Type 2 Diabetes Risk Variants Research Articles

Genetic underpinnings of fasting and oral glucose-stimulated based insulin sensitivity indices.

Insulin sensitivity (IS) is an important factor in type 2 diabetes (T2D) and can be estimated by many different indices. We aimed to compare the genetic components underlying IS indices obtained from fasting and oral glucose-stimulated plasma glucose and serum insulin levels. We computed 21 IS indices, classified as fasting, OGTT0,120 and OGTT0,30,120 indices, using fasting and oral glucose tolerance test (OGTT) data in two cohorts. We used data from a family cohort (n=313) to estimate the heritability and the genetic and phenotypic correlations of IS indices. The population cohort, Inter99 (n=5,343), was used to test for associations between IS indices and 426 genetic variants known to be associated with T2D. Heritability estimates of IS indices ranged between 19% and 38%. Fasting and OGTT0,30,120 indices had high genetic (ρG) and phenotypic (ρP) pairwise correlations (ρG and ρP: 0.88 to 1) The OGTT0,120 indices displayed a wide range of pairwise correlations (ρG: 0.17-1.00 and ρP: 0.13-0.97). We identified statistically significant associations between IS indices and established T2D-associated variants. The PPARG rs11709077 was associated only with fasting indices, and PIK3R rs4976033 only with OGTT0,30,120 indices. The variants in FAM63A/MINDY1, GCK, C2CD4A/B, and FTO loci were associated only with OGTT0,120 indices. Even though the IS indices mostly share a common genetic background, notable differences emerged between OGTT0,120 indices. The fasting and OGTT based indices have distinct associations with T2D risk variants. This work provides a basis for future large-scale genetic investigations into the differences between IS indices.

135-OR: Mapping Cis-Regulatory Programs Affecting Diabetes Risk in Pancreatic Islet Cell Types Using Single-Cell Multimodal Profiling

Genetic variants influencing type 1 diabetes (T1D) risk identified by genome-wide association studies (GWAS) are primarily non-coding and affect cis-regulatory element (cRE) activity in specific cell types. A more detailed understanding of cell type-specific cis-regulatory programs in the pancreas is therefore crucial for deciphering the functional impact of T1D risk. Recently developed assays that generate multimodal single nucleus RNA-seq (snRNA-seq) and ATAC-seq (snATAC-seq) profiles from the same cell enable high-resolution mapping of cis-regulatory programs in specific cell types. We generated deeply sequenced data from 28 nondiabetic pancreatic islet samples from the Alberta Diabetes Institute IsletCore using 10x multiome assays. After extensive quality control and filtering, we clustered multimodal profiles from 174,819 cells and identified ten major cell types. For several cell types we identified evidence of heterogeneous sub-populations; for example, in beta cells there were six sub-populations corresponding to cellular states associated with insulin secretion, stress response, and others. We identified 263,223 cREs across all cell types and developed a novel method to predict target genes of cREs based on the correlation of multimodal profiles across single cells. In beta cells, there were 122,352 cRE-target gene links involving 16,907 unique genes. We mapped chromatin accessibility quantitative trait loci (caQTLs) for cREs in each cell type and identified a total of 14,535 caQTLs at FDR<.10. Finally, we annotated T1D risk variants from high-resolution fine-mapping of 136 T1D signals with cRE-target gene links and caQTLs. At 27 T1D signals including at the INS, MAPT, and DLK1 loci, variants affecting chromatin accessibility were linked to putative target genes in beta cells suggesting mechanisms of action at these loci. Experimental follow up of variant activity at these loci using genome editing in beta cell lines is ongoing. Disclosure H. Mummey: None. W. Elison: None. K. Korgaonkar: None. Y.S. Lee: None. J.M. Newsome Ashmus: None. P. Benaglio: Employee; Shoreline Biosciences. M.T. Miller: None. J.E. Manning Fox: None. A.L. Gloyn: Other Relationship; Genentech, Inc., Roche Pharmaceuticals. P. MacDonald: None. S. Preissl: None. K.J. Gaulton: Stock/Shareholder; Neurocrine Biosciences, Inc. Consultant; Genentech, Inc. Stock/Shareholder; Vertex Pharmaceuticals Incorporated. Funding National Institutes of Health (HG012059, DK105554, DK122607, DK114650)

Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes.

