Genome-wide association study provides novel insight into the genetic architecture of severe obesity

Genome-wide association study provides novel insight into the genetic architecture of severe obesity | 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 study provides novel insight into the genetic architecture of severe obesity Mohanraj Krishnan , Mohammad Yaser Anwar , View ORCID Profile Anne E Justice , Geetha Chittoor , Hung-Hsin Chen , Rashedeh Roshani , Roelof A.J Smit , Michael H Preuss , Nathalie Chami , Benjamin S Hadad , Esteban J Parra , Miguel Cruz , Qin Hui , Peter W.F Wilson , View ORCID Profile Yan V Sun , Xiaoyu Zhang , Gregorio V Linchangco , Sharon L.R Kardia , Jessica D Faul , David R Weir , Lawrence F Bielak , Heather M Highland , Kristin L Young , Baiyu Qi , Yujie Wang , View ORCID Profile Myriam Fornage , Christopher Haiman , Iona Cheng , Ulrike Peters , Charles Kooperberg , Steven Buyske , Joseph B McCormick , Susan P Fisher-Hoch , Frida Lona-Durazo , Jesus Peralta , Jamie Gomez-Zamudio , Stephen S Rich , Kendra R Ferrier , Ethan M Lange , Christopher G Gignoux , Eimear E Kenny , Genevieve L Wojcik , Kelly Cho , Michael J Gaziano , View ORCID Profile Luc Djousse , View ORCID Profile Shuwei Liu , View ORCID Profile Dhananjay Vaidya , Renée de Mutsert , Navya S Josyula , Christopher R Bauer , Wei Zhao , Ryan W Walker , View ORCID Profile Jennifer A Smith , Leslie A Lange , Mariah C Meyer , View ORCID Profile Ching-Ti Liu , View ORCID Profile Lisa R Yanek , Miryoung Lee , View ORCID Profile Laura M Raffield , Ruth J.F Loos , Penny Gordon-Larsen , Jennifer E Below , Kari E North , Mariaelisa Graff doi: https://doi.org/10.1101/2025.02.25.25322870 Mohanraj Krishnan 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , NC, USA 2 Carolina Population Center, University of North Carolina , NC, USA 34 Biobehavioral Health Department, Pennsylvania State University, University Park , PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mohammad Yaser Anwar 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anne E Justice 3 Population Health Sciences , Geisinger, Danville, PA. USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anne E Justice Geetha Chittoor 3 Population Health Sciences , Geisinger, Danville, PA. USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hung-Hsin Chen 4 Division of Human Genetics, Vanderbilt University Medical Center , TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rashedeh Roshani 4 Division of Human Genetics, Vanderbilt University Medical Center , TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Roelof A.J Smit 5 Department of Clinical Epidemiology, Leiden University Medical Center , Leiden, South-Holland, the Netherlands 6 Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Hovedstaden, Denmark 7 Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael H Preuss 7 Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nathalie Chami 7 Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA 8 The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benjamin S Hadad 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Esteban J Parra 9 Department of Anthropology, University of Toronto at Mississauga , Mississauga, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Miguel Cruz 10 Unidad de Investigación Medica en Bioquímica Hospital de Especialidades, Centro Medico Siglo XXI , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qin Hui 11 Department of Epidemiology, Emory University Rollins School of Public Health , Atlanta, GA, USA 12 Atlanta VA Health Care System , Decatur, GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter W.F Wilson 12 Atlanta VA Health Care System , Decatur, GA, USA 13 Department of Medicine, Emory University School of Medicine , Atlanta, GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yan V Sun 11 Department of Epidemiology, Emory University Rollins School of Public Health , Atlanta, GA, USA 12 Atlanta VA Health Care System , Decatur, GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yan V Sun Xiaoyu Zhang 14 Department of Biostatistics, Boston University School of Public Health , Boston MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gregorio V Linchangco 11 Department of Epidemiology, Emory University Rollins School of Public Health , Atlanta, GA, USA 12 Atlanta VA Health Care System , Decatur, GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sharon L.R Kardia 15 Department of Epidemiology, School of Public Health, University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jessica D Faul 16 Survey Research Center, Institute for Social Research, University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site David R Weir 16 Survey Research Center, Institute for Social Research, University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lawrence F Bielak 15 Department of Epidemiology, School of Public Health, University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Heather M Highland 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristin L Young 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Baiyu Qi 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yujie Wang 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Myriam Fornage 17 Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, TX, USA 18 Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Myriam Fornage Christopher Haiman 19 Preventive Medicine, Keck School of Medicine, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Iona Cheng 20 Cancer Prevention Institute of California , Fremont, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ulrike Peters 21 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Charles Kooperberg 21 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steven Buyske 22 Department of Statistics, Rutgers University , New