Genome-wide association studies of binge-eating behaviour and anorexia nervosa yield insights into the unique and shared biology of eating disorder phenotypes
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Genome-wide association studies of binge-eating behaviour and anorexia nervosa yield insights into the unique and shared biology of eating disorder phenotypes | 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 Genome-wide association studies of binge-eating behaviour and anorexia nervosa yield insights into the unique and shared biology of eating disorder phenotypes View ORCID Profile Jet D Termorshuizen , View ORCID Profile Helena L Davies , View ORCID Profile Sang-Hyuck Lee , View ORCID Profile Jessica K Dennis , View ORCID Profile Christopher Hübel , View ORCID Profile Jessica S Johnson , View ORCID Profile Yi Lu , View ORCID Profile Melissa A Munn-Chernoff , View ORCID Profile Triinu Peters , View ORCID Profile Baiyu Qi , View ORCID Profile Katherine E Schaumberg , View ORCID Profile Rebecca H Signer , View ORCID Profile Karanvir Singh , View ORCID Profile Abigail R ter Kuile , View ORCID Profile Laura M Thornton , View ORCID Profile Jiayi Xu , View ORCID Profile Shuyang Yao , View ORCID Profile Zeynep Yilmaz , View ORCID Profile Ruyue Zhang , View ORCID Profile Johan Zvrskovec , View ORCID Profile Mohamed Abdulkadir , View ORCID Profile Ziada Ayorech , View ORCID Profile Elizabeth C Corfield , View ORCID Profile Alexandra Havdahl , View ORCID Profile Kristi Krebs , View ORCID Profile Taralynn M Mack , View ORCID Profile Maria Niarchou , View ORCID Profile Teemu Palviainen , View ORCID Profile Julia M Sealock , View ORCID Profile Jessica H Baker , View ORCID Profile Andrew W Bergen , View ORCID Profile Andreas Birgegård , View ORCID Profile Vesna Boraska Perica , View ORCID Profile Katharina Bühren , View ORCID Profile Roland Burghardt , View ORCID Profile Matteo Cassina , Giovanni Castellini , View ORCID Profile Enrico Collantoni , View ORCID Profile James J Crowley , View ORCID Profile Unna N Danner , View ORCID Profile Franziska Degenhardt , View ORCID Profile Janiece E DeSocio , View ORCID Profile Christian Dina , View ORCID Profile Monika Dmitrzak-Węglarz , View ORCID Profile Laramie E Duncan , View ORCID Profile Karin M Egberts , View ORCID Profile Lenka Foretova , View ORCID Profile Ina Giegling , View ORCID Profile Fragiskos Gonidakis , View ORCID Profile Scott D Gordon , View ORCID Profile Jakob Grove , View ORCID Profile Sébastien Guillaume , View ORCID Profile Jerry D Guintivano , View ORCID Profile Annette M Hartmann , View ORCID Profile Konstantinos Hatzikotoulas , View ORCID Profile Stefan Herms , View ORCID Profile Hartmut Imgart , View ORCID Profile Susana Jiménez-Murcia , View ORCID Profile Antonio Julià , View ORCID Profile Gursharan Kalsi , View ORCID Profile Deborah Kaminská , View ORCID Profile Leila J Karhunen , View ORCID Profile Hannah L. 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Luykx , View ORCID Profile Sarah L Maguire , View ORCID Profile Nicholas G Martin , View ORCID Profile Manuel Mattheisen , View ORCID Profile Sarah E Medland , View ORCID Profile Philip Mehler , View ORCID Profile Nadia Micali , View ORCID Profile James E Mitchell , View ORCID Profile Palmiero Monteleone , View ORCID Profile Preben Bo Mortensen , View ORCID Profile Benedetta Nacmias , View ORCID Profile Roel A Ophoff , View ORCID Profile Hana Papezova , View ORCID Profile Nancy L Pedersen , View ORCID Profile Liselotte V Petersen , View ORCID Profile Luisa S Rajcsanyi , View ORCID Profile Nicolas Ramoz , View ORCID Profile Ted Reichborn-Kjennerud , View ORCID Profile Valdo Ricca , View ORCID Profile Stephan Ripke , View ORCID Profile Dan Rujescu , View ORCID Profile Filip Rybakowski , View ORCID Profile Stephen W Scherer , View ORCID Profile Margarita CT Slof-Op ‘t Landt , View ORCID Profile Howard Steiger , View ORCID Profile Patrick F Sullivan , View ORCID Profile Beata Świątkowska , View ORCID Profile Eric F van Furth , View ORCID Profile Tracey D Wade , View ORCID Profile Thomas Werge , View ORCID Profile David C Whiteman , View ORCID Profile D. Blake Woodside , View ORCID Profile Ya-Ke Wu , View ORCID Profile Stephan Zipfel , Eating Disorders Working Group of the Psychiatric Genomics Consortium , Estonian Biobank (EstBB) , View ORCID Profile Cynthia M Bulik , View ORCID Profile Laura M Huckins , View ORCID Profile Gerome Breen , View ORCID Profile Jonathan RI Coleman doi: https://doi.org/10.1101/2025.01.31.25321397 Jet D Termorshuizen 1 Department of Medical Epidemiology and Biostatistics; Karolinska Institutet ; Stockholm; Sweden PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jet D Termorshuizen Helena L Davies 2 Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre; King’s College London ; London; United Kingdom 3 Center for Eating and Feeding Disorders Research, Mental Health Center Ballerup; Copenhagen University Hospital - Mental Health Services ; Copenhagen; Denmark 4 Institute of Biological Psychiatry; Mental Health Center Sct. Hans; Mental Health Services Copenhagen ; Roskilde; Denmark PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Helena L Davies Sang-Hyuck Lee 2 Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre; King’s College London ; London; United Kingdom 5 National Institute for Health Research Biomedical Research Centre; King’s College London and South London and Maudsley National Health Service Trust ; London; United Kingdom MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sang-Hyuck Lee Jessica K Dennis 6 Department of Medical Genetics; University of British Columbia ; Vancouver; British Columbia; Canada 7 Graduate Program in Bioinformatics; University of British Columbia ; Vancouver; British Columbia; Canada PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica K Dennis Christopher Hübel 2 Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre; King’s College London ; London; United Kingdom 8 National Centre for Register-based Research; Aarhus University ; Aarhus; Denmark 9 Clinic for Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics; German Red Cross Hospitals Berlin ; Berlin; Germany MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher Hübel Jessica S Johnson 10 Department of Psychiatry; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States MPH, MFA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica S Johnson Yi Lu 1 Department of Medical Epidemiology and Biostatistics; Karolinska Institutet ; Stockholm; Sweden PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yi Lu Melissa A Munn-Chernoff 11 Department of Community, Family, and Addiction Sciences; Texas Tech University ; Lubbock; Texas; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Melissa A Munn-Chernoff Triinu Peters 12 Section for Molecular Genetics in Mental Disorders; LVR University Clinic Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany 13 Institute of Sex and Gender-Sensitive Medicine; University Hospital Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany 14 Center for Translational Neuro- and Behavioral Sciences; University Hospital Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Triinu Peters Baiyu Qi 15 Department of Epidemiology; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Baiyu Qi Katherine E Schaumberg 16 Department of Psychiatry; University of Wisconsin ; Madison; Wisconsin; United States 17 Department of Psychology; University of Texas; Austin ; Texas; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katherine E Schaumberg Rebecca H Signer 18 Department of Genetics and Genomic Sciences; Icahn School of Medicine at Mount Sinai ; New York; New York; United States MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rebecca H Signer Karanvir Singh 7 Graduate Program in Bioinformatics; University of British Columbia ; Vancouver; British Columbia; Canada MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karanvir Singh Abigail R ter Kuile 2 Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre; King’s College London ; London; United Kingdom 5 National Institute for Health Research Biomedical Research Centre; King’s College London and South London and Maudsley National Health Service Trust ; London; United Kingdom 19 Department of Clinical, Educational, and Health Psychology; University College London ; London; United Kingdom PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Abigail R ter Kuile Laura M Thornton 10 Department of Psychiatry; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura M Thornton Jiayi Xu 20 Department of Psychiatry; Yale University ; New Haven; Connecticut; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jiayi Xu Shuyang Yao 1 Department of Medical Epidemiology and Biostatistics; Karolinska Institutet ; Stockholm; Sweden PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shuyang Yao Zeynep Yilmaz 1 Department of Medical Epidemiology and Biostatistics; Karolinska Institutet ; Stockholm; Sweden 8 National Centre for Register-based Research; Aarhus University ; Aarhus; Denmark 10 Department of Psychiatry; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States 21 Department of Biomedicine; Aarhus University ; Aarhus; Denmark PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zeynep Yilmaz Ruyue Zhang 1 Department of Medical Epidemiology and Biostatistics; Karolinska Institutet ; Stockholm; Sweden 22 Department of Genetics; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ruyue Zhang Johan Zvrskovec 2 Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre; King’s College London ; London; United Kingdom 5 National Institute for Health Research Biomedical Research Centre; King’s College London and South London and Maudsley National Health Service Trust ; London; United Kingdom PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Johan Zvrskovec Mohamed Abdulkadir 8 National Centre for Register-based Research; Aarhus University ; Aarhus; Denmark PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mohamed Abdulkadir Ziada Ayorech 23 Department of Psychology; PROMENTA Research Centre; University of Oslo ; Oslo; Norway PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ziada Ayorech Elizabeth C Corfield 24 PsychGen Centre for Genetic Epidemiology and Mental Health; Norwegian Institute of Public Health ; Oslo; Norway 25 Psychiatric Genetic Epidemiology Group, Research Department; Lovisenberg Diakonale Hospital ; Oslo; Norway 26 MRC Integrative Epidemiology Unit, Population Health Sciences; Bristol Medical School; University of Bristol ; Bristol; United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elizabeth C Corfield Alexandra Havdahl 23 Department of Psychology; PROMENTA Research Centre; University of Oslo ; Oslo; Norway 24 PsychGen Centre for Genetic Epidemiology and Mental Health; Norwegian Institute of Public Health ; Oslo; Norway 25 Psychiatric Genetic Epidemiology Group, Research Department; Lovisenberg Diakonale Hospital ; Oslo; Norway PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexandra Havdahl Kristi Krebs 27 Estonian Genome Centre, Institute of Genomics; University of Tartu ; Tartu; Estonia PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kristi Krebs Taralynn M Mack 28 Vanderbilt Genetics Institute; Vanderbilt University ; Nashville; Tennessee; United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Taralynn M Mack Maria Niarchou 29 Department of Genetic Medicine; Vanderbilt University Medical Center ; Nashville; Tennessee; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maria Niarchou Teemu Palviainen 30 Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science HiLIFE; University of Helsinki ; Helsinki; Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Teemu Palviainen Julia M Sealock 31 Analytic and Translational Genetics Unit; Broad Institute of the Massachusetts Institute of Technology and Harvard University; Massachusetts General Hospital ; Boston; Massachusetts; United States 32 Stanley Center for Psychiatric Research; Broad Institute of the Massachusetts Institute of Technology and Harvard University ; Cambridge; Massachusetts; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julia M Sealock Jessica H Baker 33 Independent Researcher ; Mebane; North Carolina; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica H Baker Andrew W Bergen 34 Oregon Research Institute ; Springfield; Oregon; United States 35 Department of Medicine; New Jersey Medical School, Rutgers University; Newark ; New Jersey; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew W Bergen Andreas Birgegård 1 Department of Medical Epidemiology and Biostatistics; Karolinska Institutet ; Stockholm; Sweden PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andreas Birgegård Vesna Boraska Perica 36 Department for Medical Biology; University of Split School of Medicine ; Split; Croatia PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vesna Boraska Perica Katharina Bühren 37 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy; Ludwig-Maximilians-Universität München; Munich ; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katharina Bühren Roland Burghardt 38 Department of Child and Adolescent Psychiatry; Oberberg Fachklinik Fasanenkiez Berlin ; Berlin; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Roland Burghardt Matteo Cassina 39 Department of Women’s and Children’s Health; University of Padova ; Padova; Italy MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Matteo Cassina Giovanni Castellini 40 Department of Health Sciences; University of Florence ; Florence; Italy PhD, MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Enrico Collantoni 41 Department of Neuroscience; University of Padova ; Padova; Italy MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Enrico Collantoni James J Crowley 22 Department of Genetics; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States 42 Department of Clinical Neuroscience; Karolinska Institutet ; Stockholm; Sweden PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James J Crowley Unna N Danner 43 Altrecht Eating Disorders Rintveld; Altrecht Mental Health Institute ; Zeist; Utrecht; The Netherlands PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Unna N Danner Franziska Degenhardt 44 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy; LVR University Clinic Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Franziska Degenhardt Janiece E DeSocio 45 College of Nursing; Seattle University ; Seattle; Washington; United States PhD, RN, PMHNP-BC Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Janiece E DeSocio Christian Dina 46 Nantes Université, CNRS, INSERM, l’institut du thorax , F-44000 Nantes, France PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian Dina Monika Dmitrzak-Węglarz 47 Department of Psychiatric Genetics, Medical Biology Center; Poznan University of Medical Sciences ; Poznan; Poland PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Monika Dmitrzak-Węglarz Laramie E Duncan 48 Department of Psychiatry and Behavioral Sciences; Stanford University; Stanford ; California; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laramie E Duncan Karin M Egberts 49 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health; University Hospital Wuerzburg ; Würzburg; Bavaria; Germany 50 