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Aims/hypothesisType 2 diabetes (T2D) is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of T2D loci can be leveraged to identify such pathways, thus facilitating improved disease management.MethodsWe developed a high-throughput pipeline to enable clustering of T2D loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for T2D and related traits to generate a matrix of 324 variant x 64 trait associations and applied Bayesian Non-negative Factorization (bNMF) to identify genetic components of T2D. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance.ResultsWe identified ten clusters, replicating the five from our prior analysis as well as novel clusters related to beta-cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein-A, and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance, and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta-cell clusters differentially enriched in functional and stressed pancreatic beta-cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple shared genetic pathways with T2D.Conclusions/interpretationOur approach stratifies T2D loci into physiologically meaningful genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

OBJECTIVE Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity. RESEARCH DESIGN AND METHODS Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459). RESULTS The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations. CONCLUSIONS Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk.

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in pre-diabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in metformin (MET, n=876) and placebo (PBO, n=887) arms. Multiple linear regression assessed association with one-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (pENOSF1 (minor allele frequency [MAF]AFR=0.07, MAFEUR=0.002) was associated with an increase in % glycated hemoglobin (per minor allele β=0.39 [95% CI 0.28, 0.50], p=2.8×10-12). Rs145591055 near OMSR (MAF=0.10 in American Indians), was associated with weight loss (kg) (per G allele β=-7.55 [95% CI -9.88, -5.22], p=3.2×10-10) in MET. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants (p(G×T)

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in pre-diabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in metformin (MET, n=876) and placebo (PBO, n=887) arms. Multiple linear regression assessed association with one-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (p

HypothesisThe prevalence of type 2 diabetes is higher in Latino populations compared with other major ancestry groups. Not only has the Latino population been systematically underrepresented in large-scale genetic analyses, but previous studies relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation reference panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The NHLBI Trans-Omics for Precision Medicine (TOPMed) reference panel represents a unique opportunity to analyze rare genetic variations in the Latino population.MethodsWe evaluate the TOPMed imputation performance using genotyping array and whole-exome sequence data in 6 Latino cohorts. To evaluate the ability of TOPMed imputation of increasing the identified loci, we performed a Latino type 2 diabetes GWAS meta-analysis in 8,150 type 2 diabetes cases and 10,735 controls and replicated the results in 6 additional cohorts including whole-genome sequence data from the All of Us cohort.ResultsWe show that, compared to imputation with 1000G, the TOPMed panel improves the identification of rare and low-frequency variants. We identified 26 distinct signals including a novel genome-wide significant variant (minor allele frequency 1.6%, OR=2.0, P=3.4×10−9) near ORC5. A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improves the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance.ConclusionsOur results demonstrate the utility of TOPMed imputation for identifying low-frequency variation in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores.

Context Polymorphisms in the gene encoding the glucagon-like peptide-1 receptor (GLP1R) are associated with type 2 diabetes but their effects on incretin levels remain unclear. Objective We evaluated the physiologic and hormonal effects of GLP1R genotypes before and after interventions that influence glucose physiology. Design Pharmacogenetic study conducted at 3 academic centers in Boston, Massachusetts. Participants A total of 868 antidiabetic drug-naïve participants with type 2 diabetes or at risk for developing diabetes. Interventions We analyzed 5 variants within GLP1R (rs761387, rs10305423, rs10305441, rs742762, and rs10305492) and recorded biochemical data during a 5-mg glipizide challenge and a 75-g oral glucose tolerance test (OGTT) following 4 doses of metformin 500 mg over 2 days. Main Outcomes We used an additive mixed-effects model to evaluate the association of these variants with glucose, insulin, and incretin levels over multiple timepoints during the OGTT. Results During the OGTT, the G-risk allele at rs761387 was associated with higher total GLP-1 (2.61 pmol/L; 95% CI, 1.0.72-4.50), active GLP-1 (2.61 pmol/L; 95% CI, 0.04-5.18), and a trend toward higher glucose (3.63; 95% CI, -0.16 to 7.42 mg/dL) per allele but was not associated with insulin. During the glipizide challenge, the G allele was associated with higher insulin levels per allele (2.01 IU/mL; 95% CI, 0.26-3.76). The other variants were not associated with any of the outcomes tested. Conclusions GLP1R variation is associated with differences in GLP-1 levels following an OGTT load despite no differences in insulin levels, highlighting altered incretin signaling as a potential mechanism by which GLP1R variation affects T2D risk.

To investigate a causal relationship between Vitamin D levels and non-infectious uveitis and scleritis using Mendelian randomization (MR) techniques.Two-sample Mendelian randomization case-control study.The study setting was a biobank of an academic, integrated health care system. The patient population comprised 375 case patients with a non-infectious uveitis and/or scleritis diagnosis and no diagnosis of infectious, trauma-related, or drug-induced uveitis/scleritis. In addition, there were 4167 controls with no uveitis or scleritis diagnosis. Causal effect estimates of low 25-hydroxy Vitamin D (25OHD) on uveitis/scleritis risk were calculated.We found an association of genetically decreased 25OHD with uveitis/scleritis risk (odds ratio [OR] = 2.16, 95% CI = 1.01-4.64, P = .049, per SD decrease in log25OHD). In a first sensitivity MR analysis excluding the genetic variants that are unlikely to have a role in biologically active 25OHD, effect estimates were consistent with those from the primary analysis (OR = 2.38, 95% CI =1.06-5.36, P = 0.035, per SD of log25OHD). Furthermore, in a second sensitivity analysis using only the 6 variants within the CYP2R1 locus (which encodes 25OHD hydroxylase, the liver enzyme responsible for converting Vitamin D to 25OHD), genetically decreased 25OHD was strongly associated with increased uveitis/scleritis risk (OR = 6.42, 95% CI = 3.19-12.89, P = 1.7 × 10Our findings suggest a causal relationship between low Vitamin D levels and higher risk of non-infectious uveitis and scleritis. Vitamin D supplementation may be a low-cost, low-risk intervention to mitigate non-infectious uveitis and scleritis risk, and should be explored in a prospective trial.