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

TIGER: The gene expression regulatory variation landscape of human pancreatic islets.

SUMMARYGenome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D.

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes.

We report the largest and most diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 78 genome-wide significant (P < 5 × 10-8) regions, including 36 novel. We define credible sets of T1D-associated variants and show they are enriched in immune cell-accessible chromatin, particularly CD4+ effector T cells. Using chromatin accessibility profiling of CD4+ T cells from 115 individuals, we map chromatin accessibility quantitative trait loci (caQTLs) and identify five regions where T1D risk variants colocalize with caQTLs. We highlight rs72928038 in BACH2 as a candidate causal T1D variant leading to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritize potential drug targets by integrating genetic evidence, functional genomic maps, and immune protein-protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D.

248-OR: Type 1 Diabetes Risk Variants Alter the Transcriptional Profile of Beta Cells

GWAS studies have identified a number of SNPs connected to the development of type 1 diabetes (T1D). It is unclear how these variants confer risk for T1D development and whether this is based on direct effects on the beta cell, or whether the systemic inflammatory environment is detrimental to the beta cell. Using CRISPR mediated homology directed repair (Life Technologies), T1D risk variants rs10517086 and rs1534422 were introduced into the BRIN-BD11 beta cell line and the Jurkat T-cell line. Experiments were conducted in BRIN-BD11 cells expressing each variant and in native BRIN-BD11 cells treated with conditioned media from Jurkat cells expressing each variant. ApoTox GloTM (Promega) was used to assess cell viability, cytotoxicity and apoptosis. In all instances, cell viability and cytotoxicity were unchanged following the introduction of the two variants. However, rs1534422 significantly (P&amp;lt;0.01) increased apoptosis in BRIN-BD11 cells. This was not observed when BRIN-BD11 cells were treated with Jurkat condition media. In all instances, modest reductions (P&amp;lt;0.05) in glucose-stimulated insulin secretion were observed (Insulin ELISA, Mercodia). Expression of beta cell markers (ABCC8, KCNJ11, KCNQ1, SCL2A2, GCK, HNF1A, PDX1, NKX6.1 NGN3) was examined by qPCR (Roche Lightcycler). Irrespective of whether BRIN-BD11 cells were exposed to Jurkat conditioned media or not, induction of rs10517086 or rs1534422 caused significant (P&amp;lt;0.05-0.001) reductions in the expression of all genes examined with two exceptions: both HNF1A and ABCC8 were significantly upregulated (P&amp;lt;0.001). The transcriptional profiles of BRIN-BD11 cells were remarkably similar regardless of whether rs10517086 and rs1534422 were directly induced in BRIN-BD11 cells or whether they were exposed to conditioned media from Jurkat cells expressing the variants. This suggests that soluble secreted factors arising from the expression of these variants may drive the changes observed in the function of BRIN-BD11 cells. Disclosure W. Ratajczak: None. S. Atkinson: None. C. Kelly: None. Funding European Union Regional Development Fund; European Union Sustainable Competitiveness Programme for Northern Ireland; Northern Ireland Public Health Agency; Ulster University; Northern Ireland Department of Employment and Learning

Pancreatic progenitor epigenome maps prioritize type 2 diabetes risk genes with roles in development.

Genetic variants associated with type 2 diabetes (T2D) risk affect gene regulation in metabolically relevant tissues, such as pancreatic islets. Here, we investigated contributions of regulatory programs active during pancreatic development to T2D risk. Generation of chromatin maps from developmental precursors throughout pancreatic differentiation of human embryonic stem cells (hESCs) identifies enrichment of T2D variants in pancreatic progenitor-specific stretch enhancers that are not active in islets. Genes associated with progenitor-specific stretch enhancers are predicted to regulate developmental processes, most notably tissue morphogenesis. Through gene editing in hESCs, we demonstrate that progenitor-specific enhancers harboring T2D-associated variants regulate cell polarity genes LAMA1 and CRB2. Knockdown of lama1 or crb2 in zebrafish embryos causes a defect in pancreas morphogenesis and impairs islet cell development. Together, our findings reveal that a subset of T2D risk variants specifically affects pancreatic developmental programs, suggesting that dysregulation of developmental processes can predispose to T2D.

