Type 2 Diabetes Genome-wide Association Studies Research Articles

Multi-ancestry polygenic mechanisms of type 2 diabetes.

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

Persistent homology analysis of type 2 diabetes genome-wide association studies in protein-protein interaction networks.

Genome-wide association studies (GWAS) involving increasing sample sizes have identified hundreds of genetic variants associated with complex diseases, such as type 2 diabetes (T2D); however, it is unclear how GWAS hits form unique topological structures in protein-protein interaction (PPI) networks. Using persistent homology, this study explores the evolution and persistence of the topological features of T2D GWAS hits in the PPI network with increasing p-value thresholds. We define an n-dimensional persistent disease module as a higher-order generalization of the largest connected component (LCC). The 0-dimensional persistent T2D disease module is the LCC of the T2D GWAS hits, which is significantly detected in the PPI network (196 nodes and 235 edges, P0.05). In the 1-dimensional homology group analysis, all 18 1-dimensional holes (loops) of the T2D GWAS hits persist over all p-value thresholds. The 1-dimensional persistent T2D disease module comprising these 18 persistent 1-dimensional holes is significantly larger than that expected by chance (59 nodes and 83 edges, P0.001), indicating a significant topological structure in the PPI network. Our computational topology framework potentially possesses broad applicability to other complex phenotypes in identifying topological features that play an important role in disease pathobiology.

283-OR: ADA Presidents' Select Abstract: An Isogenic hPSC-Based Platform for Functional Interrogation of the Role of Multiple T2D-Associated Genes in Pancreatic Beta-Cell Failure

Type 2 diabetes (T2D) mellitus is a complex polygenic disease. Multiple genome-wide association studies (GWAS) have identified hundreds of T2D-related genetic variants, the vast majority of which are non-coding. Pinpointing the actual causal variants and their target gene transcripts has been challenging. Building on recent advances in CRISPR technology and protocols that allow differentiation into functional pancreatic beta cells, we created isogenic hPSCs encompassing knockout (KO) mutations in 20 GWAS-identified T2D risk genes, and systematically examined their roles in beta cell differentiation, insulin production, glucose response, and apoptosis. As a result, loss of TCF7L2, HNF4A, TLE4, TGFB1, GIPR and COBLL1 severely impaired the differentiation capacities of hPSCs into functional beta cells and over half of the 20 T2D genes shared implications in beta cell dysfunctions including decreased insulin content, impaired insulin secretion and high susceptibility to lipotoxicity. Next, we sorted beta cells from each KO and performed RNA-seq and ATAC-seq assays. An integrative analysis revealed KO of HNF4A, HNF1A, ABCC8, KCNJ11, TLE4 and WDR13 resulted in extensive transcriptomic and epigenetic changes in the beta cells. We identify that KO of HNF4A results in dramatic expressional changes of dozens of known T2D effector genes and additional ones critical to beta cell functions and biology. An analysis of T2D GWAS credible set of SNPs using ATAC-seq and RNA-seq coupled with functional assays, helped us to nominate a single likely T2D causal SNP (rs7132908) at FAIM2 locus. Finally, a correlation analysis of gene expression levels and insulin content across KO lines identified 1,867 differential genes including PCSK1N that likely serves as a hub gene by mediating insulin production in beta cells. This study thus offers a powerful strategy to interrogate shared molecular pathways among multiple risk genes for diabetes. Disclosure D.Xue: None. J.Vandana: None. A.Chong: None. F.S.Collins: None. S.Chen: Consultant; Vesalius Therapeutics, Stock/Shareholder; Oncobeat Theraputics. N.Narisu: None. T.Yan: None. M.Zhang: None. C.Grenko: None. X.Tang: None. J.Zhu: None. L.L.Bonnycastle: None. M.Erdos: None. Funding American Diabetes Association (9-22-PDFPM-06 to D.X.)

