Validation and genetic heritability estimation of known type 2 diabetes related variants in the Korean population

Genome-wide association studies (GWASs) facilitated the discovery of countless disease-associated variants. However, GWASs have mostly been conducted in European ancestry samples. Recent studies have reported that these European-based association results may reduce disease prediction accuracy when applied in non-Europeans. Therefore, previously reported variants should be validated in non-European populations to establish reliable scientific evidence for precision medicine. In this study, we validated known associations with type 2 diabetes (T2D) and related metabolic traits in 125,850 samples from a Korean population genotyped by the Korea Biobank Array (KBA). At the end of December 2020, there were 8,823 variants associated with glycemic traits, lipids, liver enzymes, and T2D in the GWAS catalog. Considering the availability of imputed datasets in the KBA genome data, publicly available East-Asian T2D summary statistics, and the linkage disequilibrium among the variants (r2 < 0.2), 2,900 independent variants were selected for further analysis. Among these, 1,837 variants (63.3%) were statistically significant (p < 0.05). Most of the non-replicated variants (n = 1,063) showed insufficient statistical power and decreased minor allele frequencies compared with the replicated variants. Moreover, known variants showed <10% genetic heritability. These results could provide valuable scientific evidence for future study designs, the current power of GWASs, and future applications in precision medicine in the Korean population.

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Identification of type 2 diabetes loci in 433,540 East Asian individuals

10.1101/685172 ◽

2019 ◽

Cited By ~ 3

Author(s):

Cassandra N Spracklen ◽

Momoko Horikoshi ◽

Young Jin Kim ◽

Kuang Lin ◽

Fiona Bragg ◽

...

Keyword(s):

Type 2 Diabetes ◽

Association Studies ◽

Meta Analysis ◽

East Asian ◽

Genome Wide Association ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Genome Wide ◽

European Populations

SUMMARYMeta-analyses of genome-wide association studies (GWAS) have identified >240 loci associated with type 2 diabetes (T2D), however most loci have been identified in analyses of European-ancestry individuals. To examine T2D risk in East Asian individuals, we meta-analyzed GWAS data in 77,418 cases and 356,122 controls. In the main analysis, we identified 298 distinct association signals at 178 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 56 loci newly implicated in T2D predisposition. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. New associations include signals in/near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect muscle and adipose differentiation. At another locus, eQTLs at two overlapping T2D signals act through two genes, NKX6-3 and ANK1, in different tissues. Association studies in diverse populations identify additional loci and elucidate disease genes, biology, and pathways.Type 2 diabetes (T2D) is a common metabolic disease primarily caused by insufficient insulin production and/or secretion by the pancreatic β cells and insulin resistance in peripheral tissues1. Most genetic loci associated with T2D have been identified in populations of European (EUR) ancestry, including a recent meta-analysis of genome-wide association studies (GWAS) of nearly 900,000 individuals of European ancestry that identified >240 loci influencing the risk of T2D2. Differences in allele frequency between ancestries affect the power to detect associations within a population, particularly among variants rare or monomorphic in one population but more frequent in another3,4. Although smaller than studies in European populations, a recent T2D meta-analysis in almost 200,000 Japanese individuals identified 28 additional loci4. The relative contributions of different pathways to the pathophysiology of T2D may also differ between ancestry groups. For example, in East Asian (EAS) populations, T2D prevalence is greater than in European populations among people of similar body mass index (BMI) or waist circumference5. We performed the largest meta-analysis of East Asian individuals to identify new genetic associations and provide insight into T2D pathogenesis.

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Novel susceptibility loci and genetic regulation mechanisms for type 2 diabetes

10.1101/284570 ◽

2018 ◽

Author(s):

Angli Xue ◽

Yang Wu ◽

Zhihong Zhu ◽

Futao Zhang ◽

Kathryn E Kemper ◽

...

Keyword(s):

Gene Expression ◽

Type 2 Diabetes ◽

Rare Variants ◽

Association Studies ◽

Meta Analysis ◽

Genetic Regulation ◽

Regulation Of Gene Expression ◽

European Ancestry ◽

Genome Wide Association Studies

AbstractWe conducted a meta-analysis of genome-wide association studies (GWAS) with ∼16 million genotyped/imputed genetic variants in 62,892 type 2 diabetes (T2D) cases and 596,424 controls of European ancestry. We identified 139 common and 4 rare (minor allele frequency < 0.01) variants associated with T2D, 42 of which (39 common and 3 rare variants) were independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2,765) and other T2D-relevant tissues (n = up to 385) with the GWAS results identified 33 putative functional genes for T2D, three of which were targeted by approved drugs. A further integration of DNA methylation (n = 1,980) and epigenomic annotations data highlighted three putative T2D genes (CAMK1D, TP53INP1 and ATP5G1) with plausible regulatory mechanisms whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. We further found evidence that the T2D-associated loci have been under purifying selection.

