Genomic analyses reveal new insights into Alzheimer’s disease

Genomic analyses reveal new insights into Alzheimer’s disease | 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 Genomic analyses reveal new insights into Alzheimer’s disease View ORCID Profile Emil Uffelmann , View ORCID Profile Douglas P. Wightman , View ORCID Profile Shahram Bahrami , Alexey A. Shadrin , Vera Fominykh , Takafumi Ojima , Chenyang Jiang , Christian Benner , View ORCID Profile Elisa Moreno , Adrian I. Campos , Jesper Q. Thomassen , Emmanuel Minois-Genin , Hei Man Wu , G. Bragi Walters , Richard Sherva , Tian Lin , Xuemin Wang , Julien Bryois , Kristi Krebs , Marijn Schipper , Akira Narita , View ORCID Profile Alessandro Serretti , Anja H. Simonsen , Anna L. van Seumeren , Anne Corbett , Anne-Brita Knapskog , View ORCID Profile Annette M. Hartmann , Anouk den Braber , Argonde C. van Harten , Arvid Harder , View ORCID Profile Arvid Rongve , Bengt O. Madsen , View ORCID Profile Betty M. Tijms , Bitten Aagaard , Bjørn Lichtwarck , Bjørn E. Kirsebom , View ORCID Profile Byron Creese , View ORCID Profile Chandra A. Reynolds , View ORCID Profile Sara Hägg , Ida Karlsson , View ORCID Profile Christian Erikstrup , Christina Mikkelsen , View ORCID Profile Clive Ballard , Dag Aarsland , Daichi Shigemizu , View ORCID Profile Dan Rujescu , Daniel Gudbjartsson , Eivind Aakhus , Erik Sørensen , Eystein Stordal , Flora H. Duits , Frank J. Wolters , Frederic Blanc , View ORCID Profile Geert Jan Biessels , Geir Selbæk , View ORCID Profile Geir Bråthen , Gen Tamiya , Gunhild Waldemar , View ORCID Profile Harro Seelaar , Helga Eyjolfsdottir , Henne Holstege , Henning Bundgaard , Henrik Zetterberg , Henrik Ullum , Ina Giegling , Ingmar Skoog , Ingrid T. Medbøen , Ingvild Saltvedt , Irena Rektorova , J. Michael Gaziano , Jan Haavik , View ORCID Profile Jens Hjerling-Leffler , Jiao Luo , Jon Snaedal , Everard G.B Vijverberg , Julia M. Sealock , Kaj Blennow , Kaja Nordengen , Karin Persson , View ORCID Profile Katja Scheffler , Koichi Matsuda , Kouichi Ozaki , View ORCID Profile Lasse Pihlstrøm , Lavinia Athanasiu , Lene Pålhaugen , Marc Hulsman , Margda Waern , Maria Averina , Marianne Wettergreen , View ORCID Profile Marta R. Moksnes , Martijn Huisman , Masayuki Yamamoto , Mathias Toft , Matthew S. Panizzon , Mie Topholm Bruun , View ORCID Profile Mohsen Ghanbari , Monique Franc , Nancy L. Pedersen , View ORCID Profile Nathaniel Y. Bell , Niccoló Tesi , Ole B. Pedersen , Oleksandr Frei , Olivier Bousiges , Per Svenningsson , View ORCID Profile Pieter J. Visser , Qingqin S. Li , Richard Hauger , Rui Zhang , Shinichi Namba , Sigrid B. Sando , Silke Kern , Srdjan Djurovic , Steinunn Thordardottir , Tanya N. Phung , View ORCID Profile Thomas Truelsen , Thomas Werge , View ORCID Profile Thomas F. Hansen , Tomoki Kyosaka , Torgeir Engstad , Tormod Fladby , Victoria Merritt , Sverre Bergh , View ORCID Profile Wiesje M. van der Flier , Rujin Wang , Eli A. Stahl , Basavaraj Hooli , 23andMe Research Institute , LifeLines Cohort Study , DBDS Genomic consortium , Regeneron Genetics Center , Penn Medicine Biobank , GHS-RGC DiscovEHR collaboration , Mayo Clinic-RGC Project Generation , Colorado Center for Personalized Medicine – RGC Collaboration , UCLA-RGC ATLAS collaboration , INDIANA-CHALASANI , Mount Sinai Million Health Discoveries Program , Estonian Biobank research team , VA Million Veteran Program , Lea K. Davis , Mark W. Logue , Kelli Lehto , Anna Zettergren , View ORCID Profile Ben M. Brumpton , View ORCID Profile Jian Zeng , Peter M. Visscher , View ORCID Profile Paul F. O’Reilly , Anubha Mahajan , Manuel Ferreira , Yukinori Okada , View ORCID Profile Sven J. van der Lee , Sisse R. Ostrowski , View ORCID Profile Ruth Frikke-Schmidt , Hreinn Stefansson , Karl Heilbron , View ORCID Profile Ole A. Andreassen , Danielle Posthuma doi: https://doi.org/10.1101/2025.10.10.25337470 Emil Uffelmann 1 Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emil Uffelmann For correspondence: e.uffelmann{at}vu.nl d.p.wightman{at}vu.nl ole.andreassen{at}medisin.uio.no d.posthuma{at}vu.nl Douglas P. Wightman 1 Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Douglas P. Wightman For correspondence: e.uffelmann{at}vu.nl d.p.wightman{at}vu.nl ole.andreassen{at}medisin.uio.no d.posthuma{at}vu.nl Shahram Bahrami 2 Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital , Oslo, Norway 3 Norwegian Association of Sheep and Goat Breeding , PO Box 104, 1431, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shahram Bahrami Alexey A. Shadrin 2 Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vera Fominykh 2 Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Takafumi Ojima 4 Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo , Tokyo, Japan 5 Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences , Yokohama, Japan 6 Department of Statistical Genetics, Osaka University Graduate School of Medicine , Suita, Japan 7 Graduate School of Medicine, Tohoku University , Sendai, Japan 8 Tohoku Medical Megabank Organization, Tohoku University , Sendai, Japan 9 Center for Advanced Intelligence Project, RIKEN , Tokyo, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chenyang Jiang 10 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, the Netherlands 11 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC , location VUmc, Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christian Benner 12 Genentech, South San Francisco , California, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elisa Moreno 13 HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology , Trondheim, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elisa Moreno Adrian I. Campos 14 Regeneron Genetics Center , Tarrytown, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jesper Q. Thomassen 15 Department of Clinical Biochemistry, Copenhagen University Hospital – Rigshospitalet , Blegdamsvej 9, 2100 Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emmanuel Minois-Genin 15 Department of Clinical Biochemistry, Copenhagen University Hospital – Rigshospitalet , Blegdamsvej 9, 2100 Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hei Man Wu 16 Department of Genetics and Genomic Sciences, Icahn School of Medicine , Mount Sinai, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site G. Bragi Walters 17 Amgen/deCODE genetics, Inc. , Sturlugata 8, Reykjavik 102, Iceland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Richard Sherva 18 Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine , Boston, Massachusetts, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tian Lin 19 Institute for Molecular Bioscience, The University of Queensland , Brisbane, Queensland, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xuemin Wang 19 Institute for Molecular Bioscience, The University of Queensland , Brisbane, Queensland, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julien Bryois 20 Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristi Krebs 21 Estonian Genome Centre, Institute of Genomics, University of Tartu , Tartu, Estonia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marijn Schipper 1 Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam Find this author on Google Scholar Find this author on PubMed Search for this author on this site Akira Narita 8 Tohoku Medical Megabank Organization, Tohoku University , Sendai, Japan 9 Center for Advanced Intelligence Project, RIKEN , Tokyo, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alessandro Serretti 22 Oasi Research Institute-IRCCS , Troina, Italy 23 Department of Medicine and Surgery, Kore University of Enna , Enna, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alessandro Serretti Anja H. Simonsen 24 Danish Dementia Research Centre, Copenhagen University Hospital - Rigshospitalet , Blegdamsvej 9, Copenhagen Ø, DK-2100, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna L. van Seumeren 1 Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anne Corbett 25 Department of Health and Community Sciences, Faculty of Health and Life Sciences, University of Exeter , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anne-Brita Knapskog 26 Department of Geriatric Medicine, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Annette M. Hartmann 27 Department of Psychiatry and Psychotherapy, Medical University of Vienna , Vienna, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Annette M. Hartmann Anouk den Braber 10 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, the Netherlands 11 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC , location VUmc, Amsterdam, the Netherlands 28 Department of Biological Psychology, Vrije Universiteit Amsterdam , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Argonde C. van Harten 10 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, the Netherlands 11 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC , location VUmc, Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arvid Harder 29 Department of Medical Biochemistry and Biophysics, Karolinska Institute , Sweden 30 Department of Medical Epidemiology and Biostatistics, Karolinska Institute , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arvid Rongve 31 Department of Research and Innovation , Helse Fonna, Haugesund, Norway 32 University of Bergen, Department of Clinical Medicine (K1) , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Arvid Rongve Bengt O. Madsen 33 Department of Geriatrics, Sorlandet Hospital Arendal , Arendal, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Betty M. Tijms 10 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, the Netherlands 11 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC , location VUmc, Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Betty M. Tijms Bitten Aagaard 34 Department of Clinical Immunology, Aalborg University Hospital , Aalborg, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bjørn Lichtwarck 35 Research Centre for Age-related Functional Decline and Disease, Innlandet Hospital Trust , Ottestad, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bjørn E. Kirsebom 36 Department of Neurology, Akershus University Hospital , Lørenskog Norway 37 Department of Psychology, Faculty of Health Sciences, UiT The Arctic University of Norway , Tromsø, Norway 38 Department of Neurology, University Hospital of North Norway , Tromsø Find this author on Google Scholar Find this author on PubMed Search for this author on this site Byron Creese 39 Department of Psychology, College of Health Medicine and Life Sciences, Brunel University of London , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Byron Creese Chandra A. Reynolds 40 Institute for Behavioral Genetics and Department of Psychology & Neuroscience, University of Colorado Boulder Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chandra A. Reynolds Sara Hägg 30 Department of Medical Epidemiology and Biostatistics, Karolinska Institute , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sara Hägg Ida Karlsson 30 Department of Medical Epidemiology and Biostatistics, Karolinska Institute , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christian Erikstrup 41 Department of Clinical Medicine, Health, Aarhus University , Aarhus, Denmark 42 Department of Clinical Immunology, Aarhus University Hospital , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian Erikstrup Christina Mikkelsen 43 Department of Clinical Immunology, Copenhagen University Hospital – Rigshospitalet , Blegdamsvej 9, Copenhagen 2100, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clive Ballard 44 Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Clive Ballard Dag Aarsland 45 Centre for Age-Related Medicine (SESAM), Stavanger University Hospital , Stavanger, Norway 46 Centre for Healthy Brain Ageing, Institute of Psychiatry, Psychology & Neuroscience, King’s College London , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daichi Shigemizu 47 Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences , Hiroshima, Japan 48 Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology , Aichi, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dan Rujescu 49 Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna , Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dan Rujescu Daniel Gudbjartsson 17 Amgen/deCODE genetics, Inc. , Sturlugata 8, Reykjavik 102, Iceland 50 School of Engineering and Natural Sciences, University of Iceland , Reykjavik, Iceland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eivind Aakhus 51 The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust , Tønsberg, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Erik Sørensen 43 Department of Clinical Immunology, Copenhagen University Hospital – Rigshospitalet , Blegdamsvej 9, Copenhagen 2100, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eystein Stordal 52 Department of Psychiatry, Namsos Hospital , Namsos, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Flora H. Duits 10 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, the Netherlands 11 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC , location VUmc, Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Frank J. Wolters 53 Department of Radiology & Nuclear Medicine and Alzheimer Centre Erasmus MC, Erasmus Medical Center , Rotterdam, The Netherlands 54 Department of Epidemiology, Erasmus Medical Center , Rotterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Frederic Blanc 55 Strasbourg University Hospital , Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Geert Jan Biessels 56 Department of Neurology, University Medical Center Utrecht Brain Center, University Medical Center Utrecht , Utrecht 3584 CX, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Geert Jan Biessels Geir Selbæk 26 Department of Geriatric Medicine, Oslo University Hospital , Oslo, Norway 51 The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust , Tønsberg, Norway 57 Institute of Clinical Medicine, University of Oslo , Oslo Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Geir Bråthen 58 Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU) , Trondheim, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Geir Bråthen Gen Tamiya 7 Graduate School of Medicine, Tohoku University , Sendai, Japan 8 Tohoku