Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

被引:0
|
作者
Ken Suzuki
Konstantinos Hatzikotoulas
Lorraine Southam
Henry J. Taylor
Xianyong Yin
Kim M. Lorenz
Ravi Mandla
Alicia Huerta-Chagoya
Giorgio E. M. Melloni
Stavroula Kanoni
Nigel W. Rayner
Ozvan Bocher
Ana Luiza Arruda
Kyuto Sonehara
Shinichi Namba
Simon S. K. Lee
Michael H. Preuss
Lauren E. Petty
Philip Schroeder
Brett Vanderwerff
Mart Kals
Fiona Bragg
Kuang Lin
Xiuqing Guo
Weihua Zhang
Jie Yao
Young Jin Kim
Mariaelisa Graff
Fumihiko Takeuchi
Jana Nano
Amel Lamri
Masahiro Nakatochi
Sanghoon Moon
Robert A. Scott
James P. Cook
Jung-Jin Lee
Ian Pan
Daniel Taliun
Esteban J. Parra
Jin-Fang Chai
Lawrence F. Bielak
Yasuharu Tabara
Yang Hai
Gudmar Thorleifsson
Niels Grarup
Tamar Sofer
Matthias Wuttke
Chloé Sarnowski
Christian Gieger
Darryl Nousome
机构
[1] University of Manchester,Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences
[2] University of Tokyo,Department of Diabetes and Metabolic Diseases, Graduate School of Medicine
[3] Osaka University Graduate School of Medicine,Department of Statistical Genetics
[4] German Research Center for Environmental Health,Institute of Translational Genomics, Helmholtz Zentrum München
[5] National Institutes of Health,Center for Precision Health Research, National Human Genome Research Institute
[6] University of Cambridge,British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care
[7] University of Cambridge,Heart and Lung Research Institute
[8] Nanjing Medical University,Department of Epidemiology, School of Public Health
[9] University of Michigan,Department of Biostatistics and Center for Statistical Genetics
[10] Corporal Michael J. Crescenz VA Medical Center,Department of Systems Pharmacology and Translational Therapeutics
[11] University of Pennsylvania Perelman School of Medicine,Department of Genetics
[12] University of Pennsylvania Perelman School of Medicine,Programs in Metabolism and Medical and Population Genetics
[13] Broad Institute of Harvard and MIT,Diabetes Unit and Center for Genomic Medicine
[14] Massachusetts General Hospital,TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women’s Hospital
[15] Harvard Medical School,William Harvey Research Institute, Barts and the London School of Medicine and Dentistry
[16] Queen Mary University of London,Graduate School of Experimental Medicine
[17] Technical University of Munich,Munich School for Data Science
[18] Helmholtz Munich,Department of Genome Informatics, Graduate School of Medicine
[19] University of Tokyo,Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives
[20] Osaka University,Laboratory for Systems Genetics
[21] RIKEN Center for Integrative Medical Sciences,Charles Bronfman Institute for Personalized Medicine
[22] Icahn School of Medicine at Mount Sinai,Department of Medicine
[23] Vanderbilt University Medical Center,Estonian Genome Centre, Institute of Genomics
[24] University of Tartu,Nuffield Department of Population Health
[25] University of Oxford,Medical Research Council Population Health Research Unit
[26] University of Oxford,Institute for Translational Genomics and Population Sciences, Department of Pediatrics
[27] Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center,Department of Epidemiology and Biostatistics
[28] Imperial College London,Department of Cardiology, Ealing Hospital
[29] London NorthWest Healthcare NHS Trust,Division of Genome Science, Department of Precision Medicine
[30] National Institute of Health,Department of Epidemiology, Gillings School of Global Public Health
[31] University of North Carolina at Chapel Hill,Department of Gene Diagnostics and Therapeutics, Research Institute
[32] National Center for Global Health and Medicine,Institute of Epidemiology, Helmholtz Zentrum München
[33] German Research Center for Environmental Health,Department of Medicine
[34] McMaster University,Population Health Research Institute
[35] Hamilton Health Sciences and McMaster University,Public Health Informatics Unit, Department of Integrated Health Sciences
[36] Nagoya University Graduate School of Medicine,MRC Epidemiology Unit, Institute of Metabolic Science
[37] University of Cambridge School of Clinical Medicine,Department of Health Data Science
[38] University of Liverpool,Division of Translational Medicine and Human Genetics
[39] University of Pennsylvania,Department of Epidemiology
[40] Brown University School of Public Health,Department of Anthropology
[41] University of Toronto at Mississauga,Saw Swee Hock School of Public Health
[42] National University of Singapore and National University Health System,Department of Epidemiology, School of Public Health
[43] University of Michigan,Center for Genomic Medicine
[44] Kyoto University Graduate School of Medicine,deCODE Genetics
[45] Amgen,Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences
[46] University of Copenhagen,Department of Biostatistics
[47] Harvard University,Division of Sleep and Circadian Disorders
[48] Brigham and Women’s Hospital,Department of Medicine
[49] Harvard University,Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center
[50] University of Freiburg,Department of Epidemiology, Human Genetics and Environmental Sciences
来源
Nature | 2024年 / 627卷
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摘要
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.
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页码:347 / 357
页数:10
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