Brunswick, NJ, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joseph B McCormick 23 The School of Public Health, University of Texas at Brownsville , Brownsville, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Susan P Fisher-Hoch 23 The School of Public Health, University of Texas at Brownsville , Brownsville, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Frida Lona-Durazo 9 Department of Anthropology, University of Toronto at Mississauga , Mississauga, Canada 24 Montréal Heart Institute , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jesus Peralta 10 Unidad de Investigación Medica en Bioquímica Hospital de Especialidades, Centro Medico Siglo XXI , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jamie Gomez-Zamudio 10 Unidad de Investigación Medica en Bioquímica Hospital de Especialidades, Centro Medico Siglo XXI , Mexico City, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephen S Rich 25 Department of Genome Sciences, University of Virginia , Charlottesville, VA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kendra R Ferrier 26 Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado , Anschutz Medical Campus, Aurora, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ethan M Lange 26 Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado , Anschutz Medical Campus, Aurora, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher G Gignoux 26 Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado , Anschutz Medical Campus, Aurora, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eimear E Kenny 7 Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Genevieve L Wojcik 27 Department of Epidemiology at John Hopkins, Bloomberg School of Public Health , Baltimore, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kelly Cho 28 Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System , Boston, MA, USA 29 Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael J Gaziano 28 Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System , Boston, MA, USA 29 Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luc Djousse 28 Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System , Boston, MA, USA 29 Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luc Djousse Shuwei Liu 30 Department of Human Genetics, School of Public Health, University of Pittsburgh , Pittsburgh, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shuwei Liu Dhananjay Vaidya 31 Department of Medicine, Johns Hopkins University School of Medicine , Baltimore, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dhananjay Vaidya Renée de Mutsert 5 Department of Clinical Epidemiology, Leiden University Medical Center , Leiden, South-Holland, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Navya S Josyula 3 Population Health Sciences , Geisinger, Danville, PA. USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher R Bauer 32 Department of Human Medical Genetics and Genomics, University of Colorado , Denver, Co, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wei Zhao 15 Department of Epidemiology, School of Public Health, University of Michigan , Ann Arbor, MI, USA 16 Survey Research Center, Institute for Social Research, University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ryan W Walker 7 Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer A Smith 15 Department of Epidemiology, School of Public Health, University of Michigan , Ann Arbor, MI, USA 16 Survey Research Center, Institute for Social Research, University of Michigan , Ann Arbor, MI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jennifer A Smith Leslie A Lange 26 Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado , Anschutz Medical Campus, Aurora, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mariah C Meyer 26 Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado , Anschutz Medical Campus, Aurora, CO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ching-Ti Liu 14 Department of Biostatistics, Boston University School of Public Health , Boston MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ching-Ti Liu Lisa R Yanek 31 Department of Medicine, Johns Hopkins University School of Medicine , Baltimore, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lisa R Yanek Miryoung Lee 23 The School of Public Health, University of Texas at Brownsville , Brownsville, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura M Raffield 33 Department of Genetics, University of North Carolina at Chapel Hill , Chapel Hill, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura M Raffield Ruth J.F Loos 6 Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen, Hovedstaden, Denmark 7 Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Penny Gordon-Larsen 2 Carolina Population Center, University of North Carolina , NC, USA 35 Department of Nutrition, Gillings School of Global Public Health, University of North Carolina , NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer E Below 4 Division of Human Genetics, Vanderbilt University Medical Center , TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kari E North 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mariaelisa Graff 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: migraff{at}email.unc.