Medical Megabank Organization, Tohoku University , Sendai, Japan 9 Center for Advanced Intelligence Project, RIKEN , Tokyo, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gunhild Waldemar 24 Danish Dementia Research Centre, Copenhagen University Hospital - Rigshospitalet , Blegdamsvej 9, Copenhagen Ø, DK-2100, Denmark 59 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Blegdamsvej 3B, Copenhagen Ø, DK-2100, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Harro Seelaar 60 Department of Neurology and Alzheimer Center Erasmus MC, Erasmus MC - University Medical Center , Rotterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Harro Seelaar Helga Eyjolfsdottir 61 University of Iceland , Reykjavik, Iceland 62 Landspitali University Hospital , Reykjavik, Iceland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henne Holstege 10 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, the Netherlands 11 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC , location VUmc, Amsterdam, the Netherlands 63 Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc , Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henning Bundgaard 59 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Blegdamsvej 3B, Copenhagen Ø, DK-2100, Denmark 64 The Capital Regions Unit for Inherited Cardiac Diseases, Department of Cardiology, The Heart Centre, Copenhagen University Hospital – Rigshospitalet , Blegdamsvej 9, Copenhagen 2100, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henrik Zetterberg 65 Institute of Neuroscience and Physiology, Deparment of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henrik Ullum 66 Statens Serum Institut , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ina Giegling 49 Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna , Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ingmar Skoog 65 Institute of Neuroscience and Physiology, Deparment of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg 67 Region Västra Götaland, Sahlgrenska University Hospital, Department of Neuropsychiatry , Gothenburg Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ingrid T. Medbøen 26 Department of Geriatric Medicine, Oslo University Hospital , Oslo, Norway 51 The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust , Tønsberg, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ingvild Saltvedt 58 Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU) , Trondheim, Norway 68 Department of Geriatrics, St. Olav’s Hospital, Trondheim University Hospital , Trondheim, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Irena Rektorova 69 First Department of Neurology, St. Anne’s University Hospital and Faculty of Medicine, Masaryk University , Brno, Czechia 70 Brain and Mind Research, Central European Institute of Technology, Masaryk University , Brno, Czechia Find this author on Google Scholar Find this author on PubMed Search for this author on this site J. Michael Gaziano 71 Million Veteran Program (MVP) Coordinating Center, VA Boston Healthcare System , Boston, MA, USA 72 Division of Aging, Brigham & Women’s Hospital, Harvard Medical School , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jan Haavik 73 Department of Biomedicine, University of Bergen , Norway 74 Division of Psychiatry, Haukeland University Hospital , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jens Hjerling-Leffler 29 Department of Medical Biochemistry and Biophysics, Karolinska Institute , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jens Hjerling-Leffler Jiao Luo 15 Department of Clinical Biochemistry, Copenhagen University Hospital – Rigshospitalet , Blegdamsvej 9, 2100 Copenhagen, Denmark 75 Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen , Blegdamsvej 3, 2200 Copenhagen Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jon Snaedal 76 Memory Clinic, Department of Geriatric Medicine, Landspitali University Hospital , Reykjavik, Iceland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Everard G.B Vijverberg 10 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, the Netherlands 11 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC , location VUmc, Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julia M. Sealock 77 Stanley Center for Psychiatric Research, Broad Institute of MIT & Harvard , Cambridge, MA, USA 78 Analytic and Translational Genetics Unit, Massachusetts General Hospital , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kaj Blennow 65 Institute of Neuroscience and Physiology, Deparment of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kaja Nordengen 36 Department of Neurology, Akershus University Hospital , Lørenskog Norway 79 Department of Neurology, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karin Persson 26 Department of Geriatric Medicine, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katja Scheffler 58 Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU) , Trondheim, Norway 80 Department of Neurology and Clinical Neurophysiology, St. Olavs Hospital, Trondheim University Hospital , Trondheim, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katja Scheffler Koichi Matsuda 81 Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo , Tokyo, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kouichi Ozaki 47 Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences , Hiroshima, Japan 48 Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology , Aichi, Japan 82 RIKEN Center for Integrative Medical Sciences , Kanagawa, Japan 83 Department of Aging Research, Nagoya University Graduate School of Medicine , Aichi, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lasse Pihlstrøm 57 Institute of Clinical Medicine, University of Oslo , Oslo Norway 79 Department of Neurology, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lasse Pihlstrøm Lavinia Athanasiu 2 Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lene Pålhaugen 36 Department of Neurology, Akershus University Hospital , Lørenskog Norway 57 Institute of Clinical Medicine, University of Oslo , Oslo Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marc Hulsman 63 Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc , Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Margda Waern 65 Institute of Neuroscience and Physiology, Deparment of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria Averina 84 Department of Laboratory Medicine, Division of Diagnostic Services, University Hospital of North Norway , Tromsø, Norway 85 Department of Clinical Medicine, Faculty of Health Sciences, UiT, The Arctic University of Norway , Tromsø, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marianne Wettergreen 36 Department of Neurology, Akershus University Hospital , Lørenskog Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marta R. Moksnes 13 HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology , Trondheim, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marta R. Moksnes Martijn Huisman 86 Department of Sociology, Faculty of Social Sciences, Vrije Universiteit Amsterdam , Amsterdam, The Netherlands 87 Department of Epidemiology & Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC– Location VU University Medical Center , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Masayuki Yamamoto 7 Graduate School of Medicine, Tohoku University , Sendai, Japan 8 Tohoku Medical Megabank Organization, Tohoku University , Sendai, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mathias Toft 79 Department of Neurology, Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matthew S. Panizzon 88 Department of Psychiatry, School of Medicine, University of California San Diego , La Jolla, CA, USA 89 Center for Behavioral Genetics of Aging, University of California San Diego , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mie Topholm Bruun 90 Department of Clinical Immunology, Odense University Hospital , Odense, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mohsen Ghanbari 54 Department of Epidemiology, Erasmus Medical Center , Rotterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mohsen Ghanbari Monique Franc 91 Neuroscience, Janssen Research & Development, LLC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nancy L. Pedersen 30 Department of Medical Epidemiology and Biostatistics, Karolinska Institute , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nathaniel Y. Bell 1 Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nathaniel Y. Bell Niccoló Tesi 63 Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc , Amsterdam, the Netherlands 92 Delft Bioinformatics Lab, Delft University of Technology , Delft, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ole B. Pedersen 59 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Blegdamsvej 3B, Copenhagen Ø, DK-2100, Denmark 93 Department of Clinical Immunology, Zealand University Hospital - Køge , Lykkebækvej 1, Køge 4600, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Oleksandr Frei 2 Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital , Oslo, Norway 94 Department of Pharmacy, Section for Pharmacology and Pharmaceutical Biosciences, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Olivier Bousiges 55 Strasbourg University Hospital , Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Per Svenningsson 95 Department of Basal and Clinical Neuroscience, King’s College London , London, UK 96 Translational Neuropharmacology, Department of Clinical Neuroscience, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pieter J. Visser 10 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, the Netherlands 11 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC , location VUmc, Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pieter J. Visser Qingqin S. Li 91 Neuroscience, Janssen Research & Development, LLC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Richard Hauger 88 Department of Psychiatry, School of Medicine, University of California San Diego , La Jolla, CA, USA 89 Center for Behavioral Genetics of Aging, University of California San Diego , La Jolla, CA, USA 97 Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System , San Diego, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rui Zhang 98 National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System , Boston, MA 02130 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shinichi Namba 4 Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo , Tokyo, Japan 5 Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences , Yokohama, Japan 6 Department of Statistical Genetics, Osaka University Graduate School of Medicine , Suita, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sigrid B. Sando 58 Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU) , Trondheim, Norway 80 Department of Neurology and Clinical Neurophysiology, St. Olavs Hospital, Trondheim University Hospital , Trondheim, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Silke Kern 65 Institute of Neuroscience and Physiology, Deparment of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg 99 Department of Neuropsychiatry, Sahlgrenska University Hospital , Gothenburg, Region Västra Götaland, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Srdjan Djurovic 2 Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital , Oslo, Norway 100 Department of Medical Genetics, Oslo University Hospital and University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steinunn Thordardottir 62 Landspitali University Hospital , Reykjavik, Iceland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tanya N. Phung 1 Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas Truelsen 59 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Blegdamsvej 3B, Copenhagen Ø, DK-2100, Denmark 101 Department of Neurology, Copenhagen University Hospital - Rigshospitalet , København Ø, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas Truelsen Thomas Werge 59 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Blegdamsvej 3B, Copenhagen Ø, DK-2100, Denmark 102 Institute of Biological Psychiatry Mental Health Centre, Sct. Hans, Copenhagen University Hospital—Roskilde , Roskilde, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas F. Hansen 103 Neurogenomics grp, Translational Research Centre, Rigshospitalet—Glostrup, Copenhagen University Hospital , Copenhagen, Denmark 104 Danish Headache Center, Department of Neurology, Rigshospitalet—Glostrup, Copenhagen University Hospital , Copenhagen, Denmark 105 Danish Multiple Sclerosis Center, Department of Neurology, Rigshospitalet - Glostrup, Copenhagen University Hospital , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas F. Hansen Tomoki Kyosaka 7 Graduate School of Medicine, Tohoku University , Sendai, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Torgeir Engstad 106 University Hospital of North Norway HF , Tromsø, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tormod Fladby 36 Department of Neurology, Akershus University Hospital , Lørenskog Norway 57 Institute of Clinical Medicine, University of Oslo , Oslo Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Victoria Merritt 88 Department of Psychiatry, School of Medicine, University of California San Diego , La Jolla, CA, USA 97 Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System , San Diego, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sverre Bergh 35 Research Centre for Age-related Functional Decline and Disease, Innlandet Hospital Trust , Ottestad, Norway 51 The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust , Tønsberg, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wiesje M. van der Flier 10 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, the Netherlands 11 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC , location VUmc, Amsterdam, the Netherlands 107 Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wiesje M. van der Flier Rujin Wang 14 Regeneron Genetics Center , Tarrytown, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eli A. Stahl 14 Regeneron Genetics Center , Tarrytown, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Basavaraj Hooli 108 Eli Lilly and Company, Lilly Corporate Center , Indianapolis IN 46285, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site 109 23andMe Research Institute , Los Altos, CA, USA 110 