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Zinc Transporter-8 Gene (SLC30A8) Is Associated with Type 2 Diabetes in Chinese

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2008-0161 ◽

2008 ◽

Vol 93 (10) ◽

pp. 4107-4112 ◽

Cited By ~ 55

Author(s):

Jie Xiang ◽

Xiao-Ying Li ◽

Min Xu ◽

Jie Hong ◽

Yun Huang ◽

...

Keyword(s):

Type 2 Diabetes ◽

Confidence Interval ◽

Glucose Tolerance ◽

Association Studies ◽

Strong Association ◽

Insulin Response ◽

Han Chinese ◽

European Ancestry ◽

Genome Wide Association Studies

Context: Several genome-wide association studies identified a strong association of SLC30A8 with type 2 diabetes in individuals of European ancestry. The effect of the association of rs13266634 with type 2 diabetes or related glycemic traits has not been fully extended to non-European populations, and a comprehensive examination of common variants in the gene has not yet been carried out in Han Chinese. Objective: The objective of the study was to investigate the association of SLC30A8 with type 2 diabetes in Chinese. Design: A comprehensive gene-based association study was performed using 14 tagging single-nucleotide polymorphism (SNPs) of SLC30A8 in Han Chinese subjects with normal glucose tolerance (NGT; n = 721), impaired glucose regulation (IGR; n = 375), and type 2 diabetes (n = 521). Results: A significant association for SNP rs13266634 was observed between patients with type 2 diabetes and NGT controls (P = 0.016). The association was also observed between combined type 2 diabetes/IGR and NGT subjects (P = 0.002). The adjusted odds ratios for homozygote CC vs. TT at this locus were 1.71 for type 2 diabetes (95% confidence interval 1.19–2.45, P = 0.002) and 1.77 for type 2 diabetes and IGR (95% confidence interval 1.29–2.42, P = 0.0001). We further studied the genotype-phenotype correlation in 70 Han Chinese using iv glucose tolerance test and found an association between SNP rs13266634 and acute insulin response to glucose and disposition index (adjusted P = 0.012 and 0.004, respectively). Conclusions: Our results provide evidence that SLC30A8 is a susceptible locus for type 2 diabetes in Chinese population, and its variant can influence insulin secretion.

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Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes

10.2337/figshare.17099780 ◽

2021 ◽

Author(s):

Mark J. O’Connor ◽

Philip Schroeder ◽

Alicia Huerta-Chagoya ◽

Paula Cortés-Sánchez ◽

Silvía Bonàs-Guarch ◽

...

Keyword(s):

Type 2 Diabetes ◽

Complex Traits ◽

Association Studies ◽

Meta Analysis ◽

Low Frequency ◽

Additive Models ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Genome Wide

Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, P=1´10-16) and a stronger effect in men than in women (interaction P=7´10-7). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides, and colocalization analysis linked this signal to reduced expression of the nearby PELO gene. These results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

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Causal Association Between Periodontitis and Type 2 Diabetes: A Bidirectional Two-Sample Mendelian Randomization Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.792396 ◽

2022 ◽

Vol 12 ◽

Author(s):

Yong-Bo Wang ◽

Si-Yu Yan ◽

Xu-Hui Li ◽

Qiao Huang ◽

Li-Sha Luo ◽

...

Keyword(s):

Type 2 Diabetes ◽

Large Scale ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Causal Association ◽

Bidirectional Association

Background: Previous observational studies have reported a bidirectional association between periodontitis and type 2 diabetes, but the causality of these relationships remains unestablished. We clarified the bidirectional causal association through two-sample Mendelian randomization (MR).Methods: We obtained summary-level data for periodontitis and type 2 diabetes from several published large-scale genome-wide association studies (GWAS) of individuals of European ancestry. For the casual effect of periodontitis on type 2 diabetes, we used five independent single-nucleotide polymorphisms (SNPs) specific to periodontitis from three GWAS. The summary statistics for the associations of exposure-related SNPs with type 2 diabetes were drawn from the GWAS in the Diabetes Genetics Replication and Meta-analysis (DIAGRAM) consortium and the FinnGen consortium R5 release, respectively. For the reversed causal inference, 132 and 49 SNPs associated with type 2 diabetes from the DIAGRAM consortium and the FinnGen consortium R5 release were included, and the summary-level statistics were obtained from the Gene-Lifestyle Interactions in Dental Endpoints consortium. Multiple approaches of MR were carried out.Results: Periodontitis was not causally related with the risk of type 2 diabetes (all p > 0.05). No causal effect of type 2 diabetes on periodontitis was found (all p > 0.05). Estimates were consistent across multiple MR analyses.Conclusion: This study based on genetic data does not support a bidirectional causal association between periodontitis and type 2 diabetes.

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Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes

10.2337/figshare.17099780.v1 ◽

2021 ◽

Author(s):

Mark J. O’Connor ◽

Philip Schroeder ◽

Alicia Huerta-Chagoya ◽

Paula Cortés-Sánchez ◽

Silvía Bonàs-Guarch ◽

...

