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Aims/hypothesis Genome-wide studies have uncovered multiple independent signals at the RREB1 locus associated with altered type 2 diabetes risk and related glycaemic traits. However, little is known about the function of the zinc finger transcription factor Ras-responsive element binding protein 1 (RREB1) in glucose homeostasis or how changes in its expression and/or function influence diabetes risk. Methods A zebrafish model lacking rreb1a and rreb1b was used to study the effect of RREB1 loss in vivo. Using transcriptomic and cellular phenotyping of a human beta cell model (EndoC-βH1) and human induced pluripotent stem cell (hiPSC)-derived beta-like cells, we investigated how loss of RREB1 expression and activity affects pancreatic endocrine cell development and function. Ex vivo measurements of human islet function were performed in donor islets from carriers of RREB1 type 2 diabetes risk alleles. Results CRISPR/Cas9-mediated loss of rreb1a and rreb1b function in zebrafish supports an in vivo role for the transcription factor in beta cell mass, beta cell insulin expression and glucose levels. Loss of RREB1 also reduced insulin gene expression and cellular insulin content in EndoC-βH1 cells and impaired insulin secretion under prolonged stimulation. Transcriptomic analysis of RREB1 knockdown and knockout EndoC-βH1 cells supports RREB1 as a novel regulator of genes involved in insulin secretion. In vitro differentiation of RREB1KO/KO hiPSCs revealed dysregulation of pro-endocrine cell genes, including RFX family members, suggesting that RREB1 also regulates genes involved in endocrine cell development. Human donor islets from carriers of type 2 diabetes risk alleles in RREB1 have altered glucose-stimulated insulin secretion ex vivo, consistent with a role for RREB1 in regulating islet cell function. Conclusions/interpretation Together, our results indicate that RREB1 regulates beta cell function by transcriptionally regulating the expression of genes involved in beta cell development and function. Graphical abstract

Population level variation and molecular mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized despite ramifications for personalized nutrition. We now define prototypical insulin secretion dynamics in response to the three macronutrients in islets from 140 cadaveric donors, including those diagnosed with type 2 diabetes. We leverage the insulin response heterogeneity and use transcriptomics and proteomics to identify molecular pathways of specific nutrient responsiveness. Surprisingly, we find robust insulin secretion to fatty acid stimulus in ∼8% of donors, challenging the idea that fat has negligible effects on insulin release. Distinct islet proteomes with differences in metabolic signalling networks convey this hyper-responsiveness to fat relative to carbohydrate. By comparing human islets to human embryonic stem cell-derived islet clusters, we show that, unlike glucose-responsiveness, fat hyper-responsiveness is equivalent and may be a hallmark of functionally immature cells. Our study represents the first comparison of dynamic responses to nutrients and multi-omics analysis in human insulin secreting cells. Responses of different people’s islets to carbohydrate, protein, and fat lay the groundwork for personalized nutrition.ONE-SENTENCE SUMMARYDeep-phenotyping and multi-omics reveal individualized nutrient-specific insulin secretion propensity.

Patients with type 2 diabetes vary in their response to currently available therapeutic agents (including GLP-1 receptor agonists) leading to suboptimal glycemic control and increased risk of complications. We show that human carriers of hypomorphic T2D-risk alleles in the gene encoding peptidyl-glycine alpha-amidating monooxygenase(PAM),as well asPam-knockout mice, display increased resistance to GLP-1in vivo.Paminactivation in mice leads to reduced gastric GLP-1R expression and faster gastric emptying: this persists during GLP-1R agonist treatment and is rescued when GLP-1R activity is antagonized, indicating resistance to GLP-1’s gastric slowing properties. Meta-analysis of human data from studies examining GLP-1R agonist response (including RCTs) reveals a relative loss of 44% and 20% of glucose lowering (measured by glycated hemoglobin) in individuals with hypomorphicPAMalleles p.S539W and p.D536G treated with GLP-1R agonist. Genetic variation inPAMhas effects on incretin signaling that alters response to medication used commonly for treatment of T2D.(Funded by the Wellcome, Medical Research Council, European Union, NIHR Oxford Biomedical Research Centre, United Kingdom, Registered on ClinicalTrials.gov,NCT02723110.)Summary ParagraphType 2 diabetes (T2D) is a leading cause of morbidity and mortality globally1. Current management of T2D patients focuses on lowering glycemic exposure and reducing complications with lifestyle and pharmacological interventions2. Despite the availability of multiple medications to lower glycated hemoglobin (HbA1c), only 53% of individuals with T2D reach the glycemic target (HbA1c 3, 4. There is potential to improve medication selection through “precision medicine” where patient specific factors (e.g. genetic markers) are used to indicate whether a patient is more or less likely to respond to a medication. Here we show that human carriers of hypomorphic T2D-risk alleles in the gene encoding peptidyl-glycine alpha-amidating monooxygenase(PAM),as well asPam-knockout mice, have reduced PAM enzyme activity, display increased resistance to glucagon like peptide 1 (GLP-1)in vivoand have reduced response to the GLP-1 receptor agonist. Meta-analysis of human data from studies examining GLP-1 receptor agonist response (including RCTs) reveals a relative loss of 44% and 20% of glucose lowering (measured by glycated hemoglobin) in individuals with hypomorphicPAMalleles p.S539W and p.D536G treated with GLP-1 receptor agonist. Genetic variation inPAMhas effects on incretin signaling that alters response to medication used commonly for treatment of T2D.