1467-P: Type 2 Diabetes Polygenic Score Predicts the Risk of Glucocorticoid-Induced Hyperglycemia in Patients without Diabetes

Introduction: Glucocorticoids are commonly prescribed medications with a known side effect of hyperglycemia due to their effect on glucose metabolism. However, only 10-50% of patients receiving glucocorticoids develop overt hyperglycemia. It is unknown whether patients with increased genetic risk for type 2 diabetes (T2D) are predisposed to develop hyperglycemia after glucocorticoid treatment. Methods: We accessed electronic health records and genetic data in the Mass General Brigham Biobank. We examined patients with no diagnosis of diabetes or prediabetes who received a glucocorticoid dose equivalent to 10 mg of prednisone or more, and who had glucose levels checked within 7 days of glucocorticoid administration. Hyperglycemia was defined as fasting blood glucose ≥126 mg/dL or random blood glucose ≥200 mg/dL. A T2D global extended polygenic score was constructed through a meta-analysis of T2D genome-wide association studies in the Million Veteran Program, DIAMANTE, and FinnGen. We performed logistic regression to analyze the association between the polygenic score and hyperglycemia, controlling for age, gender, BMI, baseline creatinine, glucocorticoid dose and duration, and the first 10 principal components of genetic ancestry. Results: Out of 552 patients who received glucocorticoids, 216 developed hyperglycemia and 336 did not. The T2D polygenic score was significantly associated with glucocorticoid-induced hyperglycemia (p = 0.032, OR = 1.5 per standard deviation of the polygenic score). Other significant covariates included baseline creatinine (p = 8.0 × 10−4, OR = 2.2 per mg/dL of creatinine) and glucocorticoid dose (p = 9.5 × 10−5, OR = 1.01 per 10 mg of prednisone). Conclusions: Patients carrying a higher burden of genetic variants that confer risk for T2D have an increased risk of hyperglycemia after receiving glucocorticoids. This finding offers a mechanism for risk stratification as part of a precision approach to medical treatment. Disclosure A.J.Deutsch: None. P.H.Schroeder: None. R.Mandla: None. F.Erenler: None. J.M.Mercader: None. M.Udler: None. J.C.Florez: Consultant; AstraZeneca, Novo Nordisk, Other Relationship; AstraZeneca, Merck & Co., Inc. L.Brenner: None. Funding National Institutes of Health (T32DK007028)

72-OR: Higher Genetic Risk for Type 2 Diabetes Is Associated with Faster Decline of Beta-Cell Function

Objective: Polygenic risk score (PRS) for type 2 diabetes (T2D) has been reported to be associated with beta cell function, but whether it is also associated with the longitudinal change of beta cell function is largely unknown. We aimed to evaluate the association of PRS and the trajectory of beta cell function in a community based prospective cohort. Method: We analyzed 6,323 participants without T2D at baseline from Ansan-Ansung cohort with 75-g 2-hour oral glucose tolerance tests taken biennially for 14 years. Disposition index (DI), derived from insulinogenic index at 60 minutes and Matsuda index, was used as a marker for beta cell function. PRS was calculated using 1.2 million variants from trans-ancestry T2D genome-wide association study using Bayesian regression (PRS-CSx). Participants were stratified into low (1st quintile), intermediate (2nd to 4th quintiles) and high genetic risk (5th quintile). Lifestyle was categorized as unfavorable, intermediate and favorable lifestyle according to Life’s Essential 8. Linear mixed model was applied. Result: During a median follow-up of 13 years, 379 (30.6%), 875 (23.0%) and 171 (13.4%) participants developed T2D in high, intermediate and low genetic risk group, respectively. Participants in high genetic risk group, compared to low genetic risk group, had a 33% lower DI at baseline (53 [95% CI 50, 56] vs 79 [74, 84]; P = 7.9×10-15), and a 1.7-fold faster rate of decline in log2(DI) per year (-0.035 [-0.028, -0.042] vs -0.020 [-0.013, -0.027]; P = 0.0026). Notably, we found significant interaction between the rate of decline in DI and T2D PRS (P = 8.5×10-5). Healthy lifestyle was associated with decreased rate of decline in DI across all genetic risk group. Conclusion: Having a high genetic risk for T2D was associated with not only a lower DI at baseline, but also a faster rate of decline in DI. Genetic information could be used to identify those at risk for rapid decline of beta cell function and emphasize lifestyle modification. Disclosure H.Lee: None. J.Choi: None. H.Son: None. K.Park: None. N.H.Cho: None. S.Kwak: None. Funding Ministry of Health & Welfare, Republic of Korea

Mendelian randomization reveals no associations of genetically-predicted obstructive sleep apnea with the risk of type 2 diabetes, nonalcoholic fatty liver disease, and coronary heart disease.