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Severe obesity (SevO) is a primary driver of cardiovascular diseases (CVD), cardiometabolic diseases (CMD) and several cancers, with a disproportionate impact on marginalized populations. SevO is an understudied global health disease, limiting knowledge about its mechanisms and impacts. In genome-wide association study (GWAS) meta-analyses of the tail end of the BMI distribution (≥95 th percentile BMI) and two SevO phenotypes [Obesity Class III BMI ≥40 kg/m 2 and Obesity Class IV BMI ≥50 kg/m 2 ] in 159,359 individuals across eleven ancestrally diverse population-based studies followed by replication in 480,897 individuals across six ancestrally diverse studies, we identified and replicated one novel signal in an unknown locus [ BHLHE40-AS1 ] and three novel signals in known loci of BMI [ TENM2 , PLCL2 , ZNF184 ], associated with SevO traits. We confirmed a large overlap in the genetic architecture of continuous BMI and severe obesity phenotypes, suggesting little genetic heterogeneity in common variants, between obesity subgroups. Systematic analyses combining functional mapping, polygenic risk scores (PRS), phenome wide association studies (PheWAS) and environmental risk factors further reinforce shared downstream comorbidities associated with continuous measures of BMI and the importance of known lifestyle factors in interaction with genetic predisposition to SevO. Our study expands the number of SevO signals, demonstrates a strong overlap in the genetic architecture of SevO and BMI and reveals a remarkable impact of SevO on the clinical phenome, affording new opportunities for clinical prevention and mechanistic insights. MAIN Severe obesity (SevO) (Body Mass Index [BMI] (≥40 kg/m 2 ) is an emerging global health disease which imparts a substantial and growing morbidity and mortality burden that disproportionately affects underserved populations 1 – 3 . With marked heterogeneity in the disease of obesity, severe obesity represents a higher degree of obesity than frank obesity, more extreme weight gain, and potential mechanistic differences 4 . Although SevO has been associated with a myriad of health complications including type 2 diabetes (T2D), coronary heart disease (CHD) and various forms of cancer 5 , 6 , very few studies have explored the biological and genetic mechanisms underlying SevO. Indeed, most studies have grouped individuals with SevO with individuals classified according to World Health Organization (WHO) class I ([BMI] ≥30 kg/m 2 to <35 kg/m 2 ) or class II ([BMI] ≥35 kg/m 2 to <40 kg/m 2 ) obesity, or they have been excluded altogether in clinical and epidemiological studies 7 , thereby masking possible detrimental effects of the critical SevO subtype. Thus, the health impacts for individuals at the highest end of the spectrum for obesity are underestimated or largely undocumented, limiting knowledge about the mechanistic pathways and impacts, including its genetic determinants. Genome wide association studies (GWAS) have mapped thousands of independent loci among people of diverse ancestries influencing both dichotomous and quantitative traits of BMI ([BMI] ≥30 kg/m 2 ) 8 – 12 , offering insights into the genetic architecture of obesity. To date, the largest known population-based GWAS of SevO was performed in 263,407 individuals of European ancestry and identified seven novel loci associated with different classes of obesity; two of which ( HS6ST3 [heparan sulfate 6-O-sulfotransferase 3] and ZZZ3 (zinc finger ZZ-type containing 3) were associated with WHO Obesity Class II ([BMI] ≥35 kg/m 2 ) 13 . The remaining loci associated with BMI tails and clinical classes of SevO had variants that intersected with studies of continuous BMI GWAS 12 , 14 and mapped highly penetrant variants in loci that affect key molecular and neural pathways involved in human energy homeostasis 13 , 15 – 22 . Given that most SevO GWAS have been limited by small sample sizes 16 , 23 , inconsistent phenotype definition (clinical classes of obesity, extremes of distribution tails) 24 , a lack of participants of diverse backgrounds (with current analyses mainly including Europeans) 23 , 25 , and a focus on monogenic causes of obesity that are difficult to detect in population-based samples, 13 we sought to perform the largest known GWAS of SevO among individuals of diverse ancestries, leveraging extremes of obesity and WHO described clinical obesity classes. We hypothesized that given its extreme form, common variants for SevO could be mapped with smaller sample sizes than GWAS for other obesity related traits and that such variants would likely overlap with variants mapped using continuous BMI and other obesity binary traits. Lastly, we performed functional mapping, polygenic risk score analysis (PRS), phenome wide association studies (PheWAS), and assessed environmental risk factors in the context of predicted high risk for, to discern the biological mechanisms underlying body weight regulation. RESULTS Demographic characteristics Genotyping, imputation quality control, and statistical platforms used for analyses are detailed in Supplementary Table 1 . We defined cases as individuals in the top 5th percentile of BMI, and controls in the 5th–50th percentile, stratified by sex after adjusting for age, age², and ancestry (BMI cases ranged from 34 kg/m 2 to 48 kg/m 2 and BMI controls ranged from 22 kg/m 2 to 27 kg/m 2 ) ( Supplementary Figures 1-4 ). We also used the WHO classification to define SevO cases as individuals with BMI ≥40 kg/m² (Obesity Class III) or ≥50 kg/m² (Obesity Class IV), and controls as a BMI of 18.5–24.9 kg/m². We defined loci as index SNPs meeting genome-wide significance (P ≤5×10⁻D) and SNPs within ±250kb in linkage disequilibrium (LD, r² <0.6) using the 1000 Genomes Project reference. If multiple independent SNPs (r² <0.1) reached significance within a locus, each was functionally annotated. We also considered rare variants (MAF <1%) given their potential higher frequencies in ancestry-stratified analyses, though acknowledging the unreliability of rare variants genotyped by SNP arrays. Stage 1 discovery and Stage 2 replication analyses included 11 and 6 population-based studies, with a total of 159,359 and 480,997 individuals across diverse ancestries ( Figure 1 ; Supplementary Tables 2and 3 ). Download figure Open in new tab Figure 1: Schematic of the study design for genomic analysis and systematic comparisons