Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries

Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries Alicia Huerta-Chagoya , Joohyun Kim , View ORCID Profile Ravi Mandla , Yingchang Lu , Ken Suzuki , Lauren E. Petty , Hong Kiat Ng , Jaewon Choi , Simon Lee , Madhusmita Rout , Kuang Lin , Linda S. Adair , View ORCID Profile Adebowale Adeyemo , Habibul Ahsan , Masato Akiyama , Ping An , View ORCID Profile Sonia S. Anand , Diane M. Becker , Alain G. Bertoni , Zheng Bian , Lawrence F. Bielak , View ORCID Profile John Blangero , Michael Boehnke , Erwin P. Bottinger , Donald W. Bowden , View ORCID Profile Fiona Bragg , Jennifer A. Brody , Thomas A. Buchanan , View ORCID Profile Brian E. Cade , Jin-Fang Chai , John C. Chambers , Giriraj R. Chandak , Li-Ching Chang , View ORCID Profile Kyong-Mi Chang , Miao-Li Chee , Chien-Hsiun Chen , Yuan-Tsong Chen , Zhengming Chen , Yii-Der I. Chen , Ji Chen , Guanjie Chen , Shyh-Huei Chen , Wei-Min Chen , Ching-Yu Cheng , Yoon Shin Cho , Hyeok Sun Choi , Lee-Ming Chuang , Miguel Cruz , Mary Cushman , Swapan K. Das , Ralph A. DeFronzo , H Janaka deSilva , Latchezar Dimitrov , View ORCID Profile Ayo P. 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Ng , D-PRISM Consortium doi: https://doi.org/10.1101/2025.07.21.25331778 Alicia Huerta-Chagoya 1 Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, 2142, USA 2 Center for Genomic Medicine, Massachusetts General Hospital , Boston, MA, 2114, USA 3 Diabetes Unit, Massachusetts General Hospital , Boston, MA, 2114, USA 4 Department of Medicine, Harvard Medical School , Boston, MA, 2215, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joohyun Kim 5 Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center , Nashville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ravi Mandla 1 Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, 2142, USA 2 Center for Genomic Medicine, Massachusetts General Hospital , Boston, MA, 2114, USA 3 Diabetes Unit, Massachusetts General Hospital , Boston, MA, 2114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ravi Mandla Yingchang Lu 5 Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center , Nashville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ken Suzuki 6 Department of Statistical Genetics, Osaka University , Osaka, 565-0871, Japan 7 Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo , Tokyo, 113-8654, Japan 8 Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester , Manchester, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lauren E. Petty 5 Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center , Nashville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hong Kiat Ng 9 Lee Kong Chian School of Medicine, Clinical Sciences Building, Nanyang Technological University , Singapore, 308232, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jaewon Choi 10 Department of Internal Medicine, Seoul National University Hospital , Seoul, 3080, South Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simon Lee 11 The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Madhusmita Rout 12 Department of Pediatrics, Section of Genetics, University of Oklahoma , Oklahoma, OK, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kuang Lin 13 Nuffield Department of Population Health, University of Oxford , Oxford, OX3 7LF, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Linda S. Adair 14 Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill , Chapel Hill, NC, 27599, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adebowale Adeyemo 15 Center for Research on Genomics and Global Health, National Human Genome Institute, National Institutes of Health , Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Adebowale Adeyemo Habibul Ahsan 16 Biological Sciences Division, Public Health Sciences, The University of Chicago , Chicago, IL, 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Masato Akiyama 17 Laboratory for Statistical Analysis, RIKEN Centre for Integrative Medical Sciences , Yokohama, 230-0045, Japan 18 Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University , Fukuoka, 812-8582, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ping An 19 Division of Statistical Genomics, Washington University School of Medicine , St. Louis, MO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sonia S. Anand 20 Department of Medicine, McMaster University , Hamilton, ON, Canada 21 Population Health Research Institute , Hamilton, ON, Canada 22 Hamilton Health Sciences , Hamilton, ON, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sonia S. Anand Diane M. Becker 23 Department of Medicine, Johns Hopkins University School of Medicine , Baltimore, MD, 21205, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alain G. Bertoni 24 Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine , Winston-Salem, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zheng Bian 25 Chinese Academy of Medical Sciences , Beijing, 100730, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lawrence F. Bielak 26 Department of Epidemiology, School of Public Health, University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Blangero 27 Human Genetics and STDOI, University of Texas Rio Grande Valley , Edinburg, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John Blangero Michael Boehnke 28 Department of Biostatistics and Center for Statistical Genetics, University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Erwin P. Bottinger 29 Digital Health Center, Digitl Engineering Faculty of Hasso Plattner Institue and University Potsdam , Potsdam, Germany 30 Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai , New York, NY, USA 11 The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Donald W. Bowden 31 Center for Diabetes Research, Wake Forest University School of Medicine , Winston-Salem, NC, USA 32 Center for Genomics and Personalized Medicine Research, Wake Forest University School of Medicine , Winston-Salem, NC, USA 33 Department of Biochemistry, Wake Forest University School of Medicine , Winston-Salem, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fiona Bragg 13 Nuffield Department of Population Health, University of Oxford , Oxford, OX3 7LF, UK 34 Health Data Research UK Oxford, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fiona Bragg Jennifer A. Brody 35 Cardiovascular Health Research Unit, Department of Medicine, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas A. Buchanan 36 Department of Medicine, Division of Endocrinology and Diabetes, Keck School of Medicine, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brian E. Cade 37 Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital , Boston, MA, USA 38 Harvard Medical School , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Brian E. Cade Jin-Fang Chai 39 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore, 117549, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site John C. Chambers 9 Lee Kong Chian School of Medicine, Clinical Sciences Building, Nanyang Technological University , Singapore, 308232, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Giriraj R. Chandak 40 Genomic Research on Complex diseases, CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB) , Hyderabad, India 41 Lightening Lives , Hyderabad, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Li-Ching Chang 42 Institute of Biomedical Sciences, Academia Sinica , Taipei, 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kyong-Mi Chang 43 Department of Biostatistics and Epidemiology, University of Pennsylvania , Philadelphia, PA, 19104, USA 44 Division of Translational Medicine and Human Genetics, University of Pennsylvania , Philadelphia, PA, 19104, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kyong-Mi Chang Miao-Li Chee 45 Singapore Eye Research Institute, Singapore National Eye Centre , Singapore, 168751, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chien-Hsiun Chen 42 Institute of Biomedical Sciences, Academia Sinica , Taipei, 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuan-Tsong Chen 42 Institute of Biomedical Sciences, Academia Sinica , Taipei, 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhengming Chen 13 Nuffield Department of Population Health, University of Oxford , Oxford, OX3 7LF, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yii-Der I. Chen 46 The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center , Torrance, CA, 90502, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ji Chen 47 Wellcome Sanger Institute , Wellcome Trust Genome Campus, Hinxton, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Guanjie Chen 15 Center for Research on Genomics and Global Health, National Human Genome Institute, National Institutes of Health , Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shyh-Huei Chen 48 Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest University Health Sciences , Winston-Salem, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wei-Min Chen 49 Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine , Charlottesville, VA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ching-Yu Cheng 50 Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School , Singapore, 169857, Singapore 51 Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System , Singapore, 119228, Singapore 45 Singapore Eye Research Institute, Singapore National Eye Centre , Singapore, 168751, Singapore 52 Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore , Singapore, 119228, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yoon Shin Cho 53 Biomedical Science, Hallym University , Chuncheon, 24252, South Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hyeok Sun Choi 53 Biomedical Science, Hallym University , Chuncheon, 24252, South Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lee-Ming Chuang 54 Department of Internal Medicine, Division of Endocrinology and Metabolism, National Taiwan University Hospital , Taipei, Taiwan 55 College of Medicine, National Taiwan University , Taipei, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Miguel Cruz 56 Unidad de Investigación Médica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mary Cushman 57 Department of Medicine, University of Vermont , Colchester, VT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Swapan K. Das 58 Department of Internal Medicine, Section on Endocrinology and Metabolism, Wake Forest University School of Medicine , Winston-Salem, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ralph A. DeFronzo 59 Department of Medicine, University of Texas Health Science Center at San Antonio , San Antonio, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site