Genetic influences on the association between fetal growth and susceptibility to type 2 diabetes

2010 ◽  
Vol 1 (2) ◽  
pp. 96-105 ◽  
Author(s):  
B. M. Shields ◽  
R. M. Freathy ◽  
A. T. Hattersley
Keyword(s):  
Type 2 Diabetes ◽  
Insulin Secretion ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Fetal Growth ◽  
Association Studies ◽  
Single Gene ◽  
Genome Wide Association Studies ◽  
The Impact

The fetal insulin hypothesis proposes that low birth weight and susceptibility to type 2 diabetes (T2D) could both be two phenotypes of the same genotype. Insulin is a key growth factor in utero, and T2D is characterized by insulin resistance and/or beta-cell dysfunction. Therefore, genetic variants impacting on insulin secretion and action are likely to alter both fetal growth and susceptibility to T2D. There are three lines of evidence in support of this hypothesis. (1) Studies of rare monogenic diabetes have shown mutations in a single gene, such as GCK or KCNJ11, can cause diabetes by reducing insulin secretion, and these mutations are also associated with reduced birth weight. (2) Epidemiological studies have indicated that children born to fathers with diabetes are born smaller. As the father cannot influence the intrauterine environment, this association is likely to reflect genes inherited by the fetus from the father. (3) The most compelling evidence comes from recent genome-wide association studies. Variants in the CDKAL1 and HHEX-IDE genes that predispose to diabetes, if present in the fetus, are associated with reduced birth weight. These data provide evidence for a genetic contribution to the association between low birth weight and susceptibility to T2D. This genetic background is important to take into consideration when investigating the impact of environmental determinants and developing strategies for intervention and prevention.

scholarly journals RFX6-mediated dysregulation defines human β cell dysfunction in early type 2 diabetes

2021 ◽  
Author(s):  
John T Walker ◽  
Diane C Saunders ◽  
Vivek Rai ◽  
Chunhua Dai ◽  
Peter Orchard ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Insulin Secretion ◽  
Association Studies ◽  
Critical Role ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Β Cells ◽  
Β Cell ◽  
Gene Regulatory

A hallmark of type 2 diabetes (T2D), a major cause of world-wide morbidity and mortality, is dysfunction of insulin-producing pancreatic islet β cells. T2D genome-wide association studies (GWAS) have identified hundreds of signals, mostly in the non-coding genome and overlapping β cell regulatory elements, but translating these into biological mechanisms has been challenging. To identify early disease-driving events, we performed single cell spatial proteomics, sorted cell transcriptomics, and assessed islet physiology on pancreatic tissue from short-duration T2D and control donors. Here, through integrative analyses of these diverse modalities, we show that multiple gene regulatory modules are associated with early-stage T2D β cell-intrinsic defects. One notable example is the transcription factor RFX6, which we show is a highly connected β cell hub gene that is reduced in T2D and governs a gene regulatory network associated with insulin secretion defects and T2D GWAS variants. We validated the critical role of RFX6 in β cells through direct perturbation in primary human islets followed by physiological and single nucleus multiome profiling, which showed reduced dynamic insulin secretion and large-scale changes in the β cell transcriptome and chromatin accessibility landscape. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs, and individuals and thus we anticipate this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits with GWAS data.

scholarly journals Type 2 diabetes-associated single nucleotide polymorphism in Sorcs1 gene results in alternative processing of the Sorcs1 protein in INS1 β-cells

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Belinda Yau ◽  
Zachary Blood ◽  
Yousun An ◽  
Zhiduan Su ◽  
Melkam A. Kebede
Keyword(s):  
Type 2 Diabetes ◽  
Insulin Secretion ◽  
Allelic Variation ◽  
Association Studies ◽  
Mutant Form ◽  
Genome Wide Association Studies ◽  
Insulin Levels ◽  
Alternative Processing ◽  
Trait Locus

AbstractA threonine-to-Isoleucine (Thr52Ile) mutation within the pro-domain of the Sorcs1 gene was positionally cloned as the gene underlying a quantitative trait locus that affects fasting insulin levels in mice. In humans, genome-wide association studies and linkage studies have shown that SORCS1 is associated with diabetes and all of diabetes complications. We have recently shown that deletion of Sorcs1 in mice made obese with the leptinob mutation results in diabetes and an insulin granule stability defect. This present study investigates the functional consequence of the Sorcs1 Thr52Ile mutation in the rat INS1 β-cell line expressing either the wildtype or mutant Sorcs1 allele. We find that Sorcs1 Thr52Ile mutation is associated with increased basal insulin secretion, reduced glucose-stimulated insulin secretion and decreased insulin content in INS1 cells. Moreover, expression of Thr52Ile causes differential processing of the Sorcs1 protein resulting in the formation of an additional 90 kDa mutant form of the protein. The mutant form of the protein is localised to the ER, retains its pro-domain, and concurrently reduces expression of the functional mature 130 kDa Sorcs1 protein. These findings provide a mechanistic clue to why this specific allelic variation in Sorcs1 was associated with reduced insulin levels and type 2 diabetes.

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

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.

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

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.

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

BMC Medicine ◽  
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.

scholarly journals Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Minako Imamura ◽  
Atsushi Takahashi ◽  
Toshimasa Yamauchi ◽  
Kazuo Hara ◽  
Kazuki Yasuda ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Japanese Population ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide
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