Obstructive sleep apnea (OSA) has been reported to affect cardiometabolic diseases. However, whether such association is causal is still unknown. Here, we attempt to explore the effect of OSA on type 2 diabetes (T2D), nonalcoholic fatty liver disease (NAFLD) and coronary heart disease (CHD). Genetic variants associated with OSA were requested from a published genome-wide association study (GWAS) and those qualified ones were selected as instrumental variables (IV). Then, the IV-outcome associations were acquired from T2D, NAFLD and CHD GWAS consortia separately. The Mendelian randomization (MR) was designed to estimate the associations of genetically-predicted OSA on T2D, NAFLD and CHD respectively, using the inverse-variance weighted (IVW) method. We applied the Bonferroni method to adjust the p-value. Besides, MR-Egger regression and weighted median methods were adopted as a supplement to IVW. The Cochran's Q value was used to evaluate heterogeneity and the MR-Egger intercept was utilized to assess horizontal pleiotropy, together with MR-PRESSO. The leave-one-out sensitivity analysis was carried out as well. No MR estimate reached the Bonferroni threshold (p < 0.017). Although the odds ratio of T2D was 3.58 (95% confidence interval (CI) [1.06, 12.11], IVW-p-value = 0.040) using 4 SNPs, such causal association turned insignificant after the removal of SNP rs9937053 located in FTO [OR = 1.30 [0.68, 2.50], IVW p = 0.432]. Besides, we did not find that the predisposition to OSA was associated with CHD [OR = 1.16 [0.70, 1.91], IVW p = 0.560] using 4 SNPs. This MR study reveals that genetic liability to OSA might not be associated with the risk of T2D after the removal of obesity-related instruments. Besides, no causal association was observed between NAFLD and CHD. Further studies should be carried out to verify our findings.

The Genetics of Type 2 Diabetes in Youth: Where We Are and the Road Ahead

The Genetics of Type 2 Diabetes in Youth: Where We Are and the Road Ahead

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.

Polygenic Prediction of Type 2 Diabetes in Africa

OBJECTIVEPolygenic prediction of type 2 diabetes (T2D) in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of T2D from Africa and the poor transferability of European-derived polygenic risk scores (PRSs) in diverse ethnicities. We set out to evaluate if African American, European, or multiethnic-derived PRSs would improve polygenic prediction in continental Africans.RESEARCH DESIGN AND METHODSUsing the PRSice software, ethnic-specific PRSs were computed with weights from the T2D GWAS multiancestry meta-analysis of 228,499 case and 1,178,783 control subjects. The South African Zulu study (n = 1,602 case and 981 control subjects) was used as the target data set. Validation and assessment of the best predictive PRS association with age at diagnosis were conducted in the Africa America Diabetes Mellitus (AADM) study (n = 2,148 case and 2,161 control subjects).RESULTSThe discriminatory ability of the African American and multiethnic PRSs was similar. However, the African American–derived PRS was more transferable in all the countries represented in the AADM cohort and predictive of T2D in the country combined analysis compared with the European and multiethnic-derived scores. Notably, participants in the 10th decile of this PRS had a 3.63-fold greater risk (odds ratio 3.63; 95% CI 2.19–4.03; P = 2.79 × 10−17) per risk allele of developing diabetes and were diagnosed 2.6 years earlier than those in the first decile.CONCLUSIONSAfrican American–derived PRS enhances polygenic prediction of T2D in continental Africans. Improved representation of non-European populations (including Africans) in GWAS promises to provide better tools for precision medicine interventions in T2D.

Exploring the Epigenetic Regulatory Role of m6A-Associated SNPs in Type 2 Diabetes Pathogenesis.

PurposeGenetic factors in type 2 diabetes (T2D) pathogenesis have been widely explored by the genome-wide association studies (GWAS), identifying a great amount of susceptibility loci. With the development of high-resolution sequencing, the N(6)-methyladenosine (m6A) RNA modification has been proved to be affected by genetic variation. In this study, we identified the T2D-associated m6A-SNPs from T2D GWAS data and explored the underlying mechanism of the pathogenesis of T2D.MethodsWe examined the association of m6A-SNPs with T2D among large-scale T2D GWAS summary statistics and further performed multi-omics integrated analysis to explore the potential role of the identified m6A-SNPs in T2D pathogenesis.ResultsAmong the 15,124 T2D-associated m6A-SNPs, 71 of them reach the genome-wide significant threshold (5.0e-05). The leading SNP rs4993986 (C>G), which is located near the m6A modification site at the 3ʹ end of the HLA-DQB1 transcript, is expected to participate in the pathogenesis of T2D by influencing m6A modification to regulate the HLA-DQB1 expression.ConclusionThe current study has suggested a potential correlation between m6A-SNPs and T2D pathogenesis and also provided new insights into the pathogenic mechanism of the T2D susceptibility loci identified by GWAS.