Circadian, Sleep and Caloric Intake Phenotyping in Type 2 Diabetes Patients With Rare Melatonin Receptor 2 Mutations and Controls: A Pilot Study.

BackgroundMelatonin modulates circadian rhythms in physiology and sleep initiation. Genetic variants of the MTNR1B locus, encoding the melatonin MT2 receptor, have been associated with increased type 2 diabetes (T2D) risk. Carriers of the common intronic MTNR1B rs10830963 T2D risk variant have modified sleep and circadian traits such as changes of the melatonin profile. However, it is currently unknown whether rare variants in the MT2 coding region are also associated with altered sleep and circadian phenotypes, including meal timing.Materials and MethodsIn this pilot study, 28 individuals [50% male; 46–82 years old; 50% with rare MT2 mutations (T2D MT2)] wore actigraphy devices and filled out daily food logs for 4 weeks. We computed circadian, sleep, and caloric intake phenotypes, including sleep duration, timing, and regularity [assessed by the Sleep Regularity Index (SRI)]; composite phase deviations (CPD) as well a sleep timing-based proxy for circadian misalignment; and caloric intake patterns throughout the day. Using regression analyses, we estimated age- and sex-adjusted mean differences (MD) and 95% confidence intervals (95%CI) between the two patient groups. Secondary analyses also compare T2D MT2 to 15 healthy controls.ResultsPatients with rare MT2 mutations had a later sleep onset (MD = 1.23, 95%CI = 0.42;2.04), and midsleep time (MD = 0.91, 95%CI = 0.12;1.70), slept more irregularly (MD in SRI = −8.98, 95%CI = −16.36;−1.60), had higher levels of behavioral circadian misalignment (MD in CPD = 1.21, 95%CI = 0.51;1.92), were more variable in regard to duration between first caloric intake and average sleep offset (MD = 1.08, 95%CI = 0.07;2.08), and had more caloric episodes in a 24 h day (MD = 1.08, 95%CI = 0.26;1.90), in comparison to T2D controls. Secondary analyses showed similar patterns between T2D MT2 and non-diabetic controls.ConclusionThis pilot study suggests that compared to diabetic controls, T2D MT2 patients display a number of adverse sleep, circadian, and caloric intake phenotypes, including more irregular behavioral timing. A prospective study is needed to determine the role of these behavioral phenotypes in T2D onset and severity, especially in view of rare MT2 mutations.

2061-P: Islet Single Cell Transcriptomes Reveal Cellular and Gene Regulatory Alterations in Type 2 Diabetes Pathogenesis