Department of Psychiatry; Reinier van Arkel; s-Hertogenbosch ; Northern Brabant; The Netherlands MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karin M Egberts Lenka Foretova 51 Department of Cancer, Epidemiology and Genetics; Masaryk Memorial Cancer Institute ; Brno; Czech Republic MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lenka Foretova Ina Giegling 52 Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH); Medical University of Vienna ; Vienna; Austria PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ina Giegling Fragiskos Gonidakis 53 First Department of Psychiatry; National and Kappodistrian University of Athens (NKUA) ; Athens; Greece MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fragiskos Gonidakis Scott D Gordon 54 Department of Genetics; Queensland Institute of Medical Research QIMR Berghofer Medical Research Institute ; Brisbane; Queensland; Australia PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Scott D Gordon Jakob Grove 21 Department of Biomedicine; Aarhus University ; Aarhus; Denmark 55 The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH); Aarhus University ; Aarhus; Denmark 56 Center for Genomics and Personalized Medicine; Aarhus University ; Aarhus; Denmark 57 Bioinformatics Research Centre; Aarhus University ; Aarhus; Denmark PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jakob Grove Sébastien Guillaume 58 Department of Emergency and Post-Emergency Psychiatry; CHU, University of Montpellier ; Montpellier; France MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sébastien Guillaume Jerry D Guintivano 10 Department of Psychiatry; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States 22 Department of Genetics; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jerry D Guintivano Annette M Hartmann 52 Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH); Medical University of Vienna ; Vienna; Austria MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Annette M Hartmann Konstantinos Hatzikotoulas 59 Helmholtz Zentrum München - German Research Centre for Environmental Health; Institute of Translational Genomics ; Neuherberg; Germany PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Konstantinos Hatzikotoulas Stefan Herms 60 Human Genomics Research Group, Department of Biomedicine; University of Basel ; Basel; Basel-Stadt; Switzerland 61 Department of Genomics, Life & Brain Center; University of Bonn ; Bonn; Northrhine- Westfalia; Germany 62 Institute of Human Genetics; University of Bonn, School of Medicine & University Hospital Bonn ; Bonn; Northrhine-Westfalia; Germany MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stefan Herms Hartmut Imgart 63 Eating Disorders Unit; Parkland-Klinik ; Bad Wildungen; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hartmut Imgart Susana Jiménez-Murcia 64 Department of Clinical Psychology; University Hospital Bellvitge; Hospitalet del Llobregat (Barcelona) ; Barcelona; Catalonia; Spain 65 Department of Clinical Sciences; School of Medicine and Health Sciences; University of Barcelona; Hospitalet del Llobregat (Barcelona); Barcelona ; Catalonia; Spain 66 Ciber Physiopathology of Obesity and Nutrition (CIBERObn); Instituto de Salud Carlos III ; Madrid; Spain 67 Psychoneurobiology of Eating and Addictive Behaviors Research Group; Bellvitge Biomedical Research Institute (IDIBELL); Hospitalet del Llobregat (Barcelona) ; Barcelona; Catalonia; Spain 68 Centre for Psychological Services; University of Barcelona ; Barcelona; Catalonia; Spain PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Susana Jiménez-Murcia Antonio Julià 69 Rheumatology Research Group; Vall d’Hebron Research Institute ; Barcelona; Catalonia; Spain PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Antonio Julià Gursharan Kalsi 2 Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre; King’s College London ; London; United Kingdom PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gursharan Kalsi Deborah Kaminská 70 Department of Psychiatry; First Faculty of Medicine; Charles University and General University Hospital ; Prague; Czech Republic PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Deborah Kaminská Leila J Karhunen 71 Institute of Public Health and Clinical Nutrition; University of Eastern Finland ; Kuopio; Finland PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leila J Karhunen Hannah L. Kennedy 72 Department of Psychological Medicine; University of Otago ; Christchurch; New Zealand PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hannah L. Kennedy Kirsty M Kiezebrink 73 Institute of Applied Health Sciences; University of Aberdeen ; Aberdeen; Scotland; United Kingdom PhD, FHEA, RNutr Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kirsty M Kiezebrink Theresa Kolb 74 Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden , Dresden, Germany 75 Department of Psychological Medicine; Institute of Psychiatry, Psychology and Neuroscience; King’s College London ; London; United Kingdom MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Theresa Kolb Janne T Larsen 8 National Centre for Register-based Research; Aarhus University ; Aarhus; Denmark 55 The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH); Aarhus University ; Aarhus; Denmark PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Janne T Larsen Dong Li 76 Center for Applied Genomics; Children’s Hospital of Philadelphia ; Philadelphia; Pennsylvania; United States 77 Division of Human Genetics; Children’s Hospital of Philadelphia ; Philadelphia; Pennsylvania; United States 78 Department of Pediatrics; University of Pennsylvania Perelman School of Medicine ; Philadelphia; Pennsylvania; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dong Li Lisa Lilenfeld 79 Clinical Psychology Program; The Chicago School, Washington DC, College of Clinical Psychology ; Washington DC; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lisa Lilenfeld Mario Maj 80 Department of Psychiatry; University of Campania “Luigi Vanvitelli” ; Naples; Italy MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mario Maj Morten Mattingsdal 81 Department of Medical Research; Vestre Viken Hospital Trust, Bærum Hospital ; Gjettum; Norway 82 Division of Mental Health and Addiction; NORMENT KG Jebsen Centre; Oslo University Hospital ; Oslo; Norway PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Morten Mattingsdal Paolo Meneguzzo 41 Department of Neuroscience; University of Padova ; Padova; Italy 83 Padova Neuroscience Center; University of Padova ; Padova; Italy MD; PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paolo Meneguzzo Allison L Miller 84 Department of Pathology and Biomedical Science; University of Otago ; Christchurch; New Zealand PGDipSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Allison L Miller Karen S Mitchell 85 National Center for PTSD; VA Boston Healthcare System ; Boston; Massachusetts; United States 86 Department of Psychiatry; Boston University Chobanian & Avedisian School of Medicine ; Boston; Massachusetts; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karen S Mitchell Alessio Maria Monteleone 87 Department of Mental and Physical Health and Preventive Medicine; University of Campania “Luigi Vanvitelli” ; Naples; Italy MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alessio Maria Monteleone Catherine M Olsen 88 Department of Population Health; Queensland Institute of Medical Research QIMR Berghofer Medical Research Institute ; Brisbane; Queensland; Australia PhD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catherine M Olsen Leonid Padyukov 89 Department of Medicine Solna; Division of Rheumatology; Karolinska Institutet ; Stockholm; Sweden MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leonid Padyukov Richard Parker 54 Department of Genetics; Queensland Institute of Medical Research QIMR Berghofer Medical Research Institute ; Brisbane; Queensland; Australia BA(Hons) Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Richard Parker Michaela A. Pettie 84 Department of Pathology and Biomedical Science; University of Otago ; Christchurch; New Zealand PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michaela A. Pettie Dalila Pinto 18 Department of Genetics and Genomic Sciences; Icahn School of Medicine at Mount Sinai ; New York; New York; United States 90 Department of Psychiatry; Division of Psychiatric Genomics; Icahn School of Medicine at Mount Sinai ; New York; New York; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dalila Pinto Anu Raevuori 91 Department of Psychiatry; Helsinki University Hospital ; Helsinki; Finland 92 Department of Public Health; University of Helsinki ; Helsinki; Finland MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anu Raevuori Samuli Ripatti 30 Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science HiLIFE; University of Helsinki ; Helsinki; Finland 31 Analytic and Translational Genetics Unit; Broad Institute of the Massachusetts Institute of Technology and Harvard University; Massachusetts General Hospital ; 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Sweden MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Androula Savva Ulrike H Schmidt 75 Department of Psychological Medicine; Institute of Psychiatry, Psychology and Neuroscience; King’s College London ; London; United Kingdom MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ulrike H Schmidt Alexandra Schosser 94 Faculty of Medicine; Sigmund Freud University ; Vienna; Austria MD, PhD, MBA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexandra Schosser Jochen Seitz 14 Center for Translational Neuro- and Behavioral Sciences; University Hospital Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany 44 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy; LVR University Clinic Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jochen Seitz Lenka LS Slachtova 95 Institute of Biology and Medical Genetics; First Faculty of Medicine; Charles University ; Prague; Czech Republic PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lenka LS Slachtova Agnieszka Slopien 96 Department of Child and Adolescent Psychiatry; Poznan University of Medical Sciences ; Poznan; Poland MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Agnieszka Slopien Sandro Sorbi 97 Department of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA); University of Florence ; Florence; Italy MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sandro Sorbi Peter S Straub 29 Department of Genetic Medicine; Vanderbilt University Medical Center ; Nashville; Tennessee; United States MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Peter S Straub Jin P Szatkiewicz 22 Department of Genetics; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jin P Szatkiewicz Friederike I Tam 74 Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden , Dresden, Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Friederike I Tam Elena Tenconi 41 Department of Neuroscience; University of Padova ; Padova; Italy 83 Padova Neuroscience Center; University of Padova ; Padova; Italy PsyD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elena Tenconi Alfonso Tortorella 98 Department of Psychiatry; University of Perugia ; Perugia; Italy MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alfonso Tortorella Artemis Tsitsika 99 Adolescent Health Unit, Second Department of Pediatrics, “P. & A. Kyriakou” Children’s Hospital; National and Kappodistrian University of Athens (NKUA) ; Athens; Greece MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Artemis Tsitsika Annemarie A van Elburg 43 Altrecht Eating Disorders Rintveld; Altrecht Mental Health Institute ; Zeist; Utrecht; The Netherlands 100 Department of Clinical Psychology, Faculty for Social Sciences; Utrecht University ; Utrecht; Utrecht; The Netherlands MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Annemarie A van Elburg Gudrun Wagner 101 Eating Disorders Unit, Department of Child and Adolescent Psychiatry; Medical University of Vienna ; Vienna; Austria Dr, MSc, DPO Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gudrun Wagner Hunna J Watson 10 Department of Psychiatry; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States 102 Discipline of Psychology; Curtin University ; Perth; Western Australia; Australia PhD, MPsychClin, MBiostats Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hunna J Watson Roger AH Adan 43 Altrecht Eating Disorders Rintveld; Altrecht Mental Health Institute ; Zeist; Utrecht; The Netherlands 103 Department of Translational Neuroscience; UMC Utrecht Brain Center; University Medical Center Utrecht, Utrecht University ; Utrecht; The Netherlands 104 Department of Physiology; Institute of Neuroscience and Physiology; Sahlgrenska Academy at University of Gothenburg ; Gothenburg; Sweden PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Roger AH Adan Lars Alfredsson 105 Institute of Environmental Medicine; Karolinska Institutet ; Stockholm; Sweden 106 Centre for Occupational and Environmental Medicine ; Region Stockholm; Stockholm; Sweden PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lars Alfredsson Ole A Andreassen 82 Division of Mental Health and Addiction; NORMENT KG Jebsen Centre; Oslo University Hospital ; Oslo; Norway 107 Centre for Precision Psychiatry; University of Oslo ; Oslo; Norway 108 KG Jebsen Centre for Neurodevelopmental Disorders Research; University of Oslo ; Oslo; Norway MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ole A Andreassen Helga Ask 23 Department of Psychology; PROMENTA Research Centre; University of Oslo ; Oslo; Norway 24 PsychGen Centre for Genetic Epidemiology and Mental Health; Norwegian Institute of Public Health ; Oslo; Norway PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Helga Ask Anders D. Børglum 21 Department of Biomedicine; Aarhus University ; Aarhus; Denmark 55 The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH); Aarhus University ; Aarhus; Denmark 56 Center for Genomics and Personalized Medicine; Aarhus University ; Aarhus; Denmark MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anders D. Børglum Harry A Brandt 109 Eating Recovery Center ; Hunt Valley; Maryland; United States 110 Department of Psychiatry; ERC Pathlight; University of Maryland, St. Joseph Medical Center; Baltimore ; Maryland; United States MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Harry A Brandt David Collier 2 Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre; King’s College London ; London; United Kingdom PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David Collier Steven Crawford 110 Department of Psychiatry; ERC Pathlight; University of Maryland, St. Joseph Medical Center; Baltimore ; Maryland; United States MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Steven Crawford Scott Crow 111 Department of Psychiatry; University of Minnesota ; Minneapolis; Minnesota; United States MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Scott Crow Lea K Davis 18 Department of Genetics and Genomic Sciences; Icahn School of Medicine at Mount Sinai ; New York; New York; United States 90 Department of Psychiatry; Division of Psychiatric Genomics; Icahn School of Medicine at Mount Sinai ; New York; New York; United States 112 The Weindrich Department of AI and Human Health; Icahn School of Medicine