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.

242-OR: Single Nuclei Skeletal Muscle Chromatin and Gene Expression Profiling across 287 Individuals Reveals Cell Type-Specific Genetic Regulatory Architectures at T2D GWAS Loci

Skeletal muscle is the largest organ, by weight (&amp;gt;40%), in the human body; insulin resistance in this and other peripheral tissues is a hallmark of type 2 diabetes (T2D). Recent genome wide association study (GWAS) results show enrichment for T2D and fasting insulin SNPs in skeletal muscle regulatory regions. Identifying gene regulatory mechanisms underlying these associations requires pinpointing the set of causal variants, the target genes these variants regulate, and the specific skeletal muscle cell populations in which they function. Here, we generated single nuclei chromatin (snATAC) and transcription (snRNA) profiles across 287 frozen human skeletal muscle biopsies. We integrated 414,982 nuclei across the modalities with LIGER and identified 13 cell types, ranging in abundance from 38.1% (type 1 fiber) to &amp;lt;1% (adipocyte) of all nuclei. We further integrated genotypes and performed expression and chromatin accessibility quantitative trait loci (e/caQTL) scans in the five most abundant cell types (type 1, type 2a, type 2x, mesenchymal stem cell, endothelial). We identified &amp;gt;70k associations (5% FDR) ranging from shared to cell type-specific and performed colocalization with T2D GWAS signals. Of 73 T2D GWAS signals that colocalize with caQTL, 39 (53.4%) are cell type-specific, highlighting the importance of sn-caQTL maps for GWAS functional studies. For example, in mesenchymal stem cells, we identified a cell type-specific caQTL for an ARL15 intronic peak (rs702634, p=6.6x10-10), which colocalizes with the T2D GWAS signal (coloc H4 posterior probability = 0.95). To nominate target genes for this caQTL peak, we scanned peaks in promoters of genes in the 1 Mb neighborhood and computed co-accessibility scores using Cicero in an allele-specific manner, which identified the gene FST, ~493 kb away. FST encodes for follistatin which increases muscle growth and reduces fat mass and insulin resistance. Disclosure S. Parker: None. A. Varshney: None. Fusion tissue study: n/a. Amp-t2d skeletal muscle working group: n/a. Funding American Diabetes Association/Pathway to Stop Diabetes (1-14-INI-07 to S.P.); National Institutes of Health (R01DK117960, U01DK06237017, UM1DK126185)

The Utility of a Type 2 Diabetes (T2D) Polygenic Score in Addition to Clinical Variables for Prediction of T2D Incidence in Birth, Youth, and Adult Cohorts