Pancreatic islet (dys)function and failure is central to both type 2 diabetes (T2D) genetic risk and pathogenesis. Islet cellular heterogeneity has impeded precise understanding of the specific gene(s) and pathway(s) in each cell type that are altered by and/or may contribute to pathophysiology in the prediabetic (PD) and type 2 diabetic (T2D) states. To identify cellular and molecular features of islet dysfunction in PD and T2D states, we completed single cell transcriptome profiling of 137k human islet cells from 11 ND, 6 PD, and 11 T2D donors (⋃5k/individual). Single cell transcriptomes aggregated into distinct clusters of endocrine (alpha, beta, delta, PP/gamma), exocrine (acinar, stellate, ductal), endothelial, and immune cell types. Targeted analysis of each endocrine cell type identified a proliferative alpha cell sub-population comprising ∼0.15-0.3% of endocrine cells and variable insulin production and endoplasmic reticulum stress beta cell states. We compared the proportion of islet endocrine cell type (sub)populations sampled between T2D, PD, and ND donors and found that T2D samples contained significantly fewer beta cells and more alpha cells than ND and, to a lesser degree, PD samples. Delta and PP/gamma cell proportions were similar between disease states. Importantly, no (sub)populations were ND, PD, or T2D-specific. We compared aggregate T2D, PD, and ND cell type-specific transcriptomes to identify differentially expressed genes in each cell type. Genes related to exocytic granule assembly/organization, including those induced by T2D risk variants (DGKB) or T2D state (GAP43), were among 99 genes induced in T2D beta cells. 103 repressed genes included those associated with glucose transport and implicated as T2D GWAS effector genes such as SLC2A2, SLC5A1, and STARD10, a gene whose deletion in mice impairs islet insulin secretion. Studies are ongoing to assess the functional role(s) of these and additional genes in islet (dys)function. Disclosure N. Lawlor: None. R. Kursawe: None. M.L. Stitzel: None. Funding American Diabetes Association/Pathway to Stop Diabetes (1-18-ACE-15 to M.L.S.)

Ancestry effects on type 2 diabetes genetic risk inference in Hispanic/Latino populations

BackgroundHispanic/Latino (HL) populations bear a disproportionately high burden of type 2 diabetes (T2D). The ability to predict T2D genetic risk using polygenic risk scores (PRS) offers great promise for improved screening and prevention. However, there are a number of complications related to the accurate inference of genetic risk across HL populations with distinct ancestry profiles. We investigated how ancestry affects the inference of T2D genetic risk using PRS in diverse HL populations from Colombia and the United States (US). In Colombia, we compared T2D genetic risk for the Mestizo population of Antioquia to the Afro-Colombian population of Chocó, and in the US, we compared European-American versus Mexican-American populations.MethodsWhole genome sequences and genotypes from the 1000 Genomes Project and the ChocoGen Research Project were used for genetic ancestry inference and for T2D polygenic risk score (PRS) calculation. Continental ancestry fractions for HL genomes were inferred via comparison with African, European, and Native American reference genomes, and PRS were calculated using T2D risk variants taken from multiple genome-wide association studies (GWAS) conducted on cohorts with diverse ancestries. A correction for ancestry bias in T2D risk inference based on the frequencies of ancestral versus derived alleles was developed and applied to PRS calculations in the HL populations studied here.ResultsT2D genetic risk in Colombian and US HL populations is positively correlated with African and Native American ancestry and negatively correlated with European ancestry. The Afro-Colombian population of Chocó has higher predicted T2D risk than Antioquia, and the Mexican-American population has higher predicted risk than the European-American population. The inferred relative risk of T2D is robust to differences in the ancestry of the GWAS cohorts used for variant discovery. For trans-ethnic GWAS, population-specific variants and variants with same direction effects across populations yield consistent results. Nevertheless, the control for bias in T2D risk prediction confirms that explicit consideration of genetic ancestry can yield more reliable cross-population genetic risk inferences.ConclusionsT2D associations that replicate across populations provide for more reliable risk inference, and modeling population-specific frequencies of ancestral and derived risk alleles can help control for biases in PRS estimation.

112-OR: Integrative Analysis of Chromatin Accessibility and Genetic Risk in T1D Patients and Controls

