RESUMO
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.
Assuntos
Diabetes Mellitus Tipo 2 , Progressão da Doença , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Adipócitos/metabolismo , Cromatina/genética , Cromatina/metabolismo , Doença da Artéria Coronariana/complicações , Doença da Artéria Coronariana/genética , Diabetes Mellitus Tipo 2/classificação , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/patologia , Diabetes Mellitus Tipo 2/fisiopatologia , Nefropatias Diabéticas/complicações , Nefropatias Diabéticas/genética , Células Endoteliais/metabolismo , Células Enteroendócrinas , Epigenômica , Predisposição Genética para Doença/genética , Ilhotas Pancreáticas/metabolismo , Herança Multifatorial/genética , Doença Arterial Periférica/complicações , Doença Arterial Periférica/genética , Análise de Célula ÚnicaRESUMO
AIMS/HYPOTHESIS: The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population. METHODS: We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort. RESULTS: Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10-9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. CONCLUSIONS/INTERPRETATION: Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores. DATA AVAILABILITY: Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).
Assuntos
Diabetes Mellitus Tipo 2 , Saúde da População , Humanos , Estudo de Associação Genômica Ampla , Diabetes Mellitus Tipo 2/genética , Medicina de Precisão , Genótipo , Hispânico ou Latino/genética , Polimorfismo de Nucleotídeo Único/genéticaRESUMO
RATIONALE: Cardiac pacemaker cells (PCs) in the sinoatrial node (SAN) have a distinct gene expression program that allows them to fire automatically and initiate the heartbeat. Although critical SAN transcription factors, including Isl1 (Islet-1), Tbx3 (T-box transcription factor 3), and Shox2 (short-stature homeobox protein 2), have been identified, the cis-regulatory architecture that governs PC-specific gene expression is not understood, and discrete enhancers required for gene regulation in the SAN have not been identified. OBJECTIVE: To define the epigenetic profile of PCs using comparative ATAC-seq (assay for transposase-accessible chromatin with sequencing) and to identify novel enhancers involved in SAN gene regulation, development, and function. METHODS AND RESULTS: We used ATAC-seq on sorted neonatal mouse SAN to compare regions of accessible chromatin in PCs and right atrial cardiomyocytes. PC-enriched assay for transposase-accessible chromatin peaks, representing candidate SAN regulatory elements, were located near established SAN genes and were enriched for distinct sets of TF (transcription factor) binding sites. Among several novel SAN enhancers that were experimentally validated using transgenic mice, we identified a 2.9-kb regulatory element at the Isl1 locus that was active specifically in the cardiac inflow at embryonic day 8.5 and throughout later SAN development and maturation. Deletion of this enhancer from the genome of mice resulted in SAN hypoplasia and sinus arrhythmias. The mouse SAN enhancer also directed reporter activity to the inflow tract in developing zebrafish hearts, demonstrating deep conservation of its upstream regulatory network. Finally, single nucleotide polymorphisms in the human genome that occur near the region syntenic to the mouse enhancer exhibit significant associations with resting heart rate in human populations. CONCLUSIONS: (1) PCs have distinct regions of accessible chromatin that correlate with their gene expression profile and contain novel SAN enhancers, (2) cis-regulation of Isl1 specifically in the SAN depends upon a conserved SAN enhancer that regulates PC development and SAN function, and (3) a corresponding human ISL1 enhancer may regulate human SAN function.
Assuntos
Arritmia Sinusal/metabolismo , Relógios Biológicos , Sequenciamento de Cromatina por Imunoprecipitação , Elementos Facilitadores Genéticos , Frequência Cardíaca , Proteínas com Homeodomínio LIM/metabolismo , Nó Sinoatrial/metabolismo , Fatores de Transcrição/metabolismo , Potenciais de Ação , Animais , Arritmia Sinusal/genética , Arritmia Sinusal/fisiopatologia , Epigênese Genética , Feminino , Regulação da Expressão Gênica no Desenvolvimento , Idade Gestacional , Humanos , Proteínas com Homeodomínio LIM/genética , Masculino , Camundongos Endogâmicos C57BL , Camundongos Transgênicos , Polimorfismo de Nucleotídeo Único , Nó Sinoatrial/fisiopatologia , Fatores de Tempo , Fatores de Transcrição/genética , Peixe-Zebra/genética , Peixe-Zebra/metabolismoRESUMO
Polygenic scores (PGS) have emerged as the tool of choice for genomic prediction in a wide range of fields. We show that PGS performance varies broadly across contexts and biobanks. Contexts such as age, sex and income can impact PGS accuracy with similar magnitudes as genetic ancestry. Here we introduce an approach (CalPred) that models all contexts jointly to produce prediction intervals that vary across contexts to achieve calibration (include the trait with 90% probability), whereas existing methods are miscalibrated. In analyses of 72 traits across large and diverse biobanks (All of Us and UK Biobank), we find that prediction intervals required adjustment by up to 80% for quantitative traits. For disease traits, PGS-based predictions were miscalibrated across socioeconomic contexts such as annual household income levels, further highlighting the need of accounting for context information in PGS-based prediction across diverse populations.