H Janaka deSilva 60 Department of Medicine, Faculty of Medicine, University of Kelaniya , Sri Lanka Find this author on Google Scholar Find this author on PubMed Search for this author on this site Latchezar Dimitrov 32 Center for Genomics and Personalized Medicine Research, Wake Forest University School of Medicine , Winston-Salem, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ayo P. Doumatey 15 Center for Research on Genomics and Global Health, National Human Genome Institute, National Institutes of Health , Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ayo P. Doumatey Shufa Du 14 Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill , Chapel Hill, NC, 27599, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qing Duan 61 Department of Genetics, University of North Carolina at Chapel Hill , Chapel Hill, NC, 27599, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ravindranath Duggirala 27 Human Genetics and STDOI, University of Texas Rio Grande Valley , Edinburg, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Leslie S. Emery 62 Department of Biostatistics, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site James C. Engert 63 Department of Medicine, McGill University , Montreal, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel S. Evans 64 California Pacific Medical Center Research Institute , San Francisco, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michele K. Evans 65 Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health , Baltimore, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michele K. Evans Sarah Finer 66 Wolfson Institute of Population Health, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jose C. Florez 1 Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, 2142, USA 4 Department of Medicine, Harvard Medical School , Boston, MA, 2215, USA 67 Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital , Boston, MA, 2114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site James S. Floyd 35 Cardiovascular Health Research Unit, Department of Medicine, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Myriam Fornage 68 Institute of Molecular Medicine, University of Texas Health Science Center at Houston , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Myriam Fornage Elizabeth G. Frankel 5 Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center , Nashville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Barry I. Freedman 69 Department of Internal Medicine, Wake Forest University School of Medicine , Winston-Salem, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lourdes García-García 70 Instituto Nacional de Salud Pública , Cuernavaca, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pauline Genter 71 Division of Endocrinology & Metabolism, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center , Torrance, CA, 90502, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hertzel C. Gerstein 20 Department of Medicine, McMaster University , Hamilton, ON, Canada 21 Population Health Research Institute , Hamilton, ON, Canada 22 Hamilton Health Sciences , Hamilton, ON, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mark O. Goodarzi 72 Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Penny Gordon-Larsen 14 Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill , Chapel Hill, NC, 27599, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mariaelisa Graff 73 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill , Chapel Hill, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Myron Gross 74 Department of Laboratory Medicine and Pathology, University of Minnesota , Minneapolis, MN, 55454, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yu Guo 25 Chinese Academy of Medical Sciences , Beijing, 100730, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiuqing Guo 46 The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center , Torrance, CA, 90502, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yang Hai 46 The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center , Torrance, CA, 90502, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Craig L. Hanis 75 Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site MGeoffrey Hayes 76 Department of Anthropology, Northwestern University , Evanston, IL, USA 77 Center for Genetic Medicine, Northwestern University Feinberg School of Medicine , Chicago, IL, USA 78 Division of Endocrinology, Metabolism, and Molecular Medicine, Northwestern University Feinberg School of Medicine , Chicago, IL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Momoko Horikoshi 79 Laboratory for Endocrinology, Metabolism and Kidney Diseases, RIKEN Centre for Integrative Medical Sciences , Yokohama, 230-0045, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Annie-Green Howard 80 Department of Biostatistics, Carolina Population Center, University of North Carolina at Chapel Hill , Chapel Hill, NC, 27599, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah Hsu 1 Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, 2142, USA 3 Diabetes Unit, Massachusetts General Hospital , Boston, MA, 2114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Willa Hsueh 81 Department of Internal Medicine, Diabetes and Metabolism Research Center, Ohio State University Wexner Medical Center , Columbus, OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wei Huang 82 Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT) , Shanghai, 200032, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mengna Huang 83 Center for Global Cardiometabolic Health, Brown University , Providence, RI, USA 84 Department of Epidemiology, Brown University School of Public Health , Providence, RI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yi-Jen Hung 85 Division of Endocrine and Metabolism, Tri-Service General Hospital Songshan Branch , Taipei, Taiwan 86 School of Medicine, National Defense Medical Center , Taipei, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mi Yeong Hwang 87 Department of Precision Medicine, Division of Genome Science, National Institute of Health , Chungcheongbuk-do, 28159, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chii-Min Hwu 88 Department of Medicine, Section of Endocrinology and Metabolism, Taipei Veterans General Hospital , Taipei, Taiwan 89 School of Medicine, National Yang-Ming University , Taipei, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chii-Min Hwu Sahoko Ichihara 90 Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine , Shimotsuke, 329-0498, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michiya Igase 91 Department of Anti-aging Medicine, Ehime University Graduate School of Medicine , Ehime, 791-0295, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eli Ipp 71 Division of Endocrinology & Metabolism, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center , Torrance, CA, 90502, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mohammad T. 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Kho 109 Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern UniversityFeinberg School of Medicine , Chicago, IL, USA 110 Department of Medicine, Division of General Internal Medicine and Geriatrics, Northwestern University Feinberg School of Medicine , Chicago, IL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chiea-Chuen Khor 111 Genome Institute of Singapore, Agency for Science, Technology and Research , Singapore, 138672, Singapore 45 Singapore Eye Research Institute, Singapore National Eye Centre , Singapore, 168751, Singapore 112 Duke-NUS Medical School , Singapore, 169857, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Muhammad Kibriya 16 Biological Sciences Division, Public Health Sciences, The University of Chicago , Chicago, IL, 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bong-Jo Kim 87 Department of Precision Medicine, Division of Genome Science, National Institute of Health , Chungcheongbuk-do, 28159, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Woon-Puay Koh 113 Health Services and Systems Research, Duke-NUS Medical School , Singapore, 169857, Singapore 39 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore, 117549, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katsuhiko Kohara 114 Department of Regional Resource Management, Ehime University Faculty of Collaborative Regional Innovation , Ehime, 790-8577, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jaspal S. 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Lim 39 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore, 117549, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jianjun Liu 111 Genome Institute of Singapore, Agency for Science, Technology and Research , Singapore, 138672, Singapore 125 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System , Singapore, 117549, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yongmei Liu 126 Department of Medicine, Division of Cardiology, Duke University School of Medicine , Durham, NC, USA 24 Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine , Winston-Salem, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simin Liu 127 Department of Epidemiology and Biostatistics, The Joe Wen School of Population & Pubic Health, UCI , Irvine, CA, USA 128 Center for Global Cardiometabolic Health and Nutrition, the University of California, Irvine (UCI) , Irvine, CA, USA 129 Division of Cardiology, Department of Medicine, School of Medicine, UCI , USA 130 Center for Global Cardiometabolic Health, Brown University School of Public Health , Providence, RI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Simin Liu Jirong Long 100 Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center , Nashville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tin Louie 62 Department of Biostatistics, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xi Luo 131 Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jun Lv 123 Department of Epidemiology and Biostatistics, Peking University Health Science Centre , Beijing, 100191, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julie A. 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Nalls 145 Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health , Bethesda, MD, USA 146 Center for Alzheimer’s and Related Dementias, National Institutes of Health , Bethesda, MD, USA 147 DataTecnica LLC , Washington, DC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Uma Nayak 148 Department of Genome Sciences, University of Virginia School of Medicine , Charlottesville, VA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aude Nicolas 145 Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health , Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kari E. 