of SevO in ancestrally diverse populations. GWAS of severe obesity (SevO) traits We analyzed >31 million genotyped or imputed SNPs in ancestry-, sex-specific, and combined analyses ( Supplementary Figures 5-13 ). In the combined ancestry and sex analysis, we identified four novel signals in or near unknown loci of obesity ( BHLHE40-AS1 , PLAS2R1 , PIWL1 and CDHR1 ) and six novel signals in or near known obesity loci ( PDGFC, SMARCAD1, TENM2, ZNF184, PLCL2, and SLC39A11) at genome-wide significance (P <5×10⁻D) ( Supplementary Table 4 ). SNP-based heritability in the discovery phase ranged from 0.27 to 0.29 for WHO Class III and 0.37 to 0.52 for Class IV obesity ( Supplementary Table 24 ). Both SevO classes showed high genetic correlation with BMI (rg = 0.94 for Class III, 0.93 for Class IV). These ten variants were carried forward to Stage 2 replication. We replicated the novel BHLHE40-AS1 signal and three novel signals near known loci ( TENM2, ZNF184, PLCL2 ) in a meta-analysis using a Bonferroni-adjusted threshold of P < 0.005 ( Table 1 ; Figure 2 ). All variants showed consistent effect directions across SevO classes. Ancestry-stratified summary statistics for these 10 variants are in Supplementary Tables 5-10 . View this table: View inline View popup Download powerpoint Table 1: Summary of unreported independent GWAS signals of severe obesity traits in the discovery and replication cohorts. Independent signals were defined as lead variants having met a genome-wide significant P value ( P □<□5□×□10 −8 ) in our discovery cohort and a Bonferroni adjusted significance ( P= 0.005) in the replication cohort across any strata (all, men, or women) had a linkage disequilibrium (LD) r 2 □<□0.6 with other variants and were defined as those independent from each other at r 2 < 0.1 inside a subset of independent significant variants. Genes were annotated based on ±250kb of the lead variant and/ or closest gene present. Download figure Open in new tab Figure 2: Forest-plot illustrating the direction of association for the four validated variants (discovery and replication) across the three obesity classes (95% class, Obesity Class 3 and Obesity Class 4) for each stratum (all, females and males). Expression Quantitative Trait Loci (eQTL), colocalization and functional annotations of novel GWAS findings We evaluated the functional and clinical significance of our four variants identified in Stage 1 discovery and validated in Stage 2 replication using colocalization with expression quantitative trait loci (eQTLs) from GTEx 26 , eQTLGen Phase 1 27 , and a large Mexican American whole blood eQTL resource. No eQTLs were found in eQTLGen Phase 1. In GTEx, the minor (C) allele of rs36118680 was associated with increased PLCL2 expression in whole blood (NES = 0.28, P = 6.3×10⁻¹D) ( Supplementary Table 11a ). In the Mexican American dataset, the minor (C) allele of rs17824177 was associated with higher BHLHE40-AS1 expression (NES = 0.395, P = 0.01) (Supplementary Table 11b). Colocalization modeling with Obesity Class III GWAS data revealed colocalization between rs36118680 and PLCL2 expression in whole blood (posterior probability = 99%). Plausible biological roles for genes surrounding novel replicated variants Many of the genes surrounding these index variants (±250kb of the index variant or closest gene present) had important roles in the pathogenesis of SevO including neuronic control of food intake, sustained low-grade chronic inflammation and adipogenesis. A summary of the plausible biological roles of genes surrounding the four novel variants with SevO using online resources and a comprehensive manual review of the published literature is presented in Supplementary Table 12. Overlap with known BMI loci We assessed the relevance of known BMI loci for Obesity Class III by examining 813 established BMI-associated variants ( P <5×10⁻D) in our ancestry-combined Obesity Class III discovery results. Of these, 464 variants (57.07%) showed consistent direction and nominal significance (P <0.05) with Obesity Class III, indicating a strong enrichment of susceptibility alleles across traits (R Pearson = 0.89, P = 2.02×10⁻²DD) ( Supplementary Figure 15 , Table 17 ). We also analyzed our four validated novel variants (or their LD proxies, R² >0.8) in the GIANT+UKBB 12 and UKBB trans-ancestry BMI meta-analyses. In GIANT+UKBB by Yengo et al , only rs17824177 showed some evidence of association with BMI ( P = 2.6×10⁻D), though not genome-wide significant. The other variants were absent due to their rarity or the older HapMap imputation panel. In the UKBB trans-ancestry meta-analysis, genome wide evidence of association with BMI was not demonstrated (rs17824177: P = 0.00112, rs36118680: P = 0.0118, rs13155681: P = 0.132, rs140919115: P = 0.246). PRS-estimation of SevO PRS were calculated using two methods: PRS-CS with HapMap3 SNPs for SevO traits (e.g., 95 th percentile BMI, Obesity Class III, and Class IV) and BMI GWAS results from the GIANT consortium (N∼300k 14 ), and genome-wide significant independent variants from SevO GWAS traits in this study. We tested the PRS on phenotypic and genotype data from 235,071 unrelated UKBB participants and 3,063 unrelated Mexican Americans from the CCHC study, all classified as cases or controls for SevO. The participants included 223,699 Europeans, 4,057 African or African Americans, 1,537 East Asians, 4,978 South Asians, and 3,063 Mexican Americans. PRS performance by population is shown in Figure 3 and Supplementary Table 15 . Download figure Open in new tab Figure 3: Phenotypic variance explained (R 2 ) by (A) using HapMap 3 SNPs in the BMI GWAS from the GIAN consortium and (B) using the PRS-CS method with HapMap3 consortium SNPs for each of the SevO GWAS traits (e.g., 95% Obesity, Obesity Class III and Obesity Class IV). Using approximately 1/3 the