Motivation: Twin studies estimate 50% of type 1 diabetes (T1D) risk is genetic. Genetic association has implicated T cells as central to the disease process. Thus, we generated chromatin accessibility profiles from CD4+ T cells isolated from 135 T1D cases and 144 controls and investigated whether (1) there are patterns of chromatin accessibility that differ from T1D patients compared to healthy controls; (2) established T1D risk variants influence chromatin accessibility. Methods: Frozen PBMCs were provided by the Type 1 Diabetes Genetics Consortium (T1DGC). CD4+ T cells were isolated using bead purification and Assay for Transposase Accessible Chromatin (ATAC-seq) was performed. ATAC-seq samples were processed using the PEPATAC pipeline and R packages edgeR and limm. Results: We tested for differences in accessibility between individuals with and without T1D adjusting for sex, age, and experimental variability. We identified 516 chromatin regions peaks with differential accessibility by T1D status (2 with increased and 514 with decreased accessibility at FDR&amp;lt;0.05). Enrichment analysis with the software LOLA indicated these regions are enriched for ETS-1, AP1, and Maf transcription factor binding motifs. Next, we tested for association between T1D-associated variants and chromatin accessibility using available T1DGC genotypes. For each T1D variant with an observed chromatin accessibility quantitative trait locus effect (caQTL), we tested for colocalisation between the two signals and used publicly available expression QTLs (eQTL) to identify potential target genes. We identified six regions where data were consistent with a single variant driving association with T1D risk, chromatin accessibility in CD4+ T cells, and gene expression in whole blood, highlighting several candidate T1D genes: ANKRD55, IL6ST, BACH2, CENPW, SUOX, GDF11, GSDMB, ORMDL3, IKZF3, ERBB2, PNMT. Conclusion: These results provide hypotheses about molecular mechanisms of T1D in CD4+ T cells. Disclosure C.C. Robertson: None. W. Chen: None. S. Onengut-Gumuscu: None. P.J. Concannon: Stock/Shareholder; Self; Amgen. Other Relationship; Self; 10x Genomics. S.S. Rich: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (DK111906)

1709-P: Assessing the Role of 211 “Established” Type 2 Diabetes Variants in American Indians

Genome-wide association studies (GWAS) have identified many variants associated with type 2 diabetes (T2D), but there is little information on their effects in high risk populations such as American Indians. We analyzed 211 primary variants, associated with T2D (P&amp;lt;5.0×10-8) in published GWAS, in 7659 American Indian participants in a population study (33.5% with T2D)- updating our previous study of 63 variants. Genotypes came from a custom Axiom array that captures common variation in American Indians; imputation was performed using whole genome sequence data from 266 Pima Indians. Variants were tested for association with T2D in a mixed model accounting for relatedness with adjustment for age, sex and the 1st 5 genetic principal components to account for population stratification. Heterogeneity was tested by comparing odds ratios (ORs) in American Indians with published ORs. A weighted polygenic risk score across all 211 variants was analyzed. Nominally significant (P&amp;lt;0.05) and directionally consistent replication was observed for 21 of the 211 variants. Notable associations were with rs2237895 in KCNQ1 (OR=1.29, P=2.5×10-8) and rs4929965 in INS/IGF2 (OR=1.28, P=9.8×10-8). Significant heterogeneity was seen for 23 variants- 21 had weaker effects in American Indians than in published values. A summary test over all variants showed effects were generally weaker in American Indians (P=4.0×10-11). The polygenic risk score was strongly associated with T2D (OR=1.38 per SD, P=2.8×10-22). The risk score was also associated with reduced insulin secretion measured by a 25 g intravenous glucose tolerance test (by 10% per SD, P=0.005, n=300 all with normal glucose tolerance), but not insulin sensitivity measured by a hyperinsulinemic-euglycemic “clamp” (P=0.78, n=557 all nondiabetic). These findings suggest that established T2D risk variants generally also affect T2D risk in American Indians (although effects tend to be weaker) and that they act predominantly through diminished insulin secretion. Disclosure R.L. Hanson: None. L.E. Wedekind: None. W. Hsueh: None. S. Kobes: None. L.J. Baier: None. C. Bogardus: None. W.C. Knowler: None.