Understanding atypical forms of diabetes may lead to personalized treatment regimens and discovery of novel pathophysiologic mechanisms of diabetes. We aimed to create a high-throughput method for identifying patients with atypical diabetes in large electronic health record (EHR) databases and validate this method in the Mass General Brigham (MGB) Biobank. Patients with likely type 2 diabetes (T2D) were identified using a validated machine-learning (ML) algorithm. “Typical” T2D was filtered out through a “base algorithm” that excluded individuals with body mass index (BMI) ever >30 kg/m2, HDL values ever < 50 mg/dL, and triglycerides ever >150 mg/dL. To remove people with typical type 1 diabetes (T1D) , we tested six additional “branch algorithms,” relying on clinical characteristics, including autoantibodies, medication usage, and T1D diagnosis determined by ML algorithm, resulting in six overlapping cohorts. Charts were then manually reviewed, and diabetes type was classified by two endocrinologists into one of three categories: atypical, not atypical, and indeterminate due to missing information. We identified 1potentially atypical cases, of whom 16 individuals were confirmed to have atypical diabetes after expert review, across the six branch algorithms. The branch algorithm which excluded T1D by removing patients who had ever used outpatient insulin had the highest percentage yield (13 of 27; 48.2%) of atypical diabetes . The 16 atypical cases had significantly lower BMI and higher HDL compared to an unselected group of individuals with T1D or T2D diagnosis by ML algorithm. Compared to the ML T1D group, the atypical group had a significantly higher T2D polygenic score and lower hemoglobin A1c. In summary, we designed an algorithm to identify individuals with atypical diabetes within an EHR database with up to 48% yield which may shed light on the heterogeneity of T2D and help generate cohorts of atypical cases for studies, such as the Rare and Atypical Diabetes Network (RADIANT) . Disclosure V.Chen: Other Relationship; Draeger. S.Cromer: Other Relationship; Johnson & Johnson Medical Devices Companies, Research Support; National Institute of Diabetes and Digestive and Kidney Diseases. C.Han: None. W.G.Marshall: None. S.Emongo: None. T.Majarian: Employee; Vertex Pharmaceuticals Incorporated. J.C.Florez: Consultant; AstraZeneca, Goldfinch Bio, Inc., Other Relationship; AstraZeneca, Merck & Co., Inc., Novo Nordisk. J.M.Mercader: None. M.Udler: None.

Introduction: Many biobanks use machine learning algorithms to predict patient phenotypes such as type 1 diabetes (T1D) . It is not known how these algorithms perform in patients with different ancestral backgrounds or whether incorporating genetic data improves phenotypic classification. Methods: We examined 40,843 patients in the Mass General Brigham (MGB) Biobank, including 34,870 white patients and 4,872 non-white patients. The MGB machine learning algorithm predicted 242 patients to have T1D. After applying a gold standard diagnosis based on chart review, we assessed accuracy of the MGB algorithm in different self-reported races and genetic ancestries. We divided the dataset into separate training and validation cohorts, and we constructed a logistic regression model using the machine learning algorithm and previously published T1D polygenic scores to classify patients as T1D cases or controls. Results: The MGB machine learning algorithm for T1D had a positive predictive value of 77.8% for white patients, but only 62.2% for non-white patients (p = 0.04) . Among patients with predominantly European genetic ancestry, adding a polygenic score derived from European populations to the MGB classification algorithm improved the area under the receiver operating curve (AUC) from 0.928 to 0.974 (p < 0.05) . Among patients whose predominant genetic ancestry was not European, adding a polygenic score derived from African populations improved the AUC from 0.889 to 0.920 (p > 0.05) . In both cases, the improvement in AUC was less substantial when using non-ancestry-matched polygenic scores. Conclusions: Automated prediction tools for T1D are imperfect, and performance may differ by patient race. Incorporating ancestry-specific polygenic scores into phenotyping algorithms can improve diagnostic accuracy. This may help reduce healthcare and research disparities by refining the automated classification of T1D in all racial and ancestral groups. Disclosure A.J.Deutsch: None. T.Majarian: Employee; Vertex Pharmaceuticals Incorporated. J.M.Mercader: None. J.C.Florez: Consultant; AstraZeneca, Goldfinch Bio, Inc., Other Relationship; AstraZeneca, Merck & Co., Inc., Novo Nordisk. M.Udler: None. Funding National Institutes of Health (K23DK114551, T32DK007028)

Introduction: Most genome-wide association studies (GWAS) of type 2 diabetes (T2D) assume an additive mode of inheritance and equal effects in men and women. These assumptions can preclude the discovery of variants with effects that are non-additive and/or sex-specific. Focused exploration of these effects may reveal novel genetic variation associated with T2D and improve the predictive performance of polygenic risk scores (PRS) . Methods: We performed a sex-stratified GWAS, using additive and recessive models for T2D, in individuals of European ancestry in the UK Biobank (UKBB) and Genetic Epidemiology Research on Aging cohort. We also generated sex-specific and non-sex-specific T2D PRSs, using PRS-CS, and assessed their performance in Mass General Brigham Biobank (MGBB) . Results: As the largest sex-stratified additive and recessive GWAS of T2D performed to date, this study included 30,625 cases and 223,442 controls. In the recessive analysis, we identified 7 novel variants, of which 1 was female-specific and 1 was male-specific. Among these variants, 4 were associated with over 10-fold increase in risk for T2D. The male-specific variant, rs35725476 (OR = 1.49, p = 3×10- 9) , is associated with higher expression of a long non-coding RNA in pancreatic islets (p = 3×10- 14) . In the additive analysis, we identified a novel female-specific protective variant, rs12109272 (OR = 0.91, p = 3×10-8) , which is associated with lower PCSK1 expression, lower risk of gestational diabetes (p = 1×10- 6) and lower fasting glucose (p = 6×10- 32) in independent cohorts. Finally, the sex-specific PRS outperformed the non-sex-specific PRS at predicting T2D in MGBB (AUC of 0.653 versus 0.643, p [of difference] = 2×10-4 in males; AUC of 0.674 versus 0.656, p [of difference] = 3×10-4 in females) . Conclusions: These findings demonstrate the value of non-additive and sex-stratified analyses for both variant discovery and improving polygenic prediction for T2D. Disclosure P.H.Schroeder: None. J.B.Cole: None. A.Leong: None. J.C.Florez: Consultant; AstraZeneca, Goldfinch Bio, Inc., Other Relationship; AstraZeneca, Merck & Co., Inc., Novo Nordisk. J.M.Mercader: None. Funding American Diabetes Association (1-19-ICTS-068) ; National Human Genome Research Institute (U01HG011723)