Department of Epidemiology, University of Groningen, University Medical Center Groningen , The Netherlands 111 Department of Pediatrics, University of Groningen, University Medical Center Groningen , The Netherlands 112 Department of Genetics, University of Groningen, University Medical Center Groningen , The Netherlands 43 Department of Clinical Immunology, Copenhagen University Hospital – Rigshospitalet , Blegdamsvej 9, Copenhagen 2100, Denmark 59 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Blegdamsvej 3B, Copenhagen Ø, DK-2100, Denmark 14 Regeneron Genetics Center , Tarrytown, NY, USA 21 Estonian Genome Centre, Institute of Genomics, University of Tartu , Tartu, Estonia 113 VA Office of Research and Development , Washington DC, USA Lea K. Davis 114 Associate Faculty, New York Genome Center , New York, New York, USA 115 Departments of Medicine, Psychiatry, and Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee, USA 116 Windreich Department of AI and Human Health & the Departments of Medicine, Genetics and Genomic Sciences, and Psychiatry, Icahn School of Medicine at Mount Sinai , New York, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mark W. Logue 18 Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine , Boston, Massachusetts, USA 98 National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System , Boston, MA 02130 117 Departments of Psychiatry, Boston University Chobanian & Avedisian School of Medicine , Boston, MA 02118 118 Department of Biostatistics, Boston University School of Public Health , Boston, MA 02118 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kelli Lehto 21 Estonian Genome Centre, Institute of Genomics, University of Tartu , Tartu, Estonia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna Zettergren 65 Institute of Neuroscience and Physiology, Deparment of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ben M. Brumpton 13 HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology , Trondheim, Norway 119 Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital , Trondheim 7030, Norway 120 HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology , Levanger 7600, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ben M. Brumpton Jian Zeng 19 Institute for Molecular Bioscience, The University of Queensland , Brisbane, Queensland, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jian Zeng Peter M. Visscher 19 Institute for Molecular Bioscience, The University of Queensland , Brisbane, Queensland, Australia 121 Nuffield Department of Population Health, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul F. O’Reilly 16 Department of Genetics and Genomic Sciences, Icahn School of Medicine , Mount Sinai, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paul F. O’Reilly Anubha Mahajan 12 Genentech, South San Francisco , California, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Manuel Ferreira 14 Regeneron Genetics Center , Tarrytown, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yukinori Okada 4 Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo , Tokyo, Japan 5 Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences , Yokohama, Japan 6 Department of Statistical Genetics, Osaka University Graduate School of Medicine , Suita, Japan 122 Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University , Suita, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sven J. van der Lee 10 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, the Netherlands 11 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC , location VUmc, Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sven J. van der Lee Sisse R. Ostrowski 43 Department of Clinical Immunology, Copenhagen University Hospital – Rigshospitalet , Blegdamsvej 9, Copenhagen 2100, Denmark 59 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Blegdamsvej 3B, Copenhagen Ø, DK-2100, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ruth Frikke-Schmidt 15 Department of Clinical Biochemistry, Copenhagen University Hospital – Rigshospitalet , Blegdamsvej 9, 2100 Copenhagen, Denmark 59 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen , Blegdamsvej 3B, Copenhagen Ø, DK-2100, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ruth Frikke-Schmidt Hreinn Stefansson 17 Amgen/deCODE genetics, Inc. , Sturlugata 8, Reykjavik 102, Iceland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karl Heilbron 77 Stanley Center for Psychiatric Research, Broad Institute of MIT & Harvard , Cambridge, MA, USA 123 Research & Development, Pharmaceuticals, Bayer AG , Berlin, Berlin, 13353, Germany 124 Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin , Berlin, 10117, Germany 125 German Center for Mental Health (DZPG), partner site Berlin/Potsdam , Berlin, Berlin, 10117, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ole A. Andreassen 2 Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ole A. Andreassen For correspondence: e.uffelmann{at}vu.nl d.p.wightman{at}vu.nl ole.andreassen{at}medisin.uio.no d.posthuma{at}vu.nl Danielle Posthuma 1 Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam 126 Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Center , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: e.uffelmann{at}vu.nl d.p.wightman{at}vu.nl ole.andreassen{at}medisin.uio.no d.posthuma{at}vu.nl Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Alzheimer’s disease (AD) is the most common cause of dementia, with global case numbers projected to reach 153 million in 2050 1 . AD is highly heritable, with twin-based heritability estimates of 60-80% 2 . While 1,200 causal loci are predicted to exist for AD 3 , approximately 80 have been associated with AD in two recent studies 4 , 5 , suggesting that many loci remain to be discovered 6 . Here, we analyzed data from 109,479 cases, 74,141 proxy cases, 2,131,799 controls, and 499,708 proxy controls from diverse ancestries, identifying 118 loci in a multi-ancestry analysis and 9 additional loci in ancestry-specific analyses, 48 of which are new. We identified new AD risk genes, prioritized potential drug targets, and identified microglia and, for the first time, several neuronal cell types enriched for AD-associated genetic risk. Moreover, we improved polygenic prediction and estimated a single-nucleotide polymorphism (SNP) heritability of 16%. Together, our findings offer insights into the genetic architecture and potential pathobiology of AD, as well as specific targets for future drug development research. Introduction Alzheimer’s disease (AD) is the most common cause of dementia, affecting tens of millions of people worldwide and projected to more than double by 2050 1 . Current disease-modifying treatments for AD typically target only one aspect of its pathology: amyloid beta aggregates. However, this has shown limited efficacy in alleviating symptoms 7 , 8 . Further insight into the disease etiology may highlight other mechanisms leading to the development of more effective drugs. Given the large twin-based heritability of AD , genetic studies can provide valuable insights into its underlying biological mechanisms. Moreover, polygenic scores (PGS) may improve clinical risk prediction models and help identify individuals for early intervention before the onset of neurodegenerative processes. Recent genome-wide association studies (GWAS) of AD have successfully identified 75 risk loci, highlighted the central role of microglia, and suggested immune function, amyloid catabolism, and lipid function as important biological processes 4 , 5 . Despite these discoveries, the PGS generated from these studies have had limited predictive performance 4 , and liability-scale summary-statistics-based heritability estimates have been low (∼5%) 9 . This discrepancy has prompted the suggestion that there may be little additional genetic risk that can be identified with GWAS 6 beyond the strongest genetic risk factor for AD, APOE . Others, however, proposed that approximately 1,200 causal loci are expected to influence genetic risk for AD 3 . To reconcile these differences, larger studies leveraging improved, well-calibrated methodologies will be critical. Such efforts may yield more accurate heritability estimates, enhance the predictive power of PGS, and, importantly, uncover additional risk genes informing disease mechanisms. Moreover, including individuals of non-European ancestry can further improve PGS prediction in underrepresented populations. Here, we report the largest multi-ancestry GWAS of AD to date, encompassing a total of 109,479 cases, 74,141 proxy cases, 2,131,799 controls, and 499,708 proxy controls. Proxy cases are genotyped individuals who reported their parents to have had AD (see Methods ). Using this sample, we identified 118 loci in a multi-ancestry analysis and 9 additional loci in ancestry-specific analyses, 48 of which are new. We estimated a of 16% and improved the out-of-sample prediction. We also found enrichment in microglia and, for the first time, in neuronal cell types, and highlighted relevant biological processes and potential drug targets. To maximize the utility of our results, we have provided publicly available summary statistics from the main analyses, as well as from multiple stratified analyses. Results Data overview Our study included 109,479 AD cases, 74,141 proxy AD cases, 2,131,799 controls, and 499,708 proxy controls across 30 cohorts (see Figure 1 Supplementary Figure 1 , and Supplementary Data 1 ), resulting in a total effective sample size 10 ( N eff ; see Methods ) of 416,493. The total effective sample size consisted of 91% individuals of European (EUR), 5% African (AFR), 3% East Asian (EAS), <1% Admixed-American (AMR), and <1% South Asian (SAS) ancestry ( Supplementary Figure 1 ). A subset of this dataset was available for sex-stratified analyses, with 20,402 female cases and 261,889 female controls, and 15,689 male cases and 214,956 male controls. Another subset with a N eff of 196,822 was available for a GWAS on the X chromosome. A full description of each cohort is available in the Supplementary Methods . Download figure Open in new tab Figure 1. Overview of study design and key findings. Flowchart summarizing the workflow and main results, including genome-wide association analysis (Manhattan plot from our multi-ancestry GWAS), gene-set enrichment, SNP heritability and polygenic risk prediction, and characterization of new loci. NFT = neurofibrillary tangles, CAA = cerebral amyloid angiopathy, pTau = phosphorylated tau, CSF = Cerebrospinal Fluid, ALS = amyotrophic lateral sclerosis. FTD = frontotemporal dementia. GWAS meta-analyses identify new loci We conducted a multi-ancestry GWAS of AD, including five broad ancestry groups: EUR ( N eff = 379,465), AFR ( N eff = 21,163), EAS ( N eff = 13,790), AMR ( N eff = 1,222), and SAS ( N eff = 852) (see Figure 2 and Supplementary Data 1 ). After QC, this resulted in 8,152,095 SNPs with a minimum minor allele frequency (MAF) of 0.004 and a mean of 0.17. We identified 118 significant loci ( p < 5×10 -8 ), 41 of which are new. We did not identify risk loci on the X chromosome. In the ancestry-specific analyses, we identified 114 significant loci (38 new) in the EUR GWAS, 3 loci in AFR GWAS ( TREM2, SORL1, APOE ), 2 loci in EAS GWAS ( SORL1 and APOE ), and no loci in SAS or AMR GWAS ( Supplementary Figure 2 ). The discrepancy in identified loci is as expected based on differences in sample size, with more than 90% of the total sample consisting of EUR individuals. After meta-analyzing the ancestry specific results, we identified 118 loci. By meta-analyzing the ancestries, an additional 13 loci were identified, while 9 of the loci identified in the EUR only analysis dropped below the significance threshold ( TMEM163, MAP3K13, GPAM, MCF2L, KIAA0125 /IGH gene cluster, GLCE, MCTP2, MNT / SGSM2 / DPH1 / RAP1GAP2, AC104532 . 2 / VMAC ). These 9 loci were still suggestive (at least p < 1×10 -5 ) in the multi-ancestry meta-analysis. Overall, we identified 127 loci across all meta-analyses; 118 in the multi-ancestry analysis and an additional 9 in the EUR only analysis ( Supplementary Data 2 ). Of the 127 loci, 48 were new (see Supplementary Data 3 and 4 for previously published studies and loci). As a sensitivity analysis to address potential heterogeneity in effect sizes across ancestries for all loci except APOE, we conducted a random-effects meta-analysis between ancestries using METASOFT 11 after performing a fixed-effects meta-analysis within each ancestry with METAL. The P-values of the lead SNPs remained almost unchanged (see Methods and Supplementary Figure 3 ). Download figure Open in new tab Figure 2. Circular Manhattan plot of the multi-ancestry GWAS. The x-axis represents the chromosomal position of SNPs, and the y-axis their strength of association measured as –log 10 ( p ). Genome-wide significant ( p < 5×10 -8 ; darker points) loci are annotated with their predicted effector gene. New genes are highlighted in red. Lighter-shaded points correspond to SNPs not reaching genome-wide significance. Only SNPs with p < 0.00005 are plotted. The y-axis is capped at 75 for visual clarity; however, the APOE locus reaches –log 10 ( p ) ∼ 5697 and the BIN1 locus reaches –log 10 ( p ) = 108. We also meta-analyzed the data stratified by sex (male vs. female) and phenotype definition (case-control vs. proxy). The sex-stratified GWAS (excluding proxy cases) identified 12 loci in the female-only GWAS ( CR1, BIN1, OR2B2/NKAPL, TREM2, CLU, MS4A2, PICALM, APH1B, MAPT, ABCA7, APOE, LILRB2 ). We identified six loci in the male-only GWAS ( CR1, BIN1, MS4A2, PICALM, APOE, NTN5 ) ( Supplementary Figure 4 ). The relatively low number of associated loci compared to the overall GWAS was expected given the much smaller effective sample size of the sex-stratified analyses ( N eff, female = 64,771; N eff, male = 49,748). The APOE-ε4 tagging SNP had a 13% larger odds ratio in females (2.85 vs. 2.52, p = 9 × 10 −8 ). We estimated the genetic correlation ( r g ) between EUR males and females with LD-Score Regression (LDSC) at 0.77 (95%-CI: 0.48 - 1.06). Likely due to small sample size and insufficient heritability, LDSC failed to compute genetic correlations for all other ancestries. Because the standard error of LDSC’s