1591-P: The Primordial Prevention Project: Motivating Family Behavior Change through Disclosure of Child's Diabetes Genetic Risk

The prevalence of obesity and type 2 diabetes (T2D) in children has risen dramatically. We hypothesized that providing child’s genetic risk information, to families at high T2D risk, would serve as a motivator to improve behaviors related to the development of risk factors for T2D. We conducted a pilot intervention study of parents (n=51) and their children (n=44) aged 2-8. Family’s physical activity, children’s eating behaviors related to obesity (CEBQ); and parent’s attitudes and motivations for lifestyle change were collected at baseline and 3-month follow-up (3MFU). At baseline, saliva samples were collected from children for genotyping of obesity and T2D risk variants identified in genome-wide association studies. Parents were on average 35±6 years old, BMI 31±7 kg/m2, 60% mothers, 31% black and 7% Latino. Children were 4±2 years with a BMI of 72±26 percentiles. At the risk disclosure session, anxiety (STAI-S) and depressive symptoms (POMS) of parents were measured immediately before and after disclosure of child’s risk; and a significant increase in tension (X =2.58, p=0.03) and anxiety (X =-2.98, p=0.002) was observed. Parents then received a quasi-motivational interviewing and behavioral education intervention. After receiving the intervention parents reported a significant increase in their confidence to change their family’s behaviors (X =-0.31, p=0.002) compared to pre-disclosure. This led to 100% of parents committing to a personalized diet or activity change. At 3MFU, children demonstrated a reduction in their desire to drink (X =1.52, p=0.002) and responsiveness to food (X =1.86, p=0.005). In parents, no elevation in depressiveness or anxiety remained. No change in BMI or physical activity was observed in either group. Our data suggest returning genetic risk results can motivate commitment to behavior change. Future work will test whether combining genetic risk with an established lifestyle intervention may facilitate lowering T2D risk. Disclosure J. Wessel: Employee; Spouse/Partner; Eli Lilly and Company. M. Smith: None. Funding American Heart Association

Association between genetic risk variants and glucose intolerance during pregnancy in north Indian women

BackgroundGestational diabetes (GDM) is a more common problem in India than in many other parts of the world but it is not known whether this is due to unique environmental factors or a unique genetic background. To address this question we examined whether the same genetic variants associated with GDM and Type 2 Diabetes (T2D) in Caucasians also were associated with GDM in North Indian women.MethodsFive thousand one hundred pregnant women of gestational age 24–28 weeks from Punjab were studied by a 75 g oral glucose tolerance test (OGTT). GDM was diagnosed by both WHO1999 and 2013 criteria. 79 single nucleotide polymorphisms (SNPs) previously associated with T2D and glycemic traits (12 of them also with GDM) and 6 SNPs from previous T2D associations based on Indian population (some also with European) were genotyped on a Sequenom platform or using Taqman assays in DNA from 4018 women.ResultsIn support of previous findings in Caucasian GDM, SNPs at KCJN11 and GRB14 loci were nominally associated with GDM1999 risk in Indian women (both p = 0.02). Notably, T2D risk alleles of the variant rs1552224 near CENTD2, rs11708067 in ADCY5 and rs11605924 in CRY2 genes associated with protection from GDM regardless of criteria applied (p < 0.025). SNPs rs7607980 near COBLL1 (p = 0.0001), rs13389219 near GRB14 (p = 0.026) and rs10423928 in the GIPR gene (p = 0.012) as well as the genetic risk score (GRS) for these previously shown insulin resistance loci here associated with insulin resistance defined by HOMA2-IR and showed a trend towards GDM. GRS comprised of 3 insulin secretion loci here associated with insulin secretion but not GDM.ConclusionsGDM in women from Punjab in Northern India shows a genetic component, seemingly driven by insulin resistance and secretion and partly shared with GDM in other parts of the world. Most previous T2D loci discovered in European studies did not associate with GDM in North India, indicative of different genetic etiology or alternately, differences in the linkage disequilibrium (LD) structure between populations in which the associated SNPs were identified and Northern Indian women. Interestingly some T2D risk variants were in fact indicative of being protective for GDM in these Indian women.

Effects of Type 1 Diabetes Risk Alleles on Immune Cell Gene Expression.