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Pankow 152 Division of Epidemiology and Community Health, School of Public Health, University of Minnesota , Minneapolis, MN, 55454, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Guillaume Paré 153 Department of Pathology and Molecular Medicine, McMaster University , Hamilton, ON, Canada 21 Population Health Research Institute , Hamilton, ON, Canada 22 Hamilton Health Sciences , Hamilton, ON, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guillaume Paré Jaehyun Park 5 Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center , Nashville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kyong Soo Park 154 Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Hospital , Seoul, 3080, South Korea 155 Department of Internal Medicine, Konkuk University Medical Center , Seoul, 3080, South Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Esteban J. 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Shuey 5 Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center , Nashville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Moneeza K. Siddiqui 66 Wolfson Institute of Population Health, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer A. Smith 26 Department of Epidemiology, School of Public Health, University of Michigan , Ann Arbor, MI, USA 167 Survey Research Center, Institute for Social Research, University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jennifer A. Smith Tamar Sofer 168 Department of Biostatistics, Harvard T.H. 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Stilp 62 Department of Biostatistics, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Meng Sun 171 Nuffield Department of Surgical Sciences, University of Oxford , Oxford, OX3 9DU, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yasuharu Tabara 108 Center for Genomic Medicine, Kyoto University Graduate School of Medicine , Kyoto, 606-8507, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site E-Shyong Tai 125 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System , Singapore, 117549, Singapore 39 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore, 117549, Singapore 112 Duke-NUS Medical School , Singapore, 169857, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Salman M. Tajuddin 65 Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health , Baltimore, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Atsushi Takahashi 101 Laboratory for Statistical and Translational Genetics, RIKEN Centre for Integrative Medical Sciences , Yokohama, 230-0045, Japan 172 Department of Genomic Medicine, National Cerebral and Cardiovascular Center , Osaka, 565-8565, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fumihiko Takeuchi 93 Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine , Tokyo, 162-8655, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fumihiko Takeuchi Jingyi Tan 46 The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center , Torrance, CA, 90502, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kent D. Taylor 46 The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center , Torrance, CA, 90502, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katherine Taylor 1 Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, 2142, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Farook Thameem 173 Department of Biochemistry, Faculty of Medicine, Health Science Center, Kuwait University , Safat, Kuwait Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lin Tong 16 Biological Sciences Division, Public Health Sciences, The University of Chicago , Chicago, IL, 60637, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fuu-Jen Tsai 174 Department of Medical Genetics and Medical Research, China Medical University Hospital , Taichung, 40402, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Philip S. Tsao 175 Department of Medicine, Stanford University , Palo Alto, CA, USA 176 VA Palo Alto Health Care System , Palo Alto, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Miriam S. Udler 1 Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, 2142, USA 4 Department of Medicine, Harvard Medical School , Boston, MA, 2215, USA 2 Center for Genomic Medicine, Massachusetts General Hospital , Boston, MA, 2114, USA 3 Diabetes Unit, Massachusetts General Hospital , Boston, MA, 2114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adan Valladares-Salgado 56 Unidad de Investigación Médica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site David A. van Heel 177 Blizard Institute, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David A. van Heel Rob M. vanDam 39 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore, 117549, Singapore 125 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System , Singapore, 117549, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rohit Varma 178 Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maheak Vora 1 Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, 2142, USA 2 Center for Genomic Medicine, Massachusetts General Hospital , Boston, MA, 2114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Niels Wacher-Rodarte 179 Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ya-Xing Wang 180 Beijing Institute of Ophthalmology, Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University , Beijing, 100005, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ellie Wheeler 181 MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine , Cambridge, UK 47 Wellcome Sanger Institute , Wellcome Trust Genome Campus, Hinxton, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eric A. Whitsel 73 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill , Chapel Hill, NC, USA 182 Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill , Chapel Hill, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ananda R. Wickremasinghe 105 Department of Public Health, Faculty of Medicine, University of Kelaniya , Sri Lanka Find this author on Google Scholar Find this author on PubMed Search for this author on this site Genevieve L. Wojcik 183 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD, 21205, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tien Y. Wong 45 Singapore Eye Research Institute, Singapore National Eye Centre , Singapore, 168751, Singapore 50 Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School , Singapore, 169857, Singapore 51 Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System , Singapore, 119228, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jer-Yuarn Wu 42 Institute of Biomedical Sciences, Academia Sinica , Taipei, 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yong-Bing Xiang 184 State Key Laboratory of Oncogene and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine , Shanghai, 200032, China 185 Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine , Shanghai, 200032, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anny H. Xiang 186 Department of Research & Evaluation, Division of Biostatistics Research, Kaiser Permanente of Southern California , Pasadena, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chittaranjan S. Yajnik 187 Diabetes Unit, King Edward Memorial Hospital and Research Centre , Pune, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chittaranjan S. Yajnik Ken Yamamoto 188 Department of Medical Biochemistry, Kurume University School of Medicine , Kurume, 830-0011, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Toshimasa Yamauchi 7 Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo , Tokyo, 113-8654, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lisa R. Yanek 23 Department of Medicine, Johns Hopkins University School of Medicine , Baltimore, MD, 21205, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lisa R. Yanek Jie Yao 46 The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center , Torrance, CA, 90502, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mitsuhiro Yokota 188 Department of Medical Biochemistry, Kurume University School of Medicine , Kurume, 830-0011, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Canqing Yu 123 Department of Epidemiology and Biostatistics, Peking University Health Science Centre , Beijing, 100191, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jian-Min Yuan 189 Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh , Pittsburgh, PA, 15232, USA 190 Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh , Pittsburgh, PA, 15232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Salim Yusuf 191 Department of Health Research Methods, Evidence, and Impact, McMaster University , Hamilton, ON, Canada 192 Population Health Research Institute , Hamilton, ON, Canada 193 Hamilton Health Sciences , Hamilton, ON, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eleftheria Zeggini 194 Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health , Neuherberg, Germany 195 Technical University of Munich (TUM), TUM University Hospital, TUM School of Medicine and Health , Munich, 81675, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Liang Zhang 45 Singapore Eye Research Institute, Singapore National Eye Centre , Singapore, 168751, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Weihua Zhang 196 Department of Epidemiology and Biostatistics, Imperial College London , London, W2 1PG, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wei Zheng 197 Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center , Nashville, TN, 37203, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wei Zheng Alan B. Zonderman 65 Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health , Baltimore, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site 198 Instituto Nacional de Salud Pública , Cuernavaca, Mexico 177 Blizard Institute, Queen Mary University of London , London, UK 199 Corporal Michael J Crescenz VA Medical Center , Philadelphia, PA, USA Carlos A. Aguilar-Salinas 200 Unidad de Investigación de Enfermedades Metabólicas, Research Direction of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán , Mexico City, Mexico 201 Tecnológico de Monterrey, Escuela de Medicina y Ciencias de la Salud , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clicerio González-Villalpando 202 Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Pública , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher A. Haiman 203 Department of Population and Public Health Sciences, Keck School of Medicine of USC , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Young Jin Kim 87 Department of Precision Medicine, Division of Genome Science, National Institute of