r g scales inversely with the SNP heritabilities of the contributing traits, and LDSC appears to underestimate AD’s (see below), we also approximated a more precise lower bound of the genetic correlation of 0.82 (95%-CI: 0.76 – 0.88) between males and females in EUR using SBayesRC (see Methods ). The case-control only meta-analysis across all ancestries ( N eff = 353,189) identified 95 unique significant loci, and the proxy-only meta-analysis ( N eff = 60,826) identified 12 significant loci ( Supplementary Figure 5 ). Ten of the 12 proxy loci were also genome-wide significant in the case-control meta-analysis, while the other 2 loci were borderline significant ( p = 6×10 -8 ; p = 9×10 -6 ). The LDSC-computed genetic correlation between the case-control and proxy meta-analysis was 0.99 (s.e. = 0.14, p = 3×10 -13 ), indicating high similarity (see Supplementary Note 1 ). A summary of association across the 127 loci in the ancestry, sex, and phenotype stratified meta-analyses is available in Supplementary Data 5 and Supplementary Figures 6 and 7 . The multi-ancestry meta-analysis incorporated several cohorts not included in previous AD GWAS 4 , 5 , allowing us to assess the replication of recent GWAS findings in samples independent from previously included samples. Overall, we found high replicability of loci ( Supplementary Note 2 and Supplementary Data 6 and 7 ), and that lead-SNP effect sizes correlated strongly ( r = 0.98; see Supplementary Figure 8 ). To maximize the value of the data collected, we have made the summary statistics from the primary and stratified meta-analyses publicly available (after the exclusion of 23andMe Research Institute data). A summary table of the available summary statistics can be found in Supplementary Data 8 . We observed heterogeneous APOE-ε4 effect sizes across ancestries (see Supplementary Figure 9 ), ranging from the smallest odds ratio (OR) in AFR (OR = 1.7) to the largest in EAS (OR = 3.4). We note the APOE-ε4 variant was not present in All of Us, the source of all AMR participants (see Supplementary Methods ). This variation is not explained by differences in proxy case representation: we find no consistent relationship between proxy case definitions and APOE-ε4 effect size, with proxy-based estimates larger than genotyped-case estimates in EUR and AFR, but smaller in EAS and SAS. However, interpretation is complicated by substantial cross-cohort heterogeneity in APOE-ε4 effect sizes even within European cohorts ( I 2 = 97.7%, p = 8.19×10 −210 ; see Supplementary Figure 10 ), suggesting that cohort-level differences may account for much of the observed variation rather than true differences in APOE-ε4 penetrance across ancestries. To ass the impact of three cohorts that did not adjust for genotyping array or batch, we repeated the meta-analysis excluding them. Although 29 loci were lost, all were near the significance threshold both before and after exclusion, consistent with reduced power rather than false-positive signal in the primary analysis ( Supplementary Note 3 ). Effector gene prediction for 127 loci For each locus, we predicted the most likely effector genes based on several methodologies and sources of evidence. We nominated genes in a locus based on associations in previous exome-wide association studies (ExWAS) of AD (see Supplementary Data 4 ), effector gene predictions from a machine learning model (FLAMES) 12 , colocalization 13 and Mendelian randomization (MR) 14 results using expression, splicing, and protein quantitative trait loci (eQTL, sQTL, and pQTL, respectively) datasets (see Supplementary Data 9 ), and gene nominations from previous GWAS (see Supplementary Data 3 ) (see Methods ). We aimed to select a single gene per locus, prioritizing ExWAS-identified genes followed by FLAMES predictions. If these were not available for a locus, we selected the genes reported in previous GWAS. For new loci without ExWAS and FLAMES predictions, we selected genes from the colocalization and Mendelian randomization analysis and assessed their evidence in previous literature. A full description of the new loci is available in the Supplementary Note 4 . For loci with conflicting evidence, we additionally searched the literature for experimental evidence for the implicated genes ( Supplementary Note 5 ). A summary of the experimental evidence from the literature for these genes is available in Supplementary Data 10 . For some loci, this approach did not consistently resolve the conflicting predictions; in such cases, we report multiple genes ( Table 1 ; Supplementary Data 2 ). View this table: View inline View popup Table 1: Genome-Wide Significant Loci and Effector Gene Predictions The novel significant loci ( p <5×10 -8 ) that were identified across the multi-ancestry and EUR only meta-analyses. The loci marked with an asterisk were only significant in the EUR-only meta-analysis. More information for all known and novel loci is available in Supplementary Data 2 . chr = chromosome, A1 = the tested allele, A1 freq = tested allele frequency, OR = odds ratio, 95% CI = 95% confidence interval around the OR. Loci with more than two effector genes list the first two followed by ellipses. New genes highlight druggable targets, pathways, and pleiotropy Among the 48 new loci identified, we found 17 genes that interacted with non-AD approved drugs (see Methods and Supplementary Data 11 ). Five of these genes have been evaluated for their potential as drug targets for AD in previous research, three showed promise in animal models ( RRM2B 15 , SYK 16 , 17 , and KEAP1 18 ), while drugs targeting the other two genes ( CACNA1S and AXL ) were unsuccessful in improving AD symptoms in humans 19 and mice 1920 (see Supplementary Note 6 ). Among the known loci, there were ten instances where the gene predicted by FLAMES differed from the gene reported in previous literature (see Supplementary Note 5 ). One of these genes ( QPCT ) is a potential drug target and was selected as an effector gene in locus 8 instead of two genes listed in previous GWAS ( PRKD3 and NDUFAF7 ). QPCT catalyzes the formation of pyroglutamate on position 3 of Aβ (AβpE3-X) 21 , which is the specific modification of Aβ targeted by an approved anti-amyloid antibody drug (donanemab); QPCT inhibitors are in development for several other neurodegenerative disorders as well 22 . Overall, multiple new genes, as well as newly prioritized genes in known loci, represent potential targets for drug development. We were interested in how the new genes we identified fit into known AD-affected pathways. We found that five new genes ( SNX2, DOK2, SYK, GGA2 , and CEBPA ) interacted with other known AD genes across two pathways (amyloid processing and microglia response to Aβ). One of the newly identified genes ( SNX2 ) encodes an endosomal membrane protein in a family of genes (sorting nexins) that have been suggested to interact with APP or APP cleaving enzymes to regulate Aβ degradation 23 . Another ( GGA2 ) affects Aβ processing through intracellular trafficking. GGA2 influences the distribution of a key enzyme for APP processing ( BACE1 ) in the Golgi apparatus, which then influences APP secretion 24 . Moreover, DOK3 and SYK interact with each other 25 and TREM2 17 , 25 , 26 to potentially mediate microglial response to Aβ. CEBPA is another new gene that interacts with TREM2 to promote CD36 expression in microglia and may influence Aβ clearance 27 . Collectively, these findings reinforce the central importance of amyloid and microglial pathways, demonstrating how newly discovered risk factors converge upon these key disease mechanisms. Across the 48 new loci identified, 5 have been associated with endophenotypes of neurodegeneration or another neurodegenerative disease ( HDAC9, C16orf95, HIP1R, MBNL1 , and UBE2V1/SPATA2 ). The HDAC9 locus has been previously associated with neurofibrillary tangles and cerebral amyloid angiopathy 28 . HDAC9 encodes a histone deacetylase, a family of genes associated with multiple diseases and neurodegeneration 29 . C16orf95 has been previously associated with phosphorylated tau (pTau) levels in cerebrospinal fluid (CSF) 30 , but the function of C16orf95 is not well characterized. While this locus harbors genome-wide significant SNPs in both AD and pTau, the local genetic correlation was not significant between them ( r g = -0.31, p = 0.12; see Supplementary Data 12 ), which suggests there may be independent mechanisms of association. The HIP1R and MBNL1 loci have been previously associated with Parkinson’s disease (PD) 31 , but colocalization analysis suggested that these loci are unlikely to be driven by shared causal variants across AD and PD ( Supplementary Data 12 ). The UBE2V1/SPATA2 locus has been previously identified as a shared locus between amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) 32 . We identified a nominally significant positive local genetic correlation between AD and ALS at this locus ( r g = 0.47, p = 0.006; see Supplementary Data 12 ). Excluding cohorts with AD definitions based on ICD code F03 (unspecified dementia) did not change this result (see Supplementary Note 7 ). Colocalization analysis indicated that this locus was driven by a shared causal variant (PP.H4=0.95) with the lead SNP (rs113558364) being the most likely shared variant ( Supplementary Note 8 ). This suggests that this may be a shared locus for multiple neurodegenerative diseases. UBE2V1 encodes a ubiquitin-conjugating enzyme and has been suggested to regulate protein aggregation 33 , 34 , and SPATA2 is a part of a signaling network that regulates apoptosis 35 . Both of these processes are relevant to multiple neurodegenerative diseases, and there is evidence in this study for both of these genes as the potential effector gene ( Supplementary Note 8 ). Among known loci, we identified that the GBA1 locus was shared between AD and PD, and the TMEM163 locus had opposite effects driven by distinct variants in AD and PD, and likely mediated through different cell types ( Supplementary Note 8 ). Overall, this analysis highlights 7 loci ( HDAC9, C16orf95, HIP1R, MBNL1, UBE2V1/SPATA2, GBA1 , and TMEM163 ) that may be involved in fundamental processes leading to neurodegeneration. Gene-set analysis highlights lipid binding, immune activation, amyloid catabolism, and cell-cell adhesion We performed gene set enrichment analyses using MAGMA 36 to highlight biological mechanisms relevant to AD. We identified 72 significant MSigDB and SynGO gene sets at a false discovery rate of 5% (Benjamini-Hochberg) (see Supplementary Note 9 and Supplementary Data 13 ). After clustering these gene sets (see Methods ), 13 clusters of more than one gene set were identified. These clusters reflected 4 biological processes identified in previous GWAS studies 4 , 5 , 37 : lipid binding, immune activation, amyloid catabolism, and cell adhesion. The immune activation and amyloid catabolism gene sets were still significant when we repeated the analysis using a meta-analysis of cohorts not included in the previous meta-analyses ( Supplementary Note 9 and Supplementary Data 13 ). In addition to the clusters, 21 gene sets were independent from any other gene set. These gene sets highlighted 5 biological processes that have been connected to AD pathology but not through GWAS gene set analysis: EphrinB reverse signaling 38 , glutathione metabolism 39 , RAS signaling 40 , sphingolipid metabolism 41 , and synapse endocytosis 42 . EphrinB has been suggested to influence amyloid beta toxicity 38 , glutathione metabolism influences oxidative stress which has been linked to AD 39 , RAS signaling regulates blood pressure and fluid balance and has been linked to multiple aspects of AD 40 , sphingolipid metabolism has been linked to AD through regulation of cell death 41 , and synapse endocytosis has been shown to be impaired in AD 42 . Overall, we highlighted multiple pathways that influence AD development through a range of biological mechanisms. Cell type analysis identifies microglia and neurons We aimed to identify cell types relevant to AD by examining the association between cell-type-specific gene expression and GWAS signal. Using gene expression data in healthy controls, we identified a significant positive association between overexpression in microglia and the GWAS gene association (see Supplementary Data 14 and Supplementary Figure 11 ). This suggests that these genes are preferentially active in microglia compared to other brain cell types, implicating the role of microglia in AD. This finding was in line with our previous GWAS of AD 4 . The association was robust to the removal of chromosome 19 and the proxy phenotype data (see Supplementary Data 14 ). Based on this finding, we also performed gene-set analysis to identify specific microglial states associated with the AD GWAS signal. While no state was significantly associated with AD, inflammatory states showed the strongest association (see Supplementary Note 10, Supplementary Figure 11 , and Supplementary Data 15 ). Next, we used cell-type-specific differential gene expression between AD cases and controls 43 for a MAGMA gene property analysis. We tested whether there was an association between the GWAS gene association and differential expression in a cell type. We identified a significant association between differential upregulation of genes in microglia and AD GWAS signal. We also identified a significant association with differential downregulation of genes in 2 types of GABAergic neurons (small neuropeptide-expressing neurons (Sncg) and somatostatin-expressing inhibitory interneurons (Sst)) and 1 type of glutamatergic neuron (layer 6 intratelencephalic projection neurons (L6 IT Car3)) (see Figure 3 and Supplementary Data 16 ). The microglia and neuronal associations were robust to the exclusion of chromosome 19 and proxy data (see Supplementary Data 16 ). After correction for the average differential gene expression across all cell types, the association of microglia remained significant (p = 1.58×10 -6 ), while the neuronal associations were rendered only nominally significant (p<0.05) (see Supplementary Data 16 ). This suggests that the associations of sncg, sst, and L6 IT Car3 neurons may not be specific