at Mount Sinai ; New York; New York; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lea K Davis Martina de Zwaan 113 Department of Psychosomatic Medicine and Psychotherapy; Hannover Medical School ; Hannover; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martina de Zwaan George Dedoussis 114 Department of Nutrition and Dietetics; 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University of Padova ; Padova; Italy 83 Padova Neuroscience Center; University of Padova ; Padova; Italy MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Angela Favaro Fernando Fernández-Aranda 64 Department of Clinical Psychology; University Hospital Bellvitge; Hospitalet del Llobregat (Barcelona) ; Barcelona; Catalonia; Spain 65 Department of Clinical Sciences; School of Medicine and Health Sciences; University of Barcelona; Hospitalet del Llobregat (Barcelona); Barcelona ; Catalonia; Spain 66 Ciber Physiopathology of Obesity and Nutrition (CIBERObn); Instituto de Salud Carlos III ; Madrid; Spain 67 Psychoneurobiology of Eating and Addictive Behaviors Research Group; Bellvitge Biomedical Research Institute (IDIBELL); Hospitalet del Llobregat (Barcelona) ; Barcelona; Catalonia; Spain PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fernando Fernández-Aranda Krista Fischer 27 Estonian Genome Centre, Institute of Genomics; University of Tartu ; Tartu; Estonia 117 Institute of Mathematics and Statistics; University of Tartu ; Tartu; Estonia PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Krista Fischer Andreas J Forstner 62 Institute of Human Genetics; University of Bonn, School of Medicine & University Hospital Bonn ; Bonn; Northrhine-Westfalia; Germany 118 Institute of Neuroscience and Medicine (INM-1) ; Research Center Juelich; Juelich; Germany 119 Centre for Human Genetics; University of Marburg ; Marburg; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andreas J Forstner Philip Gorwood 120 Université Paris Cité, INSERM U1266 (IPNP); Institute of Psychiatry and Neuroscience of Paris ; Paris; Ile de France; France 121 Sainte-Anne hospital (CMME); GHU Paris Psychiatrie et Neurosciences ; Paris; Ile de France; France MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philip Gorwood Hakon Hakonarson 76 Center for Applied Genomics; Children’s Hospital of Philadelphia ; Philadelphia; Pennsylvania; United States 78 Department of Pediatrics; University of Pennsylvania Perelman School of Medicine ; Philadelphia; Pennsylvania; United States MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hakon Hakonarson Johannes Hebebrand 44 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy; LVR University Clinic Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Johannes Hebebrand Beate Herpertz-Dahlmann 122 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy; RWTH Aachen University ; Aachen; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Beate Herpertz-Dahlmann Anke Hinney 12 Section for Molecular Genetics in Mental Disorders; LVR University Clinic Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany 13 Institute of Sex and Gender-Sensitive Medicine; University Hospital Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anke Hinney James I Hudson 123 Biological Psychiatry Laboratory; McLean Hospital; Harvard Medical School ; Belmont; Massachusetts; United States MD, ScD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James I Hudson Craig Johnson 124 Eating Recovery Center ; Denver; Colorado; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Craig Johnson Jennifer Jordan 72 Department of Psychological Medicine; University of Otago ; Christchurch; New Zealand 125 Specialist Mental Health Clinical Research Unit; Health New Zealand - Canterbury ; Christchurch; New Zealand PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jennifer Jordan Allan S Kaplan 126 Department of Psychiatry; Centre for Addiction and Mental Health; University of Toronto ; Toronto; Ontario; Canada MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Allan S Kaplan Jaakko Kaprio 30 Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science HiLIFE; University of Helsinki ; Helsinki; Finland MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jaakko Kaprio Andreas FK Karwautz 127 Department of C & A Psychiatry; Medical University of Vienna ; Vienna; Austria MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andreas FK Karwautz Martien JH Kas 105 Institute of Environmental Medicine; Karolinska Institutet ; Stockholm; Sweden 128 Groningen Institute for Evolutionary Life Sciences; University of Groningen ; Groningen; The Netherlands PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martien JH Kas Walter H Kaye 129 Department of Psychiatry; University of California San Diego ; San Diego; California; United States MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Walter H Kaye James L Kennedy 130 Department of Psychiatry; University of Toronto ; Toronto; Ontario; Canada 131 Tanenbaum Centre; Centre for Addiction and Mental Health ; Toronto; Ontario; Canada MD, FRCP(C) Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James L Kennedy Martin A Kennedy 84 Department of Pathology and Biomedical Science; University of Otago ; Christchurch; New Zealand PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin A Kennedy Anna Keski-Rahkonen 92 Department of Public Health; 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Li 136 Department of Neuroscience; Janssen Research & Development, LLC ; Titusville; New Jersey; United States 137 Human Genetics & Genomics; CHDI Management, Inc. ; Princeton, New Jersey; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Qingqin S. Li Jolanta Lissowska 138 Maria Sklodowska-Curie National research Institute of Oncology ; Warsaw; Poland PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jolanta Lissowska Jurjen J. Luykx 139 Department of Psychiatry; Amsterdam University Medical Center ; Amsterdam; The Netherlands 140 Department of Psychiatry and Neuropsychology; School for Mental Health and Neuroscience, Maastricht University Medical Center ; Maastricht; The Netherlands 141 GGZ InGeest ; Amsterdam; The Netherlands MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jurjen J. Luykx Sarah L Maguire 142 InsideOut Institute; University of Sydney ; Sydney; Australia BScPsych(Hons), M.A., DCP, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sarah L Maguire Nicholas G Martin 54 Department of Genetics; Queensland Institute of Medical Research QIMR Berghofer Medical Research Institute ; Brisbane; Queensland; Australia PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nicholas G Martin Manuel Mattheisen 143 Department of Community Health and Epidemiology; Dalhousie University ; Halifax; Nova Scotia; Canada 144 Institute of Psychiatric Phenomics and Genomics (IPPG); Ludwig-Maximilians-Universität München ; Munich; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Manuel Mattheisen Sarah E Medland 145 Department of Mental Health and Neuroscience; Queensland Institute of Medical Research QIMR Berghofer Medical Research Institute ; Brisbane; Queensland; Australia 146 School of Psychology; University of Queensland ; Brisbane; Queensland; Australia 147 School of Psychology and Counselling; Queensland University of Technology ; Brisbane; Queensland; Australia PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sarah E Medland Philip Mehler 124 Eating Recovery Center ; Denver; Colorado; United States 148 University of Colorado School of Medicine ; Aurora; Colorado; United States MD, FACP, FAED, CEDS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philip Mehler Nadia Micali 3 Center for Eating and Feeding Disorders Research, Mental Health Center Ballerup; Copenhagen University Hospital - Mental Health Services ; Copenhagen; Denmark 4 Institute of Biological Psychiatry; Mental Health Center Sct. Hans; Mental Health Services Copenhagen ; Roskilde; Denmark 149 Great Ormond Street Institute of Child Health; University College London ; London; United Kingdom MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nadia Micali James E Mitchell 150 Department of Psychiatry and Behavioral Science; University of North Dakota ; Fargo; North Dakota; United States MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James E Mitchell Palmiero Monteleone 151 Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”; University of Salerno ; Salerno; Italy MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Palmiero Monteleone Preben Bo Mortensen 8 National Centre for Register-based Research; Aarhus University ; Aarhus; Denmark MD, DrMedSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Preben Bo Mortensen Benedetta Nacmias 97 Department of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA); 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Aarhus University ; Aarhus; Denmark 55 The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH); Aarhus University ; Aarhus; Denmark PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Liselotte V Petersen Luisa S Rajcsanyi 12 Section for Molecular Genetics in Mental Disorders; LVR University Clinic Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany 13 Institute of Sex and Gender-Sensitive Medicine; University Hospital Essen, University of Duisburg-Essen ; Essen; Northrhine-Westfalia; Germany PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luisa S Rajcsanyi Nicolas Ramoz 153 Université Paris Cité ; Paris; Ile de France; France 154 INSERM U1266; INSERM U1266 ; Paris; Ile de France; France PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nicolas Ramoz Ted Reichborn-Kjennerud 24 PsychGen Centre for Genetic Epidemiology and Mental Health; Norwegian Institute of Public Health ; Oslo; Norway 155 Institute of Clinical Medicine; University of Oslo ; Oslo; Norway MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ted Reichborn-Kjennerud Valdo Ricca 40 Department of Health Sciences; University of Florence ; Florence; Italy MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Valdo Ricca Stephan Ripke 32 Stanley Center for Psychiatric Research; Broad Institute of the Massachusetts Institute of Technology and Harvard University ; Cambridge; Massachusetts; United States 156 German Center for Mental Health (DZPG) ; Berlin-Potsdam; Germany 157 Department of Psychiatry and Psychotherapy; Charité - Universitätsmedizin ; Berlin; Germany MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephan Ripke Dan Rujescu 52 Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH); Medical University of Vienna ; Vienna; Austria MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dan Rujescu Filip Rybakowski 158 Department of Adult Psychiatry; Poznan University of Medical Sciences ; Poznan; Poland MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Filip Rybakowski Stephen W Scherer 159 The Centre for Applied Genomics, Program in Genetics and Genomic Biology; The Hospital for Sick Children ; Toronto; Ontario; Canada 160 McLaughlin Centre and Department of Molecular Genetics; University of Toronto ; Toronto; Ontario; Canada PhD, FRSC Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephen W Scherer Margarita CT Slof-Op ‘t Landt 161 GGZ Rivierduinen Eating Disorders Ursula ; Leiden; The Netherlands 162 Department of Psychiatry; Leiden University Medical Centre ; Leiden; The Netherlands PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Margarita CT Slof-Op ‘t Landt Howard Steiger 163 Psychiatry Department; McGill University ; Montreal; Quebec; Canada 164 Eating Disorders Continuum; Douglas Mental Health University Institute ; Montreal; Quebec; Canada PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Howard Steiger Patrick F Sullivan 10 Department of Psychiatry; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States 22 Department of Genetics; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States MD, FRANZCP Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Patrick F Sullivan Beata Świątkowska 165 Department of Environmental Epidemiology; Nofer Institute of Occupational Medicine ; Lodz; Poland PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Beata Świątkowska Eric F van Furth 161 GGZ Rivierduinen Eating Disorders Ursula ; Leiden; The Netherlands PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eric F van Furth Tracey D Wade 166 Discipline of Psychology; Flinders Institute for Mental Health and Wellbeing ; Adelaide; South Australia; Australia PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tracey D Wade Thomas Werge 4 Institute of Biological Psychiatry; Mental Health Center Sct. Hans; Mental Health Services Copenhagen ; Roskilde; Denmark 167 Department of Clinical Medicine; University of Copenhagen ; Copenhagen; Denmark PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas Werge David C Whiteman 88 Department of Population Health; Queensland Institute of Medical Research QIMR Berghofer Medical Research Institute ; Brisbane; Queensland; Australia MBBS(Hons), PhD, FAFPHM Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David C Whiteman D. Blake Woodside 130 Department of Psychiatry; University of Toronto ; Toronto; Ontario; Canada MD, MSc, FRCPC Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for D. Blake Woodside Ya-Ke Wu 10 Department of Psychiatry; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States 168 School of Nursing; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States PhD, RN Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ya-Ke Wu Stephan Zipfel 169 Department of Psychosomatic Medicine and Psychotherapy; University Medical Hospital Tuebingen ; Tuebingen; Germany 170 German Centre for Mental Health, Tuebingen; University Tuebingen ; Tuebingen; Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephan Zipfel Cynthia M Bulik 1 Department of Medical Epidemiology and Biostatistics; Karolinska Institutet ; Stockholm; Sweden 10 Department of Psychiatry; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States 171 Department of Nutrition; University of North Carolina at Chapel Hill ; Chapel Hill; North Carolina; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cynthia M Bulik Laura M Huckins 20 Department of Psychiatry; Yale University ; New Haven; Connecticut; United States PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura M Huckins Gerome Breen 2 Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre; King’s College London ; London; United Kingdom 5 National Institute for Health Research Biomedical Research Centre; King’s College London and South London and Maudsley National Health Service Trust ; London; United Kingdom PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gerome Breen Jonathan RI Coleman 2 Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre; King’s College London ; London; United Kingdom 5 National Institute for Health Research Biomedical Research Centre; King’s College London and South London and Maudsley National Health Service Trust ; London; United Kingdom PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonathan RI Coleman For correspondence: jonathan.coleman{at}kcl.ac.uk Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Eating disorders—including anorexia nervosa (AN), bulimia nervosa, and binge eating disorder—are clinically distinct but exhibit symptom overlap and diagnostic