Keyword(s):

Type 2 Diabetes ◽

Complex Traits ◽

Association Studies ◽

Meta Analysis ◽

Low Frequency ◽

Additive Models ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Genome Wide

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Genome-Wide Association Studies Identify Two Novel Loci Conferring Susceptibility to Diabetic Retinopathy in Japanese Patients with Type 2 Diabetes

Human Molecular Genetics ◽

10.1093/hmg/ddab044 ◽

2021 ◽

Author(s):

Minako Imamura ◽

Atsushi Takahashi ◽

Masatoshi Matsunami ◽

Momoko Horikoshi ◽

Minoru Iwata ◽

...

Keyword(s):

Type 2 Diabetes ◽

Diabetic Retinopathy ◽

Association Studies ◽

Meta Analysis ◽

Japanese Patients ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Stage 1

Abstract Several reports have suggested that genetic susceptibility contributes to the development and progression of diabetic retinopathy. We aimed to identify genetic loci that confer susceptibility to diabetic retinopathy in Japanese patients with type 2 diabetes. We analysed 5 790 508 single nucleotide polymorphisms (SNPs) in 8880 Japanese patients with type 2 diabetes, 4839 retinopathy cases and 4041 controls, as well as 2217 independent Japanese patients with type 2 diabetes, 693 retinopathy cases, and 1524 controls. The results of these two genome-wide association studies (GWAS) were combined with an inverse variance meta-analysis (Stage-1), followed by de novo genotyping for the candidate SNP loci (p < 1.0 × 10−4) in an independent case–control study (Stage-2, 2260 cases and 723 controls). After combining the association data (Stage-1 and -2) using meta-analysis, the associations of two loci reached a genome-wide significance level: rs12630354 near STT3B on chromosome 3, p = 1.62 × 10−9, odds ratio (OR) = 1.17, 95% confidence interval (CI) 1.11–1.23, and rs140508424 within PALM2 on chromosome 9, p = 4.19 × 10−8, OR = 1.61, 95% CI 1.36–1.91. However, the association of these two loci were not replicated in Korean, European, or African American populations. Gene-based analysis using Stage-1 GWAS data identified a gene-level association of EHD3 with susceptibility to diabetic retinopathy (p = 2.17 × 10−6). In conclusion, we identified two novel SNP loci, STT3B and PALM2, and a novel gene, EHD3, that confers susceptibility to diabetic retinopathy; however, further replication studies are required to validate these associations.

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A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

International Journal of Epidemiology ◽

10.1093/ije/dyaa262 ◽

2021 ◽

Author(s):

Guanghao Qi ◽

Nilanjan Chatterjee

Keyword(s):

Type 2 Diabetes ◽

Mendelian Randomization ◽

Association Studies ◽

Real Data ◽

Causal Effects ◽

Type I ◽

Genome Wide Association Studies ◽

Simulation Studies ◽

Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

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Evaluation of glycemic traits in susceptibility to COVID-19 risk: a Mendelian randomization study

BMC Medicine ◽

10.1186/s12916-021-01944-3 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Shiu Lun Au Yeung ◽

Jie V Zhao ◽

C Mary Schooling

Keyword(s):

Type 2 Diabetes ◽

Genetic Predisposition ◽

Mendelian Randomization ◽

Fasting Glucose ◽

Association Studies ◽

Sensitivity Analyses ◽

Genome Wide Association Studies ◽

Inverse Association ◽

Wide Confidence Interval

Abstract Background Observational studies suggest poorer glycemic traits and type 2 diabetes associated with coronavirus disease 2019 (COVID-19) risk although these findings could be confounded by socioeconomic position. We conducted a two-sample Mendelian randomization to clarify their role in COVID-19 risk and specific COVID-19 phenotypes (hospitalized and severe cases). Method We identified genetic instruments for fasting glucose (n = 133,010), 2 h glucose (n = 42,854), glycated hemoglobin (n = 123,665), and type 2 diabetes (74,124 cases and 824,006 controls) from genome wide association studies and applied them to COVID-19 Host Genetics Initiative summary statistics (17,965 COVID-19 cases and 1,370,547 population controls). We used inverse variance weighting to obtain the causal estimates of glycemic traits and genetic predisposition to type 2 diabetes in COVID-19 risk. Sensitivity analyses included MR-Egger and weighted median method. Results We found genetic predisposition to type 2 diabetes was not associated with any COVID-19 phenotype (OR: 1.00 per unit increase in log odds of having diabetes, 95%CI 0.97 to 1.04 for overall COVID-19; OR: 1.02, 95%CI 0.95 to 1.09 for hospitalized COVID-19; and OR: 1.00, 95%CI 0.93 to 1.08 for severe COVID-19). There were no strong evidence for an association of glycemic traits in COVID-19 phenotypes, apart from a potential inverse association for fasting glucose albeit with wide confidence interval. Conclusion We provide some genetic evidence that poorer glycemic traits and predisposition to type 2 diabetes unlikely increase the risk of COVID-19. Although our study did not indicate glycemic traits increase severity of COVID-19, additional studies are needed to verify our findings.

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