and could instead represent a general association with downregulated genes in neurons. Together, these results support the known involvement of microglia in AD. Additionally, we found an enrichment of AD GWAS signal in genes under expressed in neuronal cell types of AD cases compared to controls. Download figure Open in new tab Figure 3. Cell-type enrichment analysis of differential gene expression data. Genes that are upregulated in AD microglia and downregulated in multiple neuronal subtypes in AD are enriched for AD GWAS signal. The dashed line marks the Bonferroni-corrected significance threshold for 42 tests (21 cell types × 2 directions; p < 0.05/42). For each cell type, only the more significant direction of effect (up- or downregulation) is displayed. Gene prioritization through specific cell types highlights microglial phagocytosis, astrocyte activation, myelination, and neuronal repair To aid translation of the GWAS results, we sought to prioritize genes that influence AD through specific biological processes and cell types. We used the colocalization, MR, and cell type enrichment analyses to link genes to individual cell types, then drew on experimental literature to connect these genes to biological processes ( Supplementary Note 11 ). Colocalization and MR analyses indicated that increased astrocytic expression of KIF21B may elevate AD risk. KIF21B upregulation has previously been linked to astrocyte activation and AD 44 , and aberrant astrocyte activation has in turn been associated with increased Aβ production and release 45 . This suggests that overexpression of KIF21B may influence AD through abnormal astrocyte activation and increased Aβ pathology. The same analysis indicated that decreased oligodendrocytic expression of MAF may also be linked to AD risk. Knockout of MAF has been associated with decreased myelination 46 , which, in turn, has been linked to AD 47 . As such, we speculate that decreased MAF expression reduces myelin production, rendering neurons more susceptible to AD pathology. Through the colocalization, MR, and cell type enrichment analyses we identified multiple genes linked to microglia expression ( Supplementary Note 11 ), 8 of which are relevant in microglia phagocytosis ( INPP5D, SYK, BLNK, HAVCR2, PICALM, AXL, FCER1G , and PLCG2 ) 47 – 51 . We also prioritized genes that contributed to the differential cell type enrichment of neurons, three of which ( ZKSCAN1, APH1B , and SPRED2 ) were nominally under expressed in neurons and significantly associated with AD. APH1B encodes a part of the γ-secretase complex that cleaves App 52 and may contribute to AD through aberrant App processing in neurons. Decreased SPRED2 expression has been observed after brain injury in a zebrafish model, where it has been implicated in neuro-regeneration 53 . While adult human neurogenesis remains debated, recent work has linked altered adult hippocampal neurogenesis to cognitive decline 54 . Therefore, we hypothesize that APH1B increases amyloid load, driving AD pathology, and that SPRED2 may impair the neuronal response to neurodegeneration. In summary, by combining GWAS associations with expression data, we prioritized four biological processes acting through specific cell types and mediated by a small set of genes, providing targets for future experimental models. Polygenic Scores Capture a Large Proportion of SNP-Based Heritability We used the meta-analysis results to estimate and improve polygenic prediction. We used SBayesRC 55 with the EUR meta-analysis (excluding proxy cases) and estimated at 16% (s.e = 0.2%) (see Figure 4a and Supplementary Data 17 ). The estimate for the proxy-case phenotype was nearly identical. The estimates for the EAS and AFR meta-analyses were 13% (s.e. = 4%) and 10% (s.e. = 6%), respectively (see Supplementary Data 17 ). The APOE-ε4 tagging variant rs429358 alone contributed 6% in EUR and EAS, but only 2% in AFR. These empirical results for APOE in EUR closely match theoretical derivations based on the liability threshold model ( Supplementary Note 12 ). These estimates were based on a commonly used population prevalence of 5%; When using a population prevalence of 20% (e.g., in individuals older than 84 1 ) the increased to approximately 25% in EUR (see Supplementary Figure 12 ). While the heritability point estimates differ between the populations for both LDSC and SBayesRC, the error bars in AFR and EAS contain the point estimates for all three populations, preventing conclusions about statistical differences between populations. Both LDSC and SBayesRC heritability estimates for AFR were not significantly different from zero, likely because of reduced power from smaller AFR sample sizes. Despite the larger AFR sample size relative to EAS, AFR populations showed larger standard errors under both methods, likely reflecting the greater genetic diversity and shorter LD blocks characteristic of African-ancestry populations, which impose a more complex correlation structure that existing LD reference panels may incompletely capture. Previous EUR GWAS of AD estimated the at approximately 5% 4 , 56 using LDSC 57 and a population prevalence of 5%. Using the EUR case-control meta-analysis, we arrive at a similar estimate of 6% when using LDSC ( s . e . = 1%; see Figure 4a ). Download figure Open in new tab Figure 4. Heritability and polygenic prediction of AD. a , Summary-statistics based analysis of heritability of AD on the liability scale using LD Score Regression and SBayesRC. The error bars represent the 95% confidence intervals. b , Polygenic prediction performance in held-out clinical and biobank samples (excluding proxy cases). The base model included the first 10 principal components, and the model to be evaluated additionally included the PGS. We computed the incremental as the difference between the models. Two EUR clinical samples (DemGene and Gothenburg H70 Birth Cohort Studies and Clinical AD from Sweden) and two EUR biobank samples (TwinGene and UK Biobank) were available for prediction. One sample (UK Biobank) was available for prediction in AFR ancestry, and one (BioBank Japan) in EAS ancestry. We applied a leave-one-cohort-out approach, in which the PGS for each testing cohort was constructed using GWAS summary statistics that excluded that cohort. The difference in estimates between SBayesRC and LDSC was further investigated using simulations based on UK Biobank data, mimicking the genetic architecture of Alzheimer’s disease ( Methods ). SBayesRC without annotations (as no annotation effects were simulated) showed a small upward bias when fitting all SNPs as random effects, but produced unbiased estimates when excluding SNPs in LD with the APOE-ε4 variant (see Supplementary Data 18 ). In contrast, LDSC estimates were substantial downward biased, consistent with the result from the real data analysis. Based on the simulation findings, we conducted real data analysis (as reported above) using the same strategy, that is, fitting the APOE-ε4 variant as a fixed effect and all other SNPs outside the APOE LD block as random effects in SBayesRC. Based on these results and previous analyses 55 , 58 , 59 , we conclude that LDSC severely underestimates the for AD. We compared the predictive performance of a multi-ancestry (EUR, AFR, and EAS) PGS (see Methods ) to that of a EUR-only PGS. We evaluated the PGS in hold-out samples of EUR, AFR, and EAS ancestry, as well as in clinical samples and biobanks (excluding proxy cases; see Methods ). We excluded the AMR and SAS ancestries due to admixture, the unavailability of LD reference panels, and small sample sizes. The multi-PGS achieved a maximum incremental of 16.9% with an average of 12.8% in the EUR cohorts. The prediction in the two clinical samples (14.5% and 16.9%) was higher than in the two biobank samples (9.8% and 10.1%) (see Figure 4b and Supplementary Data 19 ). A PGS based only on EUR proxy cases achieved an average of 9.6% (see Supplementary Figure 13 ). We note the values represent point estimates without empirical standard errors. The multi-ancestry PGS performed similarly to the EUR PGS in most cohorts, except in the AFR sample of the UK Biobank. This is likely due to heterogeneous APOE effect sizes across ancestries (see Supplementary Figure 9 ). Because the APOE effect size is smallest in AFR, the inclusion of PGSs based on ancestries with larger APOE effects, particularly EAS, negatively affects prediction in AFR. However, we also note that the AFR sample of the UK Biobank was particularly small (N = 550, see Supplementary Data 20 ). As expected, both PGS performed worse in non-EUR cohorts. We note that the majority of the samples in the multi-PGS were of EUR ancestry. Furthermore, we found that the SBayesRC-based PGS is a better predictor than the combined APOE -e2 and APOE-ε4 counts for all EUR cohorts (see Methods and Supplementary Figure 14 ). When applying the PGS (UKB excluded) to unrelated individuals 65 and older of EUR ancestry in the UK Biobank, we found a 5.5 fold increase in case prevalence in the top 10% PGS group compared to the bottom 10% and a 3 fold increase when comparing the top 10% to the case prevalence of the whole sample (Supplementary Figure 15 ; Supplementary Data 19 ). This suggests that individuals with high PGS are at considerably higher risk of developing AD compared to the general population. In conclusion, when compared to previous studies, we obtained higher estimates of and demonstrated that a PGS derived from our meta-analysis explains a substantial and larger proportion of this heritability. Notably, the values based on SBayesRC 55 exceeded the estimates obtained from LDSC, reinforcing that LDSC underestimates for AD. Discussion We conducted the largest common variant analysis of AD to date and identified 127 independent loci, of which 48 were new. We applied multiple methodologies to prioritize genes in each locus, many of which were linked to pathways previously implicated in AD, most notably immune processes, microglia function, and amyloid catabolism. Our cell-type analyses using RNAseq data showed enrichment in microglia, a well-established finding 4 , 5 , 60 . However, we also found that genes overexpressed in microglia and genes underexpressed in neurons in cases compared to controls were enriched for GWAS signal, expanding earlier work 4 . This suggests that AD risk variants may lead to overexpression of microglia genes and underexpression of neuronal genes. This is supported by previous transcriptomic and epigenomic studies that identified downregulation of neuronal functions and upregulation of innate immune response as relevant to AD 61 , 62 . Of the three types of neurons significantly underexpressing AD genes in cases (Sncg, Sst, and L6 IT Car3), Sncg and Sst neurons have been previously shown to be vulnerable early on in the AD disease process 63 , 64 . This suggests that the variants associated with AD in this GWAS may influence gene expression in vulnerable neuronal subtypes leading to neuronal cell death. Among the genes prioritized for the neuronal enrichment signal, we identified one linked to App processing ( APH1B ) and another implicated in post-brain-injury repair in zebrafish ( SPRED2 ). These findings alone have limited ability to determine whether the neuronal enrichment reflects a primary or secondary disease process. AD remains a challenging disease to treat. While new disease-modifying drugs have recently been approved, their effectiveness remains a subject of dispute 7 , 8 , and new avenues need to be explored that go beyond amyloid clearance. Here, we highlighted five potential drug targets among the new genes we identified. Two of these ( SYK , and AXL ) are involved in the immune response to Aβ. Existing treatments targeting these genes have shown promise in AD mouse models 16 , 20 , 65 , 66 . We further suggested that AXL and SYK along with the target of SYK ( BLNK ) were linked to AD through microglia. These genes, along with other known AD genes ( INPP5D, FCER1G , and PLCG2 ) 48 and a new gene prioritized in this study ( CEBPA) 27 , have been suggested to play a role in microglial phagocytosis. Further research into these genes and microglial phagocytosis as drug targets for treating the microglial component of AD disease progression may identify them as complementary treatments to drugs targeting Aβ. Additionally, we identified that UBE2V1 and SPATA2 may be relevant to multiple neurodegenerative diseases. Further research targeting these genes may help address core mechanisms of neurodegeneration and provide therapeutic benefit across multiple conditions. The SBayesRC estimate of (16%) in EUR is substantially higher than the LDSC estimate (6%). The LDSC estimate was downward biased, as the APOE-ε4 variant alone explains 6% of the variance on the liability scale as shown in theory and empirical analyses, consistent with our simulation results. When running a standard SBayesRC model that fits all SNPs jointly, we observed a 2% inflation in estimation in simulations and an estimate of 18% in the real data analysis. This upward bias is likely attributable to the use of logistic regression for GWAS, where the correlations between the estimated effects of the APOE-ε4 variant, which has a very large effect, and neighbouring SNPs in LD is not exactly proportional to their LD correlations. After removing SNPs within the same LD block as the APOE-ε4 variant, the SBayesRC estimates were unbiased in simulations and 2% lower in the real data analysis. Our approach therefore provides a conservative estimate of , under the assumption that there is no additional genetic variation in LD with APOE. Our PGS prediction achieved an average incremental of 13%, exceeding the LDSC estimate (which is the theoretical upper limit of the ) and further underscores that LDSC substantially underestimates in AD. There are several methodological differences between SBayesRC and LDSC that may explain the observed differences. SBayesRC explicitly models a mixture of SNP effect size distributions, accommodating variants with large effects, whereas LDSC’s regression framework implicitly assumes a highly polygenic architecture with normally distributed effect sizes 57 , 58 , 67 . This distinction