sample size in our SevO PRS we show similar predictive power in explaining phenotypic variance of SevO traits when compared to the PRS generated from the BMI GWAS. As expected, R² estimates were low when using only GWAS-significant SNPs to calculate PRS. However, the PRS-CS method with HapMap 3 variants improved R² across all ancestries. For Obesity Class III, R² improved to 6.0% for African participants, 11.4% for Europeans, 13.8% for Hispanics, and 14.4% for South Asians. For the 95th percentile BMI, R² was 3.3% for Africans, 2.7% for East Asians, 7.4% for Europeans, 6.1% for Hispanics, and 6.8% for South Asians. For Obesity Class IV, R² was 2.4% for Africans, 6.5% for Europeans, and 13.5% for Hispanics ( Supplementary Table 15 ). Overall, PRS performance was highest for Obesity Class III, except in the Hispanic population. We also calculated PRS using BMI GWAS results and compared the performance to SevO GWAS-based PRS. R² performance was similar across ancestries, though the SevO GWAS had a smaller sample size (∼120,000 for the 95th percentile, ∼80,000 for Obesity Class III, ∼50,000 for Class IV) compared to the BMI GWAS (∼300,000) ( Supplementary Table 15 ). Only three East Asian participants had BMI ≥40 kg/m², and no East or South Asian participants had BMI ≥50 kg/m², so PRS were not calculated for these groups. PRS-PheWAS in the UKBB We conducted a phenome-wide association study (PheWAS) of PRS-SevO across diverse populations in the UKBB ( Supplementary Figure 14; Supplementary Tables 16-20 ). Obesity Class III PRS was associated with 37% of phenotypes (out of 1,668) in Europeans, including known obesity-related traits like BMI, body fat, waist circumference, metabolic comorbidities, and bone density. We also found strong associations with inflammation markers (C-reactive protein, gamma-glutamyltransferase) and hematopoietic traits (immature reticulocyte fraction, reticulocyte count) ( Figure 4 ; Supplementary Tables 16-20 ). Download figure Open in new tab Figure 4: PheWAS analysis of secondary phenotypes (max 1668) within the UKBB Europeans with our Obesity Class III PRS derived from our discovery analyses. PRS deciles and lifestyle behavior modelling A recent PRS for BMI, based on 2.1 million genetic variants, identified individuals at risk of SevO 28 . Khera et al. found that those in the top 10% (90th percentile) of PRS had an average BMI 2.9 kg/m² higher than those in the lower 90%. We replicated this approach with our PRS-CS, separating them into deciles (1-10) according to BMI categories (1) BMI <25 kg/m 2 , 2) BMI ≥25 kg/m 2 & BMI <30 kg/m 2 , 3) BMI ≥30 kg/m 2 & BMI <40 kg/m 2 and 4) BMI ≥40 kg/m 2 ) among diverse ancestries in the UKBB ( Supplementary Table 21 ). We then assessed the predictive power of our SevO PRS (90 th percentile vs. 10 th percentile) on Obesity Class III, in our ancestry -specific and -combined samples from the UKBB ( Supplementary Table 22; Supplementary Figures 16 and 17 ). Focusing on the combined samples, we found that SevO was present in 4.9% of those >90 th percentile for PRS compared to 0.55% of those in <10 th percentile category, corresponding to a 10-fold increased risk of SevO ( Figure 5 ). These differences in distribution indicate a different pattern of BMI distribution across the tail ends of the PRS distribution. Supplementary Table 25 provides estimates of the specificity, sensitivity, and positive and negative predictive values of our SevO PRS for Obesity Class III across the European, African, and South Asian samples from UKBB and the Hispanic samples from the CCHC cohort. The predictive power of the SevO PRS, assessed using receiver operating characteristic (ROC) curves, showed AUC values ranging from 0.62 to 0.77 when using PRS alone, and improved to 0.75 to 0.83 when combined with covariates across all ancestries. Sensitivity for Obesity Class III ranged from 15% to 39%, with positive predictive values from 16% to 33%. Download figure Open in new tab Figure 5: Relationship of SevO PRS > 90 th percentile with < 10 th percentile across BMI categories in ancestry - combined samples from the UKBB. We also compared the pattern of association of individual lifestyle factors with BMI, within each of the PRS groups ( Figure 6 ; Supplementary Table 23 ). Of particular interest were participants with a PRS in the ≥ 90 th percentile and a BMI 90 th percentile for PRS and Obesity Class III, 3) < 10 th percentile for PRS and SevO 4) <10 th percentile and BMI <25 kg/m 2 . The “Resilient group” reported significantly healthier lifestyle behaviors when compared to participants with SevO or even those with BMI <25 kg/m 2 and with PRS <10 th percentile, including improved dietary patterns (lower meat intake, increased fruit, vegetable, fiber and fish intake), increased physical activity and sleep duration and insomnia ( Figure 6 ; Supplementary Table 23 ). The “Resilient group” also smoked slightly more and consumed more alcohol than participants with SevO but consumed less alcohol than those with BMI <25 kg/m 2 and a PRS in the <10 th percentile range ( Figure 6 ; Supplementary Table 23 ). The “Resilient group” was also less likely than participants with SevO but more likely than those with BMI <25 kg/m 2 and a PRS in the <10 th percentile range to say they were ‘plumper’ than their peers at age 10 years ( Figure 6 ; Supplementary Table 2 3. Download figure Open in new tab Figure 6: Lifestyle adherence comparisons between sample groups defined by PRS upper and lower deciles and BMI categories. The “Resilient group” was designated as individuals having a PRS in the ≥ 90 th percentile and a BMI 90 th percentile for PRS and SevO Class III, 3) < 10 th percentile for PRS and SevO and 4) <10 th percentile and BMI <25 kg/m 2 with different lifestyle factors including dietary, physical activity and sleep patterns, alcohol and