The prevalence of type 2 diabetes in youth has increased substantially, yet the genetic underpinnings remain largely unexplored. To identify genetic variants predisposing to youth-onset type 2 diabetes, we formed ProDiGY, a multiethnic collaboration of three studies (TODAY, SEARCH, and T2D-GENES) with 3,006 youth case subjects with type 2 diabetes (mean age 15.1 ± 2.9 years) and 6,061 diabetes-free adult control subjects (mean age 54.2 ± 12.4 years). After stratifying by principal component–clustered ethnicity, we performed association analyses on ∼10 million imputed variants using a generalized linear mixed model incorporating a genetic relationship matrix to account for population structure and adjusting for sex. We identified seven genome-wide significant loci, including the novel locus rs10992863 in PHF2 (P = 3.2 × 10−8; odds ratio [OR] = 1.23). Known loci identified in our analysis include rs7903146 in TCF7L2 (P = 8.0 × 10−20; OR 1.58), rs72982988 near MC4R (P = 4.4 × 10−14; OR 1.53), rs200893788 in CDC123 (P = 1.1 × 10−12; OR 1.32), rs2237892 in KCNQ1 (P = 4.8 × 10−11; OR 1.59), rs937589119 in IGF2BP2 (P = 3.1 × 10−9; OR 1.34), and rs113748381 in SLC16A11 (P = 4.1 × 10−8; OR 1.04). Secondary analysis with 856 diabetes-free youth control subjects uncovered an additional locus in CPEB2 (P = 3.2 × 10−8; OR 2.1) and consistent direction of effect for diabetes risk. In conclusion, we identified both known and novel loci in the first genome-wide association study of youth-onset type 2 diabetes.

OBJECTIVE Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed. RESEARCH DESIGN AND METHODS Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD). RESULTS Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway. CONCLUSIONS Our findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

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.

The burden of several common diseases including obesity, diabetes, hypertension, asthma, and depression is increasing in most world populations. However, the mechanisms underlying the numerous epidemiological and genetic correlations among these disorders remain largely unknown. We investigated whether common polymorphic inversions underlie the shared genetic influence of these disorders. We performed an inversion association analysis including 21 inversions and 25 obesity-related traits on a total of 408,898 Europeans and validated the results in 67,299 independent individuals. Seven inversions were associated with multiple diseases while inversions at 8p23.1, 16p11.2, and 11q13.2 were strongly associated with the co-occurrence of obesity with other common diseases. Transcriptome analysis across numerous tissues revealed strong candidate genes for obesity-related traits. Analyses in human pancreatic islets indicated the potential mechanism of inversions in the susceptibility of diabetes by disrupting the cis-regulatory effect of SNPs from their target genes. Our data underscore the role of inversions as major genetic contributors to the joint susceptibility to common complex diseases., This research has received funding from Ministerio de Ciencia, Innovación y Universidades (MICIU), Agencia Estatal de Investigación (AEI), and Fondo Europeo de Desarrollo Regional, UE (RTI2018-100789-B-I00) also through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S); and the Catalan Government (SGR2017/801 and #016FI_B 00272 to C.R.-A.) through the CERCA Program. J.G. is funded by the European Commission (H2020-ERC-2014-CoG-647900) and the MINECO/AEI/FEDER, EU (BFU2017-82937-P). The L.A.P.-J. lab was funded by the Spanish Ministry of Science and Innovation (ISCIII-FEDER P13/02481), the Catalan Department of Economy and Knowledge (SGR2014/1468, SGR2017/1974, and ICREA Acadèmia), and also acknowledges support from the Spanish Ministry of Economy and Competiveness“Programa de Excelencia María de Maeztu” (MDM-2014-0370). This research was conducted using the UK Biobank Resource under Application Number 43983. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.

BackgroundCharacterization of genetic variation that influences response to glucose-lowering medications is instrumental to precision medicine for treatment of type 2 diabetes (T2D). SUGAR-MGH examined the acute response to two anti-diabetes medications in order to understand the functional relevance of known T2D- and glycaemic trait-associated genetic loci.Methods1,000 participants at risk for T2D from diverse ancestries underwent sequential glipizide and metformin challenges. A genome-wide association study was performed using the Illumina Multi-Ethnic Genotyping Array. Imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between variants and primary endpoints of drug response. We evaluated the influence of 804 unique T2D and glycaemic trait-associated variants on SUGAR-MGH outcomes and performed colocalization analyses to identify shared genetic signals.FindingsFive genome-wide significant variants were associated with metformin or glipizide response. The strongest association was between an African ancestry-specific variant (minor allele frequency=0·026) at rs149403252 and lower fasting glucose following metformin, adjusted for baseline glucose (p=1·9×10−9), with a 0·94 mmol/L larger decrease in fasting glucose after metformin. We identified associations between T2D-associated variants and glycaemic response, including the T2D-protective C allele of rs703972 near ZMIZ1 and increased levels of active GLP-1 (p=1·6×10−5), supporting the role of alterations in incretin levels in T2D pathophysiology.InterpretationWe present a well phenotyped, densely genotyped, multi-ancestry resource to study gene-drug interactions, uncover novel variation associated with response to common anti-diabetes medications, and provide insight into mechanisms of action of T2D-related variation.FundingUS National Institutes of Health.