crossover. Genomic analyses have mostly examined AN. We conducted the first genomic meta-analysis of binge-eating behaviour (BE; 39,279 cases, 1,227,436 controls), alongside new analyses of AN (24,223 cases, 1,243,971 controls) and its subtypes (all European ancestries). We identified six loci associated with BE, including loci associated with higher body mass index (BMI) and impulse-control behaviours. AN GWAS yielded eight loci, validating six loci. Subsequent polygenic risk score analysis demonstrated an association with AN in two East Asian ancestry cohorts. BE and AN exhibited similar positive genetic correlations with psychiatric disorders, but opposing genetic correlations with anthropometric traits. Most of the genetic signal in BE and AN was not shared with BMI. We have extended eating disorder genomics beyond AN; future work will incorporate multiple diagnoses and global ancestries. Eating disorders include anorexia nervosa (AN), bulimia nervosa, and binge eating disorder, among others. They are diagnostically distinct, yet show considerable overlap in symptoms, as reflected in diagnostic migration over time 1 – 3 . AN is characterised by low weight, fear of weight gain, and an inability to recognise the seriousness of the low weight. It has two subtypes, which both exhibit low weight achieved by caloric restriction and increased energy expenditure. In the binge eating/purging subtype (AN-BP), this is coupled with binge eating (BE) and/or purging behaviours, whereas the restricting subtype (AN-R) lacks these features. Bulimia nervosa occurs in individuals at normal or high weight and is characterised by the combination of BE and compensatory behaviours (e.g., fasting, self-induced vomiting, laxative use, diuretic use). Binge eating disorder shares the BE component of bulimia nervosa and also occurs at both normal and high weights, but lacks recurrent compensatory behaviours 1 . Genome-wide association studies (GWASs) of eating disorders have focused primarily on AN 4 – 7 , in part due to its elevated mortality 8 , with only a few GWASs of other eating disorder-related phenotypes to date 9 , 10 . The most recent AN GWAS 6 included 16,992 cases and identified eight genome-wide significant loci. Genetic correlations based on common single nucleotide polymorphisms (SNP- r g s) showed high correlations with other psychiatric disorders, and suggested that metabolic and anthropometric factors might also underlie AN pathophysiology 6 , 7 . The metabolic aspect of AN is reflected by a positive SNP- r g with high-density lipoprotein cholesterol and negative SNP- r g s with insulin resistance, leptin, and type 2 diabetes. Importantly, these SNP- r g s were independent of body mass index (BMI), a significant finding given that low BMI is a defining feature of AN. To fully understand the genetic landscape of eating disorders, it is essential to advance beyond AN. A substantial genetic correlation between AN and bulimia nervosa has been shown in family and twin studies 11 , 12 , suggesting that these phenotypes share genetic risk. This may partially reflect the presence of BE as a transdiagnostic symptom, common both to bulimia nervosa and to AN-BP. Psychiatric diagnoses defined by the DSM and ICD have been criticised for failing to capture patient experiences or biological realities, given that disorders often exist on a continuum, overlap, co-occur, present heterogeneously, and morph over time 13 . Studying only those who meet diagnostic thresholds can bias research toward the most severe cases and excludes individuals who have significant pathology but do not meet strict diagnostic criteria. Alternative, symptom-based approaches have been proposed to complement diagnostic approaches and advance the field of eating disorders 14 . Here, we present the first GWAS of BE across multiple cohorts. We additionally conducted an AN GWAS meta-analysis with augmented sample size and increased statistical power, and the first GWASs of explicitly defined AN subtypes (AN-R and AN-BP). We identified genetic commonalities and differences between BE and AN. RESULTS Summary of phenotypes We operationalised five phenotypes ( Table 1 ): broadly-defined BE (BE-BROAD) and narrowly-defined BE (BE-NARROW), and AN, AN-R, and AN-BP. We primarily report results for BE-BROAD (which captures the common genetic component of BE with greater statistical power than BE-NARROW; Methods) and for AN. Analyses of BE-NARROW, AN-R, and AN-BP are reported in Supplementary Results. We contrast our BE-BROAD results with those of a previous publication that assessed a model-derived BE phenotype in the Million Veteran Program 10 (Supplementary Material). View this table: View inline View popup Download powerpoint Table 1: Phenotype definitions. Diagnostic codes are shown with coding system in round brackets, and equivalent codes in square brackets. Control definitions are shown in order of preference. Footnotes: *If assessed with terms such as “psychological overeating”, “compulsive eating”, etc., these individuals will not be included as BE-BROAD cases. **If Other Specified Feeding or Eating Disorder or Eating Disorder Not Otherwise Specified is diagnosed, these are included only if clearly stated ‘subthreshold bulimia nervosa’ or ‘subthreshold binge eating disorder’. GWAS meta-analyses Our BE-BROAD GWAS included 17 European-ancestry datasets with 39,279 cases and 1,227,436 controls, assessing 6,244,919 common (minor allele frequency≥1%), high-confidence (imputation INFO score>0.6), autosomal single nucleotide polymorphisms (SNPs; Supplementary Table 1). Conditional and joint analyses confirmed six independently associated loci ( Figure 1 ; Table 2 ; Supplementary Figures 1-6; Supplementary Table 2). The liability-scale SNP-based heritability for BE-BROAD was 5% (SE=0.4%, assuming population prevalence of 4.5% 15 ). The intercept was 1.03, significantly >1 and the attenuation ratio was 0.14 (SE=0.04) (Supplementary Table 3). While an intercept >1 can indicate confounding, this ratio suggests that inflation was primarily due to polygenicity 16 . Download figure Open in new tab Figure 1: Miami plots showing results from the BE-BROAD (top) and AN (bottom) meta-analyses. The dotted red line is the genome-wide significance threshold ( P ≤5x10 -8 ). a . Main GWAS analyses, with variants reaching genome-wide significance coloured in blue if significant in the anorexia nervosa GWAS and in purple if significant in the binge eating broad GWAS. Variants reaching genome-wide significance in case-case GWAS of BE-BROAD vs AN are coloured in red. b . GWAS-by-subtraction analyses, showing results from the non-BMI genetic component. Variants reaching genome-wide significance coloured in blue for anorexia nervosa non-BMI component and purple in binge eating non-BMI component. For AN, we identified eight independently associated loci in 26 European-ancestry datasets with 24,223 cases and 1,243,971 controls, across 6,926,820 common, high-confidence, autosomal SNPs ( Figure 1 ; Table 2 ; Supplementary Figures 7-14; Supplementary Tables 1 and 2 ). Six of these loci were detected in a previous AN GWAS 6 and two were newly identified. Three previously significant loci (on chromosome 2 and 3 from Watson et al 6 , and on chromosome 12 from Duncan et al 7 ) did not reach genome-wide significance ( P =2x10 -7 –6x10 -7 ). The liability-scale SNP-based heritability was 13% (SE=0.7%, assuming population prevalence of 1.5% 17 ), with an intercept of 1.02, significantly >1 and an attenuation ratio of 0.07 (SE=0.03), suggesting inflation was largely due to polygenicity (Supplementary Table 3). View this table: View inline View popup Download powerpoint Table 2: Association statistics for genome-wide significant loci for BE-BROAD and AN. Loci are numbered sequentially within-analysis from chromosome 1 to chromosome X. Base pair start and end positions correspond to hg19. Odds ratios (OR) are given relevant to the ALT allele. Genes are listed if at least one transcript in GENCODE version 47 lies at least partially within the locus, with >3 genes defined as multigenic. We also conducted analyses on chromosome X for a subset of studies with available data (Supplementary Table 4). No genome-wide significant loci were identified for BE-BROAD nor AN, although one genome-wide significant locus was identified for BE-NARROW (Supplementary Results, Supplementary Figure 15, Supplementary Table 5). Given known sex differences in eating disorders, and mostly female cases in our data (96% in BE-BROAD, 94% in AN), we carried out female-only GWASs as sensitivity analyses. Results were similar to the main analyses, with differences attributable to the reduction in sample size (Supplementary Results, Supplementary Table 6). Genetic relationship between eating phenotypes and other traits We assessed the genetic similarity of BE-BROAD and AN through examining their SNP- r g with each other and with other traits, via univariate and bivariate causal mixture modelling (“MiXeR analysis”) and via case-case GWAS 18 , 19 . The SNP- r g between BE-BROAD and AN was 0.46 (SE=0.04, P= 3.44x10 -30 ), indicating moderate genetic overlap (Supplementary Table 7). MiXeR analysis resulted in a similar estimate of SNP- r g (0.49, SE=0.01). Bivariate MiXeR indicated an overlap of 2654 causal variants between BE-BROAD and BE-AN (83% of all BE-BROAD causal variants) with highly concordant effects (95%; Figure 2a ). However, the bivariate MiXeR model was not distinguishable from the minimal possible overlap model, which requires an overlap >2450 variants given the genetic correlation observed (Supplementary Results, Supplementary Table 8). Case-case GWAS leverages a genetic distance measure representing the average squared difference in allele frequency at causal SNPs 18 . The genetic distance was highest between individuals with AN and controls (0.46), similar to that between case groups (0.40), whereas the genetic distance between individuals with BE-BROAD and controls was smaller (0.25, Figure 2b ). We identified case-divergent loci on chromosomes 1, 3, and 5, overlapping with independent loci identified in the AN GWAS ( Figure 1 ), suggesting that some loci differentiating AN from controls also differentiate AN from BE-BROAD. Download figure Open in new tab Figure 2: a) Bivariate causal mixture modelling results from MiXeR. (Left) Venn diagram showing overlap of putative causal variants in AN (blue) and BE BROAD (orange), demonstrating high overlap (grey), and moderate genetic correlation (bar beneath). (Middle) Q-Q plots of variant effects for (left) AN conditional on BE BROAD and (right) BE BROAD conditional on AN, demonstrating enrichment for associations with each trait conditional on the other. (Right) Log-likelihood plot of model fit under differing models, from the minimal overlap model (leftmost) to the full overlap model (rightmost), showing best model fit (lowest y-axis value) is not distinguishable from the minimal overlap. b) Genetic distance between cases and controls of BE-BROAD and AN estimated by case-case GWAS. The genetic distance, is calculated by taking the square root of the product of m , the number of independent causal variants estimated here as 10,000 based on the polygenic nature of BE-BROAD and AN, and F ST , casual , the average normalized squared differences in allele frequencies, derived based on the SNP-based heritabilities, genetic correlations, and population prevalences of the two traits. We used LDSC to calculate pairwise SNP- r g for both BE-BROAD and AN with 225 traits including psychiatric, personality, metabolic, and anthropometric traits ( Table 3 , Supplementary Table 9). We generally observed positive SNP- r g of BE-BROAD with psychiatric traits and anthropometric phenotypes, except for persistent thinness and pubertal growth which displayed negative SNP- r g . In contrast, significant SNP- r g with metabolic traits were absent for BE-BROAD except for BMI-adjusted fasting insulin. We validated and extended previously observed SNP- r g patterns with AN 6 ( Table 3 , Supplementary Table 9). We found positive SNP- r g across psychiatric disorders, as well as with neuroticism, educational attainment, and physical activity. We observed negative SNP- r g across anthropometric traits, and notably, a non-significant SNP- r g with persistent thinness. Metabolic SNP- r g mirrored Watson et al., with predominantly negative SNP- r g , except for total cholesterol in high-density lipoprotein 6 . View this table: View inline View popup Download powerpoint Table 3: A representative subset of significant genetic correlations of BE-BROAD and AN with external traits. Note . The full set of genetic correlations can be found in Supplementary Table 9. PMID = PubMed ID of GWAS for external trait, BMI = body mass index, adj = adjusted, ADHD = attention deficit/hyperactivity disorder, MDD = major depressive disorder, OCD = obsessive-compulsive disorder, PTSD = post-traumatic stress disorder, AUDIT-P = Alcohol Use Disorder Identification Test Problem items. Next, we tested for significant differences between the SNP- r g of BE-BROAD and SNP- r g of AN with other traits ( Figure 3 , Supplementary Figure 16, Supplementary Table 10). Most psychiatric and behavioural traits and disorders showed similar SNP- r g with BE-BROAD and AN. However, attention deficit/hyperactivity disorder (ADHD) showed greater SNP- r g (difference P= 1.06x10 -7 ) with BE-BROAD than with AN, and obsessive-compulsive disorder showed greater SNP- r g ( P= 4.79x10 -5 ) with AN than with BE-BROAD. BE-BROAD was positively correlated with Alcohol Use Disorder Identification Test problem items, four smoking-related phenotypes, and general risk tolerance, while AN was not ( P ≤4.83x10 -7 ). AN was negatively correlated with automobile speeding propensity, while BE-BROAD was 16 not ( P =1.21x10 -4 ). Download figure Open in new tab Figure 3: Genetic correlations (rg) of selected external traits with BE-BROAD and AN, split by those that differ significantly between the eating phenotypes (left) and those that do not (right). The rgs were computed by Linkage Disequilibrium Score Regression (LDSC). The rg estimates are indicated by the dots and standard errors are indicated by the lines on either side of each dot. rg estimates have been corrected for multiple testing via the Bonferroni method. Information about the summary statistics used in our analysis can be found in Supplementary Table 9. BE-BROAD = binge eating broad definition; AN = anorexia nervosa; MDD = major depressive disorder; PGC = Psychiatric Genomics Consortium; BMI = Body mass index; F = female; M = male; FFM = fat-free mass; AUDIT-P = Alcohol Use Disorder Identification Test problem items BE-BROAD and AN also diverged in their associations with anthropometric and metabolic traits. For example, BE-BROAD was positively correlated with waist-to-hip ratio while AN was negatively correlated ( P= 2.05x10 -31 ). BE-BROAD showed a stronger pattern of SNP- r g with certain socio-demographic traits than AN ( P <2x10 -4 ), displaying negative SNP- r g s with age at menarche and age at first birth in females, and positive SNP- r g s with social deprivation and loneliness. In contrast, AN showed no significant SNP- r g with these traits but was