Background: There is limited information on how polygenic scores (PSs) — based on variants from genome-wide association studies (GWAS) of T2D — add to clinical variables in predicting T2D incidence. Methods: For participants in a longitudinal study in an Indigenous population from the Southwest United States (US) with high T2D prevalence, we analyzed 10 constructions of PS using publicly available GWAS summary statistics. T2D incidence was examined in three cohorts of individuals without T2D at baseline. The adult cohort, comprised of 2333 participants followed from age≥20y, had 640 T2D cases. The youth cohort was comprised of 2229 participants followed from age 5-19y (228 cases). The birth cohort was comprised of 2894 participants followed from birth (438 cases). We assessed contributions of the PS and clinical variables in predicting T2D incidence. Results: Of the 10 PS constructions, a PS using 293 genome-wide significant variants from a large T2D GWAS meta-analysis in European-ancestry populations performed best. In the adult cohort, area under the receiver operating characteristic curve (AUC) for clinical variables for prediction of T2D incidence was 0·728; with the PS, 0·735. The PS’s hazard ratio (HR) was 1.27 per SD (p=1·6×10-8; 95% CI 1·17-1·38). In the youth cohort, the analogous model’s AUC was 0·805; with the PS, 0·812. The PS’s HR was 1·49 (p=4·3×10-8; 95% CI 1·29-1·72). In the birth cohort, AUC for the clinical variables was 0·614; with the PS, 0·685. The PS’s HR was 1·48 (p=2·8×10-16; 95% CI 1·35-1·63). To further assess the potential impact of including PS for assessing individual T2D risk, the net reclassification improvement (NRI) was calculated: NRI for the PS was 0·264, 0·249, and 0·309 for adult, youth, and birth cohorts, respectively. For comparison, NRI for HbA1c was 0·292 and 0·150 for adult and youth cohorts, respectively. Across all cohorts, as estimated using decision curve analyses, the net benefit of including the PS in addition to clinical variables was most pronounced at moderately stringent threshold probability (pt) values for instituting a preventive intervention. Discussion: This study demonstrates that the PS contributes significantly to prediction of T2D incidence in addition to the information provided by clinical factors; however, overall improvement of the prediction model was modest. Discriminatory power of the PS for predicting incident T2D was similar to that of other commonly measured clinical factors (e.g. HbA1c). Including T2D PS in addition to clinical factors may be clinically beneficial for identifying individuals at higher risk for T2D, especially at younger ages. Funding Statement: This study used computational resources of the Biowulf system at the National Institutes of Health (NIH), Bethesda, MD. The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Intramural Research Program provided support for NIDDK-based coauthors. The NIH Oxford-Cambridge Scholars Program provided funding support for L.E.W.’s doctoral programme. M.I.M. was a Wellcome Senior Investigator and an NIHR Senior Investigator. This work was funded in Oxford by the Wellcome Trust (098381, 106130, 203141), NIH (U01- DK105535; U01-DK085545) and National Institute for Health Research (NIHR) (NF-SI-0617- 10090) and Oxford Biomedical Research Centre (BRC). Declaration of Interests: The views expressed in this article are those of the author(s) and not necessarily those of NIH, NHS, NIHR, or UK Department of Health. M.I.M. has served on advisory panels for Pfizer, Novo Nordisk, and Zoe Global; has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly; and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, M.I.M. and A.M. are employees of Genentech and holders of Roche stock. Ethics Approval Statement: Protocols were approved by the institutional review board of the National Institute of Diabetes and Digestive and Kidney Diseases.

Telomere length and type 2 diabetes: Mendelian randomization study and polygenic risk score analysis.

Recent epidemiological studies suggest an association between shorter telomere length and higher risk for type 2 diabetes (T2D). However, results from observational studies are susceptible to confounding and reverse causation, and it is not clear whether there is a causal association between telomere length and T2D. Using Mendelian randomization (MR) and polygenic risk score (PRS) approaches, we had evaluated the causal effect of telomere length on T2D in the Chinese Han population. Using 8 telomere-length associated genetic variants as instrumental variables, an analysis of genetically predicted telomere length and T2D risk was performed in the MR study based on data from a T2D genome-wide association study (GWAS) in 2632 individuals (1318 cases and 1314 controls). We also applied a PRS approach to investigate the causal relationship using Chinese GWAS data. The inverse-variance weighted, MR-Egger regression, simple median, and weighted median methods yielded no evidence of association between genetically predicted longer telomere length and risk of T2D (OR = 0.78, 95% CI: 0.36 ~ 1.68, P = 0.522; OR = 0.23, 95% CI: 0.01 ~ 7.64, P = 0.412; OR = 0.60, 95% CI: 0.28 ~ 1.28, P = 0.185; OR = 0.64, 95% CI: 0.31 ~ 1.33,P = 0.233; respectively). Further, PRS analysis did not produce consistent genetic overlap between telomere length and T2D. Accordingly, this study found no evidence supporting a causal association between telomere length and T2D. Further studies with larger cohorts could yield more reliable results and conclusions.

244-OR: Single Nuclei Chromatin and Transcriptome Profiling across Human and Rat Skeletal Muscle Identifies Regulatory Elements, Genes, and Cell Types Associated with Diabetes GWAS Signals

