3782889 doesn't remain statistically significant after adjusting for SNP rs11066015 (proxy SNP with BRAP (r(2)=1)). But new possible CAD-associated variant was observed for rs9508025 (FLT1), even though its statistical significance did marginally reach at the genome-wide a significance level (combined P=6.07 × 10(-7)). This study shows that three CAD susceptibility loci, which were previously identified in European can be directly replicated in Koreans and also provides additional evidences implicating suggestive loci as risk variants for CAD in East Asian.

Genome-wide association study of coronary artery disease in the Japanese.

A new understanding of the genetic basis of coronary artery disease (CAD) has recently emerged from genome-wide association (GWA) studies of common single-nucleotide polymorphisms (SNPs), thus far performed mostly in European-descent populations. To identify novel susceptibility gene variants for CAD and confirm those previously identified mostly in populations of European descent, a multistage GWA study was performed in the Japanese. In the discovery phase, we first genotyped 806 cases and 1337 controls with 451 382 SNP markers and subsequently assessed 34 selected SNPs with direct genotyping (541 additional cases) and in silico comparison (964 healthy controls). In the replication phase, involving 3052 cases and 6335 controls, 12 SNPs were tested; CAD association was replicated and/or verified for 4 (of 12) SNPs from 3 loci: near BRAP and ALDH2 on 12q24 (P=1.6 × 10(-34)), HLA-DQB1 on 6p21 (P=4.7 × 10(-7)), and CDKN2A/B on 9p21 (P=6.1 × 10(-16)). On 12q24, we identified the strongest association signal with the strength of association substantially pronounced for a subgroup of myocardial infarction cases (P=1.4 × 10(-40)). On 6p21, an HLA allele, DQB1(*)0604, could show one of the most prominent association signals in an ∼8-Mb interval that encompasses the LTA gene, where an association with myocardial infarction had been reported in another Japanese study. CAD association was also identified at CDKN2A/B, as previously reported in different populations of European descent and Asians. Thus, three loci confirmed in the Japanese GWA study highlight the likely presence of risk alleles with two types of genetic effects - population specific and common - on susceptibility to CAD.