more strongly positively associated with educational traits such as college/university completion than was BE-BROAD. The genetic signal in our BE-BROAD GWAS may partly be influenced by AN, given that 18% of our BE-BROAD cases had (known) AN (Supplementary Table 11). As a sensitivity analysis, we conducted an additional BE-BROAD GWAS, excluding cohorts that specifically focused on AN recruitment (Supplementary Results). The SNP- r g between BE-BROAD and the reduced GWAS did not differ from unity (0.96, SE=0.07), but SNP- r g s with anthropometric traits were stronger in the reduced GWAS, suggesting that AN cases with BE 7 may mask BE-anthropometric genetic associations (Supplementary Figure 17, 8 Supplementary Table 12). Role of BMI genetics The role of BMI in eating disorders is complex, with low BMI being pathognomonic of AN and individuals with binge eating disorder often being overweight 1 . If the genetics of BMI were the only genetic cause of BE and AN (in opposite directions), then our GWASs would just be proxy GWASs for BMI. To assess this, we applied GWAS-by-subtraction to remove the genetic variance of BMI from BE-BROAD and from AN separately 20 . We modelled a factor shared between each eating phenotype and BMI, and a non-BMI factor only loaded on by the eating phenotype. The shared factor explained 12% (SE=2.7%) of genetic variance in BE-BROAD, leaving 88% (SE=7.9%) accounted for by the non-BMI factor. In AN, the shared factor accounted for 10% (SE=1.4%) of genetic variance, leaving 90% (SE=5.5%) accounted for by the non-BMI factor. GWASs of the non-BMI factor for BE-BROAD and for AN generally resulted in larger p-values for lead SNPs, but larger effect sizes in the same direction as the original GWAS ( Figure 1 , Supplementary Table 13). In pairwise SNP- r g analyses of the non-BMI component of BE-BROAD and AN, SNP- r g with psychiatric disorders typically remained stable or slightly increased relative to the respective full GWAS, whereas SNP- r g with anthropometric and metabolic traits were typically attenuated (Supplementary Figure 18, Supplementary Table 14). We also conducted exploratory two-sample Mendelian randomisation (MR) analyses of each eating phenotype with BMI, testing causal effects in both directions. We used SNPs in linkage equilibrium as genetic instruments, with P <5x10 -6 for BE-BROAD and AN, and P <5x10 -9 for BMI 21 . For each analysis, we used the full BE-BROAD or AN GWAS, and the GWAS of the respective non-BMI component (Supplementary Results, Supplementary Table 15). Significant results were found in both directions between increased BE-BROAD risk and increased risk for higher BMI. BE-BROAD was still associated with increased risk for higher BMI when using just the non-BMI component. When examining the effect of BMI on the non-BMI component of BE-BROAD, odds ratios (ORs) were consistently >1, but the different methods were not consistently significant. Significant results were found in both directions between increased risk of AN and decreased BMI. However, the non-BMI component of AN risk was not associated with BMI, and results were inconsistent across MR methods when examining the effect of BMI on the non-BMI component of AN. Genetically-regulated gene expression We used S-PrediXcan 22 to identify predicted genetically-regulated gene expression associated with our phenotypes (Supplementary Figure 19; Supplementary Table 16). For BE-BROAD, two gene-tissue associations were significant at the experiment-wide threshold ( PRKAR2A -Sigmoid colon and KLHDC8B -Heart, atrial appendage; P <8.32x10 -8 ). In AN, we observed 300 experiment-wide significant gene-tissue associations ( P <8.32x10 -8 ) with 29 unique genes, predominantly from the gene-dense locus on chromosome 3:47-52Mb. Among the experiment-wide significant associations, 94 were in central nervous system tissues and 41 in gastrointestinal tissues. Within-tissue significant results for both phenotypes are described in the Supplementary Results. We calculated cross-tissue predicted genetically-regulated gene expression using S-MultiXcan 23 to identify apparent tissue-specific associations that are better interpreted as cross-tissue (Supplementary Results, Supplementary Figure 20, Supplementary Table 17). Ten genes had significant ( P< 2.25x10 -6 ) cross-tissue expression in BE-BROAD, four of which were identified as tissue-level associations (including KLHDC8B but not PRKAR2A ). Similarly, 43 genes showed significant cross-tissue expression in AN, of which 23 were identified as tissue-level associations. Gene-level associations We used MAGMA v1.10 24 to conduct gene-wise analyses of the aggregate effect of SNPs mapped to protein-coding genes (Supplementary Table 18); gene-set analyses of groups of genes with shared functional, biological, or other characteristics (Supplementary Table 19); and gene-set analyses restricted to genes targeted by medications (Supplementary Table 20; Supplementary Results). We also examined enrichment of signal within drug-sets belonging to the same class of drugs (Supplementary Table 21; Supplementary Results). For BE-BROAD, MAGMA identified 22 genes significant after Bonferroni correction ( P< 2.59x10 -6 ). FTO had the strongest association ( P= 2.8x10 -22 ), while 9/22 (43%) of the Bonferroni-significant genes were linked to the gene-dense locus on chromosome 3:47-52Mb. The MAGMA gene set, drug set, and drug-class analysis yielded no significant results for BE-BROAD. For AN, MAGMA identified 76 significant genes ( P< 2.58x10 -6 ). Most (54/76, 71%) were again mapped to chromosome 3:47-52Mb. Gene-set analysis identified enrichment in three biological pathways, related to the binding targets of RBFOX1-3 (RNA binding proteins that regulate neuronal alternative splicing 25 ), and to mutation-constrained genes with pLI>0.9. Drug-set analysis revealed no significant drug sets, but antimigraine preparations as a class were significantly associated with AN, consistent with previous associations between AN and migraine polygenic risk scores 24 (PRS) 26 . Genes implicated both through proximity (MAGMA) and through effects on gene expression (S-PrediXcan) are more likely to be functionally relevant than those implicated through proximity alone 27 . Further restricting our MAGMA gene-wise results to genes that were at least tissue-level significant in S-PrediXcan resulted in seven prioritised genes across four loci in BE-BROAD, and 38 prioritised genes across eight loci in AN (Supplementary Table 22; Supplementary Results). Tissue and cell-type analyses We used stratified LDSC 28 to estimate the enrichment of SNP-based heritability for BE-BROAD and AN among genes specifically expressed in GTEx human tissues (Supplementary Figure 21, Supplementary Table 23; Supplementary Results) and in cell types from the Human Brain Atlas (Supplementary Figure 22, Supplementary Table 24) 29 , 30 . After accounting for multiple testing, no associations were significant. Polygenic prediction We tested whether higher BE-BROAD and AN PRS were associated with a higher risk of BE-BROAD and AN, using a leave-one-cohort-out design in well-powered cohorts representative of the ascertainment methods employed in the study (Methods; Supplementary Table 25). Individuals with 1 SD higher BE-BROAD PRS had an average OR of 1.11 for BE-BROAD (average 95% confidence interval [CI] across cohorts: 1.08–1.14; P range: 2.38x10 -15 –0.022; Supplementary Figure 23). The average liability-scale variance explained was 0.32%. Individuals with 1 SD higher AN PRS had an average OR of 1.50 for AN (average 95% CI 1.42–1.59, all P <2x10 -16 ; Supplementary Figure 23) and AN PRS explained 2.32% liability-scale variance on average. We additionally tested the cross-ancestry prediction of AN in two East Asian ancestry cohorts from Korea and Japan. The European AN PRS was positively associated with AN in the combined Korean and Japanese cohort, with an OR of 1.36 (95% CI 1.09 – 1.70, P =0.0066), explaining 1.3% of the variance (assuming population prevalence of AN at 26 1.5%). Next, we tested whether genetic risk of BE-BROAD and AN were shared across males and females, using a similar leave-one-cohort-out design. BE-BROAD PRS based on female-only GWAS were positively associated with BE-BROAD risk in males (OR 1.06– 1.20), but not all results were significant, possibly due to low case numbers in some cohorts. Similarly, AN PRS calculated based on female-only GWAS were positively associated with AN risk in males (OR 1.07–1.41), but the results were not consistently significant (Supplementary Figure 24, Supplementary Table 26; Supplementary Results). We further assessed if BE-BROAD and AN PRS differed across BE and AN subgroups, comparing control individuals to (a) those with BE-BROAD only; (b) with both BE-BROAD and AN (BE+AN); and (c) with AN only. Overall, we found both BE-BROAD and AN PRS to be elevated in all subgroups compared to controls ( P≤ 0.019). Among the subgroups, the BE+AN and BE-BROAD-only groups did not significantly differ on BE-BROAD PRS, and typically had a significantly higher BE-BROAD PRS than the AN-only group (Supplementary Figure 25). The BE+AN and AN-only groups did not significantly differ from each other on AN PRS, while the BE-BROAD-only group had a significantly lower AN PRS than both other groups in one cohort, but not in another (Supplementary Figure 25, Supplementary Table 27; Supplementary Results). DISCUSSION In this first genome-wide analysis of BE, we implicate six genomic loci. These have been previously associated with smoking 31 , risk-taking behaviour 32 , and age at menarche 33 . Overlap between BE and impulse-control behaviours was further observed in positive SNP- r g with smoking, general risk tolerance, and problematic alcohol use. Loss of control is a key component of BE, and impulse-control behaviours have been associated with binge-type eating disorders clinically 34 – 36 . Alongside significant polygenic overlap with a range of psychiatric disorders, our findings imply that BE shares genetic underpinnings with psychiatric disorders and impulse-control behaviours. Loci associated with BE-BROAD have also been implicated in anthropometric traits, including a BMI-related signal near FTO 21 , 37 that was not associated with AN or its subtypes. The FTO locus has been studied extensively (summarised in Loos and Yeo 38 ), but it has been challenging to determine the causal mechanism that contributes to a high BMI 38 . One study found that FTO was related to BE independent of BMI 39 , and suggested that BE could mediate the pathway between FTO and a high BMI. Together with our results, this implies that the relationship between FTO and high BMI could result partly from binge-eating behaviours. Further research should examine whether broader disordered eating behaviours mediate the relationship between FTO and high BMI. Genetic correlations of BE with anthropometric traits and impulse-control behaviours mirror clinical observations in individuals with BE, and suggest the relationship of BE to these traits and behaviours is partly due to pleiotropy rather than being purely environmental or a consequence of BE itself. For AN, we validated six previously identified loci, identified two new loci, and identified one locus for AN-R. The four single-gene loci identified in Watson et al. 6 remained genome-wide significant, suggesting that genes located in these regions— CADM1, MGMT, FOXP1, PTBP2— may warrant further investigation in the aetiology of AN 40 . The AN-R-identified locus narrowly missed genome-wide significance in AN ( P =5.89x10 -8 ) and has previously been implicated in schizophrenia 41 . The locus contains several genes, but only DLX1 was indicated by both proximity-based and expression-based gene mapping. DLX1 is differentially expressed in the brain and may be involved in several processes of neural development 42 . Further studies are needed to confirm that DLX1 is implicated in AN-R aetiology, given the multigenic nature of the locus. Our gene-level results should generally be viewed cautiously, as greater power is needed to effectively fine-map associated loci and link causal variants to genes. Despite increasing our effective sample size for AN by 64% since our previous freeze 6 , we identified only two new loci, and two previously implicated loci were no longer genome-wide significant. We speculate that this is because our new cohorts were primarily population-based and used more lenient case criteria compared to the clinical diagnoses and targeted AN-specific recruitment previously used 43 . Consistent with this, AN PRS typically captured less variance in AN in new cohorts (Supplementary Results, Supplementary Table 25). Genetic signal also tends to become more heterogeneous as GWAS sample sizes increase 44 , 45 . This is supported by our data. Although none of the lead SNPs associated with AN showed a heterogeneous effect (Supplementary Figures 7-14), variant heterogeneity statistics (I 2 ) were higher in our AN GWAS than in the AN GWAS published by Watson et al (Supplementary Results, Supplementary Table 28) 6 . Nonetheless, our AN GWAS measured variants with greater precision than that of Watson et al (average variant SE=0.0199 vs SE=0.0255 in Watson et al), indicating that power bincreased despite the increase in heterogeneity (Supplementary Results). We have previously hypothesised that AN is a metabo-psychiatric disorder 6 —this study yields for the first time the ability to investigate shared and distinct metabolic and psychiatric components across multiple eating phenotypes. BE-BROAD has typical genetic features of a psychiatric disorder, including significant SNP- r g with psychiatric traits akin to previous findings 6 , 7 , 26 , 46 . The SNP- r g pattern for BE-BROAD is similar to that of AN, with notable exceptions. AN had a positive SNP- r g with obsessive-compulsive disorder, whereas BE-BROAD showed no significant association. Conversely, BE-BROAD was positively genetically correlated with ADHD, whilst AN showed no significant association. However, there was a significant positive SNP- r g between ADHD and non-BMI AN, suggesting that opposing SNP- r g s of AN and ADHD with BMI were previously masking this association. We observed some key differences for SNP- r g s with non-psychiatric traits. BE-BROAD displayed a significantly stronger, negative SNP- r g with age at menarche whilst AN showed no genetic overlap, consistent with previous research that showed early-onset AN was negatively associated with age at menarche, but typical-onset AN was not 47 . Earlier age at menarche has been associated with increased impulse-associated traits like substance use and risky behaviour 48 , and with BMI 49 . Both impulsivity and BMI are often higher in those who binge eat 1 , 34 , 35 . Observational studies find that later age of menarche is associated with AN 48 , 50 ; however, disentangling this from the effects of starvation and low body weight is difficult. Significant SNP- r g s between BE-BROAD and anthropometric traits were positive compared with the negative SNP- r g s observed in AN, consistent with previous research 26 , 46 . We also validated our previous finding that significant SNP- r g s with AN are concentrated in metabolic-related