Skeletal muscle is the largest organ, by weight (&amp;gt;40%), in the human body; insulin resistance in this and other peripheral tissues is a hallmark of type 2 diabetes (T2D). Recent genome wide association study (GWAS) results show significant enrichment for fasting insulin single nucleotide polymorphisms (SNPs) in skeletal muscle tissue chromatin-defined regulatory regions. The next steps towards mechanistic translation are identification of causal variants, the target genes these variants regulate, and the specific cell populations in which they function. To accomplish this, we optimized and performed single-nuclei resolution chromatin (snATAC-seq) and transcriptome (snRNA-seq) profiling across human and rat frozen skeletal muscle, collectively measuring ’omic profiles on 38,386 nuclei. We used integrative non-negative matrix factorization for data fusion across ’omics modalities and species, revealing seven distinct clusters that range in abundance from 58% to 2.1% of all nuclei. The clusters are consistent across independent human and rat samples and across ’omic modalities. We find heterogeneity in fasting insulin GWAS enrichment across snATAC peaks for the muscle cell types (2/7 significantly enriched), one of which is mesenchymal stem cells (∼4% of nuclei). We use genetically fine-mapped SNPs from the largest T2D GWAS and find the highest posterior probability 99% credible set SNP (rs702634) out of three at the ARL15 locus resides in a muscle mesenchymal stem cell-specific snATAC peak. We are nearly finished snRNA+snATAC profiling across 400 additional samples, which will enable muscle cell-specific e/chromQTL analyses and GWAS colocalization reports for the ADA meeting. This work represents a unique resource for the genetic signature of skeletal muscle biology at unprecedented epigenomic, transcriptomic, and cellular scales. Disclosure S. Parker: None. Funding American Diabetes Association (1-14-INI-07 to S.P.); National Institutes of Health (R01DK117960)

240-OR: Combining Type 2 Diabetes Polygenic Scores Improves Prediction in 36,000 European and Non-European Participants in the Partners Healthcare Biobank

Polygenic scores constructed by the aggregation of effect sizes of associated genetic variants have been shown to predict type 2 diabetes (T2D). Polygenic scores can be defined as “restricted-to-significant polygenic scores” (rsPS), and “globally expanded polygenic scores” (gePS), which can include millions of variants beyond those that are genome-wide significant. Most polygenic scores have been generated based on genome-wide association studies (GWAS) conducted predominantly in participants of European ancestry (EU); how these polygenic scores perform in non-European individuals in the healthcare system has not been thoroughly evaluated. We applied the rsPS and gePS for T2D in 30,240 EU participants, 3,029 Latinos (LA), and 2,201 African Americans (AA) from the Partners HealthCare Biobank, which maintains blood and DNA samples from patients seen at Partners HealthCare-affiliated hospitals in Boston. We compared rsPS based on the DIAMANTE consortium meta-analysis, with 398 variants (rsPS_398), rsPS based on the latest Million Veterans Program meta-analysis, with 682 variants (rsPS_682), and a gePS based on ldpred, including 6.9M variants adjusted for their effects based on linkage disequilibrium. rsPS_682 was most strongly associated with T2D in all ancestries (OR per SD in EU=1.73 p=7×10-142, OR per SD in LA =1.88, p=7×10-19, OR per SD in AA =1.47, p=1×10-8 compared to the other polygenic scores. We observed that rsPS_682 and the gePS provided both complementary information, and that combining them into the model improved the prediction in all ancestries, with 16%, 22%, and 16% increase in accuracy in EU, LA, and AA, respectively. We show the advantage of combining rsPS and gePS to improve prediction of T2D in multi-ethnic populations within a healthcare system. The results suggest that larger and multi-ethnic T2D GWAS are needed to avoid major health disparities. Disclosure J.M. Mercader: None. J.C. Florez: Advisory Panel; Self; Doris Duke Charitable Foundation. Other Relationship; Self; Novo Nordisk Inc., Park Street School. A. Leong: None. V. Kaur: None. M. Udler: None. J.B. Meigs: Consultant; Self; Quest Diagnostics. B. Porneala: None. Funding American Diabetes Association (1-19-ICTS-068 to J.M.M.)

1460-P: Type 2 Diabetes Polygenic Score in Addition to Clinical Factors for Prediction of Diabetes Incidence in an Indigenous American Population