Objective: Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However there exists significant variability in glycaemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction. Research design and methods: As an initiative of the Metformin Genetics Plus (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortia, 5,485 white Europeans with type 2 diabetes treated with sulfonylurea were recruited from 6 referral centres in Europe and North America. We first estimated heritability using generalized restricted maximum likelihood (REML) and then undertook GWAS of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy followed by meta-analysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, cis-eQTLs and functional validation in cellular models. Finally, we examined for a possible drug-drug-gene interactions. Results: After establishing that sulfonylurea response is heritable (37±11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction at a genome-wide scale (p < 5×10-8). The C-allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), p=2.39×10−8) lower reduction in HbA1c. Similarly, the C-allele was associated with higher glucose trough levels (β=1.61, p=0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). In 3, 029 human whole blood samples, the C-allele is a cis-eQTL for increased expression of GXYLT1 (β=0.21, p=2.04×10-58). The C-allele of rs10770791, in an intronic region of SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA1c (p=4.80×10−8). In 1,183 human liver samples, the C-allele at rs10770791 is a cis-eQTL for reduced SLCO1B1 expression (p=1.61×10−7) which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use, sulfonylurea response and SCLO1B1 genotype was observed (p=0.001). In statin non-users, C-allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48±0.12% (5.2±1.26 mmol/mol)), equivalent to initiating a DPP4 inhibitor. Conclusion: We have identified clinically important genetic effects at genome wide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important when prescribing glucose-lowering drugs.

OBJECTIVE LDL cholesterol (LDLc)-lowering drugs modestly increase body weight and type 2 diabetes risk, but the extent to which the diabetogenic effect of lowering LDLc is mediated through increased BMI is unknown. RESEARCH DESIGN AND METHODS We conducted summary-level univariable and multivariable Mendelian randomization (MR) analyses in 921,908 participants to investigate the effect of lowering LDLc on type 2 diabetes risk and the proportion of this effect mediated through BMI. We used data from 92,532 participants from 14 observational studies to replicate findings in individual-level MR analyses. RESULTS A 1-SD decrease in genetically predicted LDLc was associated with increased type 2 diabetes odds (odds ratio [OR] 1.12 [95% CI 1.01, 1.24]) and BMI (β = 0.07 SD units [95% CI 0.02, 0.12]) in univariable MR analyses. The multivariable MR analysis showed evidence of an indirect effect of lowering LDLc on type 2 diabetes through BMI (OR 1.04 [95% CI 1.01, 1.08]) with a proportion mediated of 38% of the total effect (P = 0.03). Total and indirect effect estimates were similar across a number of sensitivity analyses. Individual-level MR analyses confirmed the indirect effect of lowering LDLc on type 2 diabetes through BMI with an estimated proportion mediated of 8% (P = 0.04). CONCLUSIONS These findings suggest that the diabetogenic effect attributed to lowering LDLc is partially mediated through increased BMI. Our results could help advance understanding of adipose tissue and lipids in type 2 diabetes pathophysiology and inform strategies to reduce diabetes risk among individuals taking LDLc-lowering medications.

SummaryA primary obstacle in translating genetics and genomics data into therapeutic strategies is elucidating the cellular programs affected by genetic variants and genes associated with human diseases. Broadly applicable high-throughput, unbiased assays offer a path to rapidly characterize gene and variant function and thus illuminate disease mechanisms. Here, we report LipocyteProfiler, an unbiased high-throughput, high-content microscopy assay that is amenable to large-scale morphological and cellular profiling of lipid-accumulating cell types. We apply LipocyteProfiler to adipocytes and hepatocytes and demonstrate its ability to survey diverse cellular mechanisms by generating rich context-, and process-specific morphological and cellular profiles. We then use LipocyteProfiler to identify known and novel cellular programs altered by polygenic risk of metabolic disease, including insulin resistance, waist-to-hip ratio and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse phenotypic profiling in lipid-storing cells, and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.

ObjectiveMost 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.Research Design and MethodsWe 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 then used two additional cohorts, FinnGen and a Danish cohort, for replication. For the most significant recessive signal, we conducted a phenome-wide association study across hundreds of traits to make inferences about the pathophysiology underlying the increased risk seen in homozygous carriers.ResultsWe identified 51 loci associated with type 2 diabetes, including five variants with recessive effects 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. 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). Colocalization analysis linked this signal to reduced expression of the nearby PELO gene, and the signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides.ConclusionsOur 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.

The high prevalence of T2D among Latino individuals emphasizes the importance of studying both environmental and genetic factors that influence disease in this population. However, Latino is a recently admixed population with complex linkage disequilibrium patterns that impose methodological challenges, especially when using genotype imputation to test association with rare variation. The recent release of the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) reference panel, comprising more than 100,000 samples (~10% are Latinos), represents a unique opportunity to explore rare genetic variation in this population. We performed the largest T2D GWAS meta-analysis in the Latino population imputed to the TOPMed panel. Our analysis includes 18,294 individuals (7,556 cases and 10,738 controls) with genotyping array and whole-exome sequencing data. We were able to impute 38M high-quality variants (rsq>0.8) with TOPMed (5M with minor allele frequency [MAF] 0.001-0.0005) compared to 16M with 1000G Phase 3 panel (1.6M with [MAF] 0.001-0.0005). This allowed us to replicate previously reported T2D-associated common variants, and to identify a novel genome-wide significant variant (MAF=0.016, OR=2.06, P=1.7×10-9) near ORC5, a gene implicated in familial hyperinsulinemic hypoglycemia 2. Two additional suggestive novel signals merit further research: a variant in ROR2 (MAF=0.066, OR=1.3, P=8.8×10-8) and a Latino specific variant in HDAC2 only identified with the use of TOPMed (MAF=0.013, OR=1.82, P=3.2×10-7) and associated with insulin secretion in individuals without diabetes in this study. Our results demonstrate the utility of the TOPMed imputation for the identification of novel and population-specific rare variation associated with T2D in understudied populations, which could lead to the identification of novel therapeutic targets and the improvement of polygenic risk scores. Disclosure A. Huerta: None. Mgb biobank: n/a. J. B. Cole: None. P. Dornbos: None. D. A. Dicorpo: None. A. Leong: None. J. C. Florez: Consultant; Self; Goldfinch Bio, Inc., Other Relationship; Self; Novo Nordisk. J. M. Mercader: None. Sigma t2d consortium: n/a. Mx biobank: n/a. Funding American Diabetes Association (1-19-ICTS-068 to J.M.M.)