traits 6 , in contrast to BE-BROAD. Eating disorders and their component features share genetic factors with anthropometric traits, but these effects act in opposite directions depending on the presentation. To investigate this further, we assessed BE-BROAD and AN after subtracting the genetic component each shares with BMI. The BMI component accounted for 12% of the genetic variance of BE-BROAD and 10% of AN, despite low BMI being a diagnostic requirement for AN. The low variance explained by the BMI component argues that neither our AN nor BE-BROAD GWAS are BMI GWAS by proxy. This is further supported by the FTO locus association with BE-BROAD, which is observed in AN-ascertained cohorts where affected individuals are likely to have lower BMI than unaffected individuals. Consistent with previous literature 51 , we also found no evidence for a genetic overlap between persistent thinness and AN, indicating that the cognitive-behavioural component of AN distinguishes these low-BMI phenotypes on a genomic level. However, BMI is a blunt measure of body composition 52 , and its relationship with eating disorders is complicated, with evidence that the negative SNP- r g between AN and BMI is driven by genetic enrichment for AN risk in females with lower BMI, rather than a uniform linear relationship across BMI 53 . Both BE and AN are heterogeneous conditions, and subtypes may have differing relationships with BMI. Subtracting the BMI component from our AN and BE-BROAD GWASs affords tentative insights into the physiological aspects of these illnesses, but BMI itself has a partly behavioural aetiology 28 . More sophisticated analyses with a wider range of body composition measurements and eating disorder presentations (including atypical AN in the normal or high BMI range) will provide deeper understanding. We present the first GWAS meta-analysis of BE, accompanied by the largest investigation of AN and AN subtypes to date, with chromosome X analysed for all phenotypes. We have extended eating disorder genetic research beyond AN alone and demonstrated that BE is a psychiatric phenotype with distinctive genetic relationships with external traits. Nonetheless, readers should consider the following limitations. Our analyses only considered individuals of European genetically inferred ancestry, limiting their generalisability. We analysed PRSs in two small East Asian cohorts, but used European prevalence estimates to convert risk to the liability scale, potentially introducing bias. Differences in population structure, genetic architecture, and environmental factors, such as lower average BMI in Korean and Japanese populations 54 , could influence the cross-ancestry prediction of AN. More GWAS and PRS studies in East Asian populations are required to improve the accuracy of genetic risk predictions and our understanding of AN genetics in these populations. This limitation extends to other global ancestries and should be considered from both phenotypic and genotypic perspectives 14 . The historic focus of eating disorder studies on females of European descent has influenced disease characterization, potentially leading to certain diagnostic criteria being over- or under-represented. Prioritizing variants common in European ancestry populations may prevent the genetic architecture of eating disorders being fully characterised. To address this, ongoing research efforts aim to enhance the generalisability and applicability of findings across a wider range of individuals through global data collection across all populations experiencing eating disorders. Given the known diagnostic crossover between eating disorders, cohorts that only contributed cross-sectional diagnoses or symptoms are unable to account for later development of a disorder or symptom; for example, an individual with AN-R might go on to develop AN-BP 2 . It is not possible to fully mitigate this limitation. However: (1) many of our cohorts include individuals beyond the typical age of diagnosis, making new diagnoses or diagnostic crossover less likely; (2) hidden diagnostic crossover likely contributes to false negatives and under-estimation of differences between GWAS, rather than introducing false positives 55 ; and (3) our previous work has shown that rates of diagnostic contamination (a similar effect to hidden diagnostic crossover) would need to occur at extremely high levels to affect locus discovery 56 . Despite our best efforts at harmonisation, heterogeneity might exist within the phenotypes due to factors such as distinct methods of ascertainment. Ideally, one approach to ascertainment would be used, such as structured diagnostic interviews within speciality clinics. However, using multiple ascertainment approaches can increase sample size and thus power despite the resulting heterogeneity. Fourth, our sample is mostly female, and results may not necessarily generalise to those who are not female. Finally, although strongly associated with BE-BROAD and AN risk, PRS remain very weak predictors of BE-BROAD and AN status. Combining PRS with other risk factors is needed to further improve prediction accuracy of BE-BROAD and AN. Historically, binge-type eating disorders have been overshadowed by research on AN, despite their higher prevalence. This paper redresses that imbalance. We identified six genetic loci relating to broadly defined BE, validated six loci related to AN, reported two novel loci, and found one locus related to the restricting subtype of AN. We demonstrate that BE is genetically related to several other psychiatric phenotypes, with both shared and distinct patterns compared to AN, providing genetic substantiation of clinically-observed comorbidity patterns. The number of loci we implicate in BE-BROAD and AN is typical of early-phase GWAS studies 57 , motivating GWAS meta-analyses of all major eating disorders (AN, bulimia nervosa, binge eating disorder, avoidant/restrictive food intake disorder) and transdiagnostic behaviours (e.g., BE, restriction) to drive variant discovery and further refine our understanding of shared and unique genetic features that distinguish presentations and inform a genetically guided nosology of eating disorders 13 . Data availability Individual level genotype data (except in countries where sharing of individual level data is prohibited by national law) and summary statistics used in this study are available to bona fide researchers working in collaboration with a member of the Eating Disorders Working Group of the PGC via secondary analysis proposals ( https://pgc.unc.edu/for-researchers/data-access-committee/data-access-information/ ). Summary statistics from this work will be made available via Figshare and the PGC website on publication ( https://pgc.unc.edu/for-researchers/download-results/ ). Code availability Code underlying this work will be made available on GitHub ( https://github.com/psychiatric-genomics-consortium/PGC3_EAD ) on publication. Author contributions Specific author contributions are listed in Supplementary Table 29. All authors made substantial contributions to the conception or design of the work, or to the acquisition, analysis, or interpretation of data for the work, and critically reviewed the work for important intellectual content, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Conflicts of Interest Susana Jiménez-Murcia and Fernando Fernández-Aranda have received consultancy and speaker honoraria from Novo Nordisk. Ole Andreassen is a consultant to Presicion Health and Cortechs.ai, and has received speaker’s honoraria from Lundbeck, Janssen, Lilly, and Otsuka. James Kennedy is a member of the Scientific Advisory Board for Myriad Neuroscience. Mikael Landén has received lecture honoraria from Lundbeck pharmaceuticals. Qingqin Li was an employee of Janssen Research & Development, LLC when the work was completed and holds company equity. Nadia Micali receives an honorarium as associate editor on the European Eating Disorders Review. Dan Rujescu served as consultant for Janssen, received honoraria from Boehringer-Ingelheim, Gerot Lannacher, Janssen and Pharmagenetix, received travel support from Angelini, Janssen and Schwabe, and served on advisory boards of AC Immune, Boehringer-Ingelheim, Roche and Rovi. Patrick Sullivan is a shareholder in Neumora Therapeutics and serves on the advisory board. Cynthia Bulik receives royalties from Pearson Education, Inc. and has served as a consultant for Orbimed. No other authors report conflicts of interest. Funding and ethics statements The Psychiatric Genomics Consortium is supported by NIH grant R01 MH124851. Author and cohort-specific funding and ethics statements are provided in the Supplementary Materials. METHODS Ethics The individual studies that comprise this investigation were conducted with advance approval by the appropriate Institutional Review Boards or equivalents at the individual study sites. We provide ethical statements for each study site in the Supplementary Note. This work represents a secondary analysis with data from these individual studies. Summary of cohorts Detailed descriptions of the ascertainment and definition of cases and controls for each cohort is provided in Supplementary Table 1. Broadly, we identified cases and controls based on clinical diagnoses, diagnostic algorithms, and/or self-report questionnaires 43 . We defined controls as individuals without a history of BE and without a history of an eating disorder, if possible. If this information was unavailable, unscreened controls were included assuming that the large control numbers would outweigh the impact of misclassified individuals in the control groups, given the collective lifetime prevalence of eating disorders 16 is ∼5% 17 . We included data from the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED; Supplementary Table 1). These data were restricted to individuals of European ancestry due to the limited availability of non-European ancestry samples at the time of analysis—we included two cohorts with individuals of East Asian ancestry for follow-up cross-ancestry polygenic risk score analyses. In total, we combined 27 European ancestry datasets totalling 14 previously analysed 6 and 13 new cohorts. Data from cohorts providing individual-level data ( n= 11) were combined with cohorts that contributed summary statistics ( n =16; Supplementary Table 1). Detailed descriptions of each of the cohorts are provided in the Supplementary Note. We included data if the total number of cases for any phenotype prior to quality control was >100. If cases for an individual phenotype were <50, we excluded that phenotype from analyses. We included five different phenotypes: broadly-defined BE (BE-BROAD, 39,279 cases), narrowly-defined BE (BE-NARROW, 15,175 cases), AN (24,145 cases), AN restricting subtype (AN-R, 2524 cases), and AN binge eating/purging subtype (AN-BP, 5245 cases). Detailed phenotypic definitions are provided in Table 1 . Genotype quality control and imputation Approaches to quality control and imputation differed between cohorts, and are described in detail in the Supplementary Material. Association analyses The statistical model used to conduct the GWAS for each cohort (described in Supplementary Table 4) depended on the design of the specific cohort. For unrelated case/control cohorts, we used PLINK2 to conduct logistic regression using an appropriate number of PCs to account for ancestry as necessary within each cohort (Supplementary Table 4). For related or imbalanced case/control cohorts we used SAIGE 58 or REGENIE 59 . Note that for whole genome regression in REGENIE step 1, we used a set of pruned SNPs with MAF>0.01, excluding high LD regions and only including autosomal chromosomes (PLINK command: --indep-pairwise 1500 150 0.2). Further details on cohort-specific aspects of association analysis are provided in the Supplementary Methods. All analyses were two-tailed. Post-GWAS processing and quality control We aligned the summary statistics of each GWAS to the TOPMed reference panel in Genome Reference Consortium Build 37 (GRCh37/hg19), using variant positions from ENSEMBL (see URLs). For cohorts that were in GRCh38, we first linked the datasets with the GRCh38 TOPMed reference panel (see URLs) by chromosome/base pair, then selected the SNP rsID labels and linked these labels to the GRCh37 TOPMed reference panel, and finally extracted the GRCh37 chromosome/base pair information for each SNP. Variants without a rsID label in the GRCh38 TOPMed reference panel were lifted over to GRCh37 using the liftOver tool (see URLs). After alignment, we applied an INFO and MAF filter to include SNPs with an INFO score of>0.3 and MAF>0.01 in cases and controls. We then used DENTIST 60 to remove variants with effects inconsistent with their linkage disequilibrium pattern with other assessed variants, estimating linkage disequilibrium from European ancestry individuals in Phase 3 of the 1000 Genomes project. Meta-analysis and quality control We used the post-imputation module of Ricopili 61 to perform meta-analyses in METAL 62 using an inverse-variance weighted fixed-effect model, including assessment of variant heterogeneity as I 2 values (Comparison of variant heterogeneity between our AN GWAS and that of Watson et al is included in the Supplementary Methods 6 .) We defined independent significant SNPs with a genome-wide significant p-value ( P <5x10 -8 ) that were independent (r 2 <0.6) from each other. We then defined significant genomic loci by merging LD blocks of these independent significant SNPs if they were close to each other (<250 kb). Furthermore, we defined independent lead SNPs if independent significant SNPs were independent of each other at r 2 <0.1. We ran a stepwise conditional analysis on our GWAS results to select independently-associated SNPs (conditional on lead SNPs) at each loci using GCTA-COJO 63 . For these analyses we used one of our largest cohorts ( usa2 ) as our reference for linkage disequilibrium. Female-only analyses and female-to-male polygenic risk scoring We conducted a supplementary female-only GWAS for BE-BROAD and AN and generated female-only PRS using PRS-CS which we applied on male-only datasets with sufficient data (i.e., n case and n control >100) available. For the BE-BROAD female-only meta-analysis, all cohorts except usa1 and biov were included, and alsp, moba , and ukd2 were included as male-only target cohorts. For the AN female-only meta-analysis, all cohorts except itgr, spa1, ukd1 , and net2 were included, and ipsy, fngn , and ukb2 were included as male-only target cohorts. SNP-based heritability and distinguishing polygenicity from other sources of inflation We used linkage disequilibrium score regression (LDSC 64 ) to estimate SNP-based heritability ( h 2 SNP ). These estimates were transformed to the liability scale, assuming population prevalences as follows: BE-BROAD 4.5% 15 , BE-NARROW 3.5% 15 , AN 1.5% 17 , AN-R 0.8% 17 , 65 , AN-BP 0.7% 17 , 65 . For all analyses using LDSC, we applied an LD reference panel based on the European subset of the 1000 Genomes Project (1kGP), restricted to SNPs present in the HapMap3 panel 66 . For N , we calculated the sum of effective N across all cohorts and specified 0.5 for sample prevalence 