smoke servings and perceived body size at age 10. DISCUSSION In our meta-analysis of GWAS of up to 159,359 individuals across 11 ancestrally diverse population-based studies, we identified numerous loci associated with SevO, many of which overlap with Class I obesity (BMI ≥30 kg/m 2 ). Ten unreported independent variants in novel and known loci were associated with obesity related traits for the first time; four of these were validated in our replication cohort of 480,897 individuals of diverse ancestries. Of our replicated signals, rs17842177 is in a novel locus ( BHLHE40-AS1 ), whereas the other three novel signals (rs36118680, rs13155681 and rs140919115) are in known BMI loci. Our study expands the number of identified SevO signals, confirms strong overlap in the genetic architecture of SevO and BMI and reveals a remarkable impact of SevO on the clinical phenome, affording new opportunities for mechanistic insights and clinical prevention and intervention. These findings further enhance the underlying biology of SevO and highlight novel genetic signals not previously implicated in SevO pathophysiology. Before severe obesity became as common as it is today, the focus was on monogenic forms of the disease. It is only more recently that the form of common complex severe obesity became more prevalent in the population. Thus, early genetic studies of severe obesity (SevO) focused on identifying gene-disrupting mutations in the leptin-melanocortin pathway (e.g., LEPR, MC4R, POMC ) 29 – 33 , which are linked to early-onset SevO through dysregulation of food intake and food preferences 34 . While these studies provided novel mechanistic insights, GWAS have shifted focus to polygenic forms of obesity. To date, GWAS has identified over 1,000 loci influencing BMI, with some high-impact variants near genes associated with extreme and early-onset obesity 11 , 14 , 35 – 38 also enriched in polygenic obesity traits. GWAS of SevO found numerous susceptibility loci that intersected with earlier-identified BMI loci including the archetypal FTO, MC4R , SH2B1 and NPC1 suggesting little etiological heterogeneity between obesity subgroups 16 , 17 , 19 . However, many of these GWAS had small sample-sizes, low coverage of markers tested, and inconsistent phenotype groupings, all which may have limited study findings. A large GWAS involving 263,407 individuals of European ancestry identified 165 genetic loci associated with BMI, height, waist-to-hip ratio, and WHO-defined obesity classes: Overweight (BMI ≥25 kg/m²), Obesity Class I (BMI ≥30 kg/m²), Obesity Class II (BMI ≥35 kg/m²), and Obesity Class III (BMI ≥40 kg/m²). Of these, only one known variant, rs1421085 in the FTO gene, was significantly associated with Obesity Class III [OR=1.47, P=2.11 × 10⁻¹D], which was identified in smaller SevO GWAS studies and replicated in our study. Additionally, 22 variants were associated with Obesity Class II, including two novel loci in HS6ST3 (heparan sulfate 6-O-sulfotransferase 3) and ZZZ3 (zinc finger ZZ-type containing 3). The absence of novel associations with Obesity Class III in this study likely reflects low statistical power, with 3,986 cases and 67,010 controls. In our discovery GWAS, we found validation for the novel Obesity Class III traits reported by Berndt et al. for HS6ST3 (rs7989336) and ZZZ3 (rs17381664), particularly in Obesity Class IV, as well as in ancestry-combined and female-specific samples. Consistent with findings in other polygenic traits, our study shows that large sample sizes are needed to identify variants associated with population extremes 13 , 39 . Using a population-based design, we identified a novel locus ( BHLHE40-AS1 ) and three unreported independent signals in known BMI loci linked to SevO traits. These loci are involved in inflammation, food intake regulation, and altered adipose differentiation. Despite the strong genetic overlap between BMI and SevO (r g s > 0.80), focusing on trait extremes allowed us to detect novel signals with smaller sample sizes. Obesity is often characterized by low-grade inflammation that is associated with a sequalae of metabolic diseases including type 2 diabetes, hypertension, and cardiovascular complications 40 . As an acute-phase reactant to inflammation and infection, C-reactive protein (CRP) is associated with obesity 41 along with inflammatory mediators such as TNF- α and IL- 6 42 . Our study identified the novel BHLHE40-AS1 as a strong candidate gene of SevO both through our GWAS findings and replication but also through eQTL evidence in whole blood samples from Mexican American CCHC participants 43 . A CADD score > 10 was also reported for rs17824177 (C score =15.27) suggesting some level of deleteriousness ( Supplementary Table 13 ). Recently, a study has demonstrated that BHLHE40-AS1 modulates pro-inflammatory cytokines and is an important mediator of IL6/STAT3 signaling 44 . In addition, common variants in BHLHE40-AS1 associate with gamma glutamyl transferase, a biomarker positively correlated with C-reactive protein 45 and increased oxidative stress 46 , which is supported by our PheWAS of Obesity Class III trait in UKBB. Our study suggests that BHLHE40-AS1 is a strong biological candidate of obesity. Accumulating evidence also suggests that distinct psychiatric disorders including schizophrenia and major depressive disorders share a common genetic etiology with obesity 47 . The intronic variant, rs140919115 (n.265+6212G>A), located in ZNF184 was associated with Obesity Class III. This variant surrounds a locus that harbor genes ( ZNF391 , POM121L2 and PRSS16I ) involved with a plethora of mood-related disorders ( see Supplementary Table 12 ) 48 , 49 . This suggests a multi-faceted association between psychiatric disorders and BMI, involving pathways that may influence ‘binge’ eating and canonical food-related