Assuntos
Estudo de Associação Genômica Ampla , Modelos Genéticos , Herança Multifatorial , Humanos , Herança Multifatorial/genética , Estudo de Associação Genômica Ampla/métodos , Feminino , Masculino , Calibragem , Bancos de Espécimes Biológicos , Fenótipo , Genômica/métodos , Polimorfismo de Nucleotídeo ÚnicoRESUMO
BACKGROUND: The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic scores (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D. METHODS: We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): (1) age and sex; (2) age, sex, body mass index (BMI), systolic blood pressure, and family history of T2D; (3) all variables in (2) and random glucose; and (4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS. RESULTS: PGS was associated with incident T2D in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of the top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk ((PGS-CRS interaction p = 0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)). CONCLUSIONS: Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.
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Diabetes Mellitus Tipo 2 , Herança Multifatorial , Humanos , Diabetes Mellitus Tipo 2/genética , Masculino , Feminino , Pessoa de Meia-Idade , Idoso , Incidência , Médicos de Atenção Primária , Adulto , Fatores de Risco , Predisposição Genética para Doença , Estudos Longitudinais , Atenção Primária à Saúde , Estudos de CoortesRESUMO
OBJECTIVE: Individuals with diabetes who carry genetic variants that lower hemoglobin A1c (HbA1c) independently of glycemia may have higher real, but undetected, hyperglycemia compared with those without these variants despite achieving similar HbA1c targets, potentially placing them at greater risk for diabetes-related complications. We sought to determine whether these genetic variants, aggregated in a polygenic score, and the large-effect African ancestry-specific missense variant in G6PD (rs1050828) that lower HbA1c were associated with higher retinopathy risk. RESEARCH DESIGN AND METHODS: Using data from 29,828 type 2 diabetes cases of genetically inferred African American/African British and European ancestries, we calculated ancestry-specific nonglycemic HbA1c polygenic scores (ngA1cPS) composed of 122 variants associated with HbA1c at genome-wide significance, but not with glucose. We tested the association of the ngA1cPS and the G6PD variant with retinopathy, adjusting for measured HbA1c and retinopathy risk factors. RESULTS: Participants in the bottom quintile of the ngA1cPS showed between 20% and 50% higher retinopathy prevalence, compared with those above this quintile, despite similar levels of measured HbA1c. The adjusted meta-analytic odds ratio for the bottom quintile was 1.31 (95% CI 1.0, 1.73; P = 0.05) in African ancestry and 1.31 (95% CI 1.15, 1.50; P = 6.5 × 10-5) in European ancestry. Among individuals of African ancestry with HbA1c below 7% units, retinopathy prevalence was higher in individuals below, compared with above, the 50th percentile of the ngA1cPS regardless of sex or G6PD carrier status. CONCLUSIONS: Genetic effects need to be considered to personalize HbA1c targets and improve outcomes of people with diabetes from diverse ancestries.
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Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.
Assuntos
Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/genética , Estudo de Associação Genômica Ampla , Fatores de Risco , Fenótipo , Herança Multifatorial/genética , Predisposição Genética para Doença/genéticaRESUMO
Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 cis-effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of these cis- and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D. These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.
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Aims: The behavior of pacemaker cardiomyocytes (PCs) in the sinoatrial node (SAN) is modulated by neurohormonal and paracrine factors, many of which signal through G-protein coupled receptors (GPCRs). The aims of the present study are to catalog GPCRs that are differentially expressed in the mammalian SAN and to define the acute physiological consequences of activating the cholecystokinin-A signaling system in isolated PCs. Methods and Results: Using bulk and single cell RNA sequencing datasets, we identify a set of GPCRs that are differentially expressed between SAN and right atrial tissue, including several whose roles in PCs and in the SAN have not been thoroughly characterized. Focusing on one such GPCR, Cholecystokinin-A receptor (CCK A R), we demonstrate expression of Cckar mRNA specifically in mouse PCs, and further demonstrate that subsets of SAN fibroblasts and neurons within the cardiac intrinsic nervous system express cholecystokinin, the ligand for CCK A R. Using mouse models, we find that while baseline SAN function is not dramatically affected by loss of CCK A R, the firing rate of individual PCs is slowed by exposure to sulfated cholecystokinin-8 (sCCK-8), the high affinity ligand for CCK A R. The effect of sCCK-8 on firing rate is mediated by reduction in the rate of spontaneous phase 4 depolarization of PCs and is mitigated by activation of beta-adrenergic signaling. Conclusions: (1) PCs express many GPCRs whose specific roles in SAN function have not been characterized, (2) Activation of the the cholecystokinin-A signaling pathway regulates PC automaticity.