67 . All LDSC analyses, including tests of the of h 2 SNP and SNP- r g from 0 and from 1, were two-tailed tests of deviation from a chi-square distribution with jack-knifed standard errors. Test statistics from GWAS of a polygenic trait are expected to be inflated, but inflation may also be due to spurious SNP associations caused by population stratification and cryptic relatedness of study participants. We used statistics from LDSC 64 to determine the source of inflation. Although the LDSC intercept is commonly used to distinguish polygenicity from spuriously inflated statistics, we calculated the attenuation ratio statistic, defined as (LDSC intercept–1)/(mean of association chi-square statistics–1), which may be a less biassed metric compared to the LDSC intercept 16 . We included variants with MAF≥0.01 and INFO≥0.6. Comparison of the two binge-eating behaviour phenotypes The two BE phenotype definitions balanced phenotypic certainty with sample size— BE-BROAD had a larger sample size and was potentially a better-powered GWAS than BE-NARROW but was likely to be more heterogeneous and could lack specificity to BE. To determine which phenotype to carry forward to follow-up analyses, we considered the LDSC intercept and attenuation ratio statistics and calculated the genetic correlation (SNP- r g ) between the two BE phenotypes. Inflation was a more sizable component of the signal in BE-NARROW (attenuation ratio 0.21 ± 0.06) than in BE-BROAD (0.14 ± 0.04). Furthermore, the SNP- r g between BE-NARROW and BE-BROAD did not differ from unity (1.00 ± 0.03). We therefore concluded that BE-BROAD appropriately captured the common genetic component of BE with greater statistical power than BE-NARROW. Genetic relationship between traits We used LDSC to calculate SNP- r g with several aims. First, we estimated SNP- r g s between both BE phenotypes, AN, and AN subtypes to assess the genetic relationships among these eating phenotypes. Second, we calculated SNP- r g between BE-BROAD/AN and 225 traits covering eight categories: 1) psychiatric trait or disorder, 2) substance use, 3) psychological/personality/behavioural, 4) anthropometric, 5) metabolism, 6) blood, 7) sociodemographic, and 8) somatic trait or disease. We selected these traits from an internal catalogue based on their power ( h 2 SNP Z-score>4) 68 . To assess statistical significance, we tested whether SNP- r g differed from zero and applied a Bonferroni-corrected p-value threshold of 2.20x10 -4 based on 225 traits. If, according to this threshold, BE-BROAD and/or AN was significantly genetically correlated with another trait, we used the LDSC block-jackknife procedure (described in more detail in Appendix S1 of Hübel et al 69 ) to statistically compare the SNP- r g between BE-BROAD and AN (Bonferroni-corrected p-value threshold of 23 2.20x10 -4 ). We used MiXeR to conduct univariate and bivariate casual mixture modelling of BE-BROAD and AN 19 . MiXeR is an extension of LDSC that uses GWAS summary statistics to model the number of causal variants underlying a trait (polygenicity), as well as inferring the average contribution of each causal variant to heritability (discoverability). Bivariate MiXeR extends cross-trait LDSC to refine the interpretation of genetic correlation. It enables the shared number of causal variants between traits to be estimated, as well as the extent to which shared causal variants have the same direction of effect. We conducted MiXeR analyses using BE-BROAD and AN summary statistics, limited to autosomal variants with INFO≥0.6 and MAF≥0.01, and with the MHC region (chr6, 26-34Mb) excluded. We followed protocols and used LD reference files provided by the authors of MiXeR (see URLs). To further investigate the genetic difference between BE-BROAD and AN we used CC-GWAS 18 . CC-GWAS uses GWAS summary statistics to test for allele frequency differences between cases of two phenotypes, as opposed to a traditional GWAS that tests for differences between cases and controls. It generates Z-scores and p-values for allele frequency differences between cases of two phenotypes as a two-tailed test, using both ordinary least squares (OLS) and exact weights. We used BE-BROAD and AN summary statistics including non-ambiguous SNPs from HapMap 3 with INFO≥0.6 and MAF≥0.01. We used the liability-scale h 2 SNP , the SNP- r g and associated covariate intercept between both traits as input. We furthermore set the BE-BROAD population prevalence to 4.5% (range 0.1 - 10%) and AN to 1.5% (range 0.1 - 4.3%) and approximated the number of effective loci to be 10,000–consistent with psychiatric disorder polygenicity 18 . We additionally defined the number of independent CC-GWAS loci with PLINK 1.9 (--clump-p1 5e-8 --clump-p2 5e-8 -- clump-r2 0.1 --clump-kb 3000) and defined a genome-wide significant SNP if P <5x10 -8 in the CC-GWAS OLS test and P <10 -4 in the CC-GWAS exact test. Further, CC-GWAS calculates genetic distances between cases and controls of the two traits using based on liability-scale h 2 SNP , SNP- r g , population prevalence and the number of independent causal variants of the two traits. Influence of BMI We used GWAS-by-subtraction 20 , an application of genomicSEM 70 , in R v4.3.1 71 to estimate the proportion of variance in BE-BROAD and AN independent of BMI (Supplementary Methods). Shared genomic covariance across traits is expected due to pleiotropy. Latent Genomic SEM factors explicitly model this covariance, making results less influenced by spurious biases than would conditioning on phenotypic traits in a GWAS 72 . We used GWAS summary statistics from BE-BROAD, AN, and BMI 21 . We specified a structural equation model (SEM) that regressed both sets of summary statistics on a shared variable (“ BMI ”) and a non-BMI variable (“ non-BMI ”) for each eating phenotypes, respectively (Supplementary Figure 26). Specifically, we specified the two latent variables as a function of BMI and (e.g.) BE-BROAD: “ BMI =∼ NA * BEBROAD + start(0.4) * BMI” and “non-BMI =∼ NA * BE-BROAD”. In line with the GWAS-by-subtraction specification as it has previously been applied 20 , we additionally set the variance of the latent variables to 1 and a covariance of 0, and constrained the model so all (co)variance in BMI and BE-BROAD was captured by BMI and non-BMI . We used the diagonally-weighted least squares estimator, which is the default setting in Genomic SEM 70 . Additional computational settings are shown in Supplementary Table 30. We then regressed the two latent factors on individual SNPs yielding a GWAS of the latent variables BMI and non-BMI . We subsequently used LDSC 64 to calculate SNP- r g of the non-BMI factor with all traits identified in initial SNP- r g s. As a sensitivity analysis, we restricted our BE-BROAD sample in our GWAS to cohorts that were not ascertained for AN, reasoning that this might better capture BE behaviour outside of AN. We applied the same GWAS-by-subtraction model on that selection of cohorts (listed in Supplementary Table 11). We also conducted exploratory two-sample Mendelian randomisation analyses of BE-BROAD with BMI and AN with BMI, testing causal effects in both directions with two-tailed tests. We used SNPs in linkage equilibrium as genetic instruments, with P <5x10 -6 for BE-BROAD and AN, and P <5x10 -9 for BMI. The instrument used for BMI was that suggested by the authors of the BMI GWAS 21 . We repeated analyses using the non-BMI factor GWAS of BE-BROAD and of AN. We view these Mendelian randomisation analyses as exploratory because SNPs not passing genome-wide significance were included in the instruments for the eating phenotypes. To ensure our analyses were robust to potential violations of the assumptions of Mendelian randomisation, we conducted analyses using multiple methods in R 4.3.2, including the packages TwoSampleMR, MendelianRandomisation , and MR-PRESSO (Supplementary Methods) 71 , 73 , 74 . These were inverse-variance weighted analysis, MR-Egger, mode-based estimation and median-based estimation. We determined the strength of association of our genetic instruments using the F-statistics 75 . We used Cochran’s Q-statistic to test for instrument heterogeneity, with P <0.05 indicating heterogeneity 76 . To investigate potential confounding via horizontal pleiotropy, we assessed the deviation of the MR-Egger intercept from 0, and performed a global bias test in MR-PRESSO. We excluded from the analysis SNPs identified by MR PRESSO as pleiotropic. We ran further methods robust to heterogeneity, including the penalised weighted median estimator, the contamination mixture method, and MR-Lasso 77 . We assessed results visually (Supplementary Methods). Identification of gene-tissue associations with eating phenotypes We used S-PrediXcan 22 to identify genetically regulated gene expression associated with our phenotypes. We tested the association of gene expression with our eating phenotypes using available GTEx v8 MASHR 22 , 78 , 79 and CommonMind DLPFC 80 , 81 tissue models. MASHR-based PredictDB models use fine-mapping methods for selection of eQTLs included in the predictor models, improving prediction 22 , 78 , 79 . We included 45 GTEx v8 MASHR models, removing non-natural tissues (cell lines), tissues with N<100 individuals (kidney cortex), and testis 82 . We performed liftover of our GWAS summary statistics to hg38, harmonisation, and imputation based on recommended preprocessing by Barbeira et. al 78 using GWAS tools (see URLs). S-PrediXcan uses GWAS summary statistics to impute the difference in genetically regulated gene expression between levels of the GWAS phenotype (in this instance, cases and controls). We tested whether these imputed differences differed from 0 as a two-tailed Z test. We used two different Bonferroni significance thresholds: an experiment-wide threshold, where we corrected for 600,382– 602,744 tests performed across all tissues ( P< 0.05/Tests Total = P< 8.32x10 -8 ), and a tissue-specific threshold, where we corrected for varying numbers of tests performed within each tissue ( P< 0.05/Tests Tissue X , Supplementary Table 16). We performed two-tailed exact binomial tests for tissue enrichment using binom . test() in R for associations at three different significance thresholds: experiment-wide significant ( P< 0.05/Tests Total ), tissue-specific significant ( P< 0.05/Tests TissueX ), and nominally significant ( P< 0.05). We reported results with a focus on central nervous system tissues (brain and cervical spinal cord) and gastrointestinal tissues (oesophagus, colon, stomach, and small intestine). S-MultiXcan is a summary-level method for measuring the joint association of genetically regulated gene expression across tissues with a phenotype of interest, leveraging shared eQTLs across tissues 23 . Using our GTEx v8 MASHR S-PrediXcan results as input, along with MASHR models, and harmonised, imputed GWAS summary statistics, we ran S-MultiXcan on each of our eating phenotypes for all genes (N=22,241). S-MultiXcan gives as output the p-value for association of multi-tissue gene expression with the trait of interest (S-MultiXcan P), along with best single-tissue p-value, both from two-tailed tests. In order to account for potential false positive associations, we removed any significant S-MultiXcan associations where the single best tissue p-value was greater than 1x10 -4 23 . We set a Bonferroni significance threshold for our results, correcting for the number of genes tested in our S-MultiXcan analysis ( P <0.05/22,241 = 2.25x10 -6 ). Gene-wise and gene set analysis, including drug target and drug class analyses Following a previously published approach 83 , we used MAGMA v1.10 24 to test the association between each phenotype and 1) the aggregate effect of SNPs mapped to protein-coding genes (gene analysis); 2) groups of genes with shared functional, biological, or other characteristics (gene-set analysis); 3) a gene-set analysis restricted to genes targeted by drugs (drug-set analysis) and 4) the enrichment of signal within classes of drug. We restricted SNPs to those with MAF≥0.01, INFO≥0.6, and which are present in 80% of the total sample and 50% of the cohorts. We mapped SNPs to protein-coding genes, applying a 35 kb upstream and 10 kb downstream window around hg19 gene positions from Ensembl release 75 84 . We obtained gene-wise p-values with the multi snp-wise model, which combines the lowest and mean p-values of all SNPs mapped to the gene. We tested 19,332-19,418 ENSEMBL genes across the five phenotypes and applied a Bonferroni correction of P< 2.60x10 -6 . We used the 1kGP reference panel for estimating between-SNP LD. For gene-set analyses, we applied a competitive analysis, which regresses the phenotype on the mean effect of genes within the gene set, with the mean effect of genes outside the gene set as a covariate, in a one-tailed test that the mean effect of genes within the gene set is greater than 0. We defined biological pathways based on gene ontology and canonical pathways from MSigDB v6.1 and psychiatric pathways identified from the literature. We tested 7324-7325 pathways across the five phenotypes and applied a Bonferroni correction of P< 6.83x10 -6 . For drug-set analyses, we defined drug sets based on drug targets from the Drug-Gene Interaction database DGIdb v4.2.0 85 ; the Psychoactive Drug Screening Database Ki DB 86 ; CheMBL v27 87 ; the Target Central Resource Database v6.7.0 88 ; and DSigDB v1.0 89 , all downloaded in October 2020. We applied a competitive analysis and subsequently grouped the results based on the Anatomical Therapeutic Chemical class of the respective drugs 90 . For drug-class analysis, we first ranked all drug-gene sets according to their association in the drug-set analysis. We then generated enrichment curves for specific drug classes, assigning a ‘hit’ if the drug-gene set belonged to the class or a “miss” if it was outside the class. We calculated the area under the curve and determined statistical significance with the Wilcoxon Mann-Whitney test, comparing drug-gene sets within the class to those outside the class as a one-tailed test of whether in-class drugs showed greater association with the disorder than out-of-class drugs. We applied a Bonferroni correction of P< 3.23x10 -5 (based on 1546-1547 drug sets) for the drug-set analysis and P< 3.08x10 -4 (based on 162 drug classes) for the drug-class analysis to account for multiple testing. Tissue and cell-type specific analyses To identify relevant tissues and cell-types related to the common genetic risk of BE-BROAD and AN, we performed tissue and cell-type heritability ( h 2 SNP ) enrichment analyses. First, we analysed the enrichment of h 2 SNP in 27 tissues from the GTEx gene expression data (v8) after excluding tissues with less than 100 donors, non-natural tissues (such as cell lines), and testis tissues (since it was an expression outlier) 91 . Second, we analysed enrichment of h 2 SNP in 31 superclusters and 461 cell clusters based on the single-nucleus RNA sequencing data including over three million nuclei from around 100 dissections across the adult human brain 29 . Within each expression dataset, we calculated the specificity of gene expression per tissue or cell type (superclusters and clusters