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.

Aims/hypothesis We examined the contribution of rare HNF1A variants to type 2 diabetes risk and age of diagnosis, and the extent to which their impact is affected by overall genetic susceptibility, across three ancestry groups. Methods Using exome sequencing data of 160,615 individuals of the UK Biobank and 18,797 individuals of the BioMe Biobank, we identified 746 carriers of rare functional HNF1A variants (minor allele frequency ≤1%), of which 507 carry variants in the functional domains. We calculated polygenic risk scores (PRSs) based on genome-wide association study summary statistics for type 2 diabetes, and examined the association of HNF1A variants and PRS with risk of type 2 diabetes and age of diagnosis. We also tested whether the PRS affects the association between HNF1A variants and type 2 diabetes risk by including an interaction term. Results Rare HNF1A variants that are predicted to impair protein function are associated with increased risk of type 2 diabetes in individuals of European ancestry (OR 1.46, p=0.049), particularly when the variants are located in the functional domains (OR 1.89, p=0.002). No association was observed for individuals of African ancestry (OR 1.10, p=0.60) or Hispanic-Latino ancestry (OR 1.00, p=1.00). Rare functional HNF1A variants were associated with an earlier age at diagnosis in the Hispanic-Latino population (β=−5.0 years, p=0.03), and this association was marginally more pronounced for variants in the functional domains (β=−5.59 years, p=0.03). No associations were observed for other ancestries (African ancestry β=−2.7 years, p=0.13; European ancestry β=−3.5 years, p=0.20). A higher PRS was associated with increased odds of type 2 diabetes in all ancestries (OR 1.61–2.11, p−5) and an earlier age at diagnosis in individuals of African ancestry (β=−1.4 years, p=3.7 × 10−6) and Hispanic-Latino ancestry (β=−2.4 years, p−16). Furthermore, a higher PRS exacerbated the effect of the functional HNF1A variants on type 2 diabetes in the European ancestry population (pinteraction=0.037). Conclusions/interpretation We show that rare functional HNF1A variants, in particular those located in the functional domains, increase the risk of type 2 diabetes, at least among individuals of European ancestry. Their effect is even more pronounced in individuals with a high polygenic susceptibility. Our analyses highlight the importance of the location of functional variants within a gene and an individual’s overall polygenic susceptibility, and emphasise the need for more genetic data in non-European populations. Graphical abstract

Since the discovery of insulin 100 years ago, our knowledge and understanding of diabetes have grown exponentially. Specifically, with regards to the genetics underlying diabetes risk, our discoveries have paralleled developments in our understanding of the human genome and our ability to study genomics at scale; these advancements in genetics have both accompanied and led to those in diabetes treatment. This review will explore the timeline and history of gene discovery and how this has coincided with progress in the fields of genomics. Examples of genetic causes of monogenic diabetes are presented and the continuing expansion of allelic series in these genes and the challenges these now cause for diagnostic interpretation along with opportunities for patient stratification are discussed.