Genome-wide significant (GWS) loci for glycemic response to MET in type 2 diabetes (T2D) reported elsewhere have not replicated in the DPP. Thus, to assess whether pharmacogenetic interactions may differ in impaired glucose tolerance (IGT), we conducted a GWAS in the DPP for MET response, defined by diabetes incidence and change in quantitative traits (fasting glucose, 2-hour glucose on oral glucose tolerance test, hemoglobin A1c [HbA1c], fasting insulin, insulin sensitivity index, and weight). Samples were genotyped on the Illumina HumanCore Exome array and imputed on the 1000 Genomes Phase3 panel. Cox proportional hazards models tested associations with diabetes incidence in MET (n=876) and placebo (PBO, n=887) arms. Multiple linear regression using an additive model evaluated for association with 1-year change in quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-environment (G×E) interaction between MET and PBO. Results were filtered to minor allele frequency (MAF) >1% and imputation score >0.7. We identified 3 GWS variants after correcting for correlated traits (P In summary, a GWAS for MET response in participants with IGT revealed novel associations that require validation in ancestry-specific populations and may have implications for tailored therapy. Disclosure J. H. Li: None. R. L. Hanson: None. S. E. Kahn: Advisory Panel; Self; Bayer AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk, Pfizer Inc. W. C. Knowler: None. T. I. Pollin: None. J. C. Florez: Consultant; Self; Goldfinch Bio, Inc., Other Relationship; Self; Novo Nordisk. D. Research group: None. J. A. Perry: None. K. A. Jablonski: None. L. Chen: None. S. Srinivasan: None. J. N. Todd: None. M. Harden: None. J. M. Mercader: None. P. W. Franks: Advisory Panel; Self; Zoe Global Limited, Consultant; Self; Eli Lilly and Company, Novo Nordisk. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK048489)

SUGAR-MGH is a pharmacogenetic resource for characterizing genetic influences on pharmacological perturbations relevant to type 2 diabetes (T2D). 1,000 participants who were naïve to T2D treatment received 5 mg glipizide, followed by 4 doses of 500 mg metformin and a 75-g oral glucose tolerance test (OGTT) a week later. Glucose and insulin were measured at pre-specified endpoints after glipizide and during the OGTT. Incretin levels were measured in a subset. We calculated physiologic endpoints reflecting insulin secretion, insulin sensitivity, and incretin secretion. The mean age was 47 years, 54% were women, >35% were non-white, and the mean BMI was 30.2 kg/m2. 890 participants underwent genome-wide genotyping using the Illumina Multi-Ethnic Genotyping Array, and high-quality imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between genetic variants and endpoints. We identified 21 genome-wide significant variants, of which 3 met experiment-wide significance (P Disclosure J. H. Li: None. V. Kaur: None. L. Brenner: Research Support; Self; Apple. J. M. Mercader: None. J. C. Florez: Consultant; Self; Goldfinch Bio, Inc., Other Relationship; Self; Novo Nordisk. Funding National Institutes of Health (R01DK088214, R03DK077675, P30DK036836, M01RR01066, 1UL1RR025758-04, 8UL1TR000170-05, T32DK007028)

Genetics and socioeconomic status (SES) are key drivers of increased type 2 diabetes (T2D) risk, but the extent to which they impact disease in an additive or non-additive manner is unknown. We examined T2D prevalence using cross-sectional electronic health record (EHR) data from 27,771 participants of European ancestry in the Mass General Brigham Biobank. A polygenic score for T2D was derived using a previously published globally expanded score (gePS). Multiple area-based socioeconomic risk estimates were evaluated (including deprivation indices and a machine learning model incorporating hundreds of census variables) with similar impact on T2D risk; here, an individual’s estimated SES risk was defined as the percent of residents in their census tract with less than high school education. T2D status was derived using a machine-learning algorithm validated by expert chart review to have 90% positive predictive value. The age and sex-adjusted odds of T2D was 1.72 (95% CI 1.63-1.81, p Disclosure S. Cromer: Employee; Spouse/Partner; Johnson & Johnson Medical Devices Companies. C. M. Lakhani: Consultant; Self; xy. ai. J. M. Mercader: None. T. Majarian: None. J. C. Florez: Consultant; Self; Goldfinch Bio, Inc., Other Relationship; Self; Novo Nordisk. A. Manning: Employee; Spouse/Partner; Emulate, Inc, Stock/Shareholder; Spouse/Partner; Invitae. J. Merino: None. M. Udler: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (T32DK007028)

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite notable anthropometric variability with ancestry proportions, and a high burden of growth stunting and overweight/obesity in Hispanic/Latino populations. This address this knowledge gap, we analyzed densely-imputed genetic data in a sample of Hispanic/Latino adults, to identify and fine-map common genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (Stage 1, n=59,769) and validated our findings in 9 additional studies (HISLA Stage 2, n=9,336). We conducted a trans-ethnic GWAS with summary statistics from HISLA Stage 1 and existing consortia of European and African ancestries. In our HISLA Stage 1+2 analyses, we discovered one novel BMI locus, as well two novel BMI signals and another novel height signal, each within established anthropometric loci. In our trans-ethnic meta- analysis, we identified three additional novel BMI loci, one novel height locus, and one novel WHRadjBMI locus. We also identified three secondary signals for BMI, 28 for height, and two for WHRadjBMI. We replicated >60 established anthropometric loci in Hispanic/Latino populations at genome-wide significance—representing up to 30% of previously-reported index SNP anthropometric associations. Trans-ethnic meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our novel findings demonstrate that future studies may also benefit from leveraging differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.