separately), defined as the expression of each gene (counts per million, CPM) in a tissue or cell type (i.e., the superclusters and clusters respectively) divided by the total expression of this gene across all tissues or cell types in the dataset 92 . We then used the genes with the top 10% specificity in each tissue or cell type to perform the heritability enrichment analysis using stratified LDSC 92 , 93 . Specifically, we compared the per-SNP heritability of SNPs within 100kb flankings of the top 10% specific genes and the per-SNP heritability of other SNPs, using the baseline model that adjusted for 53 baseline annotations 93 . We then used the coefficient z -scores to calculate the one-sided p-values. Finally, we accounted for multiple comparisons by calculating FDR per trait for the GTEx dataset (27 tests), the human brain superclusters (31 tests) and clusters (461 tests) respectively. Polygenic prediction We used PRS-CS 94 to generate polygenic risk scores (PRS) for BE-BROAD and AN. First, we performed leave-one-cohort-out (LOO) analyses to generate LOO GWAS summary statistics from all cohorts except the target cohort and used this as the base data to calculate individual-level PRS in each target cohort. We included non-ambiguous SNPs with INFO≥0.6 and MAF≥0.01 in the PRS calculation. We used the 1000 Genomes Project Phase 3 EUR reference as the LD reference panel and provided median sample size per LOO meta-analysis as input for PRS-CS. Posterior SNP effect size estimates from PRS-CS were then combined across chromosomes to calculate individual PRS via PLINK (--score 2 4 6 sum) 95 . We standardised the individual PRS by applying the scale function in R (version 4.3.2) 71 . Using the standardised PRS scores, we first assessed the proportion of variance explained by PRS for each phenotype through calculating the nested Nagelkerke’s pseudo- R 2 (that is,the pseudo-R 2 of the full model minus that of the model excluding the PRS) 96 . The inclusion of target cohorts for each phenotype was based on the effective sample size (N eff half>1000), study characteristics, and availability of individual-level data (more detailed information in the Supplementary Methods). Two-tailed logistic regression was performed for BE-BROAD and AN PRS on BE-BROAD and AN, adjusting for cohort-specific PCs. We then converted the variance explained by each PRS ( R 2 ) to the liability scale using populationprevalences of BE-BROAD and AN as used for LDSC 97 . In addition, we divided individuals into PRS decile groups and assessed their relative risk of BE-BROAD and AN compared to the group of individuals in the lowest PRS decile. We also examined predictive performance of each PRS regarding their sensitivity, specificity, and precision to predict BE-BROAD and AN status using the pROC 98 and pracma 99 packages in R by comparing the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the precision recall (PR) curve of the full model including PRS and PCs as predictors against the null model including PCs only as predictors. We used female-only GWAS summary statistics as base data in PRS-CS 94 to assess whether female BE-BROAD PRS could be applied to male individuals to assess their BE-BROAD risk in three cohorts ( alsp, moba, ukd2 , N case_male_total =1055, N control_male_total =38,046). The same analysis was performed to examine the association of female AN PRS with male AN risk in three cohorts ( ukb2, fngn, ipsy , N case_male_total =1524, N control_male_total =388,891). We assessed whether the levels of BE-BROAD and AN PRS differed across subgroups, including a) those with BE-BROAD only, b) those with both BE-BROAD and AN, and c) those with AN only. Target cohorts for this analysis included cohorts with sufficient data available on some/all subgroups (Supplementary Methods, Supplementary Table 1). Specifically, we selected aunz and sedk to assess individuals with both BE-BROAD and AN and to assess the AN only group. We selected ukb2 and ukd2 for assessing BE-BROAD and AN PRS levels in all subgroups. We assessed differences between subgroups and controls by performing linear regression on each PRS for each subgroup compared to controls, adjusting for cohort-specific genetic PCs. In addition to setting controls as the reference group, we also set different subgroups as the reference group to compare differences in BE-BROAD PRS and AN PRS across different subgroups. Last, we generated AN PRS from the main meta-analysis – including solely individuals of European genetic ancestry – and applied this to two East Asian genetic ancestry cohorts (N case =77, N control =117 in a Japanese cohort, N case =75, N control =109 in a Korean cohort). Korean data were merged with Japanese data from GCAN cohorts. The same pre-imputation quality control and imputation method used for the European ancestry cohorts was conducted. We used the 1000 Genomes Project Phase 3 EUR reference as the LD reference panel for PRS, as the reference panel should align with the ancestry in the base GWAS 94 . We used the European AN prevalence estimate for liability scale conversion, as estimates of AN prevalence in East Asia are sparse and what estimates exist are approximately consistent with estimates in countries with primarily European genetic ancestries 100 . Sensitivity analyses using down-sampled BE-BROAD data A considerable portion (13%) of the cases for the BE-BROAD GWAS includes individuals who were recruited through studies that focused on AN ascertainment. Even though the BE-BROAD phenotype is expected to be heterogeneous given its transdiagnostic nature, we sought to understand the influence of AN on this BE-BROAD phenotype. We therefore conducted an additional BE-BROAD meta-analysis where we excluded individuals with BE-BROAD who were identified in cohorts that were specifically ascertained for AN (e.g., the ANGI cohorts). This resulted in an additional analysis including 87% of the BE-BROAD GWAS, consisting of the following cohorts: sebe, agds, alsp, biov, esbb, fngn, jans, moba, ukb2, ukd2 . We then calculated SNP- r g to compare the genetic relationship of the “not-ascertained-for-AN” BE-BROAD GWAS and the original BE-BROAD GWAS with selected traits significantly correlated with the original BE-BROAD GWAS or with the AN GWAS (hypothesising that AN-related effects may mask or drive SNP- r g s between such traits and the original BE-BROAD GWAS). URLS TopMED Hg37 VCF: https://ftp.ensembl.org/pub/grch37/release-113/data_files/homo_sapiens/GRCh37/variation_genotype/TOPMED_GRCh37.vcf.gz TopMED Hg38 VCF: https://ftp.ensembl.org/pub/grch37/release-113/data_files/homo_sapiens/GRCh38/variation_genotype/TOPMED_GRCh38.vcf.gz LiftOver: https://ftp.ensembl.org/pub/grch37/release-113/data_files/homo_sapiens/GRCh38/variation_genotype/TOPMED_GRCh38.vcf.gz MiXeR: https://github.com/precimed/mixer SPrediXcan best practice: https://github.com/hakyimlab/MetaXcan/wiki/Best-practices-for-integrating-GWAS-and-GTEX-v8-transcriptome-prediction-models GWAS tools : https://github.com/hakyimlab/summary-gwas-imputation/wiki/GWAS-Harmonization-And-Imputation Footnotes ↵ * Joint first authors ↵ ^ Joint senior authors Additional analyses added: per-variant heterogeneity in AN; BE Broad and AN univariate and bivariate mixture modelling, with SBayesRC validation; additional power analysis for Watson et al AN GWAS. Minor edits to text. 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Share Genome-wide association studies of binge-eating behaviour and anorexia nervosa yield insights into the unique and shared biology of eating disorder phenotypes Jet D Termorshuizen , Helena L Davies , Sang-Hyuck Lee , Jessica K Dennis , Christopher Hübel , Jessica S Johnson , Yi Lu , Melissa A Munn-Chernoff , Triinu Peters , Baiyu Qi , Katherine E Schaumberg , Rebecca H Signer , Karanvir Singh , Abigail R ter Kuile , Laura M Thornton , Jiayi Xu , Shuyang Yao , Zeynep Yilmaz , Ruyue Zhang , Johan Zvrskovec , Mohamed Abdulkadir , Ziada Ayorech , Elizabeth C Corfield , Alexandra Havdahl , Kristi Krebs , Taralynn M Mack , Maria Niarchou , Teemu Palviainen , Julia M Sealock , Jessica H Baker , Andrew W Bergen , Andreas Birgegård , Vesna Boraska Perica , Katharina Bühren , Roland Burghardt , Matteo Cassina , Giovanni Castellini , Enrico Collantoni , James J Crowley , Unna N Danner , Franziska Degenhardt , Janiece E DeSocio , Christian Dina , Monika Dmitrzak-Węglarz , Laramie E Duncan , Karin M Egberts , Lenka Foretova , Ina Giegling , Fragiskos Gonidakis , Scott D Gordon , Jakob Grove , Sébastien Guillaume , Jerry D Guintivano , Annette M Hartmann , Konstantinos Hatzikotoulas , Stefan Herms , Hartmut Imgart , Susana Jiménez-Murcia , Antonio Julià , Gursharan Kalsi , Deborah Kaminská , Leila J Karhunen , Hannah L. Kennedy , Kirsty M Kiezebrink , Theresa Kolb , Janne T Larsen , Dong Li , Lisa Lilenfeld , Mario Maj , Morten Mattingsdal , Paolo Meneguzzo , Allison L Miller , Karen S Mitchell , Alessio Maria Monteleone , Catherine M Olsen , Leonid Padyukov , Richard Parker , Michaela A. Pettie , Dalila Pinto , Anu Raevuori , Samuli Ripatti , Marion E Roberts , Paolo Santonastaso , Androula Savva , Ulrike H Schmidt , Alexandra Schosser , Jochen Seitz , Lenka LS Slachtova , Agnieszka Slopien , Sandro Sorbi , Peter S Straub , Jin P Szatkiewicz , Friederike I Tam , Elena Tenconi , Alfonso Tortorella , Artemis Tsitsika , Annemarie A van Elburg , Gudrun Wagner , Hunna J Watson , Roger AH Adan , Lars Alfredsson , Ole A Andreassen , Helga Ask , Anders D. Børglum , Harry A Brandt , David Collier , Steven Crawford , Scott Crow , Lea K Davis , Martina de Zwaan , George Dedoussis , Danielle M Dick , Stefan Ehrlich , Xavier Estivill , Angela Favaro , Fernando Fernández-Aranda , Krista Fischer , Andreas J Forstner , Philip Gorwood , Hakon Hakonarson , Johannes Hebebrand , Beate Herpertz-Dahlmann , Anke Hinney , James I Hudson , Craig Johnson , Jennifer Jordan , Allan S Kaplan , Jaakko Kaprio , Andreas FK Karwautz , Martien JH Kas , Walter H Kaye , James L Kennedy , Martin A Kennedy , Anna Keski-Rahkonen , Youl-Ri Kim , Kelly L Klump , Mikael Landén , Stéphanie Le Hellard , Kelli Lehto , Qingqin S. Li , Jolanta Lissowska , Jurjen J. Luykx , Sarah L Maguire , Nicholas G Martin , Manuel Mattheisen , Sarah E Medland , Philip Mehler , Nadia Micali , James E Mitchell , Palmiero Monteleone , Preben Bo Mortensen , Benedetta Nacmias , Roel A Ophoff , Hana Papezova , Nancy L Pedersen , Liselotte V Petersen , Luisa S Rajcsanyi , Nicolas Ramoz , Ted Reichborn-Kjennerud , Valdo Ricca , Stephan Ripke , Dan Rujescu , Filip Rybakowski , Stephen W Scherer , Margarita CT Slof-Op ‘t Landt , Howard Steiger , Patrick F Sullivan , Beata Świątkowska , Eric F van Furth , Tracey D Wade , Thomas Werge , David C Whiteman , D. Blake Woodside , Ya-Ke Wu , Stephan Zipfel , Eating Disorders Working Group of the Psychiatric Genomics Consortium , Estonian Biobank (EstBB) , Cynthia M Bulik , Laura M Huckins , Gerome Breen , Jonathan RI Coleman medRxiv 2025.01.31.25321397; doi: https://doi.org/10.1101/2025.01.31.25321397 Share This Article: Copy Citation Tools Genome-wide association studies of binge-eating behaviour and anorexia nervosa yield insights into the unique and shared biology of eating disorder phenotypes Jet D Termorshuizen , Helena L Davies , Sang-Hyuck Lee , Jessica K Dennis , Christopher Hübel , Jessica S Johnson , Yi Lu , Melissa A Munn-Chernoff , Triinu Peters , Baiyu Qi , Katherine E Schaumberg , Rebecca H Signer , Karanvir Singh , Abigail R ter Kuile , Laura M Thornton , Jiayi Xu , Shuyang Yao , Zeynep Yilmaz , Ruyue Zhang , Johan Zvrskovec , Mohamed Abdulkadir , Ziada Ayorech , Elizabeth C Corfield , Alexandra Havdahl , Kristi Krebs , Taralynn M Mack , Maria Niarchou , Teemu Palviainen , Julia M Sealock , Jessica H Baker , Andrew W Bergen , Andreas Birgegård , Vesna Boraska Perica , Katharina Bühren , Roland Burghardt , Matteo Cassina , Giovanni Castellini , Enrico Collantoni , James J Crowley , Unna N Danner , Franziska Degenhardt , Janiece E DeSocio , Christian Dina , Monika Dmitrzak-Węglarz , Laramie E Duncan , Karin M Egberts , Lenka Foretova , Ina Giegling , Fragiskos Gonidakis , Scott D Gordon , Jakob Grove , Sébastien Guillaume , Jerry D Guintivano , Annette M Hartmann , Konstantinos Hatzikotoulas , Stefan Herms , Hartmut Imgart , Susana Jiménez-Murcia , Antonio Julià , Gursharan Kalsi , Deborah Kaminská , Leila J Karhunen , Hannah L. Kennedy , Kirsty M Kiezebrink , Theresa Kolb , Janne T Larsen , Dong Li , Lisa Lilenfeld , Mario Maj , Morten Mattingsdal , Paolo Meneguzzo , Allison L Miller , Karen S Mitchell , Alessio Maria Monteleone , Catherine M Olsen , Leonid Padyukov , Richard Parker , Michaela A. Pettie , Dalila Pinto , Anu Raevuori , Samuli Ripatti , Marion E Roberts , Paolo Santonastaso , Androula Savva , Ulrike H Schmidt , Alexandra Schosser , Jochen Seitz , Lenka LS Slachtova , Agnieszka Slopien , Sandro Sorbi , Peter S Straub , Jin P Szatkiewicz , Friederike I Tam , Elena Tenconi , Alfonso Tortorella , Artemis Tsitsika , Annemarie A van Elburg , Gudrun Wagner , Hunna J Watson , Roger AH Adan , Lars Alfredsson , Ole A Andreassen , Helga Ask , Anders D. Børglum , Harry A Brandt , David Collier , Steven Crawford , Scott Crow , Lea K Davis , Martina de Zwaan , George Dedoussis , Danielle M Dick , Stefan Ehrlich , Xavier Estivill , Angela Favaro , Fernando Fernández-Aranda , Krista Fischer , Andreas J Forstner , Philip Gorwood , Hakon Hakonarson , Johannes Hebebrand , Beate Herpertz-Dahlmann , Anke Hinney , James I Hudson , Craig Johnson , Jennifer Jordan , Allan S Kaplan , Jaakko Kaprio , Andreas FK Karwautz , Martien JH Kas , Walter H Kaye , James L Kennedy , Martin A Kennedy , Anna Keski-Rahkonen , Youl-Ri Kim , Kelly L Klump , Mikael Landén , Stéphanie Le Hellard , Kelli Lehto , Qingqin S. Li , Jolanta Lissowska , Jurjen J. Luykx , Sarah L Maguire , Nicholas G Martin , Manuel Mattheisen , Sarah E Medland , Philip Mehler , Nadia Micali , James E Mitchell , Palmiero Monteleone , Preben Bo Mortensen , Benedetta Nacmias , Roel A Ophoff , Hana Papezova , Nancy L Pedersen , Liselotte V Petersen , Luisa S Rajcsanyi , Nicolas Ramoz , Ted Reichborn-Kjennerud , Valdo Ricca , Stephan Ripke , Dan Rujescu , Filip Rybakowski , Stephen W Scherer , Margarita CT Slof-Op ‘t Landt , Howard Steiger , Patrick F Sullivan , Beata Świątkowska , Eric F van Furth , Tracey D Wade , Thomas Werge , David C Whiteman , D. Blake Woodside , Ya-Ke Wu , Stephan Zipfel , Eating Disorders Working Group of the Psychiatric Genomics Consortium , Estonian Biobank (EstBB) , Cynthia M Bulik , Laura M Huckins , Gerome Breen , Jonathan RI Coleman medRxiv 2025.01.31.25321397; doi: https://doi.org/10.1101/2025.01.31.25321397 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 Psychiatry and Clinical Psychology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4436) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4538) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (542) 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 (3333) 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 (9232) 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:'a00fff1f5ad40db4',t:'MTc3OTY2NDQ2NQ=='};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())}}}})();
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