Type 2 diabetes (T2D) is highly prevalent and is a strong contributor for cardiovascular disease. However, there is significant heterogeneity in disease pathogenesis and the risk of complications. Enormous progress has been made in our ability to catalog genetic variation associated with T2D risk and variation in disease-relevant quantitative traits. These discoveries hold the potential to shed light on tractable targets and pathways for safe and effective therapeutic development, but the promise of precision medicine has been slow to be realized. Recent studies have identified subgroups of individuals with differential risk for intermediate phenotypes (eg, lipid levels, fasting insulin, body mass index) that contribute to T2D risk, helping to account for the observed clinical heterogeneity. These "partitioned genetic risk scores" not only have the potential to identify patients at greatest risk of cardiovascular disease and rapid disease progression, but also could aid patient stratification bridging the gap toward precision medicine for T2D.

We examined the contribution of rare HNF1A variants to type 2 diabetes risk and age of diagnosis, and the extent to which their impact is affected by overall genetic susceptibility, across three ancestry groups.Using exome sequencing data of 160,615 individuals of the UK Biobank and 18,797 individuals of the BioMe Biobank, we identified 746 carriers of rare functional HNF1A variants (minor allele frequency ≤1%), of which 507 carry variants in the functional domains. We calculated polygenic risk scores (PRSs) based on genome-wide association study summary statistics for type 2 diabetes, and examined the association of HNF1A variants and PRS with risk of type 2 diabetes and age of diagnosis. We also tested whether the PRS affects the association between HNF1A variants and type 2 diabetes risk by including an interaction term.Rare HNF1A variants that are predicted to impair protein function are associated with increased risk of type 2 diabetes in individuals of European ancestry (OR 1.46, p=0.049), particularly when the variants are located in the functional domains (OR 1.89, p=0.002). No association was observed for individuals of African ancestry (OR 1.10, p=0.60) or Hispanic-Latino ancestry (OR 1.00, p=1.00). Rare functional HNF1A variants were associated with an earlier age at diagnosis in the Hispanic-Latino population (β=-5.0 years, p=0.03), and this association was marginally more pronounced for variants in the functional domains (β=-5.59 years, p=0.03). No associations were observed for other ancestries (African ancestry β=-2.7 years, p=0.13; European ancestry β=-3.5 years, p=0.20). A higher PRS was associated with increased odds of type 2 diabetes in all ancestries (OR 1.61-2.11, p10We show that rare functional HNF1A variants, in particular those located in the functional domains, increase the risk of type 2 diabetes, at least among individuals of European ancestry. Their effect is even more pronounced in individuals with a high polygenic susceptibility. Our analyses highlight the importance of the location of functional variants within a gene and an individual's overall polygenic susceptibility, and emphasise the need for more genetic data in non-European populations.

Aims/hypothesisGenome-wide studies have uncovered multiple independent signals at the RREB1 locus associated with altered type 2 diabetes risk and related glycemic traits. However, little is known about the function of the zinc finger transcription factor RREB1 in glucose homeostasis or how changes in its expression and/or function influence diabetes risk.MethodsA zebrafish model lacking rreb1a and rreb1b was used to study the effect of RREB1 loss in vivo. Using transcriptomic and cellular phenotyping of a human beta cell model (EndoC-βH1) and human induced pluripotent stem cell (hiPSC)-derived beta-like cells, we investigated how loss of RREB1 expression and activity affects pancreatic endocrine cell development and function. Ex vivo measurements of human islet function were performed in donor islets from carriers of RREB1 T2D-risk alleles.ResultsCRISPR-Cas9-mediated loss of rreb1a and rreb1b function in zebrafish supports an in vivo role for the transcription factor in beta cell mass, beta cell insulin expression, and glucose levels. Loss of RREB1 reduced insulin gene expression and cellular insulin content in EndoC-βH1 cells, and impaired insulin secretion under prolonged stimulation. Transcriptomic analysis of RREB1 knockdown and knockout EndoC-βH1 cells supports RREB1 as a novel regulator of genes involved in insulin secretion. In vitro differentiation of RREB1KO/KO hiPSCs revealed a dysregulation of pro-endocrine cell genes, including RFX family members, suggesting that RREB1 also regulates genes involved in endocrine cell development. Human donor islets from carriers of T2D-risk alleles in RREB1 have altered glucose-stimulated insulin secretion ex vivo, consistent with RREB1 regulating islet cell function.Conclusions/interpretationTogether, our results indicate that RREB1 regulates beta cell function by transcriptionally regulating the expression of genes involved in beta cell development and function.Research in contextWhat is already known about this subject?Human genetic variation in RREB1 is associated with altered diabetes risk, variation in glycemic, and anthropometric traitsRREB1 is a transcription factor that binds to Ras-responsive elements and is expressed in multiple diabetes relevant tissues, including pancreatic isletsWhat is the key question?How does altered expression or function of RREB1 influence diabetes risk?What are the new findings?Knockdown and knockout of RREB1 in mature human EndoC-βH1 cells reduces expression of insulin transcript and cellular content, as well as insulin secretion under prolonged stressCarriers of the T2D-risk RREB1 coding allele trend towards reduced insulin content, but have improved glucose-stimulated insulin secretionA loss-of-function zebrafish model suggests that RREB1 is required for insulin expressionHow might this impact on clinical practice in the foreseeable future?RREB1 controls beta cell function and whole-body glucose homeostasis by transcriptionally regulating the development and function of pancreatic beta cells