is particularly consequential for AD, which exhibits a more oligogenic architecture due to the large-effect APOE locus. We expect this to be the principal methodological difference underlying the higher heritability estimates from SBayesRC. Additionally, unlike LDSC, SBayesRC incorporates functional annotations to inform variant prioritization and effect size distributions. Our study marks the largest multi-ancestry genomic study of AD to date, encompassing 2.8 million individuals including 8673 cases, 15005 proxy cases, 105,525 controls and 116,426 proxy controls of non-EUR ancestry. Nonetheless, the proportion of non-EUR samples was still relatively low, which made the findings of ancestry-specific effects and the PGS prediction performance in non-EUR samples less precise. Future studies including more individuals of non-EUR ancestry are essential to improve equity in AD genomic research. To accelerate this process, we have made the summary statistics from our ancestry-specific meta-analyses publicly available, as well as sex- and phenotype-stratified (case-control vs. proxy) summary statistics, X chromosome summary statistics, and data excluding the UK Biobank and cohorts included in previous GWAS. Our study included 74,141 proxy cases (40% of total cases) with the goal of boosting power, which yielded 32 additional genome-wide significant loci. The use of proxy cases is particularly valuable for AD, given its late-onset nature: biobanks predominantly recruit middle-aged participants who are often too young to have developed AD themselves but can report parental diagnoses through family history questionnaires. Genotyped individuals with an affected parent can be included as proxy cases, since they carry half of the parental genotype and therefore a portion of the risk variants. While a recent study suggests that including such AD proxy cases can introduce some statistical bias, this appears to be limited to genetic correlation analyses, particularly for educational attainment, with no observed impact on locus discovery, which is our primary aim. The proportion of proxy cases varied by ancestry: 37% in EUR samples, 63% in AFR, 62% in EAS, 68% in AMR, and 82% in SAS. This disparity reflects differential data accessibility: Non-EUR samples derived primarily from national biobanks with family history data and liberal data access policies, whereas established clinical networks in Europe facilitated direct case ascertainment in EUR populations from hospitals and memory clinics. In conclusion, we identified 127 AD risk loci that implicate several new genes, reveal new molecular and cellular mechanisms with potential for future drug development, and improve PGS prediction and heritability estimates. Methods Quality control of individual-level data Newly participating cohorts received an analysis plan detailing quality control, principal component analysis, imputation, phenotype definitions, GWAS models, and results formats (see Supplementary Methods ). Briefly, pre-imputation quality control followed RICOPILI 68 guidelines, namely removal of SNPs with a call rate lower than 0.98, palindromic SNPs with minor allele frequency higher than 0.3, invariant and multi-allelic SNPs, SNPs with Hardy-Weinberg-Equilibrium p-value in controls smaller than 1 × 10 6 , and SNPs with Hardy-Weinberg-Equilibrium p-value in cases smaller than 1 × 10 10 . Individuals were removed if their call rate was smaller than 0.98, their inbreeding coefficient was outside of the range -0.2 to 0.2, or where the genetic sex was not concordant with the reported sex. We note that many cohorts performed quality control independently from this analysis plan. Detailed descriptions and divergences are provided in the Supplementary Methods . Quality Control of GWAS summary statistics Every set of summary statistics underwent a standardized quality control protocol. Because the majority of summary statistics were on genomic build 37, we used this as the consensus build. All other summary statistics on build 38 were lifted to 37 using the snp_modifyBuild() function in the bigsnpr package 69 . snp_modifyBuild() does not account for strand changes between genomic builds, which we manually corrected. We removed SNPs with missing values, indels, duplicated, multiallelic or monomorphic SNPs, SNPs with minor allele frequency below 0.001, SNPs with nonsense values, palindromic SNPs with minor allele frequency larger than 0.4, SNPs with allele frequency differences larger than 0.2 with a reference sample, or SNPs with INFO scores below 0.6 for dosage and 0.8 for hardcall data. Case and control definitions We applied the following AD definition for most cohorts; however, there was heterogeneity in how AD was ascertained across studies. Details on the specific definition used in each cohort are provided in the Supplementary Methods . AD cases were defined based on ICD-10 codes (G30, F00*, F03) or clinical diagnosis. When possible, to minimize the inclusion of familial (Mendelian) AD cases, individuals with age-of-onset < 40 were excluded. If age-of-onset data were unavailable, we used age-at-assessment, prioritizing the earliest visit at which the individual was identified as a case. Detailed age and sex information can be found in Supplementary Data 21 . Controls (coded as 0) were defined as individuals with no diagnosis or self-reported history of AD, dementia, or mild cognitive impairment (ICD-10: G30, F00*, F03, F06.7), nor any other neurodegenerative disorders (ICD-10: G20–G26, G31–G32, G35– G37, G10–G14, F02). Parental diagnoses were also considered, and individuals with a parent affected by any of these conditions were excluded from the study. Wherever possible, we aimed to include at least four times as many controls as cases in each GWAS to maximize power. This strategy resulted in several cohorts including controls below the typical age of onset, which risks misclassifying future cases as controls and may attenuate statistical power. However, published simulation work demonstrates that, at a population prevalence of 5%, a large set of unscreened controls yields greater power than a smaller set of screened controls at half the sample size 70 . Proxy-case analysis Individuals with first-degree relatives with AD will, on average, carry half of the alleles that affect the risk of AD. As such, genotyped individuals can be used as proxies for their affected first-degree relatives, even though they are not affected themselves. In the present study, a subject was designated a proxy case (coded as 1) if either their mother or father is reported to have AD / Dementia. If the genotyped individuals are themselves AD cases, we excluded them from this analysis. We ran the GWAS separately in individuals who reported their mothers versus their fathers to have AD, and meta-analyzed the resulting summary statistics 71 . If genotyped individuals reported both their parents to have AD, half were randomly assigned to the paternal, and the other half to the maternal GWAS. Resulting GWAS effect sizes and standard errors were multiplied by 2 to correct for proxy status and transform them to the same scale as the case-control GWAS 72 . We found the proxy GWAS effect sizes after transformation to be well calibrated (See Supplementary Figure 16 ). GWAS analysis A GWAS was run if at least 100 cases or 200 proxy-cases were available for analysis. When several ancestries were available, a GWAS was run separately in each ancestry. In the main analysis, we used the following regression model correcting for sex, age, principal components (PC), and other study-specific covariates: In the sex-stratified analyses, we use the following regression model: Not all cohorts had the same information available for every individual. As such, slightly different models were applied in some cohorts. Details for each cohort can be found in the Supplementary Methods . GWAS meta-analysis We used metal (version 2020-05-05) to run all inverse-variance weighted fixed-effect meta-analyses. For the main analyses, we first ran ancestry- and phenotype-stratified (i.e., case-control vs. proxy) meta-analyses. Subsequently, we meta-analyzed all ancestries within the same phenotype definitions. Lastly, we meta-analyzed the case-control and proxy summary statistics within and across all ancestries. After having performed all meta-analyses on unfiltered summary statistics, the results were filtered for SNPs with at least 60% of the maximum effective sample size in a given set. Effective sample size The effective sample size ( N eff ) is the size of a balanced case-control study (ratio 1:1) that would yield the same statistical power as the actual study with an imbalanced case-control ratio. It is calculated as the sum of N eff for each cohort 10 , where . For samples with proxy-cases, N eff was further divided by four 72 . FLAMES We fine-mapped genome-wide significant loci with the PolyFUN (v1.0.0) 73 implementation of SuSiE 74 . Because of the nature of large meta-analyses and the sharing restrictions present on raw genetic data, the generation of in-sample LD reference panels is infeasible. We therefore restricted fine-mapping to a single causal variant per locus. MAGMA 36 scores were generated using the SNP-wise mean model with an ancestry-matched reference panel from the UKB and with a reference panel of all individuals included in the 1000 Genomes project (analysis implemented in FUMA v1.8.3). The UKB reference panels consisted of random sets of 10,000 individuals of each ancestry in the UK Biobank 75 . For ancestries with fewer individuals, all were used. For the multi-ancestry UKB reference panel, 10,000 individuals were selected such that the proportion of each ancestry matched that of the multi-ancestry GWAS. PoPS (v0.1) 76 scores were generated for each ancestry, using their corresponding MAGMA scores as input. Credible set annotation and effector gene prediction were performed using FLAMES (v1.1.2) 12 . Credible sets were annotated with the MAGMA and PoPS scores matching the ancestry of the summary statistics from which the credible set was derived. Genes selected by FLAMES for each locus were reported from four FLAMES analyses: multi-ancestry summary statistics using 1000 Genomes and UKB reference panel, and EUR only summary statistics using the 1000 Genomes and UKB reference panel. Co-localization and Mendelian Randomization To identify potential risk genes driven by GWAS signals in non-coding regions, we implemented a multi-faceted approach integrating molecular quantitative trait loci (QTL) colocalization analysis and Mendelian Randomization (MR) 77 . First, we defined the boundaries of each GWAS locus by identifying all SNPs in LD (r 2 > 0.1) with the lead GWAS SNP. This LD analysis was based on the EUR population from the 1000 Genomes Project Phase 3, utilizing LDlink 78 . Next, for each locus, we performed colocalization analysis to assess whether the GWAS signal shared a causal variant with molecular QTLs (eQTLs for gene expression, sQTLs for splicing, and pQTLs for protein abundance). We considered all genes for which QTL association data were available for the SNPs within the defined locus boundaries. This analysis was conducted using the coloc R package 13 under the assumption of a single causal variant per signal. We leveraged a comprehensive set of QTL datasets from relevant human brain tissues or cell types 79 – 90 . Following colocalization, we performed two-sample Mendelian Randomization (MR) to infer a potential causal relationship between the gene’s expression/splicing/protein level and AD. For each gene that showed evidence of colocalization, the top associated eQTL/sQTL/pQTL from the respective study was used as the genetic instrument. A gene was considered a high-confidence candidate if it met three stringent criteria: 1) strong evidence of colocalization between the GWAS signal and the gene’s expression/splicing/protein level, defined as a posterior probability of a shared signal (PP.H4.abf) of 0.8 or greater; 2) a statistically significant MR result after correcting for multiple testing across all genes with PP.H4 ≥ 0.8 (False Discovery Rate < 0.05); and 3) the transcription start site (TSS) of the gene was located within 500kb of the lead GWAS SNP. We performed the same colocalization and MR analyses on the loci shared with ALS and PD using summary statistics from van Rheenen et al . (2021) 91 and Nalls et al . (2019) 31 . Common and rare variant GWAS literature search To identify which loci and genes have been reported in previous literature, we conducted a literature search of all GWAS of AD listed in the GWAS catalog, which yielded 206 studies ( https://www.ebi.ac.uk/gwas/efotraits/MONDO_0004975 ; Accessed 7 October 2024). We then filtered these for common variant GWAS where AD was the primary phenotype, resulting in 49 studies ( Supplementary Data 3 ). Next, we extracted all significant lead SNPs ( p < 5×10 -8 ) reported in these studies and the genes assigned to them. We defined 250kb windows around the lead SNPs and merged overlapping windows into single loci. This yielded a final set of 174 non-overlapping loci identified in previous AD GWAS ( Supplementary Data 3 ). We were also interested in whether our common variant GWAS implicated the same genes as previous rare variant studies of AD. We created a list of rare variant studies of AD (largely based on two reviews 92 , 93 ). We identified 32 studies that highlighted 25 genes ( Supplementary Data 4 ). Drug–Gene Interaction Analysis To investigate the potential translational relevance of genes prioritized in our Alzheimer’s disease (AD) genome-wide association study (GWAS), we performed a systematic drug–gene interaction analysis using the Drug–Gene Interaction Database (DGIdb)(ref=82), a curated compendium of interactions collated from multiple evidence sources. The input comprised prioritized effector genes derived from our genetic and post-GWAS annotation pipeline (see Gene Prioritization). These genes were queried against DGIdb to retrieve all reported drug–gene interaction pairs, yielding an initial set of 1,525 interactions (drug_gene_interactions_all.csv). To focus on interactions of potential therapeutic significance, we applied the following criteria. First, we