GWAS have identified more than 700 genetic signals associated with type 2 diabetes (T2D). To gain insight into the underlying molecular mechanisms, we created the Translational human pancreatic Islet Genotype tissue-Expression Resource (TIGER), aggregating >500 human islet RNA-seq and genotyping datasets. We imputed genotypes using 4 reference panels and meta-analyzed cohorts to improve coverage of expression quantitative trait loci (eQTL) and developed a method to combine allele-specific expression across samples (cASE). We identified >1 million islet eQTLs (56% novel), of which 53 colocalize with T2D signals (60% novel). Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identified 8 novel cASE colocalizations, among which an SLC30A8 T2D associated variant. We make all the data available through the open-access TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translate this into therapeutic insight and precision medicine for T2D.

Context There is substantial heterogeneity in insulin sensitivity, and genetics may suggest possible mechanisms by which common variants influence this trait. Objectives We aimed to evaluate an 11-variant polygenic lipodystrophy genetic risk score (GRS) for association with anthropometric, glycemic and metabolic traits in the Diabetes Prevention Program (DPP). In secondary analyses, we tested the association of the GRS with cardiovascular risk factors in the DPP. Design In 2713 DPP participants, we evaluated a validated GRS of 11 common variants associated with fasting insulin-based measures of insulin sensitivity discovered through genome-wide association studies that cluster with a metabolic profile of lipodystrophy, conferring high metabolic risk despite low body mass index (BMI). Results At baseline, a higher polygenic lipodystrophy GRS was associated with lower weight, BMI, and waist circumference measurements, but with worse insulin sensitivity index (ISI) values. Despite starting at a lower weight and BMI, a higher GRS was associated with less weight and BMI reduction at one year and less improvement in ISI after adjusting for baseline values but was not associated with diabetes incidence. A higher GRS was also associated with more atherogenic low-density lipoprotein peak-particle-density at baseline but was not associated with coronary artery calcium scores in the Diabetes Prevention Program Outcomes Study. Conclusions In the DPP, a higher polygenic lipodystrophy GRS for insulin resistance with lower BMI was associated with diminished improvement in insulin sensitivity and potential higher cardiovascular disease risk. This GRS helps characterize insulin resistance in a cohort of individuals at high risk for diabetes, independent of adiposity.

Background Epidemiological studies report associations of diverse cardiometabolic conditions including obesity with COVID-19 illness, but causality has not been established. We sought to evaluate the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using 2-sample Mendelian randomization (MR) analyses. Methods and findings We selected genetic variants associated with each exposure, including body mass index (BMI), at p < 5 × 10−8 from genome-wide association studies (GWASs). We then calculated inverse-variance-weighted averages of variant-specific estimates using summary statistics for susceptibility and severity from the COVID-19 Host Genetics Initiative GWAS meta-analyses of population-based cohorts and hospital registries comprising individuals with self-reported or genetically inferred European ancestry. Susceptibility was defined as testing positive for COVID-19 and severity was defined as hospitalization with COVID-19 versus population controls (anyone not a case in contributing cohorts). We repeated the analysis for BMI with effect estimates from the UK Biobank and performed pairwise multivariable MR to estimate the direct effects and indirect effects of BMI through obesity-related cardiometabolic diseases. Using p < 0.05/34 tests = 0.0015 to declare statistical significance, we found a nonsignificant association of genetically higher BMI with testing positive for COVID-19 (14,134 COVID-19 cases/1,284,876 controls, p = 0.002; UK Biobank: odds ratio 1.06 [95% CI 1.02, 1.10] per kg/m2; p = 0.004]) and a statistically significant association with higher risk of COVID-19 hospitalization (6,406 hospitalized COVID-19 cases/902,088 controls, p = 4.3 × 10−5; UK Biobank: odds ratio 1.14 [95% CI 1.07, 1.21] per kg/m2, p = 2.1 × 10−5). The implied direct effect of BMI was abolished upon conditioning on the effect on type 2 diabetes, coronary artery disease, stroke, and chronic kidney disease. No other cardiometabolic exposures tested were associated with a higher risk of poorer COVID-19 outcomes. Small study samples and weak genetic instruments could have limited the detection of modest associations, and pleiotropy may have biased effect estimates away from the null. Conclusions In this study, we found genetic evidence to support higher BMI as a causal risk factor for COVID-19 susceptibility and severity. These results raise the possibility that obesity could amplify COVID-19 disease burden independently or through its cardiometabolic consequences and suggest that targeting obesity may be a strategy to reduce the risk of severe COVID-19 outcomes., Aaron Leong and co-workers investigate causal risk factors for COVID-10 illness and severity., Author summary Why was this study done? Diverse cardiometabolic risk factors have been described in the literature to be associated with COVID-19 illness, but causality has not been established. Preventive strategies targeting cardiometabolic risk factors that are both causal and modifiable may reduce the risk of COVID-19 illness, whereas interventions targeting risk factors that are only correlated with the outcome may not. What did the researchers do and find? We used 2-sample Mendelian randomization analyses to test whether 17 cardiometabolic diseases and traits had a causal relationship with risk of COVID-19 illness. We found that higher body mass index was the only cardiometabolic risk factor among those we studied that was associated with a higher risk of hospitalization for COVID-19 compared to the general population. Obesity-related cardiometabolic diseases—type 2 diabetes, chronic kidney disease, stroke, and coronary heart disease—may be mediators of the relationship between body mass index and higher risk of hospitalization for COVID-19. What do these findings mean? Our results suggest that people with a higher body mass index have a higher risk for hospitalization for COVID-19. If other cardiometabolic risk factors have causal associations with COVID-19 illness, their effects are likely modest. We provide genetic evidence supporting body mass index as a causal risk factor for COVID-19 severity, raising the possibility that obesity could have amplified the COVID-19 pandemic, directly or through obesity-related cardiometabolic diseases.