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OBJECTIVE: To assess whether increased genetic risk of type 2 diabetes (T2D) is associated with the development of hyperglycemia after glucocorticoid treatment. RESEARCH DESIGN AND METHODS: We performed a retrospective analysis of individuals with no diagnosis of diabetes who received a glucocorticoid dose of ≥10 mg prednisone. We analyzed the association between hyperglycemia and a T2D global extended polygenic score, which was constructed through a meta-analysis of two published genome-wide association studies. RESULTS: Of 546 individuals who received glucocorticoids, 210 developed hyperglycemia and 336 did not. T2D polygenic score was significantly associated with glucocorticoid-induced hyperglycemia (odds ratio 1.4 per SD of polygenic score; P = 0.038). CONCLUSIONS: Individuals with increased genetic risk of T2D have a higher risk of glucocorticoid-induced hyperglycemia. This finding offers a mechanism for risk stratification as part of a precision approach to medical treatment.
Assuntos
Diabetes Mellitus Tipo 2 , Hiperglicemia , Humanos , Diabetes Mellitus Tipo 2/tratamento farmacológico , Diabetes Mellitus Tipo 2/genética , Glucocorticoides/efeitos adversos , Estudos Retrospectivos , Estudo de Associação Genômica Ampla , Hiperglicemia/induzido quimicamente , Hiperglicemia/genética , Hiperglicemia/diagnóstico , Fatores de RiscoRESUMO
OBJECTIVE: The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic score (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D. RESEARCH DESIGN AND METHODS: We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): 1) age and sex, 2) age, sex, BMI, systolic blood pressure, and family history of diabetes; 3) all variables in (2) and random glucose; 4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS. RESULTS: PGS was associated with incident diabetes in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk [(PGS-CRS interaction p =0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)]. CONCLUSIONS: Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.
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OBJECTIVE: The study aimed to develop and validate algorithms for identifying people with type 1 and type 2 diabetes in the All of Us Research Program (AoU) cohort, using electronic health record (EHR) and survey data. RESEARCH DESIGN AND METHODS: Two sets of algorithms were developed, one using only EHR data (EHR), and the other using a combination of EHR and survey data (EHR+). Their performance was evaluated by testing their association with polygenic scores for both type 1 and type 2 diabetes. RESULTS: For type 1 diabetes, the EHR-only algorithm showed a stronger association with T1D polygenic score (p=3×10-5) than the EHR+. For type 2 diabetes, the EHR+ algorithm outperformed both the EHR-only and the existing AoU definition, identifying additional cases (25.79% and 22.57% more, respectively) and showing stronger association with T2D polygenic score (DeLong p=0.03 and 1×10-4, respectively). CONCLUSIONS: We provide new validated definitions of type 1 and type 2 diabetes in AoU, and make them available for researchers. These algorithms, by ensuring consistent diabetes definitions, pave the way for high-quality diabetes research and future clinical discoveries.
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Aims: The behavior of pacemaker cardiomyocytes (PCs) in the sinoatrial node (SAN) is modulated by neurohormonal and paracrine factors, many of which signal through G-protein coupled receptors (GPCRs). The aims of the present study are to catalog GPCRs that are differentially expressed in the mammalian SAN and to define the acute physiological consequences of activating the cholecystokinin-A signaling system in isolated PCs. Methods and results: Using bulk and single cell RNA sequencing datasets, we identify a set of GPCRs that are differentially expressed between SAN and right atrial tissue, including several whose roles in PCs and in the SAN have not been thoroughly characterized. Focusing on one such GPCR, Cholecystokinin-A receptor (CCKAR), we demonstrate expression of Cckar mRNA specifically in mouse PCs, and further demonstrate that subsets of SAN fibroblasts and neurons within the cardiac intrinsic nervous system express cholecystokinin, the ligand for CCKAR. Using mouse models, we find that while baseline SAN function is not dramatically affected by loss of CCKAR, the firing rate of individual PCs is slowed by exposure to sulfated cholecystokinin-8 (sCCK-8), the high affinity ligand for CCKAR. The effect of sCCK-8 on firing rate is mediated by reduction in the rate of spontaneous phase 4 depolarization of PCs and is mitigated by activation of beta-adrenergic signaling. Conclusion: (1) PCs express many GPCRs whose specific roles in SAN function have not been characterized, (2) Activation of the cholecystokinin-A signaling pathway regulates PC automaticity.