Health , Chungcheongbuk-do, 28159, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Young Jin Kim Soo Heon Kwak 154 Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Hospital , Seoul, 3080, South Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aaron Leong 1 Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, 2142, USA 2 Center for Genomic Medicine, Massachusetts General Hospital , Boston, MA, 2114, USA 204 Department of Medicine, Massachusetts General Hospital , Boston, MA, 2114, USA 3 Diabetes Unit, Massachusetts General Hospital , Boston, MA, 2114, USA 141 Division of General Internal Medicine, Massachusetts General Hospital , Boston, MA, 2114, USA 205 Endocrine Division, Massachusetts General Hospital , Boston, MA, 2114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ruth J.F. Loos 11 The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA 206 The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andres Moreno-Estrada 207 Aging Research Center, Cinvestav Sede Sur, Center for Research and Advanced Studies of the National Polytechnic Institute , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrew P. Morris 8 Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester , Manchester, UK 208 NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre , Manchester, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lorena Orozco 209 Instituto Nacional de Medicina Genómica , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jerome I. Rotter 46 The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center , Torrance, CA, 90502, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dharambir Sanghera 12 Department of Pediatrics, Section of Genetics, University of Oklahoma , Oklahoma, OK, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Teresa Tusie-Luna 210 Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México/Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benjamin F. Voight 211 Corporal Michael J. Crescenz Philadelphia VA Medical Center , Philadelphia, PA, USA 212 Department of Genetics, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA, USA 213 Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA, 19104, USA 214 Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Benjamin F. Voight Marijana Vujkovic 215 Department of Medicine, University of Pennsylvania , Philadelphia, PA, 19104, USA 211 Corporal Michael J. Crescenz Philadelphia VA Medical Center , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marijana Vujkovic Robin G. Walters 13 Nuffield Department of Population Health, University of Oxford , Oxford, OX3 7LF, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Robin G. Walters Tian Ge 216 Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital , Boston, MA, 2114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alisa K. Manning 217 Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital , Boston, MA, 2114, USA 1 Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, 2142, USA 4 Department of Medicine, Harvard Medical School , Boston, MA, 2215, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marie Loh 9 Lee Kong Chian School of Medicine, Clinical Sciences Building, Nanyang Technological University , Singapore, 308232, Singapore 218 Department of Epidemiology and Biostatistics, Imperial College London , St Mary’s Campus, London, UK 219 Research Division, National Skin Centre , Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer E. Below 5 Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center , Nashville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xueling Sim 39 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore, 117549, Singapore Find this author on Google Scholar Find this author on PubMed Search for this author on this site Josep M. Mercader 1 Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, 2142, USA 4 Department of Medicine, Harvard Medical School , Boston, MA, 2215, USA 2 Center for Genomic Medicine, Massachusetts General Hospital , Boston, MA, 2114, USA 3 Diabetes Unit, Massachusetts General Hospital , Boston, MA, 2114, USA 220 Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard , Cambridge, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Josep M. Mercader For correspondence: mercader{at}broadinstitute.org maggie.ng{at}vumc.org Maggie C.Y. Ng 5 Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center , Nashville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: mercader{at}broadinstitute.org maggie.ng{at}vumc.org Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Summary Background Polygenic risk scores (PRSs) improve type 2 diabetes (T2D) prediction beyond clinical risk factors but perform poorly in non-European populations, where T2D burden is often higher, undermining their global clinical utility. Methods We conducted the largest global effort to date to harmonize T2D genome-wide association study (GWAS) meta- analyses across five ancestries—European (EUR), African/African American (AFR), Admixed American (AMR), South Asian (SAS), and East Asian (EAS)—including 360,000 T2D cases and 1·8 million controls (41% non-EUR). We constructed ancestry-specific and multi-ancestry PRSs in training datasets including 11,000 T2D cases and 32,000 controls, and validated their performance in independent datasets including 39,000 T2D cases and 126,000 controls of diverse ancestries. In the All of Us Research Program, we compared these PRSs to those from the Polygenic Score Catalog and assessed their ability to predict diabetes micro- and macrovascular complications. Findings Ancestry-specific PRSs showed limited prediction power for T2D in AFR, AMR, and SAS compared to EUR and EAS. In contrast, multi-ancestry PRSs, built using GWAS data from five ancestries, substantially improved T2D prediction across all ancestries. Compared to those in the interquartile range, individuals at the 97·5 th percentile of their PRSs had a 6-fold increased T2D risk in AMR, EAS, and EUR, and ≥3-fold in AFR and SAS. These PRSs were also associated with the development of microvascular complications and outperformed all previously reported PRSs for all ancestries. Interpretation We developed and extensively validated the most up-to-date T2D PRSs across diverse ancestry groups. These PRSs are publicly available to support further evaluation of their clinical utility in diverse ancestries. Introduction Type 2 diabetes (T2D) represents one of the largest health problems of the 21st century, affecting 537 million people globally, and is predicted to increase to 783 million by 2045. 1 Both genetic and environmental factors contribute to T2D susceptibility. Genome-wide association studies (GWAS) have identified 1,289 genetic signals associated with T2D in diverse ancestries. 2 , 3 The aggregation of risk alleles in polygenic risk scores (PRSs) for T2D 2 , 4 may provide insights into disease progression and prognosis, or help identify people at risk for prioritization of therapeutic or lifestyle intervention. 5 Several initiatives are beginning to test their utility in clinical settings 6 , 7 , and an observational study in Electronic Health Record (EHR) data has shown that T2D PRSs provide data orthogonal to standard clinical risk factors and are therefore particularly valuable in identifying at-risk individuals among those perceived to be low risk based on standard clinical risk factors. 8 T2D disproportionately affects people of ancestral genetic backgrounds other than European, with South Asian, African American, and Hispanic/Latin American populations having a higher prevalence of diabetes and related complications. 9 Yet, most PRSs are based on GWAS, including predominantly individuals of European ancestry, and have poor performance in individuals of other ancestries, which may further exacerbate health disparities if PRS were to be deployed for disease prediction in ancestrally diverse populations. 2 , 10 While efforts have been made to enhance PRS performance in diverse populations 11 , a comprehensive, standardized, and harmonized approach for the development and validation of PRSs across continental ancestries for T2D remains lacking. In particular, recent efforts have aimed at improving the transferability of PRS across diverse ancestries. First, the Polygenic Risk Methods in Diverse Populations (PRIMED) consortium and others have developed new methods, such as those incorporating GWAS and linkage disequilibrium (LD) data from multiple ancestries, showing improved prediction. 11 , 12 Second, large-scale T2D GWAS data for populations other than European have become available, which may improve the power of PRS development in these populations. Third, the emergence of large-scale biobank data from around the world — such as the All of Us Research Program (AoU) — offers valuable opportunities to develop, train, and validate novel PRSs, as data from these participants has not yet been included in GWAS meta-analyses. 13 , 14 As part of the Type 2 Diabetes Global Genomics Initiative (T2DGGI), we published the largest multi-ancestry T2D GWAS meta-analysis to date, based on 2·5 million individuals, including 428,452 with T2D, and enumerating 1,289 signals for T2D risk. 3 Here, in collaboration with T2DGGI, we present results from the Diabetes Polygenic Risk Scores in Multiple ancestries (D-PRISM) 2 , 11 , an international consortium focusing on improving PRS prediction of different types of diabetes and progression across the lifespan in diverse ancestries. We leveraged these two consortia to aggregate extensive T2D GWAS data from five major continental ancestry groups, trained and validated PRS models in independent cohorts to evaluate their performance in predicting T2D and diabetes complications using a unified pipeline. We tested the optimal PRS model for each of the five ancestries and made them available to the community for downstream analyses, as well as future testing for clinical utility and implementation. Methods An overview of the overall strategy is shown in Fig.1 and detailed in the supplementary methods. We leveraged T2D GWAS from selected cohorts participating in three large consortia: the Diabetes Meta-analysis of Trans-ethnic Association Studies (DIAMANTE) 15 , the Million Veteran Program (MVP) 16 , and FinnGen 17 . Cohorts were categorized by genetic similarity to one or more of the five ancestries available in the 1000 Genomes (1KG) project 18 , or the country of origin: African/African American (AFR), Admixed American (AMR), East Asian (EAS), European (EUR), and South Asian (SAS). While ancestry labels do not represent or account for the full continuum of human genetic diversity, they were