It is important to study the genetics of complex traits in diverse populations. Here, we introduce covariate-adjusted linkage disequilibrium (LD) score regression (cov-LDSC), a method to estimate SNP-heritability (${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}})$ and its enrichment in homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the genome-wide association studies samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ by 10-60% in admixed populations; in contrast, cov-LDSC is robustly accurate. We apply cov-LDSC to genotyping data from 8124 individuals, mostly of admixed ancestry, from the Slim Initiative in Genomic Medicine for the Americas study, and to approximately 161 000 Latino-ancestry individuals, 47 000 African American-ancestry individuals and 135 000 European-ancestry individuals, as classified by 23andMe. We estimate ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and detect heritability enrichment in three quantitative and five dichotomous phenotypes, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals to date. Most traits have high concordance of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$. We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size $ \tau $* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in the Latino-, African American- and European-ancestry populations, respectively. Our approach is a powerful way to analyze genetic data for complex traits from admixed populations.

Genome-wide association studies (GWAS) are not fully comprehensive, as current strategies typically test only the additive model, exclude the X chromosome, and use only one reference panel for genotype imputation. We implement an extensive GWAS strategy, GUIDANCE, which improves genotype imputation by using multiple reference panels and includes the analysis of the X chromosome and non-additive models to test for association. We apply this methodology to 62,281 subjects across 22 age-related diseases and identify 94 genome-wide associated loci, including 26 previously unreported. Moreover, we observe that 27.7% of the 94 loci are missed if we use standard imputation strategies with a single reference panel, such as HRC, and only test the additive model. Among the new findings, we identify three novel low-frequency recessive variants with odds ratios larger than 4, which need at least a three-fold larger sample size to be detected under the additive model. This study highlights the benefits of applying innovative strategies to better uncover the genetic architecture of complex diseases., Most genome-wide association studies assume an additive model, exclude the X chromosome, and use one reference panel. Here, the authors implement a strategy including non-additive models and find that the number of loci for age-related traits increases as compared to the additive model alone.

Background: Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However there exists significant variability in glycaemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction. Methods: As an initiative of the Metformin Genetics Plus (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortia, 5,485 white European adults with type 2 diabetes treated with sulfonylurea were recruited from 6 referral centres in Europe and North America. We first estimated heritability using generalized restricted maximum likelihood (REML) and then undertook GWAS of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy in each cohort using linear regression followed by meta-analysis. These results were supported by cis-eQTLs and functional validation in cellular models. Findings: After establishing that sulfonylurea response is heritable (37±11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction. The C-allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), p=2.4×10−8) lower HbA1c reduction. Similarly, the C-allele was associated with higher glucose trough levels (β=1.61, p=0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). The C-allele of rs10770791, near SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater HbA1c reduction (p=4.8×10 −8 ). In 1,183 human liver samples, the C-allele at rs10770791 is a cis-eQTL for reduced SLCO1B1 expression (p=1.61×10−7) which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use, sulfonylurea response and SCLO1B1 genotype was observed (p=0.001). In statin non-users (but not users), C-allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48±0.12% (5.2±1.26 mmol/mol)), equivalent to initiating a DPP4 inhibitor. Interpretation: We have identified clinically important genetic effects at genomewide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important when prescribing glucose-lowering drugs. Funding Statement: Innovative Medicines Initiative, the National Institutes of Health, the Geisinger Health Plan Quality Pilot Fund. Declaration of Interests: ERP has received honoraria for speaking from Lilly and Sanofi. JCF has received honoraria for speaking at scientific conferences from Novo, and for consulting from Goldfinch Bio. All other authors declare no competing interest. Ethics Approval Statement: This study was approved by respective research ethics review boards and participants provided written informed consent.

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier will develop the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we applied clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias displayed effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers averaged below 60% in both studies for all conditions except monogenic diabetes. We assessed additional epidemiologic and genetic factors contributing to risk prediction, demonstrating that inclusion of common polygenic variation significantly improved biomarker estimation for two monogenic dyslipidemias.

We assembled an ancestrally diverse collection of genome-wide association studies of type 2 diabetes (T2D) in 180,834 cases and 1,159,055 controls (48.9% non-European descent). We identified 277 loci at genome-wide significance (p-8), including 237 attaining a more stringent trans-ancestry threshold (p-9), which were delineated to 338 distinct association signals. Trans-ancestry meta-regression offered substantial enhancements to fine-mapping, with 58.6% of associations more precisely localised due to population diversity, and 54.4% of signals resolved 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 foundations for functional investigations. Trans-ancestry genetic risk scores enhanced transferability across diverse populations, providing a step towards more effective clinical translation to improve global health.

Glucocorticoids have multiple therapeutic benefits and are used both for immunosuppression and treatment purposes. Notwithstanding their benefits, glucocorticoid use often leads to hyperglycemia. Owing to the pathophysiologic overlap in glucocorticoid-induced hyperglycemia (GIH) and type 2 diabetes (T2D), we hypothesized that genetic variation in glucocorticoid pathways contributes to T2D risk. To determine the genetic contribution of glucocorticoid action on T2D risk, we conducted multiple genetic studies. First, we performed gene-set enrichment analyses on 3 collated glucocorticoid-related gene sets using publicly available genome-wide association and whole-exome data and demonstrated that genetic variants in glucocorticoid-related genes are associated with T2D and related glycemic traits. To identify which genes are driving this association, we performed gene burden tests using whole-exome sequence data. We identified 20 genes within the glucocorticoid-related gene sets that are nominally enriched for T2D-associated protein-coding variants. The most significant association was found in coding variants in coiled-coil α-helical rod protein 1 (CCHCR1) in the HLA region (P = .001). Further analyses revealed that noncoding variants near CCHCR1 are also associated with T2D at genome-wide significance (P = 7.70 × 10–14), independent of type 1 diabetes HLA risk. Finally, gene expression and colocalization analyses demonstrate that variants associated with increased T2D risk are also associated with decreased expression of CCHCR1 in multiple tissues, implicating this gene as a potential effector transcript at this locus. Our discovery of a genetic link between glucocorticoids and T2D findings support the hypothesis that T2D and GIH may have shared underlying mechanisms.