Type 2 diabetes (T2D) is classically defined by measures of glycemia which do not take into account its physiologic basis, which differs between individuals. Defining the relative contribution of fundamental physiologic processes to glucose dysregulation would facilitate targeted treatment approaches for diabetes prevention and treatment. We propose that muscle insulin resistance, beta cell dysfunction, incretin defect, and hepatic insulin resistance are present to varying degrees in individuals predisposed to T2D, who can be thus classified according to their metabolic subphenotype. While this transformative approach to T2D is appealing, traditional metabolic tests are invasive and unavailable outside research units. We tested the ability of the shape of the glucose response curve during an oral glucose tolerance test (OGTT) to identify metabolic subphenotypes. 32 participants underwent gold-standard metabolic tests including frequently-sampled 3-hour OGTT, isoglycemic intravenous glucose infusion for quantification of incretin effect, steady-state plasma glucose measurement of insulin resistance, and calculated hepatic insulin resistance. We developed a machine learning framework to predict metabolic subphenotypes of an individual using dynamic features of the glucose time series during the OGTT. Metabolic testing revealed extensive inter-individual heterogeneity and the existence of four major metabolic subphenotypes. The features of the glucose time series identified insulin resistance, beta cell deficiency, and incretin deficiency with auROCs of 94%, 76%, and 75%, respectively. These were superior to standard clinical and laboratory measures of metabolic phenotypes. We suggest that identification of distinct metabolic subphenotypes using features of the glucose time series during OGTT may both enhance early identification of at-risk individuals as well as inform targeted therapeutic approaches to prevent and treat T2D. Disclosure A.A.Metwally: None. D.Perelman: None. H.Park: None. A.L.Gloyn: Other Relationship; Genentech, Inc., Roche Pharmaceuticals. T.Mclaughlin: Board Member; January, Inc., Research Support; Merck & Co., Inc., Novo Nordisk, Stock/Shareholder; Eiger BioPharmaceuticals. M.Snyder: Stock/Shareholder; January, Inc. Funding National Institutes of Health (R01DK110186-01) National Institute of Diabetes and Digestive and Kidney Diseases (U01-DK105535; U01-DK085545, UM1DK126185)

In an effort to identify disease-causing mechanisms, numerous genome-wide studies across different ancestries have discovered genetic variants influencing type 2 diabetes (T2D) risk. Amongst identified diabetes-associated loci, a PAX4 coding variant (p.Arg192His) is uniquely and reproducibly associated with altered T2D-risk in East Asian populations. PAX4 encodes a transcription factor, and in mice Pax4 is essential for beta cell development. Whether PAX4 plays a similar role in humans and how diabetes associated PAX4 variants impact beta cell development and/or function is unknown. We combined in vivo studies in human variant carriers with disease modelling in human stem cell models to address these questions. Carriers of the PAX4 p.Arg192His allele have reduced islet cell function compared to non-carriers (AIRg: p.Arg192 756.9 vs. p.His192 530.8 p In conclusion, we have demonstrated the importance of PAX4 for human endocrine cell development and provide a potential mechanism for diabetes-associated alleles through de-repression of alpha cell gene expression. Disclosure N. Krentz: None. S. Hoon: None. D. Gardner: None. S. Kao: None. E. Tai: Advisory Panel; Amgen Inc., Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk, Stock/Shareholder; Abbott, AbbVie Inc. A. L. Gloyn: Other Relationship; Genentech, Inc., Roche Pharmaceuticals. A. Teo: Stock/Shareholder; BetaLife Pte Ltd. H. Lau: None. F. Abaitua: Employee; Vertex Pharmaceuticals Incorporated. M. Perez-alcantara: None. J. Chan: None. J. N. Ajeian: None. B. Champon: None. H. Sun: None. A. Jha: None. Funding Wellcome (200837) ; National Institutes of Health (U01-DK105535, U01-DK085545, UM1DK126185, U01DK123743, U24DK098085, P30DK116074) ; Lee Foundation Grant (SHTX/LFG/002/2018) Paris-NUS 2021-06-R/UP-NUS (ANR-18-IDEX-0001)