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We meta-analyzed array data imputed with the TOPMed reference panel and whole-genome sequence (WGS) datasets and performed the largest, rare variant (minor allele frequency as low as 5×10-5) GWAS meta-analysis of type 2 diabetes (T2D) comprising 51,256 cases and 370,487 controls. We identified 52 novel variants at genome-wide significance (p<5 × 10-8), including 8 novel variants that were either rare or ancestry-specific. Among them, we identified a rare missense variant in HNF4A p.Arg114Trp (OR=8.2, 95% confidence interval [CI]=4.6-14.0, p = 1.08×10-13), previously reported as a variant implicated in Maturity Onset Diabetes of the Young (MODY) with incomplete penetrance. We demonstrated that the diabetes risk in carriers of this variant was modulated by a T2D common variant polygenic risk score (cvPRS) (carriers in the top PRS tertile [OR=18.3, 95%CI=7.2-46.9, p=1.2×10-9] vs carriers in the bottom PRS tertile [OR=2.6, 95% CI=0.97-7.09, p = 0.06]. Association results identified eight variants of intermediate penetrance (OR>5) in monogenic diabetes (MD), which in aggregate as a rare variant PRS were associated with T2D in an independent WGS dataset (OR=4.7, 95% CI=1.86-11.77], p = 0.001). Our data also provided support evidence for 21% of the variants reported in ClinVar in these MD genes as benign based on lack of association with T2D. Our work provides a framework for using rare variant imputation and WGS analyses in large-scale population-based association studies to identify large-effect rare variants and provide evidence for informing variant pathogenicity.
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We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.
RESUMO
We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.
RESUMO
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10-8) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.
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Cardiac pacemaker cells differentiate and functionally specialize early in embryonic development through activation of critical gene regulatory networks. In general, cellular specification and differentiation require that combinations of cell type-specific transcriptional regulators activate expression of key effector genes by binding to DNA regulatory elements including enhancers and promoters. However, because genomic DNA is tightly packaged by histones that must be covalently modified in order to render DNA regulatory elements and promoters accessible for transcription, the process of development and differentiation is intimately connected to the epigenetic regulation of chromatin accessibility. Although the difficulty of obtaining sufficient quantities of pure populations of pacemaker cells has limited progress in this field, the advent of low-input genomic technologies has the potential to catalyze a rapid growth of knowledge in this important area. The goal of this review is to outline the key transcriptional networks that control pacemaker cell development, with particular attention to our emerging understanding of how chromatin accessibility is modified and regulated during pacemaker cell differentiation. In addition, we will discuss the relevance of these findings to adult sinus node function, sinus node diseases, and origins of genetic variation in heart rhythm. Lastly, we will outline the current challenges facing this field and promising directions for future investigation.
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The NR4A family of orphan nuclear receptors (Nr4a1-3) plays redundant roles to establish and maintain Treg identity; deletion of multiple family members in the thymus results in Treg deficiency and a severe inflammatory disease. Consequently, it has been challenging to unmask redundant functions of the NR4A family in other immune cells. Here we use a competitive bone marrow chimera strategy, coupled with conditional genetic tools, to rescue Treg homeostasis and unmask such functions. Unexpectedly, chimeras harboring Nr4a1-/- Nr4a3-/- (double-knockout, DKO) bone marrow developed autoantibodies and a systemic inflammatory disease despite a replete Treg compartment of largely WT origin. This disease differs qualitatively from that seen with Treg deficiency and is B cell extrinsic. Negative selection of DKO thymocytes is profoundly impaired in a cell-intrinsic manner. Consistent with escape of self-reactive T cells into the periphery, DKO T cells with functional, phenotypic, and transcriptional features of anergy accumulated in chimeric mice. Nevertheless, we observed upregulation of genes encoding inflammatory mediators in anergic DKO T cells, and DKO T cells exhibited enhanced capacity for IL-2 production. These studies reveal cell-intrinsic roles for the NR4A family in both central and peripheral T cell tolerance and demonstrate that each is essential to preserve immune homeostasis.