Glucocorticoids have multiple therapeutic benefits and are used both for immunosuppression and treatment purposes. Notwithstanding their benefits, glucocorticoid use often leads to hyperglycemia. Owing to the pathophysiologic overlap in glucocorticoid-induced hyperglycemia (GIH) and type 2 diabetes (T2D), we hypothesized that genetic variation in glucocorticoid pathways contributes to T2D risk. To determine the genetic contribution of glucocorticoid action on T2D risk, we conducted multiple genetic studies. First, we performed gene-set enrichment analyses on 3 collated glucocorticoid-related gene sets using publicly available genome-wide association and whole-exome data and demonstrated that genetic variants in glucocorticoid-related genes are associated with T2D and related glycemic traits. To identify which genes are driving this association, we performed gene burden tests using whole-exome sequence data. We identified 20 genes within the glucocorticoid-related gene sets that are nominally enriched for T2D-associated protein-coding variants. The most significant association was found in coding variants in coiled-coil α-helical rod protein 1 (CCHCR1) in the HLA region (P = .001). Further analyses revealed that noncoding variants near CCHCR1 are also associated with T2D at genome-wide significance (P = 7.70 × 10–14), independent of type 1 diabetes HLA risk. Finally, gene expression and colocalization analyses demonstrate that variants associated with increased T2D risk are also associated with decreased expression of CCHCR1 in multiple tissues, implicating this gene as a potential effector transcript at this locus. Our discovery of a genetic link between glucocorticoids and T2D findings support the hypothesis that T2D and GIH may have shared underlying mechanisms.

Patients with type 2 diabetes (T2D) are at two-fold increased risk of cardiovascular disease (CVD). CVD is the leading cause of T2D morbidity and mortality. The aim of this study was to identify genetic risk factors associated with incident CVD in patients with T2D. We performed survival genome-wide association study (GWAS) with patients with T2D in the UK Biobank (UKBB), Partners HealthCare Biobank (PBB), and Korean Genome and Epidemiology (KoGES) studies. The primary outcome of the study was a composite of myocardial infarction and stroke that occurred at least one year after the diagnosis of T2D. We conducted survival GWAS using ’GWASTools’ R package adjusting for age, and sex. The results of the three cohorts were meta-analyzed using inverse variance weighted method. A total of 19,121 participants were included in the analyses (15,643 from UKBB, 1,161 from PBB and 2,317 from KoGES). The incidence rate of CVD was higher in the hospital-based study of PBB (33.5%) compared to population-based studies of UKBB (9.5%), and KoGES (8.0%). Although there was no genetic association reaching genome-wide significance, there were four genomic loci that had suggestive association evidence for incident CVD with P In summary, we have implemented survival GWAS analysis to investigate genetic risk factors for incident CVD in patients with T2D. However, further increase in sample size is required to identify molecular features leading to increased CVD risk among individuals with T2D. Disclosure S. Kwak: None. J.M. Mercader: None. A. Leong: None. B. Porneala: None. P. Wu: None. J.B. Cole: None. J. Merino: None. K. Park: None. N.H. Cho: None. J. Dupuis: Consultant; Self; Merck Sharp & Dohme Corp. J.C. Florez: Advisory Panel; Self; Doris Duke Charitable Foundation. Other Relationship; Self; Novo Nordisk Inc., Park Street School. J.B. Meigs: Consultant; Self; Quest Diagnostics.

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.)

Improved understanding of disease-causing pathways for type 2 diabetes (T2D) may lead to novel therapeutic targets and individualized treatment. Rapid expansion in the number of T2D genetic loci over the past few years can be leveraged to identify pathways via cluster analysis. We developed an automated pipeline to enable clustering of T2D genetic loci starting with genome-wide association study (GWAS) summary statistics from 6 European T2D studies. The pipeline 1) extracts variants reaching genome-wide significance, 2) replaces multi-allelic, ambiguous (A/T, C/G), or low-trait count SNPs with appropriate proxies, and 3) filters for independent signals using r2=0. Traits in the T2D Knowledge Portal with summary GWAS data in European populations were included if they met a minimum Bonferroni p-value across the selected variants. This pipeline generated a matrix of associations for 259 independent T2D variants and 32 traits to which we applied Bayesian Non-negative Matrix Factorization (bNMF) clustering. We identified 6 robust clusters of T2D loci, 5 of which overlapped with a previously published analysis of 94 loci. Three clusters indicated variant-trait associations related to insulin deficiency, while the other three clusters displayed insulin resistance-related features including: obesity, lipodystrophy, and liver-lipid metabolism. The new cluster identified in this analysis is related to beta cell function. Increased polygenic scores for clusters were associated with distinct clinical outcomes in GWAS, including coronary artery disease (UK BioBank CARDIoGRAM), ischemic stroke (MEGASTROKE), chronic kidney disease (CKDGen). Clusters also displayed tissue-specific epigenomic enrichment. Our approach expands upon previous clustering work of T2D loci and allows for efficient updating as additional GWAS results become available. This method can also be readily applied to other diseases beyond T2D to identify key pathways. Disclosure H. Kim: None. M. von Grotthuss: None. J.M. Mercader: None. J. Chiou: None. K.J. Gaulton: Consultant; Self; Genentech, Inc. Employee; Spouse/Partner; Vertex Pharmaceuticals. A. Manning: None. J.C. Florez: Advisory Panel; Self; Doris Duke Charitable Foundation. Other Relationship; Self; Novo Nordisk Inc., Park Street School. M. Udler: None. Funding National Institutes of Health