Genetic studies have identified 61 variants associated with the risk of developing Type 1 Diabetes (T1D). The functions of most of the non-HLA (Human Leukocyte Antigen) genetic variants remain unknown. We found that only 16 of these risk variants could potentially be linked to a protein-coding change. Therefore, we investigated whether these variants affected susceptibility by regulating changes in gene expression. To do so, we examined whole transcriptome profiles of 600 samples from the Type 1 Diabetes Genetics Consortium (T1DGC). These comprised four different immune cell types (Epstein-Barr virus (EBV)-transformed B cells, either basal or after stimulation; and cluster of differentiation (CD)4+ and CD8+ T cells). Many of the T1D-associated risk variants regulated expression of either neighboring (cis-) or distant (trans-) genes. In brief, 24 of the non-HLA T1D variants affected the expression of 31 nearby genes (cis) while 25 affected 38 distant genes (trans). The effects were highly significant (False Discovery Rate p < 0.001). In addition, we searched in public databases for expression effects of T1D single nucleotide polymorphisms (SNPs) in other immune cell types such as CD14+ monocytes, lipopolysaccharide (LPS) stimulated monocytes, and CD19+ B cells. In this paper, we review the (expression quantitative trait loci (eQTLs) associated with each of the 60 T1D variants and provide a summary of the genes impacted by T1D risk alleles in various immune cells. We then review the methodological steps involved in analyzing the function of genome wide association studies (GWAS)-identified variants, with emphasis on those affecting gene expression. We also discuss recent advancements in the methodologies and their advantages. We conclude by suggesting future study designs that will aid in the study of T1D risk variants.

Diabetes Pathology and Risk of Primary Open-Angle Glaucoma: Evaluating Causal Mechanisms by Using Genetic Information.

Although type 2 diabetes (T2D) predicts glaucoma, the potential for unmeasured confounding has hampered causal conclusions. We performed separate sample genetic instrumental variable analyses using the Genetic Epidemiology Research Study on Adult Health and Aging cohort (n = 69,685; 1995-2013) to estimate effects of T2D on primary open-angle glaucoma (POAG; 3,554 cases). Genetic instrumental variables for overall and mechanism-specific (i.e., linked to T2D via influences on adiposity, β-cell function, insulin regulation, or other metabolic processes) T2D risk were constructed by using 39 genetic polymorphisms established to predict T2D in other samples. Instrumental variable estimates indicated that T2D increased POAG risk (odds ratio = 2.53, 95% confidence interval: 1.04, 6.11). The instrumental variable for β-cell dysregulation also significantly predicted POAG (odds ratioβ-cell = 5.26, 95% confidence interval: 1.75, 15.85), even among individuals without diagnosed T2D, suggesting that metabolic dysregulation may increase POAG risk prior to T2D diagnosis. The T2D risk variant in the melatonin receptor 1B gene (MTNR1B) predicted risk of POAG independently of T2D status, indicating possible pleiotropic physiological functions of melatonin, but instrumental variable effect estimates were significant even excluding MTNR1B variants. To our knowledge, this is the first genetic instrumental variable study of T2D and glaucoma, providing a novel approach to evaluating this hypothesized relationship. Our findings substantially bolster observational evidence that T2D increases POAG risk.

Genetic determinants for gestational diabetes mellitus and related metabolic traits in Mexican women.

Epidemiological and physiological similarities among Gestational Diabetes Mellitus (GDM) and Type 2 Diabetes (T2D) suggest that both diseases, share a common genetic background. T2D risk variants have been associated to GDM susceptibility. However, the genetic architecture of GDM is not yet completely understood. We analyzed 176 SNPs for 115 loci previously associated to T2D, GDM and body mass index (BMI), as well as a set of 118 Ancestry Informative Markers (AIMs), in 750 pregnant Mexican women. Association with GDM was found for two of the most frequently replicated T2D loci: a TCF7L2 haplotype (CTTC: rs7901695, rs4506565, rs7903146, rs12243326; P=2.16x10-06; OR=2.95) and a KCNQ1 haplotype (TTT: rs2237892, rs163184, rs2237897; P=1.98x10-05; OR=0.55). In addition, we found two loci associated to glycemic traits: CENTD2 (60’ OGTT glycemia: rs1552224, P=0.03727) and MTNR1B (HOMA B: rs1387153, P=0.05358). Remarkably, a major susceptibility SLC16A11 locus for T2D in Mexicans was not shown to play a role in GDM risk. The fact that two of the main T2D associated loci also contribute to the risk of developing GDM in Mexicans, confirm that both diseases share a common genetic background. However, lack of association with a Native American contribution T2D risk haplotype, SLC16A11, suggests that other genetic mechanisms may be in play for GDM.