Background: Type 2 diabetes (T2D)-associated variants, derived from genome-wide association studies (GWAS), have largely been reproducible across populations, but there is limited information on how polygenic scores (PS) based on these variants add to clinical factors for predicting diabetes incidence, particularly in non-European populations. Methods: A longitudinal study of diabetes was conducted in an Indigenous study population native to the Southwestern U.S. At each exam, a 75-g oral glucose tolerance test was administered with measurement of fasting and 2-hour plasma glucose (FPG, 2hPG). Genotypes were available from a GWAS for 2783 participants aged ≥ 20 years without diabetes at baseline, with imputed genotypes based on whole genome sequence data from 296 individuals from the population. PSs for participants were constructed and weighted using genome-wide significant variants in a T2D GWAS meta-analysis in European-descent populations (n=245; Mahajan 2018). Survival analyses were conducted to predict diabetes incidence in 2 models: (1) age, sex, BMI, FPG; (2) age, sex, BMI, FPG, T2D PS. Area under the receiver-operating characteristic curve (AUC) was calculated using a nonparametric method for survival analysis. Results: 903 cases (32.5%) of diabetes occurred during 25,110 person-years of follow-up. The hazard ratio for T2D PS, controlled for age, sex, BMI and FPG, was 1.27 per SD (P=1.5×10-12). Cumulative incidence at 10 years was 0.076 in the lowest quartile for the prediction score based on clinical factors alone and 0.509 in the highest quartile. For the model including PS, corresponding values were 0.075 and 0.524. AUC was 0.710 for the model with clinical factors alone and 0.721 for the model including T2D PS. Conclusion: Weighted T2D PSs based on 245 variants were informative in predicting T2D incidence in Indigenous Americans; however, the improvement in prediction beyond clinical factors alone was modest. Disclosure L.E. Wedekind: None. S. Kobes: None. W. Hsueh: None. L. Baier: None. W.C. Knowler: None. A. Mahajan: None. M.I. McCarthy: Advisory Panel; Self; Illumina. Consultant; Self; Eli Lilly and Company, Novo Nordisk Inc., Zoe Global Ltd. Employee; Self; Genentech, Inc. Research Support; Self; AbbVie Inc., Merck &amp; Co., Inc., Sanofi-Aventis, Servier, Takeda UK. R.L. Hanson: None.

Computational and functional analyses of T2D GWAS SNPs for transcription factor binding

Genome-wide association studies (GWASs) have successfully identified numerous non-coding genetic variants for type 2 diabetes (T2D), but the functional roles underlying these non-coding variants remain largely unknown. The effects of T2D GWAS lead SNPs on transcriptional factors binding motifs were firstly analyzed via JASPAR, followed by functional validations including dual-luciferase reporter assays, biotin-based DNA pull-down assays, real-time quantitative PCR, and western blotting. The results showed that GWAS SNP rs4430796 conferred T allele specific transcriptional enhancer activity via a PAX6 binding element, and upregulated the expression of HNF1B. GWAS SNP rs4607103 showed a bidirectional modulation of ADAMTS9-AS2 and ADAMTS9 by TCF7L2 in a T allele-specific manner. GWAS SNP rs849135 conferred C allele-specific bidirectional transcriptional enhancer activity via a CREB1 binding element. Our findings have uncovered the functional mechanisms of three T2D GWAS SNPs via affecting the binding of transcription factors, providing new insights into the genetics and molecular pathogenesis of T2D.

Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes

Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer clusters or super-enhancers. So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in three-dimensional (3D) space. Furthermore, their target genes are often unknown. We have created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers to their target genes, often located hundreds of kilobases away. It also revealed >1,300 groups of islet enhancers, super-enhancers and active promoters that form 3D hubs, some of which show coordinated glucose-dependent activity. We demonstrate that genetic variation in hubs impacts insulin secretion heritability, and show that hub annotations can be used for polygenic scores that predict T2D risk driven by islet regulatory variants. Human islet 3D chromatin architecture, therefore, provides a framework for interpretation of T2D genome-wide association study (GWAS) signals.

Impact of variants on type-2 diabetes risk genes identified through genomewide association studies in polycystic ovary syndrome: a case–control study

Polycystic ovary syndrome (PCOS) is a common endocrine disorder in females, and is associated with altered metabolic processes in particular insulin resistance and diabetes mellitus. PCOS shares with type-2 diabetes (T2D) a number of features, including beta cell dysfunction, impaired glucose tolerance and dyslipidaemia. Recently, genomewide association studies (GWAS) have reported a number of genes with reproducible associations and susceptibilities to T2D. To address this, we examined the association between the T2D GWAS candidate genes (CDKAL1, CDKN2B, COL8A1, HHEX, IGF2BP2, KCNJ1, KCNQ1 and SLC30A8) and PCOS in Saudi women. A case-control study, includes 162 cases and 162 controls was enrolled. Genotyping was carried out by the allelicdiscrimination method. Our results showed that the variants including rs792837 of COL8A1, rs61873498 of KCNQ1 and rs13266634 of SLC30A8 genes to be significantly more frequent in PCOS patients than in controls. Our results suggest that COL8A1, KCNQ1 and SLC30A8, which are previously identified through GWAS as T2D-associated genes, are associated with PCOS.