retained only drugs with documented approval by major regulatory agencies, specifically the U.S. Food and Drug Administration (FDA) or the European Medicines Agency (EMA); approval status was taken from DGIdb annotations and cross-referenced with authoritative regulatory records. Second, we filtered on DGIdb’s interaction confidence metric, retaining pairs with an interaction score ≥ 0.1 to exclude low-confidence associations while preserving interactions supported by at least minimal evidence. The final curated set comprised 220 approved drug–gene interactions (drug_gene_interactions_approved_SC0.1.csv), representing drugs with established regulatory status and evidence for interaction with prioritized AD-implicated genes. For interpretative context, drugs in the curated set were assigned to broad mechanistic categories reflecting their primary mode of action or their relevance to biological processes implicated in AD (e.g., cholinergic signalling, amyloid homeostasis, lipid metabolism, vascular regulation, and neuroinflammation) based on documented mechanism-of-action information and literature review. Full interaction results and drug categorizations are provided in Supplementary Data 11 . Polygenic prediction using SBayesRC SBayesRC 55 is a Bayesian hierarchical mixture model for polygenic prediction, jointly fitting genome-wide SNPs and incorporating various functional genomic annotations into the estimation of SNP effects. We applied SBayesRC to GWAS summary statistics of AD from different ancestry groups, along with LD matrices derived from reference samples of the same ancestry in the UK Biobank 75 , and 96 functional annotations from the BaselineLD model (v2.2) 94 . These annotations include evolutionary conservation, functional roles of DNA sequence (e.g., coding, promoter, and enhancers), and LD or MAF-related features. In SBayesRC, the annotations inform the probabilities that a SNP has zero, small, or large effects, with both annotation and SNP effects estimated using Markov chain Monte Carlo (MCMC). We ran SBayesRC using 4 independent MCMC chains, each with 5,000 iterations, discarding the first 3,000 as burn-in. The SNP effect estimates were obtained as posterior means across iterations and chains. Since SBayesRC accounts for LD between SNPs, all SNPs were included in the prediction equation. For cross-ancestry prediction, we first estimated SNP effects separately within each ancestry group, then linearly combined the ancestry-specific effect estimates using a tuning sample from the target ancestry and finally applied the combined SNP effects to the testing data (see Supplementary Data 20 for the tuning and testing sample sizes). We computed PGSs based on the SBayesRC posterior means using PLINK1.9 (version Linux 64-bit 6th June, 2021; command “--score sum center”). To evaluate prediction, we used the coefficient of determination on the liability scale . We converted the R 2 from the observed scale to the liability scale based on the transformation introduced in Lee et al. (2012) 95 . The base model included the first 10 principal components, and the model to be evaluated additionally included the PGS. We computed the incremental as the difference between the models. We note we did not include other covariates, such as age and sex, in the polygenic score analyses because doing so can lead to collider bias and reduce power in ascertained samples 96 , 97 (the case ratio in the clinical samples was up to 43%). We evaluated the PGSs in five cohorts: DemGene, Gothenburg H70 Birth Cohort Studies and Clinical AD from Sweden, TwinGene, UK Biobank, and BioBank Japan. DemGene and Gothenburg H70 Birth Cohort Studies and Clinical AD from Sweden are clinical samples, where AD status was ascertained in memory clinics in Norway and Sweden. TwinGene, UK Biobank, and BioBank Japan are biobanks with limited phenotypic appraisal. A sixth cohort available for prediction, STSA, was excluded because it did not clearly fall into either the clinical or biobank category, representing a mixture of both. We applied a leave-one-cohort-out approach, in which the PGS for each testing cohort was constructed using GWAS summary statistics that excluded that cohort. More details for each cohort can be found in the Supplementary Methods. We compared the prediction of the PGS to APOE counts. APOE-ε4 dosage is fully determined by the count of the T allele at SNP 19:45411941:C:T (rs429358). Similarly, the number of T alleles at SNP 19:45412079:C:T (rs7412) determines the dosage of APOE-e2. We included both counts as predictors in a regression. SNP-based heritability estimation using SBayesRC Besides SNP effects, SBayesRC provides estimates of genetic architecture parameters, including SNP-based heritability . To estimate on the liability scale, we first converted GWAS marginal effects obtained from logistic regression ( b logit ) to the liability scale using b l = b logit × K × (1 − K )/ z , where K is the population lifetime prevalence (set to 0.05), and z = dnorm(qnorm(K)). Given the allele frequency ( p ) in the LD reference samples, we estimated the sample size on the liability scale by for each SNP. We then ran SBayesRC using b l , n l and p as input. The SNP-based heritability on the liability scale was calculated in each MCMC iteration as β ′ Rβ , where β is the vector of sampled SNP effects and R is the LD correlation matrix. As shown in the simulation study, a small inflation in the estimation could occur when including SNPs in LD with the APOE-ε4 tagging SNP (rs429358), probably due to a combination of the large effect of APOE-ε4 allele and the imperfect approximation of LD correlation to the correlation between logistic regression coefficient estimates. To avoid the potential inflation, we therefore fit rs429358 as a fixed effect and all other SNPs as random, excluding SNPs in the same LD block of rs429358 from the model. LD blocks were defined based on the quasi-independent LD blocks in the human genome 98 , merging small blocks to have a minimum size of 2cM. We estimated the total , fixed-effect , and random-effect . The posterior mean across MCMC iterations was reported as the estimate of , and the posterior standard error was calculated as the standard deviation of the MCMC samples. To assess convergence, we computed the potential scale reduction factor (PSRF) 99 based on 4 independent chains. Simulation based on UK Biobank data To investigate the difference in estimate between LDSC and SBayesRC, we simulated a phenotype whose genetic architecture mirrors that of Alzheimer’s disease and controls in 348,494 unrelated participants of the European ancestry in the UK Biobank with a disease prevalence of 5% (i.e. 17,424 cases and 331,070 controls) and an of 12% with the APOE-ε4 tagging SNP (rs429358) accounting for 6% of the and another 1,000 SNPs randomly selected across the autosomes for the other 6% of the . Under the additive model, individual liabilities were computed as the sum of allelic effects across all causal variants, plus Gaussian noise scaled to achieve the target heritability. The 5% of individuals with the highest liabilities were classified as cases. Simulations were replicated 5 times using the GCTA software version 1.95.1 100 . GWAS were performed on the simulated binary phenotype using logistic regression implemented in PLINK2 101 . We first converted GWAS marginal effects and standard errors from logistic regression and sample size to the liability scale using b l = b logit × (1 − K )/ z, se l = se logit × K × (1 − K)/z and n l = 348,494 × z 2 / ( K × (1 − K )) = 78,040 following Yang et al. 102 . Then, we ran SBayesR to estimate of the simulated phenotypes of the five replicates. We also ran SBayesR by considering the APOE-ε4 tagging SNP (rs429358) as fixed effects and skipping SNPs with an LD r2 > 0.001 with rs429358 or skipping all variants in the same LD block containing rs429358 while taking all other variants as random effects. We used 10,000 unrelated individuals of European ancestry in the UK Biobank as an LD reference sample, same as in the empirical analysis. For LDSC, we first estimated of the simulated pehnotypes using LD scores of the HapMap3 variants; since rs429358 is absent in the HapMap3 LD scores files, we also ran LDSC using the full LD scores (not restricted to the HapMap3 variants) prepared by the pan-UK Biobank project 103 ( https://pan-dev.ukbb.broadinstitute.org/ ). Global genetic correlation analysis using SBayesRC We performed sex-stratified SBayesRC analyses and obtained posterior means of SNP effect sizes. During each MCMC iteration, we computed the correlation between these SNP effects in males and females, and estimated the posterior mean and standard deviation of the global genetic correlation as the mean and standard deviation of these correlations across all iterations. This approach may underestimate the true correlation because LD can lead to different but correlated SNPs being selected in males and females within a given iteration. Thus, the estimated correlation should be regarded as a lower bound of the true global genetic correlation. Local genetic correlation analysis We aimed to use local genetic correlations with traits relevant for AD to help explain the associations of the new loci with AD. We first lifted over the locus boundaries of the new loci to build GRCh37 from GRCh38 using the UCSC liftover tool 104 . Next we queried the GWAS catalog 105 (version e114_r2025-05-1) to find neurodegenerative phenotypes reported as having an association within the locus. We used the available summary statistics from EUR ancestry individuals for these traits for a local genetic correlation analysis with AD using LAVA 106 . For each summary statistics file, we removed SNPs with a sample size smaller than 60% of the maximum sample size. For dichotomous disease traits, we used the effective sample size (neff) as input in the summary statistic file, and defined the case and control sample sizes in the LAVA info files as . To account for potential sample overlap, we supplied the intercepts from cross-trait LDSC 57 to LAVA. We used the European 1000 Genomes sample as an LD reference panel and defined the LAVA locus file based on the GWAS loci. We only computed local genetic correlations for locus pairs with sufficient genetic signal. To this end, we applied a threshold of p < 0.05. Gene-set and Cell type analyses We used MAGMA v1.10 36 to perform gene-set enrichment, and cell type enrichment analyses. We performed these analyses using the gene-level associations from the FLAMES analysis using the UKB reference panel. As a sensitivity analyses, we also reported the P-value of all significant findings when using the gene level associations derived from using the 1000 Genomes data as a reference panel. The gene-level analysis results used for gene-set and cell type enrichment analyses were the same gene-level analysis results (snp-wise=mean) used for gene prioritization (FLAMES). We performed gene-set enrichment analyses for all of the gene-sets included in MSigDB 107 v2024.1.Hs C2 (curated), C5 GO (gene ontology), C7 (immunologic) gene-set groups, and the SynGO 108 v20231201 gene-sets. The MSigDB gene-sets were downloaded as gene symbols and converted to Ensembl gene IDs using BioMart 109 . We excluded gene-sets with fewer than 10 genes; This resulted in a total of 18,782 tested gene-sets. We then selected gene-sets based on a false discovery rate of 5% using the Benjamini-Hochberg procedure. As a sensitivity analysis for the effect of APOE , we performed the gene-set analysis with the whole of chromosome 19 excluded. We performed pairwise conditional analysis with MAGMA to calculate how much of each association could be explained by the other gene-sets . This proportion of explained association was then used to cluster the gene-sets by edge betweenness (cluster_edge_betweenness in the R package igraph 110 ). Only connections between gene-sets where more than 30% of the marginal association was explained by the other gene-set were included in the clustering. Clusters of gene-sets were assigned names based on the function of the gene-sets that make up the cluster. We performed MAGMA gene property analysis using FUMA v1.7 111 to highlight cell types relevant to AD. We tested whether the gene expression pattern of a cell type was significantly correlated with the association of the genes with AD. This regression model included the average gene expression for that tissue as a covariate. The association test was one-sided, only considering a positive relationship between gene expression and AD association. We tested this across 4 brain datasets: Siletti et al. (2023) 112 hippocampus (datasets 59-69 level 2); Allen Human Brain Atlas 113 (Allen_Human_MTG_level2); GSE168408 human prefrontal cortex 114 (GSE168408_Human_Prefrontal_Cortex_level2_Adult); and PsychENCODE 115 (PsychENCODE_Adult). We performed the full 3-step procedure outlined by FUMA to identify cell types associated with AD after conditioning on associations within and between datasets. We selected significant cell types in step 1 using Bonferroni correction for 337 cell type dataset pairs. As a sensitivity analysis for the effect of APOE , we performed gene property analysis excluding the whole of chromosome 19. We also used MAGMA gene property analysis to highlight cell types where differentially expressed genes across AD cases and controls were enriched for AD GWAS signal. We extracted gene differential expression P-values per cell type from Nakatsuka et al. (2025) 43 (SumRank up and down regulation from Supplementary File 3). We used the - − log 10 ( p ) of the differential up and down regulation analyses from each cell type as the gene property for the MAGMA gene property analysis. As with the FUMA cell type analysis, we tested whether more significant differential expression was associated with increased association in the AD GWAS. As a sensitivity analysis, we included the average association of each gene across all cell types as a covariate. Differential expression P-values of 0 were converted to 1×10 -10 to avoid infinite values of – log 10 ( p ). The differential expression P-values were originally calculated with 10,000 permutations, so 1×10 -10 