necessary for statistical analyses where no individual-level data were available. We included 2,185,548 individuals (359,819 cases, 1,825,729 controls) across 125 T2D GWAS datasets to conduct ancestry-specific meta-analyses, with summary statistics from EUR and EAS ancestry groups representing 86% of the total sample size (EUR=68%, EAS=18%, AFR=6%, AMR=4%, and SAS=4%; Fig.1a , Supplementary Tables 1,2). Download figure Open in new tab Fig. 1. Overall analysis approach. a, Overview of the 125 T2D GWAS datasets, variant sets, and pairwise LD information used to generate five ancestry-specific T2D GWAS meta-analyses and 20 LD reference panels for PRSs training. b, Independent, ancestry-specific cohorts used to train the PRS models and select the optimal continuous shrinkage prior from five phi values (i.e., 0·01, 0·001, 1×10 −4 , 1×10 −5 , 1×10 −6 ) based on predictive performance. Single-ancestry PRSs using GWAS summary statistics and LD panels matched to the validation ancestry or using data from the EAS or EUR ancestries. Multi-ancestry PRSs jointly modeled GWAS summary statistics and LD panels from all five ancestry groups. c, Set of 23 ancestry-specific and independent cohorts used to validate the 18 best-performing PRS. d, Evaluation of PRS predictive performance, including: i) incremental AUC, calculated as the difference between the AUC of the full model (PRS + covariates) and the model without the PRS, ii) proportion of variation in T2D status explained by the PRS, estimated using Nagelkerke’s r 2 , iii) odds ratio per standard deviation (OR per SD) of the PRS distribution or odds ratio (OR) comparing PRS distribution extremes relative to the interquartile range. We leveraged cohorts not included in previous meta-analyses and, when necessary, conducted held-out meta-analyses to allow some cohorts to train and validate PRS. We aggregated data from a total of 42,784 individuals (10,992 cases, 31,792 controls) from five ancestries for training (tuning PRS construction parameters; Fig.1b ) and 164,972 individuals (39,148 cases, 125,824 controls) for validation (testing PRS performance; Fig.1c ). We used the five ancestry-specific T2D GWAS summary statistics to construct single-ancestry and multi-ancestry PRSs, using the PRS-CS (Continuous Shrinkage) 19 and PRS-CSx 20 methods, respectively (Fig.1b) . Both methods use LD panels from reference datasets to model the pairwise correlations between SNPs in GWAS summary statistics during PRS construction. The SNPs in the most widely used LD panels are based on the HapMap3 (HM3), which generally do not tag well in non-EUR populations and may miss ancestry-specific signals. In addition, the samples used to model LD structure are either from the 1KG project or the UK Biobank (UKBB). The former has a limited sample size, while the latter lacks sufficient representation of populations other than the EUR. To improve tagging, we developed new LD panels with an expanded set of SNPs generated using the Tag(ging) It(erative) of SNPs in multiple populations (TagIt) program 21 and variants with ancestry-specific minor allele frequency (MAF) ≥0·01 across samples from the 1KG. We also use >8,000 in-house samples to compute pairwise LD in each ancestry, enabling more accurate modeling of the LD structure ( Fig.1a ). We compared the impact of several input parameters (GWAS ancestry, two SNP sets, and two LD sources) on PRS performance in the training cohorts. We defined the best models as maximizing the incremental AUC (iAUC) for predicting prevalent T2D, comparing a full model, including the PRS, sex, age, and genetic principal components (PCs), to a model without the PRS ( Fig.1d ). We validated the best-performing models for association with T2D in at least four independent cohorts from each ancestry group ( Fig.1c ) and fitted secondary models with body mass index (BMI) as an additional covariate. We estimated the effect size of the PRSs as the odds ratio per standard deviation (OR per SD) unit of the PRS, and calculated OR for individuals at the 90 th , 95 th , and 97·5 th PRS percentiles compared with the interquartile range as the reference. In the AoU cohort, we also compared the association of our best-performing T2D PRSs with other published T2D PRSs from the Polygenic Score (PGS) catalog, and tested their association with common diabetes complications ( Fig.1d ). Results Development of new LD reference panels to improve variant coverage and LD estimation in diverse populations Compared to the LD panels using the HM3 set of variants, our new TagIt-based LD panels increased the proportion of SNPs being tagged (r 2 ≥0·8) up to 2 folds. The best improvement was 7 folds for SNPs with MAF 0·01-0·05 in AFR ancestry (Supplementary Table 7, Supplementary Fig.1). In all ancestry groups, the best-performing PRSs were those constructed using the expanded TagIt set of variants and/or the recomputed pairwise LD with large sample sizes (Supplementary Table 8, Supplementary Fig.2), suggesting that the prediction performance benefited from increasing SNP coverage, better LD modeling, or both. Matched single-ancestry PRS performance is positively correlated with the sample size of GWAS summary statistics We first trained PRSs (Supplementary Tables 3,4) using GWAS summary statistics and four different LD panels, each matched to the ancestry of the validation cohorts (Supplementary Tables 5,6). When using a single-ancestry summary statistics, matching the GWAS and LD panel ancestries to that of the validation dataset resulted in the best prediction performance for the EUR (iAUC =0·07-0·14) and EAS (iAUC=0·02-0·16) ancestries. However, the prediction was poorer for AFR (iAUC=0·02-0·03), AMR (iAUC=0·02-0·04), and SAS (iAUC=0·02-0·04) ( Fig.2a-e , Supplementary Table 9). Download figure Open in new tab Fig. 2. Performance of the T2D PRSs in the validation cohorts across ancestry groups. a-e: Incremental AUC (iAUC) of the T2D PRS in the validation cohorts across ancestry groups: a, AFR, b, AMR, c, EAS, d, EUR, e, SAS. For each ancestry, the best-performing single-ancestry and multi-ancestry PRSs were evaluated. Each bar represents a single cohort. Bar colors represent the ancestry group: purple for AFR, yellow for AMR, green for EAS, red for EUR, and blue for SAS. Line colors represent the ancestry of the T2D GWAS summary statistics and LD panels used to train the PRS, using the same color codes for single-ancestry PRSs, and black for multi-ancestry PRSs *De Long test p<0·05. f-j: Odds ratio from the meta-analysis of validation cohorts across ancestry groups: f, AFR, g, AMR, h, EAS, i, EUR, j, SAS. Points represent the odds ratio per standard deviation (OR per SD) of the PRS distribution or the odds ratio (OR) comparing different PRS distribution extremes relative to the interquartile range. Error bars show the 95% confidence intervals (95% CI). Point colors represent the ancestry of the T2D GWAS summary statistics and LD panels used to train the PRS. *De Long p<0.05. The effect size of the PRS on T2D risk showed a similar pattern with larger OR per SD from the meta-analysis of the EUR (OR per SD [95% CI] = 2·31 [2·26-2·35]) and EAS (2·19 [2·12-2·27]) validation cohorts, compared to the AFR (1·38 [1·34-1·43]), AMR (1·64 [1·57-1·71]) and SAS (1·6 [1·5-1·74]). The EUR and EAS PRSs also showed better power to identify individuals at the highest T2D genetic risk than those trained for the other ancestries. For instance, individuals at the 90 th percentile of the respective PRS distribution had ∼4-fold increased risk of T2D in the EUR (OR [95% CI] = 4·01 [3·82-4·21]) and in the EAS (3·58 [3·24-3·94]) ancestries, compared to ∼2-fold increased risk for individuals in the AFR (1·82 [1·64-2·01], AMR (2·1 [1·84-2·39]) and SAS (2·22 [1·75-2·81]) ancestries ( Fig.2f-j , Supplementary Table 9). We observed consistent results regardless of adjustment for BMI, indicating that the predictive performance of the PRSs was robust to the inclusion of this well-established T2D risk factor (Supplementary Table 10, Supplementary Fig.3). Using GWAS data from large non-matched ancestries improves PRS performance in ancestries with limited GWAS sample size We then tested whether constructing PRS using GWAS summary statistics from ancestries with larger sample sizes, such as EUR and EAS, could improve T2D prediction in other ancestries with limited GWAS data, despite the larger ancestral differences between discovery and validation cohorts. Compared to the matched single-ancestry PRSs, those based on the EUR GWAS improved the T2D prediction in the AFR, AMR, and SAS validation cohorts but had lower predictions in the EAS. A PRS based on EAS GWAS showed modestly improved T2D prediction in the AMR and SAS validation cohorts, but worse performance in the AFR and EUR ( Fig.2a-e , Supplementary Table 9). The effect sizes of the PRSs were consistent with their prediction performance. The best-performing single-ancestry PRSs were the one derived from EUR GWAS for AFR (OR per SD (95% CI) = 1·75 [1·67-1·82]), AMR (3·07 [2·89-3·27]), SAS (2·21 [2·03-2·42]), and EUR (2·31 [2·26-2·35]) validation cohorts, while for EAS cohorts (2·19 [2·12-2·27]), the best-performing single-ancestry PRS was based on EAS GWAS ( Fig.2f-j , Supplementary Table 9). Multi-ancestry PRSs show the best prediction performance in all ancestries We further applied a multi-ancestry PRS method, PRS-CSx 20 , which jointly models ancestry-specific GWAS and LD panel data from multiple ancestries. This approach leverages the increased statistical power from GWAS of five continental ancestries, which are jointly modelled to maximize the power of variants that are present in all ancestries, while still incorporating the effects of variants that are specific or enriched in specific ancestries, even if their sample size is modest. Compared to the best single-ancestry PRSs, the prediction performance of the multi-ancestry PRSs was the highest across validation cohorts from all five ancestries (iAUC ranging from 0·02-0·06 in AFR, 0·06-0·09 in AMR, 0·04-0·17 in EAS, 0·07-0·14 in EUR, and 0·06-0·10 in SAS) ( Fig.2a-e , Supplementary Table 9). The multi-ancestry PRSs also had the highest effect sizes along with smaller confidence intervals across all ancestries (OR per SD [95% CI] = 1·73 [1·67-1·80] in AFR, 2·82 [2·67-2·97] in AMR, 2·45 [2·36-2·54] in EAS, 2·36 [2·32-2·41] in EUR, and 2·23 [2·05-2·42] in SAS). The improvement was particularly notable for individuals at the extremes of the PRS distributions. For instance, the individuals in the 97·5 th percentile of the multi-ancestry PRSs had 3 to 7-fold increased T2D risk compared to those in the interquartile range (OR [95% CI] = 3·43 [2·8-4·21] in AFR, 7·47 [5·64-9·89] in AMR, 6·62 [5·58-7·85] in EAS, 6·25 [5·72-6·82] in EUR and 4·50 [2·70-7·53] in SAS ( Fig.2f-j , Supplementary Table 9). Multi-ancestry T2D PRSs outperform previously published T2D PRSs We then leveraged the AoU cohort to compare this study’s best-performing multi-ancestry PRSs against the published T2D PRSs from the PGS catalog. 