A Systems Genetics Approach Identifies Genes and Pathways for Type 2 Diabetes in Human Islets

Close to 50 genetic loci have been associated with type 2 diabetes (T2D), but they explain only 15% of the heritability. In an attempt to identify additional T2D genes, we analyzed global gene expression in human islets from 63 donors. Using 48 genes located near T2D risk variants, we identified gene coexpression and protein-protein interaction networks that were strongly associated with islet insulin secretion and HbA(1c). We integrated our data to form a rank list of putative T2D genes, of which CHL1, LRFN2, RASGRP1, and PPM1K were validated in INS-1 cells to influence insulin secretion, whereas GPR120 affected apoptosis in islets. Expression variation of the top 20 genes explained 24% of the variance in HbA(1c) with no claim of the direction. The data present a global map of genes associated with islet dysfunction and demonstrate the value of systems genetics for the identification of genes potentially involved in T2D.

Consistent Directions of Effect for Established Type 2 Diabetes Risk Variants Across Populations

Common genetic risk variants for type 2 diabetes (T2D) have primarily been identified in populations of European and Asian ancestry. We tested whether the direction of association with 20 T2D risk variants generalizes across six major racial/ethnic groups in the U.S. as part of the Population Architecture using Genomics and Epidemiology Consortium (16,235 diabetes case and 46,122 control subjects of European American, African American, Hispanic, East Asian, American Indian, and Native Hawaiian ancestry). The percentage of positive (odds ratio [OR] >1 for putative risk allele) associations ranged from 69% in American Indians to 100% in European Americans. Of the nine variants where we observed significant heterogeneity of effect by racial/ethnic group (Pheterogeneity < 0.05), eight were positively associated with risk (OR >1) in at least five groups. The marked directional consistency of association observed for most genetic variants across populations implies a shared functional common variant in each region. Fine-mapping of all loci will be required to reveal markers of risk that are important within and across populations.

Genetic Risk Assessment of Type 2 Diabetes–Associated Polymorphisms in African Americans

OBJECTIVEMultiple single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) susceptibility have been identified in predominantly European-derived populations. These SNPs have not been extensively investigated for individual and cumulative effects on T2D risk in African Americans.RESEARCH DESIGN AND METHODSSeventeen index T2D risk variants were genotyped in 2,652 African American case subjects with T2D and 1,393 nondiabetic control subjects. Individual SNPs and cumulative risk allele loads were assessed for association with risk for T2D. Cumulative risk was assessed by counting risk alleles and evaluating the difference in cumulative risk scores between case subjects and control subjects. A second analysis weighted risk scores (ln [OR]) based on previously reported European-derived effect sizes.RESULTSFrequencies of risk alleles ranged from 8.6 to 99.9%. Eleven SNPs had ORs >1, and 5 from ADAMTS9, WFS1, CDKAL1, JAZF1, and TCF7L2 trended or had nominally significant evidence of T2D association (P < 0.05). Individuals carried between 13 and 29 risk alleles. Association was observed between T2D and increase in risk allele load (unweighted OR 1.04 [95% CI 1.01–1.08], P = 0.010; weighted 1.06 [1.03–1.10], P = 8.10 × 10−5). When TCF7L2 SNP rs7903146 was included as a covariate, the risk score was no longer associated with T2D in either model (unweighted 1.02 [0.98–1.05], P = 0.33; weighted 1.02 [0.98–1.06], P = 0.40).CONCLUSIONSThe trend of increase in risk for T2D with increasing risk allele load is similar to observations in European-derived populations; however, these analyses indicate that T2D genetic risk is primarily mediated through the effect of TCF7L2 in African Americans.