Glucokinase (GCK) regulates insulin secretion to maintain appropriate blood glucose levels. Sequence variants can alter GCK activity to cause hyperinsulinemic hypoglycemia (HH) or hyperglycemia associated with GCK-maturity-onset diabetes of the young (GCK-MODY), collectively affecting up to 10 million people worldwide. Patients with GCK-MODY are frequently misdiagnosed and treated unnecessarily. Genetic testing can prevent this but is hampered by the challenge of interpreting novel missense variants. Here we exploited a multiplexed yeast complementation assay to measure both hyper- and hypoactive GCK variation, capturing 97% of all possible missense and nonsense variants. Activity scores correlated with in vitro catalytic efficiency, fasting glucose levels in carriers of GCK variants and with evolutionary conservation. Hypoactive variants were concentrated at buried positions, near the active site, and at a region of known importance for GCK conformational dynamics. Some hyperactive variants shifted the conformational equilibrium towards the active state through a relative destabilization of the inactive conformation. Our comprehensive assessment of GCK variant activity promises to facilitate variant interpretation and diagnosis, expand our mechanistic understanding of hyperactive variants, and inform development of therapeutics targeting GCK.

Type 2 diabetes mellitus (T2DM) is an increasingly prevalent multifactorial disease that has both genetic and environmental risk factors, resulting in impaired glucose homeostasis. Genome-wide association studies (GWAS) have identified over 400 genetic signals that are associated with altered risk of T2DM. Human physiology and epigenomic data support a central role for the pancreatic islet in the pathogenesis of T2DM. This Review focuses on the promises and challenges of moving from genetic associations to molecular mechanisms and highlights efforts to identify the causal variant and effector transcripts at T2DM GWAS susceptibility loci. In addition, we examine current human models that are used to study both β-cell development and function, including EndoC-β cell lines and human induced pluripotent stem cell-derived β-like cells. We use examples of four T2DM susceptibility loci (CDKAL1, MTNR1B, SLC30A8 and PAM) to emphasize how a holistic approach involving genetics, physiology, and cellular and developmental biology can disentangle disease mechanisms at T2DM GWAS signals.

The critical cellular transitions that govern human pancreas development are largely unknown. We performed large-scale single-cell RNA-sequencing (scRNA-Seq) to interrogate human fetal pancreas development from 8-20 weeks post conception. We identified 103 distinct cell types, including four novel endocrine progenitor subtypes displaying unique transcriptional features and differentiation potency. Integration with single-nucleus Assay for Transposase Accessible Chromatin Sequencing (snATAC-Seq) identified candidate regulators of human endocrine cell fate and revealed development-specific regulatory annotation at diabetes risk loci. Comparison of in vitro stem cell-derived and endogenous endocrine cells predicted aberrant genetic programs leading to the generation of off-target cells. Finally, knock-out studies revealed that the gene FEV regulates human endocrine differentiation. This work establishes a roadmap of human pancreatic development, highlights previously unappreciated cellular diversity and lineage dynamics, and provides a blueprint for understanding pancreatic disease and physiology, as well as generating human stem cell-derived islet cells in vitro for regenerative medicine purposes.

Detailed characterization of human pancreatic islets is key to elucidating the pathophysiology of all forms of diabetes, especially type 2 diabetes. However, access to human pancreatic islets is limited. Pancreatic tissue for islet retrieval can be obtained from brain-dead organ donors or from individuals undergoing pancreatectomy, often referred to as 'living donors'. Different protocols for human islet procurement can substantially impact islet function. This variability, coupled with heterogeneity between individuals and islets, results in analytical challenges to separate genuine disease pathology or differences between human donors from experimental noise. There are currently no international guidelines for human donor phenotyping, islet procurement and functional characterization. This lack of standardization means that substantial investments from multiple international efforts towards improved understanding of diabetes pathology cannot be fully leveraged. In this Perspective, we overview the status of the field of human islet research, highlight the challenges and propose actions that could accelerate research progress and increase understanding of type 2 diabetes to slow its pandemic spreading.