22 We tested 55 out of 147 available PRS for T2D (accessed on October 07, 2024), after excluding pathway-specific PRSs or those for which the AoU cohort was used as a training dataset due to potential model overfitting. In all ancestry groups, our best-performing multi-ancestry PRSs showed better predictive performance than the previously published T2D PRSs from the PGS catalog. The differences with the best published PRSs from the PGS catalog were statistically significant for AFR, AMR, and EUR (De Long p<9×10 −4 , Bonferroni-corrected threshold=0·05/55 PRSs tested; iAUC for D-PRISM multi-ancestry PRS-CSx vs. best iAUC from PGS Catalog: 0·041 vs. 0·029 in AFR, 0·086 vs. 0·073 in AMR, 0·143 vs. 0·123 in EUR) ( Fig.3a,b,d ). The improvement was nominally significant for EAS (0·074 vs. 0·058, p=0·03) ( Fig.3c ), but not for SAS (p=0·58) ( Fig.3e ), likely due in part to their limited sample sizes in the AoU cohort for these two ancestries (Supplementary Table 11, Supplementary Fig.4-8). Download figure Open in new tab Fig. 3. Performance of D-PRISM multi-ancestry PRSs compared to published T2D PRSs from the PGS Catalog and other sources in the All of Us cohort. a-e: Incremental AUC (iAUC) across ancestry groups: a, AFR, b, AMR, c, EAS, d, EUR, e, SAS. Black bars highlight this study’s multi-ancestry PRSs. *De Long p<0.05; **Bonferroni-corrected De Long p<9×10 −4 . We also compared different strategies for PRS construction. Some studies leverage ancestry diversity using the inverse variance-weighted (IVW) meta-analysis results from multi-ancestry GWAS, typically applying a single LD panel — often of EUR ancestry— which may fail to model LD patterns and tag ancestry-specific variants accurately. In contrast, PRS-CSx jointly models GWAS from multiple ancestries while accounting for differences in allele frequencies and LD patterns across ancestries. To assess the value of PRS-CSx, we constructed a PRS-CS model using summary statistics from the IVW GWAS meta-analysis of the D-PRISM ancestry-specific GWAS summary statistics and tested its performance in the validation cohorts. In all ancestry groups, the T2D prediction performance of the multi-ancestry PRS-CSx was better than that of the PRS-CS models constructed using the IVW GWAS meta-analysis. The improvement was statistically significant in the AMR, EAS and EUR (iAUC for the multi-ancestry PRS-CSx vs. the multi-ancestry PRS-CS: 0·086 vs. 0·077 in AMR, 0·074 vs. 0·061 in EAS, and 0·143 vs. 0·126 in EUR, De Long p<0·05) ( Fig.3b-d ) but not significantly different for AFR and SAS (0·041 vs. 0·038 in AFR, p=0·07, 0·061 vs. 0·060 in SAS, p=0·91) ( Fig.3a,e , Supplementary Table 11, Supplementary Figures 4-8). Download figure Open in new tab Fig. 4. Association of D-PRISM multi-ancestry PRS with common complications of diabetes in the All of Us cohort. Odds ratio of this study’s multi-ancestry PRS for the following outcomes: CAD (cardiovascular disease), IS (ischemic stroke), DN (diabetic nephropathy), ESDN (end-stage diabetic nephropathy), DR (diabetic retinopathy), and PDR (proliferative diabetic retinopathy). Points represent the odds ratios per standard deviation (OR per SD) and are colored according to the genetic ancestry of the individuals tested: purple for AFR, yellow for AMR, and red for EUR. Error bars show the 95% confidence intervals (95% CI). The largest multi-ancestry T2D GWAS meta-analysis to date, conducted by Suzuki et al., 3 includes a sample size 16% larger than our D-PRISM GWAS datasets (N=2,535,601, 428,452 cases, 2,107,149 controls), as we intentionally held out several cohorts for PRS training and validation in this study. Nevertheless, since the AoU cohort was not included in the discovery GWAS by Suzuki et al., we also evaluated the performance of a PRS-CS model built using those summary statistics. Despite a smaller sample size, our D-PRISM multi-ancestry PRS-CSx showed better prediction performance in the AMR, EUR, and EAS ancestry groups (iAUC for D-PRISM multi-ancestry PRS-CSx vs. Suzuki et al. PRS-CS: 0·086 vs. 0·077 in AMR, 0·074 vs. 0·059 in EAS, and 0·143 vs. 0·128 in EUR, De Long p<0·05) ( Fig.3b-d ). In the AFR and SAS ancestry groups, the D-PRISM multi-ancestry PRS-CSx yielded prediction performance compared to the Suzuki et al. PRS-CS model (0·041 vs. 0·041, p=0·82 in AFR, 0·061 vs. 0·068, p=0·34 in SAS) ( Fig.3a,e ). In addition, our D-PRISM multi-ancestry PRS-CSx models showed consistently better prediction performance in four of the five ancestries (i.e., AMR, EAS, EUR, and SAS, p<0·05) ( Fig.3b-e ) compared to a multi-ancestry PRS that was restricted to the 1,289 genome-wide significant variants (rsPRS) identified in the discovery GWAS by Suzuki et al. We did not observe significant improvement in the AFR ancestry ( Fig.3a , Supplementary Table 11, Supplementary Figs 4-8). Multi-ancestry T2D PRSs are associated with the risk of microvascular diabetes complications T2D adversely affects the functioning of multiple organs, and the long-term complications accompanying the disease contribute the greatest morbidity for patients suffering from T2D. 23 We leveraged the AoU cohort to assess if the best-performing multi-ancestry T2D PRSs may be helpful to identify individuals who are at high risk of developing diabetes associated macrovascular [i.e., cardiovascular disease (CAD), ischemic stroke (IS)] and microvascular complications [i.e., diabetic retinopathy (DR), proliferative diabetic retinopathy (PDR), diabetic nephropathy (DN), and end-stage diabetic nephropathy (ESDN)]. Given the sample size constraints of the AoU cohort, we only assessed the AFR, AMR, and EUR ancestries. We restricted microvascular complication analyses to individuals with T2D, as these outcomes are largely diabetes specific. For macrovascular complications, which also occur in those without T2D, we included all individuals and adjusted for T2D status in the models. In each of the three ancestries, the D-PRISM multi-ancestry PRS-CSx was associated with increased risk of developing DR among individuals with T2D: OR per SD [95% CI] = 1·24 [1·12-1·37] in AFR, 1·44 [1·27-1·63] in AMR, 1·32 [1·21-1·44] in EUR. For the more severe form, PDR, the PRSs were also associated with increased risk in AFR, AMR, and EUR ancestries: 1·35 [1·09-1·67] in AFR, 1·94 [1·51-2·49] in AMR, 1·55 [1·19-2·01] in EUR. Additionally, the multi-ancestry PRSs predicted the risk for developing DN: 1·49 [1·27-1·76] in AMR and 1·23 [1·12-1·35] in EUR, as well as ESDN: 1·75 [1·35-2·27] in AMR and 1·41 [1·1-1·8] in EUR. It was also associated with increased risk of CAD in EUR only: 1·05 [1·02-1·08] (p<0·008, Bonferroni-corrected threshold from 0·05/6 diabetes complications tested) ( Fig.4 , Supplementary Table 12). Discussion Identifying individuals with high T2D risk is essential to prioritize those who will benefit from lifestyle or therapeutic interventions to delay the disease or its complications. 24 While clinical risk factors can identify individuals at risk, polygenic risk scores can be estimated at birth and can identify at risk individuals who may clinically be perceived at low risk, e.g., without a family history of T2D, young, lean, etc. 8 Multiple efforts have been made to identify individuals at high genetic risk for T2D. More than a hundred PRS for T2D have been published 22 , yet they have poorer predictive performance in underrepresented populations. Several reasons influence the low transferability, including the overrepresentation of European populations in T2D GWAS and differential LD patterns between variants across ancestries. Importantly, populations in which PRSs are less predictive, including the AFR, AMR, and SAS, are disproportionately affected by diabetes and its complications, highlighting the risk of exacerbating health disparities by applying PRSs derived only from European genetic information. 11 To improve the transferability and predictive accuracy of PRSs across diverse populations, including those underrepresented in GWAS efforts, various multi-ancestry PRS methods have been developed, while in parallel, several efforts have focused on expanding the representation of diverse ancestry populations in GWAS. 3,15,16,25,26 Beyond GWAS discovery, multiple and sufficiently large sample sizes are also essential for training and validating PRSs, and rigorous data aggregation and harmonization following best practices are crucial to determine PRS accuracy. In this study, we developed the most comprehensive PRSs for T2D across five continental ancestries and conducted extensive evaluations in at least four independent cohorts per ancestry. To accomplish this we leveraged nearly all available genetic datasets with T2D phenotype information to: i) maximize ancestry diversity by harmonizing 125 T2D GWAS datasets, including up to 2·2 million individuals; ii) enhance the representation and tagging of the genetic variants contributing to the PRS by generating new ancestry-specific LD reference panels, iii) train PRS models across ancestries and iv) thoroughly validate the PRSs in multiple independent harmonized cohorts and assess their association with diabetes-related complications. As previously described 12 , 27 , we observed that PRSs have lower accuracy when the validation sample is genetically distant from the discovery GWAS sample. However, underrepresented populations in GWAS have limited power and imprecise variant effect size estimates. For this reason, PRSs derived from EUR GWAS continue to outperform those derived from ancestry-matched GWAS in AFR, AMR, and SAS ancestries, likely due to the EUR GWAS sample size being at least 11 times larger. In contrast, applying a EUR PRS yielded worse performance for EAS ancestry than using a matched-ancestry EAS GWAS, even though the latter was four times smaller than the EUR GWAS. This suggests that non-matched ancestry PRSs (i.e., PRSs based on GWAS from a different ancestry than the validation cohort) improve performance only when the GWAS sample size in the other ancestry is orders of magnitude larger than that of the matched-ancestry GWAS, which may lack power to capture ancestry-specific genetic effects. However, when power is sufficient, ancestry-matched GWAS can improve accuracy compared to