was chosen as a P-value small enough to be beyond the calculation of the permutations but close to the boundary. Gene symbols were converted to Ensembl gene ID using BioMart 109 . As a sensitivity analysis for the effect of APOE , we performed the gene property analysis with the whole of chromosome 19 excluded. We performed MAGMA gene-set enrichment analyses to determine if genes linked to specific microglia states were enriched for AD GWAS association. We extracted the gene-sets that were used to define the 12 microglia states from Sun et al. (2023) 116 (extracted from Supplementary Data 2 ). Again, we removed chromosome 19 as a sensitivity analysis. METASOFT To assess whether effect sizes were heterogeneous across ancestries, we applied METASOFT 11 (version 2.0.1) to genome-wide significant loci identified in the primary meta-analysis. METASOFT is a random-effects meta-analysis framework specifically designed to detect and quantify heterogeneity in genetic associations across multiple studies or populations. Unlike fixed-effects approaches that assume a single true effect size shared across all studies, METASOFT’s RE2 (random-effects) model allows effect sizes to vary across populations and explicitly models this between-study heterogeneity using a random-effects variance component (τ 2 ). The RE2 model estimates the probability that an effect exists in each individual study while simultaneously testing for overall association across all studies, making it particularly well-suited for identifying ancestry-specific or population-stratified genetic effects. Data availability Summary statistics will be made available upon acceptance. Code availability Analysis code can be downloaded from: https://github.com/euffelmann/paper-pgc_alzheimers Author contributions The following authors contributed to: Project coordination AH, AIC, AM, AMH, AZ, BMB, CAR, DR, DP, EAS, EU, FB, GS, GT, HMW, HZ, IK, ISk, ITM, JH, JHL, JMG, JMS, JZ, KB, KH, KL, KP, LA, MWL, MWa, MaF, OAA, OB, OF, PFO, RH, RZ, SH, SK, TL, TO, VF, VM, YO Data collection 23andMe, AAS, ABK, AC, ACvH, AHS, AM, AR, AS, AZ, AdB, BA, BC, BEK, BH, BL, BMT, BOM, CCPM, CE, CJ, CM, ClB, DA, DBDS, DG, DPW, DS, EA, ErS, EstBB, EyS, FHD, FJW, GB, GBW, GHS, GJB, GS, GT, GW, HB, HE, HH, HU, HZ, HaS, HrS, IG, IR, ISa, ISk, ITM, Indiana, JL, JS, JV, KB, KK, KM, KN, KO, KP, KS, LKD, LaP, LeP, LifeLines, MA, MC, MG, MH, MSP, MT, MTB, MVP, MWL, MWa, MWe, MY, Mayo, MoF, MtS, NLP, NT, OBP, OF, PJV, PMBB, PS, QSL, RFS, RS, Regeneron, SBS, SD, SJvdL, SK, SRO, ST, ShB, SvB, TE, TF, TFH, TO, TT, TW, UCLA, VF, WMvdF, YO GWAS Analysis 23andMe, AAS, CJ, ChB, DPW, DS, EM, EMG, EU, GT, JQT, KO, MRM, NT, PMV, RS, RW, SJvdL, ShB, TO, YO Quality Control AAS, AM, AN, AZ, ChB, DPW, HZ, ISk, KB, MWa, OF, QSL, SK, SN, TK, TO, VF Post GWAS statistical analysis ALvS, AM, CJ, ChB, DPW, DS, EU, JB, KO, MS, NYB, PMV, QSL, RS, SJvdL, TNP, TO, YO Writing AAS, AZ, DP, DPW, EAS, EM, EU, GS, HZ, ISk, JB, KB, MS, MWa, OAA, RW, SD, SK, ShB, TO, VF Declaration of interests DR served as consultant for Boehringer-Ingelheim and Janssen, received honoraria from Boehringer-Ingelheim, Gerot Lannacher, Indorsia, Janssen and Pharmagenetix, received research/ travel support from Angelini, Boehringer-Ingelheim, Janssen and Schwabe, and served on advisory boards of AC Immune, Boehringer-Ingelheim, Indorsia, Roche and Rovi. SJvdL is part of the GeneMINDS consortium, which is powered by Health∼Holland, Top Sector Life Sciences & Health and receives co-financing from Vigil Neuroscience, Prevail therapeutics and Brain Research Center. All funding is paid to his institution. WMvdF has been an invited speaker at Biogen MA Inc, Danone, Eisai, WebMD Neurology (Medscape), NovoNordisk, Springer Healthcare, European Brain Council. All funding is paid to her institution. WMvdF is consultant to Oxford Health Policy Forum CIC, Roche, Biogen MA Inc, Eisai, Eli-Lilly, Owkin France, Nationale Nederlanden Ventures. All funding is paid to her institution. WMvdF participated in advisory boards of Biogen MA Inc, Roche, and Eli Lilly. WMvdF is member of the steering committee of phase 3 EVOKE/EVOKE+ studies (NovoNordisk). WMvdF is member of the steering committee op phase 3 Trontinemab study (Roche). All funding is paid to her institution. WMvdF is member of the steering committee of PAVE, and Think Brain Health. WMvdF is chair of the Scientific Leadership Group of InRAD. WMvdF was associate editor of Alzheimer, Research & Therapy in 2020/2021. WMvdF is associate editor at Brain. WMvdF is member of Supervisory Board (Raad van Toezicht) Trimbos Instituut. ChB and AM are holders of Roche stock. AHS received a one-time consulting fee, paid to the institution from Eisai / BioArctic (2025) SK has served at scientific advisory boards, speaker and / or as consultant for Roche, Eli Lilly, Geras Solutions, Optoceutics, Biogen, Eisai, Merry Life, Triolab, Novo Nordisk and Bioarctic, unrelated to present study content. JB is an employee of Roche. GS has participated in Advisory Board meetings for Roche, Eli-Lilly and Eisai regarding disease-modifying drugs for Alzheimer’s disease. GS has received honoraria for delivering lectures at symposia sponsored by Eisai and Eli-Lilly. BEK has previously served on an advisory board for Eisai and as a consultant for Biogen. He is currently a consultant and advisory board member for Eli Lilly. OF is a consultant to Precision Health. BH is employed by Eli Lilly and Company and owns publicly traded stocks of this company. Acknowledgements This project was supported by the NWO Gravitation grant BRAINSCAPES: a roadmap from neurogenetics to neurobiology (grant no. 024.004.012), the European Research Council Advanced Grant (grant no. ERC-2018-ADG 834057), the Research Council of Norway (grants #344121, #324499, and #353629), the European Union’s Horizon 2020 Research and Innovation Action (grant #964874), NordForsk (grant #164218), and the NIMH (award 5R01MH124839-05). This research has been conducted using the UK Biobank Resource under Application no. 16406. Acknowledgements for each cohort can be found in the Supplementary Info. Footnotes This revised version addresses peer-review comments received on the previous version. The principal changes are as follows. Gene prioritization. We have substantially extended our analyses to prioritize a small set of genes causally implicated in Alzheimer's disease, each with experimentally testable hypotheses. By combining colocalization with Mendelian randomization, cell-type enrichment, and differential expression, we now assign prioritized genes to a specific cell type, biological process, and hypothesized direction of effect. The results implicate astrocytes and oligodendrocytes alongside the established microglial and neuronal contributions, and are presented in a new Results section. Heritability estimation. We added simulation analyses comparing heritability estimates from SBayesRC and LD-Score Regression (LDSC) in a scenario with known ground truth. These highlighted a severe downward bias of LDSC under a major-gene architecture, as in Alzheimer's disease, and revealed a small upward bias in SBayesRC that was easily corrected, yielding a new European estimate of 16 percent (compared with 6 percent from LDSC, the field standard). Figure 4 has been updated accordingly. Proxy-case sensitivity analyses. We conducted numerous sensitivity analyses for the inclusion of proxy cases, showing that it does not bias our results but instead substantially increases statistical power. All sets of summary statistics will be made publicly available with and without proxy cases. Additional sensitivity analyses. We conducted further sensitivity analyses to address concerns about phenotype definition, meta-analysis approach, and cohort-level covariate adjustment. Excluding cohorts that used ICD-10 code F03 (unspecified dementia) did not attenuate pleiotropic correlations with other neurodegenerative diseases. 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Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Genomic analyses reveal new insights into Alzheimer’s disease Emil Uffelmann , Douglas P. Wightman , Shahram Bahrami , Alexey A. Shadrin , Vera Fominykh , Takafumi Ojima , Chenyang Jiang , Christian Benner , Elisa Moreno , Adrian I. Campos , Jesper Q. Thomassen , Emmanuel Minois-Genin , Hei Man Wu , G. Bragi Walters , Richard Sherva , Tian Lin , Xuemin Wang , Julien Bryois , Kristi Krebs , Marijn Schipper , Akira Narita , Alessandro Serretti , Anja H. Simonsen , Anna L. van Seumeren , Anne Corbett , Anne-Brita Knapskog , Annette M. Hartmann , Anouk den Braber , Argonde C. van Harten , Arvid Harder , Arvid Rongve , Bengt O. Madsen , Betty M. Tijms , Bitten Aagaard , Bjørn Lichtwarck , Bjørn E. Kirsebom , Byron Creese , Chandra A. Reynolds , Sara Hägg , Ida Karlsson , Christian Erikstrup , Christina Mikkelsen , Clive Ballard , Dag Aarsland , Daichi Shigemizu , Dan Rujescu , Daniel Gudbjartsson , Eivind Aakhus , Erik Sørensen , Eystein Stordal , Flora H. Duits , Frank J. Wolters , Frederic Blanc , Geert Jan Biessels , Geir Selbæk , Geir Bråthen , Gen Tamiya , Gunhild Waldemar , Harro Seelaar , Helga Eyjolfsdottir , Henne Holstege , Henning Bundgaard , Henrik Zetterberg , Henrik Ullum , Ina Giegling , Ingmar Skoog , Ingrid T. Medbøen , Ingvild Saltvedt , Irena Rektorova , J. Michael Gaziano , Jan Haavik , Jens Hjerling-Leffler , Jiao Luo , Jon Snaedal , Everard G.B Vijverberg , Julia M. Sealock , Kaj Blennow , Kaja Nordengen , Karin Persson , Katja Scheffler , Koichi Matsuda , Kouichi Ozaki , Lasse Pihlstrøm , Lavinia Athanasiu , Lene Pålhaugen , Marc Hulsman , Margda Waern , Maria Averina , Marianne Wettergreen , Marta R. Moksnes , Martijn Huisman , Masayuki Yamamoto , Mathias Toft , Matthew S. 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Stahl , Basavaraj Hooli , 23andMe Research Institute , LifeLines Cohort Study , DBDS Genomic consortium , Regeneron Genetics Center , Penn Medicine Biobank , GHS-RGC DiscovEHR collaboration , Mayo Clinic-RGC Project Generation , Colorado Center for Personalized Medicine – RGC Collaboration , UCLA-RGC ATLAS collaboration , INDIANA-CHALASANI , Mount Sinai Million Health Discoveries Program , Estonian Biobank research team , VA Million Veteran Program , Lea K. Davis , Mark W. Logue , Kelli Lehto , Anna Zettergren , Ben M. Brumpton , Jian Zeng , Peter M. Visscher , Paul F. O’Reilly , Anubha Mahajan , Manuel Ferreira , Yukinori Okada , Sven J. van der Lee , Sisse R. Ostrowski , Ruth Frikke-Schmidt , Hreinn Stefansson , Karl Heilbron , Ole A. Andreassen , Danielle Posthuma medRxiv 2025.10.10.25337470; doi: https://doi.org/10.1101/2025.10.10.25337470 Share This Article: Copy Citation Tools Genomic analyses reveal new insights into Alzheimer’s disease Emil Uffelmann , Douglas P. Wightman , Shahram Bahrami , Alexey A. Shadrin , Vera Fominykh , Takafumi Ojima , Chenyang Jiang , Christian Benner , Elisa Moreno , Adrian I. Campos , Jesper Q. Thomassen , Emmanuel Minois-Genin , Hei Man Wu , G. Bragi Walters , Richard Sherva , Tian Lin , Xuemin Wang , Julien Bryois , Kristi Krebs , Marijn Schipper , Akira Narita , Alessandro Serretti , Anja H. Simonsen , Anna L. van Seumeren , Anne Corbett , Anne-Brita Knapskog , Annette M. Hartmann , Anouk den Braber , Argonde C. van Harten , Arvid Harder , Arvid Rongve , Bengt O. Madsen , Betty M. Tijms , Bitten Aagaard , Bjørn Lichtwarck , Bjørn E. Kirsebom , Byron Creese , Chandra A. Reynolds , Sara Hägg , Ida Karlsson , Christian Erikstrup , Christina Mikkelsen , Clive Ballard , Dag Aarsland , Daichi Shigemizu , Dan Rujescu , Daniel Gudbjartsson , Eivind Aakhus , Erik Sørensen , Eystein Stordal , Flora H. Duits , Frank J. Wolters , Frederic Blanc , Geert Jan Biessels , Geir Selbæk , Geir Bråthen , Gen Tamiya , Gunhild Waldemar , Harro Seelaar , Helga Eyjolfsdottir , Henne Holstege , Henning Bundgaard , Henrik Zetterberg , Henrik Ullum , Ina Giegling , Ingmar Skoog , Ingrid T. Medbøen , Ingvild Saltvedt , Irena Rektorova , J. Michael Gaziano , Jan Haavik , Jens Hjerling-Leffler , Jiao Luo , Jon Snaedal , Everard G.B Vijverberg , Julia M. Sealock , Kaj Blennow , Kaja Nordengen , Karin Persson , Katja Scheffler , Koichi Matsuda , Kouichi Ozaki , Lasse Pihlstrøm , Lavinia Athanasiu , Lene Pålhaugen , Marc Hulsman , Margda Waern , Maria Averina , Marianne Wettergreen , Marta R. Moksnes , Martijn Huisman , Masayuki Yamamoto , Mathias Toft , Matthew S. Panizzon , Mie Topholm Bruun , Mohsen Ghanbari , Monique Franc , Nancy L. Pedersen , Nathaniel Y. Bell , Niccoló Tesi , Ole B. Pedersen , Oleksandr Frei , Olivier Bousiges , Per Svenningsson , Pieter J. Visser , Qingqin S. Li , Richard Hauger , Rui Zhang , Shinichi Namba , Sigrid B. Sando , Silke Kern , Srdjan Djurovic , Steinunn Thordardottir , Tanya N. Phung , Thomas Truelsen , Thomas Werge , Thomas F. Hansen , Tomoki Kyosaka , Torgeir Engstad , Tormod Fladby , Victoria Merritt , Sverre Bergh , Wiesje M. van der Flier , Rujin Wang , Eli A. Stahl , Basavaraj Hooli , 23andMe Research Institute , LifeLines Cohort Study , DBDS Genomic consortium , Regeneron Genetics Center , Penn Medicine Biobank , GHS-RGC DiscovEHR collaboration , Mayo Clinic-RGC Project Generation , Colorado Center for Personalized Medicine – RGC Collaboration , UCLA-RGC ATLAS collaboration , INDIANA-CHALASANI , Mount Sinai Million Health Discoveries Program , Estonian Biobank research team , VA Million Veteran Program , Lea K. Davis , Mark W. Logue , Kelli Lehto , Anna Zettergren , Ben M. Brumpton , Jian Zeng , Peter M. Visscher , Paul F. O’Reilly , Anubha Mahajan , Manuel Ferreira , Yukinori Okada , Sven J. van der Lee , Sisse R. Ostrowski , Ruth Frikke-Schmidt , Hreinn Stefansson , Karl Heilbron , Ole A. Andreassen , Danielle Posthuma medRxiv 2025.10.10.25337470; doi: https://doi.org/10.1101/2025.10.10.25337470 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 (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15228) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6598) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9230) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) 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:'a004eb06f8c48e2e',t:'MTc3OTU0ODI5OQ=='};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())}}}})();