Genetic variants that influence transcriptional regulation in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D). For many susceptibility loci, however, the mechanisms are unknown. We examined splicing QTLs (sQTLs) in islets from 399 donors and observed that genetic variation has a widespread influence on splicing of genes with important functions in islet biology. In parallel, we profiled expression QTLs, and used transcriptome-wide association and co-localization studies to assign islet sQTLs or eQTLs to T2D susceptibility signals that lacked candidate effector genes. We found novel T2D associations, including an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effectors revealed overrepresented pathways, including regulators of G-protein-mediated cAMP production. This data exposes an underappreciated layer of genetic regulation in pancreatic islets, and nominates molecular mediators of T2D susceptibility.

Identification of the genes and processes mediating genetic association signals for complex disease represents a major challenge. Since many of the genetic signals for type 2 diabetes exert their effects through pancreatic islet-cell dysfunction, we performed a genome-wide pooled CRISPR loss-of- function screen in human pancreatic beta cells. We focused on the regulation of insulin content as a disease-relevant readout of beta cell function. We identified 580 genes influencing this phenotype: integration with genetic and genomic data provided experimental support for 20 candidate type 2 diabetes effector transcripts including the autophagy receptorCALCOCO2. Our study highlights how cellular screens can augment existing multi-omic efforts to accelerate biological and translational inference at GWAS loci.

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.

Identification of the genes and processes mediating genetic association signals for complex diseases represents a major challenge. As many of the genetic signals for type 2 diabetes (T2D) exert their effects through pancreatic islet-cell dysfunction, we performed a genome-wide pooled CRISPR loss-of-function screen in a human pancreatic beta cell line. We assessed the regulation of insulin content as a disease-relevant readout of beta cell function and identified 580 genes influencing this phenotype. Integration with genetic and genomic data provided experimental support for 20 candidate T2D effector transcripts including the autophagy receptor CALCOCO2. Loss of CALCOCO2 was associated with distorted mitochondria, less proinsulin-containing immature granules and accumulation of autophagosomes upon inhibition of late-stage autophagy. Carriers of T2D-associated variants at the CALCOCO2 locus further displayed altered insulin secretion. Our study highlights how cellular screens can augment existing multi-omic efforts to support mechanistic understanding and provide evidence for causal effects at genome-wide association studies loci.

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.

Monogenic diabetes refers to diabetes mellitus (DM) caused by a mutation in a single gene and accounts for approximately 1%-5% of diabetes. Correct diagnosis is clinically critical for certain types of monogenic diabetes, since the appropriate treatment is determined by the etiology of the disease (e.g., oral sulfonylurea treatment of HNF1A/HNF4A-diabetes vs. insulin injections in type 1 diabetes). However, achieving a correct diagnosis requires genetic testing, and the overlapping of the clinical features of monogenic diabetes with those of type 1 and type 2 diabetes has frequently led to misdiagnosis. Improvements in sequencing technology are increasing opportunities to diagnose monogenic diabetes, but challenges remain. In this Review, we describe the types of monogenic diabetes, including common and uncommon types of maturity-onset diabetes of the young, multiple causes of neonatal DM, and syndromic diabetes such as Wolfram syndrome and lipodystrophy. We also review methods of prioritizing patients undergoing genetic testing, and highlight existing challenges facing sequence data interpretation that can be addressed by forming collaborations of expertise and by pooling cases.

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.

Glycaemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycaemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated haemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% with no significant evidence of between-ancestry heterogeneity. Analyses restricted to European ancestry individuals with equivalent sample size would have led to 24 fewer new loci. Compared to single-ancestry, equivalent sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.

A recent study by Esteghamat et al., published in Nature Genetics, has reported that CELA2A, encoding a pancreatic enzyme, is a novel genetic cause of metabolic syndrome and atherosclerosis.