larger cross-ancestry GWAS. The minimum ancestry-specific GWAS sample size for powerful PRS prediction may vary depending on the trait and ancestry and may require further investigation. Regardless, we observe that for all the ancestry groups, including EUR, which has the largest GWAS sample size 15 , the optimal PRS strategy is to combine PRSs from multiple ancestries, here using PRS-CSx. This approach can leverage the most accurate estimates from variants present in all ancestries while still accounting for population-specific or enriched variants, improving the overall prediction accuracy. For all ancestries, the multi-ancestry PRSs showed the strongest associations with T2D risk among individuals at the extremes of the risk distribution. For instance, individuals of EUR, EAS, or AMR ancestry in the top 97·5 th percentile of the PRS distribution were associated with a seven-fold increased risk of developing T2D than those with average scores. Despite limited GWAS representation, the multi-ancestry PRS still shows the most predictive accuracy for individuals of AFR and SAS ancestry relative to the matched-ancestry PRS. However, overall performance remains lower than in other ancestry groups, with individuals in the 97·5 th percentile associated with three-fold and four-fold increased risk of T2D. These results underscore that, despite our best efforts, there is still a substantial gap among the performance of PRSs, particularly in these two ancestry groups. Using data from the AoU cohort, we demonstrate that our multi-ancestry PRSs outperformed all previously available PRSs for T2D, likely because we used the largest and most diverse GWAS data and multi-ancestry-based methodology for PRS development. Additionally, we constructed our multi-ancestry PRSs using a standardized and rigorous approach and tested them extensively across diverse ancestry groups, supporting their broader applicability. For example, the multi-ancestry PRSs outperformed PRSs derived from the largest trans-ancestry meta-analysis. These findings underscore that, in addition to increasing representation in GWAS, leveraging methods that jointly model GWAS and LD panels across multiple ancestries can enhance the tagging of causal variants, thereby improving predictive performance. In contrast, standard methods that use multi-ancestry IVW GWAS meta-analysis results rely on a single LD reference panel and may fail to capture ancestry-specific LD patterns, limiting their ability to model genetic risk prediction accurately. Previous studies have reported significant associations between T2D PRS, proliferative retinopathy, and end-stage diabetic nephropathy across and within ancestries. 15 , 16 We confirm and extend previous findings by showing that multi-ancestry PRSs predict microvascular complications in T2D individuals of AFR, AMR, and EUR ancestries. Notably, our results reveal stronger associations and capture a broader range of diabetes complications severity than previously reported. Our PRSs still face some limitations, mainly due to the lack of diversity in available genetic data. First, while constructing multi-ancestry PRS including GWAS from diverse ancestries—even those with smaller sample sizes— has proven beneficial to capture ancestry-specific effects, the SNP effect sizes are still strongly influenced by the largest European cohorts. Second, we acknowledge that using discrete population categories or restricting the analyses to groups defined by genetic similarity is sub-optimal, particularly in highly admixed populations where such groupings do not fully capture heterogeneous ancestry. While our current approach partially addresses these challenges, it remains limited by the need to group individuals into discrete ancestries, rather than a continuum. A critical next step will be adopting continuous genetic ancestry methods, which will require broader data access and new analytical strategies. Despite criticism regarding PRSs’ limited added value to clinical risk factors, it has been shown that incorporating PRSs can help identify high-risk individuals among people who are clinically perceived as low risk, such as the young, the lean, or those with sparse clinical data 8 , and that PRSs improve prediction over family history alone 28 . Ongoing randomized controlled trials are starting to evaluate the impact of PRS implementation into adult primary care as an additional risk factor for the primary care provider to consider. 7 , 29 Importantly, for any PRS-informed interventions to be viable, a state-of-the-art T2D PRS is a necessary first step. Among the characteristics to prioritize when using a PRS are its generalizability, portability, and implementation feasibility. For instance, Lennon et al . 6 prioritized a multi-ancestry T2D PRS 30 trained using EUR, EAS, and AFR GWAS data, and proposed a high-risk cut-off of 2%. Compared to the rest of the non-high-risk population, this PRS showed ORs ranging from 4·44 [3·60-5·49] in EUR to a maximum of 2·35 [1·54-3·60] in AFR cohorts. Notably, our multi-ancestry PRSs consistently improved the prediction performance of T2D irrespective of the individual’s continental ancestry over any previously existing PRS. Compared to people with average genetic risk, those above the high genetic risk cut-off (2·5%) have ORs ranging from 3·43 to 7·47 across ancestries, compared to the interquartile group. We therefore propose using the T2D PRS models presented here for future clinical applications as they show improved predictive ability across all ancestries. In summary, this study addresses a critical gap by delivering the most comprehensive and rigorously tested set of multi-ancestry PRSs for T2D. These scores improve risk prediction across diverse populations and enhance the identification of individuals at high genetic risk of developing T2D and microvascular complications, including mild and severe forms. By making the PRS weights publicly available, we provide a valuable resource for researchers and clinicians seeking to advance genetic risk stratification and develop prevention strategies for T2D. Contributors J.M.M. and M.C.Y.N. conceived the study. A.H-C. and J.K. led the analyses. R.M., Y.L., K.S., L.E.P., H.K.N., J.C., S.L., M.R., and K.L. performed the training and/or validation of the PRS analyses. Y.J.K., R.G.W., D.S., S.H.K., and R.J.F.L. supervised the training and/or validation of the PRS analyses. T.G., A.K.M., M.L., J.E.B., X.S., J.M.M and M.C.Y.N. jointly directed this work. The rest of the authors provided data for the different stages of the analyses. A.H.-C., J.K., J.M.M., and M.C.Y.N. wrote the first draft of the manuscript. All authors contributed to interpreting the data, and they read, revised, and approved the final manuscript. Declaration of interests H.C.G. holds the McMaster-Sanofi Population Health Institute Chair in Diabetes Research and Care. He reports research grants from Eli Lilly, Novo Nordisk, and Hanmi Pharmaceutical; grants to support continuing education programs from Eli Lilly, Abbott, Sanofi, Novo Nordisk, and Boehringer Ingelheim; honoraria for speaking from AstraZeneca, Eli Lilly, Zuellig, and Jiangsu Hanson; and consulting fees from Abbott, Bayer, Biolinq, Eli Lilly, Novo Nordisk, Pfizer, Shionogi, and Zealand. M.S.U. has consulting activity and research funded in collaboration with Novo Nordisk. A.K.M. has research funded in collaboration with Novo Nordisk. M.S.U. has research funded in collaboration with Novo Nordisk and is an unpaid research collaborator with AstraZeneca. J.M.M. has research funded in collaboration with Novo Nordisk. S.R.P. has had research funded by Philips Respironics and consulting fees from Apnimed, Bayer, Philips Respironics, Mineralys, and SleepRes. M.A.N. ‘s participation in this project was part of a competitive contract awarded to DataTecnica LLC by the National Institutes of Health to support open science research. He also currently owns stock in Character Bio and Neuron23 Inc. Data sharing Individual participant data is not available because they are subject to data protection laws and restrictions imposed by the ethics committee to ensure study participants’ privacy. The study protocol and the individual methods are included in the methods section. The developed PRS weights are available without restrictions through the PGS catalog ( https://www.pgscatalog.org ). Data Availability The study protocol and the individual methods are included in the methods section. The developed PRS weights are available without restrictions through the PGS catalog ( https://www.pgscatalog.org ). Acknowledgments This work is supported by National Human Genome Research Institute (NHGRI) of the National Institutes of Health (NIH) U01HG011723. A.H.-C. is supported by the American Diabetes Association (ADA) grant 11-23-PDF-35. Y.L. is supported by R56HL150186, R01HL158884, and R01DK135938. M.O.G. was supported in part by NIH grants from the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK) (P30-DK063491) and from the National Center for Advancing Translational Sciences (NCATS) (UL1TR001420, UL1TR001881) and the Eris M. Field Chair in Diabetes Research. S.S.R. is supported by NHGRI U01HG011723. M.A.N. research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging (NIA), NIH, Department of Health and Human Services; project number ZO1 AG000535, as well as the National Institute of Neurological Disorders and Stroke (NINDS). This work utilized the computational resources of the NIH HPC Biowulf cluster. ( http://hpc.nih.gov ). A.L. is supported by grant 2020096 from the Doris Duke Foundation, the ADA grant 7-22-ICTSPM-23, and NHGRI U01HG011723. A.P.M. acknowledges support from the NIHR Manchester Biomedical Research Centre (NIHR203308). C.N.S. was supported by the NIH (R01DK118011; R01DK136671) and the ADA (11-22-JDFPM-06). J.B.M. is supported by grant UMDK078616. B.F.V. is grateful for support from the NIDDK DK138521 and DK126194. A.K.M. is supported by grant UMDK078616. J.M.M. is supported by ADA grant #11-22-ICTSPM-16 and by NHGRI U01HG011723, by the NIDDK under Award Number R01DK137993 and U01 DK140757, AMP CMD award from RFP 6 from the Foundation for the NIH, and a Medical University of Bialystok (MUB) grant from the Ministry of Science and Higher Education (Poland). This work is supported by the Novo Nordisk Foundation (NNF21SA0072102). M.C.Y.N. is supported by U01HG011723, R01DK066358 and U01DK105556. Footnotes ↵ ^ These authors jointly directed this work. 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OpenUrl CrossRef PubMed ↵ Ge T , Irvin MR , Patki A , et al. Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations . Genome Med 2022 ; 14 : 70 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted July 23, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries Alicia Huerta-Chagoya , Joohyun Kim , Ravi Mandla , Yingchang Lu , Ken Suzuki , Lauren E. Petty , Hong Kiat Ng , Jaewon Choi , Simon Lee , Madhusmita Rout , Kuang Lin , Linda S. Adair , Adebowale Adeyemo , Habibul Ahsan , Masato Akiyama , Ping An , Sonia S. Anand , Diane M. Becker , Alain G. Bertoni , Zheng Bian , Lawrence F. Bielak , John Blangero , Michael Boehnke , Erwin P. Bottinger , Donald W. Bowden , Fiona Bragg , Jennifer A. Brody , Thomas A. Buchanan , Brian E. Cade , Jin-Fang Chai , John C. Chambers , Giriraj R. Chandak , Li-Ching Chang , Kyong-Mi Chang , Miao-Li Chee , Chien-Hsiun Chen , Yuan-Tsong Chen , Zhengming Chen , Yii-Der I. Chen , Ji Chen , Guanjie Chen , Shyh-Huei Chen , Wei-Min Chen , Ching-Yu Cheng , Yoon Shin Cho , Hyeok Sun Choi , Lee-Ming Chuang , Miguel Cruz , Mary Cushman , Swapan K. Das , Ralph A. DeFronzo , H Janaka deSilva , Latchezar Dimitrov , Ayo P. Doumatey , Shufa Du , Qing Duan , Ravindranath Duggirala , Leslie S. Emery , James C. Engert , Daniel S. Evans , Michele K. Evans , Sarah Finer , Jose C. Florez , James S. Floyd , Myriam Fornage , Elizabeth G. Frankel , Barry I. Freedman , Lourdes García-García , Pauline Genter , Hertzel C. Gerstein , Mark O. Goodarzi , Penny Gordon-Larsen , Mariaelisa Graff , Myron Gross , Yu Guo , Xiuqing Guo , Yang Hai , Craig L. Hanis , MGeoffrey Hayes , Momoko Horikoshi , Annie-Green Howard , Sarah Hsu , Willa Hsueh , Wei Huang , Mengna Huang , Yi-Jen Hung , Mi Yeong Hwang , Chii-Min Hwu , Sahoko Ichihara , Michiya Igase , Eli Ipp , Mohammad T. Islam , Masato Isono , Hye-Mi Jang , Farzana Jasmine , Jost B. Jonas , Yoonjung Y. Joo , Edmond Kabagambe , Takashi Kadowaki , Yoichiro Kamatani , Fouad R. Kandeel , Sharon L.R. Kardia , Elizabeth W. Karlson , Anuradhani Kasturiratne , Norihiro Kato , Tomohiro Katsuya , Varinderpal Kaur , Takahisa Kawaguchi , Jacob M. Keaton , Abel N. Kho , Chiea-Chuen Khor , Muhammad Kibriya , Bong-Jo Kim , Woon-Puay Koh , Katsuhiko Kohara , Jaspal S. Kooner , Charles Kooperberg , Raymond J. Kreienkamp , Amel Lamri , Leslie A. Lange , Nanette R. Lee , Myung-Shik Lee , Jung-Jin Lee , Donna M. Lehman , Liming Li , Yun Li , Victor JY. Lim , Jianjun Liu , Yongmei Liu , Simin Liu , Jirong Long , Tin Louie , Xi Luo , Jun Lv , Julie A. Lynch , Shiro Maeda , Anubha Mahajan , Nisa M. Maruthur , Fumihiko Matsuda , Mark I. McCarthy , Roberta McKean-Cowdin , James B. Meigs , Iona Y. Millwood , Karen L. Mohlke , Ayesha A. Motala , Girish N. Nadkarni , Jerry L. Nadler , Masahiro Nakatochi , Mike A. Nalls , Uma Nayak , Aude Nicolas , Kari E. North , Darryl Nousome , Yukinori Okada , Ian Pan , James S. Pankow , Guillaume Paré , Jaehyun Park , Kyong Soo Park , Esteban J. Parra , Sanjay R. Patel , Mark A. Pereira , Patricia A. Peyser , Fraser J. Pirie , Michael Preuss , Michael A. Province , Bruce M. Psaty , Leslie J. Raffel , Laura M. Raffield , Laura J. Rasmussen-Torvik , Susan Redline , Alexander P. Reiner , Stephen S. Rich , Rebecca Rohde , Kathryn Roll , Rashedeh Roshani , Charles N. Rotimi , Charumathi Sabanayagam , Danish Saleheen , Kevin Sandow , Claudia Schurmann , Mohammad Shahriar , Douglas M. Shaw , Wayne H-H. Sheu , Jinxiu Shi , Xiao-Ou Shu , Megan M. Shuey , Moneeza K. Siddiqui , Jennifer A. Smith , Tamar Sofer , Cassandra N. Spracklen , Adrienne M. Stilp , Meng Sun , Yasuharu Tabara , E-Shyong Tai , Salman M. Tajuddin , Atsushi Takahashi , Fumihiko Takeuchi , Jingyi Tan , Kent D. Taylor , Katherine Taylor , Farook Thameem , Lin Tong , Fuu-Jen Tsai , Philip S. Tsao , Miriam S. Udler , Adan Valladares-Salgado , David A. van Heel , Rob M. vanDam , Rohit Varma , Maheak Vora , Niels Wacher-Rodarte , Ya-Xing Wang , Ellie Wheeler , Eric A. Whitsel , Ananda R. Wickremasinghe , Genevieve L. Wojcik , Tien Y. Wong , Jer-Yuarn Wu , Yong-Bing Xiang , Anny H. Xiang , Chittaranjan S. Yajnik , Ken Yamamoto , Toshimasa Yamauchi , Lisa R. Yanek , Jie Yao , Mitsuhiro Yokota , Canqing Yu , Jian-Min Yuan , Salim Yusuf , Eleftheria Zeggini , Liang Zhang , Weihua Zhang , Wei Zheng , Alan B. Zonderman , ENSA Genomics Consortium , Genes & Health Research Team , VA Million Veteran Program , Carlos A. Aguilar-Salinas , Clicerio González-Villalpando , Christopher A. Haiman , Young Jin Kim , Soo Heon Kwak , Aaron Leong , Ruth J.F. Loos , Andres Moreno-Estrada , Andrew P. Morris , Lorena Orozco , Jerome I. Rotter , Dharambir Sanghera , Teresa Tusie-Luna , Benjamin F. Voight , Marijana Vujkovic , Robin G. Walters , Tian Ge , Alisa K. Manning , Marie Loh , Jennifer E. Below , Xueling Sim , Josep M. Mercader , Maggie C.Y. Ng , D-PRISM Consortium medRxiv 2025.07.21.25331778; doi: https://doi.org/10.1101/2025.07.21.25331778 Share This Article: Copy Citation Tools Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries Alicia Huerta-Chagoya , Joohyun Kim , Ravi Mandla , Yingchang Lu , Ken Suzuki , Lauren E. Petty , Hong Kiat Ng , Jaewon Choi , Simon Lee , Madhusmita Rout , Kuang Lin , Linda S. Adair , Adebowale Adeyemo , Habibul Ahsan , Masato Akiyama , Ping An , Sonia S. Anand , Diane M. Becker , Alain G. Bertoni , Zheng Bian , Lawrence F. Bielak , John Blangero , Michael Boehnke , Erwin P. Bottinger , Donald W. Bowden , Fiona Bragg , Jennifer A. Brody , Thomas A. Buchanan , Brian E. Cade , Jin-Fang Chai , John C. Chambers , Giriraj R. Chandak , Li-Ching Chang , Kyong-Mi Chang , Miao-Li Chee , Chien-Hsiun Chen , Yuan-Tsong Chen , Zhengming Chen , Yii-Der I. Chen , Ji Chen , Guanjie Chen , Shyh-Huei Chen , Wei-Min Chen , Ching-Yu Cheng , Yoon Shin Cho , Hyeok Sun Choi , Lee-Ming Chuang , Miguel Cruz , Mary Cushman , Swapan K. Das , Ralph A. DeFronzo , H Janaka deSilva , Latchezar Dimitrov , Ayo P. Doumatey , Shufa Du , Qing Duan , Ravindranath Duggirala , Leslie S. Emery , James C. Engert , Daniel S. Evans , Michele K. Evans , Sarah Finer , Jose C. Florez , James S. Floyd , Myriam Fornage , Elizabeth G. Frankel , Barry I. Freedman , Lourdes García-García , Pauline Genter , Hertzel C. Gerstein , Mark O. Goodarzi , Penny Gordon-Larsen , Mariaelisa Graff , Myron Gross , Yu Guo , Xiuqing Guo , Yang Hai , Craig L. Hanis , MGeoffrey Hayes , Momoko Horikoshi , Annie-Green Howard , Sarah Hsu , Willa Hsueh , Wei Huang , Mengna Huang , Yi-Jen Hung , Mi Yeong Hwang , Chii-Min Hwu , Sahoko Ichihara , Michiya Igase , Eli Ipp , Mohammad T. Islam , Masato Isono , Hye-Mi Jang , Farzana Jasmine , Jost B. Jonas , Yoonjung Y. Joo , Edmond Kabagambe , Takashi Kadowaki , Yoichiro Kamatani , Fouad R. Kandeel , Sharon L.R. Kardia , Elizabeth W. Karlson , Anuradhani Kasturiratne , Norihiro Kato , Tomohiro Katsuya , Varinderpal Kaur , Takahisa Kawaguchi , Jacob M. Keaton , Abel N. Kho , Chiea-Chuen Khor , Muhammad Kibriya , Bong-Jo Kim , Woon-Puay Koh , Katsuhiko Kohara , Jaspal S. Kooner , Charles Kooperberg , Raymond J. Kreienkamp , Amel Lamri , Leslie A. Lange , Nanette R. Lee , Myung-Shik Lee , Jung-Jin Lee , Donna M. Lehman , Liming Li , Yun Li , Victor JY. Lim , Jianjun Liu , Yongmei Liu , Simin Liu , Jirong Long , Tin Louie , Xi Luo , Jun Lv , Julie A. Lynch , Shiro Maeda , Anubha Mahajan , Nisa M. Maruthur , Fumihiko Matsuda , Mark I. McCarthy , Roberta McKean-Cowdin , James B. Meigs , Iona Y. Millwood , Karen L. Mohlke , Ayesha A. Motala , Girish N. Nadkarni , Jerry L. Nadler , Masahiro Nakatochi , Mike A. Nalls , Uma Nayak , Aude Nicolas , Kari E. North , Darryl Nousome , Yukinori Okada , Ian Pan , James S. Pankow , Guillaume Paré , Jaehyun Park , Kyong Soo Park , Esteban J. Parra , Sanjay R. Patel , Mark A. Pereira , Patricia A. Peyser , Fraser J. Pirie , Michael Preuss , Michael A. Province , Bruce M. Psaty , Leslie J. Raffel , Laura M. Raffield , Laura J. Rasmussen-Torvik , Susan Redline , Alexander P. Reiner , Stephen S. Rich , Rebecca Rohde , Kathryn Roll , Rashedeh Roshani , Charles N. Rotimi , Charumathi Sabanayagam , Danish Saleheen , Kevin Sandow , Claudia Schurmann , Mohammad Shahriar , Douglas M. Shaw , Wayne H-H. Sheu , Jinxiu Shi , Xiao-Ou Shu , Megan M. Shuey , Moneeza K. Siddiqui , Jennifer A. Smith , Tamar Sofer , Cassandra N. Spracklen , Adrienne M. Stilp , Meng Sun , Yasuharu Tabara , E-Shyong Tai , Salman M. Tajuddin , Atsushi Takahashi , Fumihiko Takeuchi , Jingyi Tan , Kent D. Taylor , Katherine Taylor , Farook Thameem , Lin Tong , Fuu-Jen Tsai , Philip S. Tsao , Miriam S. Udler , Adan Valladares-Salgado , David A. van Heel , Rob M. vanDam , Rohit Varma , Maheak Vora , Niels Wacher-Rodarte , Ya-Xing Wang , Ellie Wheeler , Eric A. Whitsel , Ananda R. Wickremasinghe , Genevieve L. Wojcik , Tien Y. Wong , Jer-Yuarn Wu , Yong-Bing Xiang , Anny H. Xiang , Chittaranjan S. Yajnik , Ken Yamamoto , Toshimasa Yamauchi , Lisa R. Yanek , Jie Yao , Mitsuhiro Yokota , Canqing Yu , Jian-Min Yuan , Salim Yusuf , Eleftheria Zeggini , Liang Zhang , Weihua Zhang , Wei Zheng , Alan B. Zonderman , ENSA Genomics Consortium , Genes & Health Research Team , VA Million Veteran Program , Carlos A. Aguilar-Salinas , Clicerio González-Villalpando , Christopher A. Haiman , Young Jin Kim , Soo Heon Kwak , Aaron Leong , Ruth J.F. Loos , Andres Moreno-Estrada , Andrew P. Morris , Lorena Orozco , Jerome I. Rotter , Dharambir Sanghera , Teresa Tusie-Luna , Benjamin F. Voight , Marijana Vujkovic , Robin G. Walters , Tian Ge , Alisa K. Manning , Marie Loh , Jennifer E. Below , Xueling Sim , Josep M. Mercader , Maggie C.Y. Ng , D-PRISM Consortium medRxiv 2025.07.21.25331778; doi: https://doi.org/10.1101/2025.07.21.25331778 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (569) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4442) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1511) Epidemiology (15230) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6610) Geriatric Medicine (668) Health Economics (998) Health Informatics (4542) Health Policy (1370) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1266) Infectious Diseases (except HIV/AIDS) (15923) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6607) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1146) Occupational and Environmental Health (957) Oncology (3338) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (664) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (712) Psychiatry and Clinical Psychology (5448) Public and Global Health (9238) Radiology and Imaging (2202) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (596) Sexual and Reproductive Health (714) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a01d7bad7e0eaa64',t:'MTc3OTgwNTg4Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();