Resolving the molecular processes that mediate genetic risk remains a challenge as most disease-associated variants are non-coding and functional and bioinformatic characterization of these signals requires knowledge of the specific tissues and cell-types in which they operate. To address this challenge, we developed a framework for integrating tissue-specific gene expression and epigenomic maps (primarily from tissues involved in insulin secretion and action) to obtain tissue-of-action (TOA) scores for each association signal by systematically partitioning posterior probabilities from Bayesian fine-mapping. We applied this scheme to credible set variants for 380 association signals from a recent GWAS meta-analysis of type 2 diabetes (T2D) in Europeans. The resulting tissue profiles underscored a predominant role for pancreatic islets and, to a lesser extent, subcutaneous adipose and liver, that was largely attributable to enhancer elements and transcribed regions, particularly among signals with greater fine-mapping resolution. We incorporated resulting TOA scores into a rule-based classifier, and validated the tissue assignments through comparison with data from cis-eQTL enrichment, functional fine-mapping, RNA co-expression, and patterns of physiological association. In addition to implicating signals with a single tissue-of-action, we also found evidence for signals with shared effects in multiple tissues as well as distinct tissue profiles between independent signals within heterogeneous loci. Lastly, we demonstrated that TOA scores can be directly coupled with eQTL colocalization to further resolve effector transcripts at T2D signals. This framework guides mechanistic inference by directing functional validation studies to the most relevant tissues and can gain power as fine-mapping resolution and cell-specific annotations become richer. This method is generalizable to all complex traits with relevant annotation data and is made available as an R package.

Heterozygous loss of function mutations in HNF1A, encoding the transcription factor hepatocyte nuclear factor 1 alpha (HNF-1A) are the most common cause of monogenic diabetes. HNF-1A is involved in both beta cell development and mature cell function. However, model organisms have often failed to faithfully recapitulate the human phenotypes. As such, we used a patient-derived HNF1A Pro291fsinsC iPSC model and CRISPR-Cas9 genome editing to correct the mutation and generate an isogenic control. iPSC clones (n=6) were differentiated towards the endocrine lineage in triplicate, with RNA-seq (bulk and single-cell) and ATAC-seq performed at definitive endoderm (DE), pancreatic endoderm (PE) and beta-like cells (BLC) stages. RNA-seq data confirmed HNF1A haploinsuffiency in the patient cell lines, with corrected cell lines demonstrating restored HNF1A expression at both transcript and protein level. At the BLC stage, there was a significant overlap in differentially expressed genes and HNF1A targets identified in an independent HNF1A knock-down experiment in the EndoC-βh1 cell line (p=4.4e-132). Corrected clones demonstrated a higher expression of INS (p=7.2e-03), insulin secretion genes and effects on pancreas progenitor cell genes indicating that HNF1A may mediate the cellular composition of the pancreatic islet. Bulk RNA-seq data were also supported in the preliminary analysis of the single-cell RNA-seq data. Open chromatin, assessed by ATAC-seq was significantly different between patient and corrected lines, with >90% of the sites being more accessible at both PE and BLC stages in the corrected clones. These sites were enriched for HNF1A binding motifs, implicating HNF1A in the establishment of open chromatin. In conclusion, our patient-derived HNF1A-MODY iPSC model recapitulates a number of features of HNF1A-MODY, as well as providing insights into the cellular and molecular phenotypes caused by HNF-1A deficiency. Disclosure C. Duff: Research Support; Self; Novo Nordisk A/S. R. Jensen: None. M. Perez-Alcantara: None. M. Rasmussen: None. A. Grotz: None. M. Jansen: None. B. Holst: None. C. Clausen: None. N.A.J. Krentz: None. M. Hansson: Employee; Self; Novo Nordisk A/S. 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 & Co., Inc., Sanofi-Aventis, Servier, Takeda UK. A.L. Gloyn: Consultant; Self; Merck Sharp & Dohme Corp. Employee; Spouse/Partner; Genentech, Inc. Research Support; Self; Novo Nordisk A/S. A. Wesolowska-Andersen: None. C. Honore: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. Funding Innovative Medicines Initiative (115439)

Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict epigenome features from DNA sequence - to support inference concerning the regulatory effects of disease-associated variants. Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set of epigenomic features collected in a single disease-relevant tissue – pancreatic islets in the case of type 2 diabetes (T2D) - as opposed to models trained on multiple human tissues. We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization – genetic fine-mapping and regulatory annotation enrichment. We demonstrate that CNN-based analyses can refine association signals at T2D-associated loci and provide experimental validation for one such signal. We anticipate that these approaches will become routine in downstream analyses of GWAS.