behaviors. In addition, ZNF184 was found to amplify FTO gene expression levels 50 , and may regulate body through FTO -mediated browning and mitochondrial thermogenesis 51 . Two additional loci ( Supplementary Table 12 ) highlight genetic mechanisms involved in the developmental shift from brite (brown) adipocytes to energy-storing white adipocytes leading to reduced thermogenesis and increased lipid deposition 34 . The intronic variant, rs13155681 (c.226+44283C>T) located in TENM2 was associated with the 95 th percentile BMI and may mediate obesity through adipocyte differentiation. TENM2 , involved in regulating synaptic plasticity of neurons, is known to maintain white adipocyte phenotype, and reduced mitochondrial respiration during adipogenic differentiation 52 leading to increased storage of fat. The intronic variant rs36118680 (c.3204+5565C>G) was associated with Obesity Class III. The PLCL2 locus mediates several biological functions, particularly those related to protein phosphatases. and is known to modulate obesity through fat lipolysis and thermogenesis regulation of adipocytes 53 , 54 . However, to translate genomic findings to meaningful outcomes, incorporation of other ‘omic’ platforms combined with advanced computational tools are needed to provide more proximal insight into the dysregulation of SevO biology in response to genetic modifications. The SevO PRS in UKBB explained 11.4%, 6.0%, 13.8% and 14.4% of the phenotypic variance for Europeans, Africans, Hispanics and South Asian participants, respectively and was similar with the PRS generated using HapMap 3 SNPs in the BMI GWAS from the GIANT consortium 14 . Notably, we had similar predictive power and performance in BMI risk assessment with just 1/3 the sample size across all ancestries. In applying our SevO PRS in PheWAS, 37% of phenotypes demonstrating significant associations (max 1668). These findings highlight a broader impact of SevO on disease morbidity and mortality than previously anticipated. Notable associations were also found with hematopoietic phenotypes, such as immature reticulocyte fraction and reticulocyte count. Genes upregulated in obesity are selectively expressed in reticulocytes 55 , aligning with studies showing higher red blood cell counts in obese individuals 56 , 57 . Future research will explore pleiotropic genetic effects to better understand the link between obesity and its comorbidities. A recent study demonstrated that PRS can identify individuals at high risk of obesity. By analyzing over 2 million variants in 300,000 people, the PRS explained 8.4% of BMI variation 28 , with those in the top decile having a BMI 2.9 kg/m² higher on average, and a 4.2-fold increased risk of SevO compared to the lowest nine deciles. Our PRS analysis, using fewer variants and a smaller sample, we found a different BMI distribution at the tail ends of the distribution of genetic susceptibility, highlighting the potential for heterogeneity in the relationship between PRS and BMI distribution. We found SevO in 4.9% of individuals in the top 90th percentile vs. 0.55% in the bottom 10th percentile, indicating a 10-fold increased risk. However, our study and previous studies showed low predictive value, with AUC ROC values ranging from 0.16 to 0.33 and sensitivity from 0.15 to 0.39 28 , 36 . This poor prediction may stem from the exclusion of lifestyle factors and selective participation bias in the UKBB. Our analysis suggests that incorporating lifestyle factors, like diet and physical activity, is crucial for improving personalized risk prediction. There were several limitations to our study. Sample sizes were small for certain populations (e.g., East Asians) and SevO classes (Obesity Class IV). There was also a sex imbalance between the predominantly female discovery cohort and predominantly male replication cohort, limiting validation of sex-specific associations. Additionally, the 95% percentile BMI trait showed heterogeneity, lower predictability, and less heritability, which may have reduced GWAS power. Despite this, we conducted a complementary GWAS using WHO SevO classification to identify additional variants. Lastly, we recognize that stratification based on self-reported background may not fully align with genetic similarity, but it was necessary due to limitations in existing cohorts. Despite these limitations, our study identified a novel locus influencing SevO and expands the number of identified SevO signals. We also confirmed a strong overlap between the genetic architecture of SevO and BMI. The integration of two replicated variants (located in or near BHLHE40-AS1, and PLCL2, ) with transcriptomic data suggests a candidate gene in each region with the top GWAS SNP likely influencing risk of SevO through differential expression. Follow-up functional studies for these regions will be prioritized in our future work along with causal modelling to infer genetic liability. Our systematic analysis combining PRS, PheWAS and lifestyle factors further reinforced the limited etiologic heterogeneity between obesity traits, the common downstream sequelae associated with SevO and BMI, and the importance of lifestyle factors in understanding genetic risk for SevO. MATERIALS AND METHODS Study Design Study-specific design, sample quality control and descriptive statistics are provided in Supplementary Tables 1-3 . We conducted a two-stage study of SevO traits: 95 th percentile [cases defined as the upper 5 th percentile and controls as the 5 th −50 th percentile], Obesity Class III [BMI ≥ 40 kg/m 2 ] vs. controls [BMI ≥ 25 kg/m 2 ] and Obesity Class IV [BMI ≥ 50 kg/m 2 ] vs. controls [BMI ≥ 25 kg/m 2 ]. Stage 1 discovery analyses consisted of up to 159,359 individuals (≥18 years to < 70 years) across 11 diverse population-based studies which includes individuals of European ( n= 108,844), African ( n= 23,237), Hispanic ( n= 20,081), East Asian ( n= 4,138), American Indian ( n= 276), Hawaiian ( n= 2,232) and mixed ancestry ( n= 508) backgrounds ( Supplementary Table 2 ). Of note, the numbers in the ancestry -combined samples do not match our ancestry-stratified samples because of the inclusion of datasets that did not initially meet our ancestry-stratified criteria (N in cases >20). Discovery meta-analyses were performed in each ancestry group separately and in an all-ancestry combined group for both sex-specific and sex-combined analyses. Given the preponderance of rare variations in our initial discovery, we assessed replication of our novel independent variants that reached GWAS significance threshold ( P = 5×10 −8 ) in a stage 2 follow-up samples of 480,897 individuals (≥18 years to < 70 years) across 6 diverse population-based studies which includes individuals of European ( n =390,569), African/African American ( n= 62,539), Hispanic ( n= 21,781), South Asian ( n= 4,759) and East Asian ( n= 1,557) ancestries ( Supplementary Table 2 ). All studies were approved by local research ethics committees across each institution, and all participants gave informed consent. All methods were performed in accordance with the relevant guidelines and regulations. Phenotype Definitions We used 3 different classes of SevO traits defined by BMI (weight (in kg)/height (in m) 2 ). Using ancestry specific quantile regression modelling nested within our cohorts, the 95 th percentile SevO class was defined as the upper 5th percentile (cases) and 5 th -50 th percentile (controls) of BMI distribution stratified by sex after controlling for age, age 2 and ancestry ( Supplementary Figures 1-4 ). For clinical obesity classes, cases were defined by BMI ≥ 40kg/m 2 for Obesity Class III and BMI ≥ 50kg/m 2 for Obesity Class IV. Controls were subjects with BMI ≥ 18.5kg/m 2 and < 25kg/m 2 . A minimum of 20 cases and 20 controls for each study-specific stratum was required for combined analyses, whereas a minimum of 10 cases and 10 controls for each study-specific stratum was required for sex-specific analyses. Association analyses Ancestry and sex specific GWAS, adjusted for age, principal components of ancestry (PCs) 1-10 and study specific covariates, were conducted for the three different classes of SevO traits. Poorly imputed variants (IMPUTE info < 0.4 and/or R 2 < 3) 58 , and those with an effective sample size less than 20 in each stratum (or 10 in each sex-specific stratum) were excluded from association analyses. A centralized quality control procedure implemented in EasyQC 58 was applied to association summary statistics to identify study specific problems. Ancestry and sex combined meta-analysis for each SevO trait was performed in METAL 59 using the fixed-effect inverse variance method based on β estimates and standard errors from each study. Variants with a minor allele count [MAC] <50, with a combined sample size of 80 (for one study) were excluded from downstream analyses. Similar GWAS, meta-analysis and quality control methods were employed for the replication analyses. Gene mapping, functional annotation, and validation of novel variants Post-GWAS analysis of gene mapping, functional annotation, and tissue expression analysis of prioritized loci in our discovery GWAS was conducted using Functional Mapping and Annotation (FUMA) 60 SNP2GENE function. We identified each locus with an index SNP that met genome-wide significant P value threshold of P 5×10 −8 and then included all SNPs surrounding the index SNP ±250kb on each side. We functionally annotated each locus by considering the index SNP and any SNP in the 500kb region that displayed linkage disequilibrium (LD) r 2 <0.6 with the index SNP using the phase all ancestry 1000G project LD reference panel. If in some loci, two or more SNPs achieved genome-wide significant evidence for association but were independent of one another ( r 2 <0.1), we functionally annotate each independent SNP effect. If no genes were present within ±250Dkb of the lead variant, the closest gene was selected. We also used PhenoScanner v2 61 , GWAS Catalog 62 , and the Integrative Epidemiology Unit (IEU) Open GWAS Project, as well as conducting a comprehensive literature review to evaluate our independent associated variants across all three SevO classes. A variant is considered novel if not previously associated with obesity related traits. Variants that meet these criteria were subsequently assessed in our replication dataset and were considered significant if they were directionally consistent and met our Bonferroni adjusted significance threshold (0.05/ n independent SNPs) in any SevO class. Independent unreported variants were annotated for predicted pathogenicity by Combined Annotation Dependent Depletion (CADD) scores using CADDv1.3 63 with a Phred-scale CADD score >10 (top 10%) being deleterious. We utilized online resources such as GeneCards, GWAS Catalog and PubMed to derive potential biological links of genes in proximity of novel signals (±250kb of the index SNP, or the closest gene if no gene was present within ±250Dkb) ( Supplementary Table 12 ). Expression Quantitative Trait Loci (eQTL) and colocalization We used eQTL summary data from GTEx v8 26 and eQTLGen Phase 1 release 27 , to test for cis associations between our novel findings with transcripts within ±1 Mb across obesity related tissues including liver, skeletal muscle, whole blood, brain, and adipose tissues surrounding the transcription start (TSS). Detailed method descriptions can be found in the main GTEx v8 26 and/or eQTLGen 27 publications. Additionally, we examined cis associations between our SevO novel variants with transcripts derived from RNA sequencing (RNAseq) within ±1 Mb in whole blood among 645 Mexican Americans from the County Hispanic Cohort (CCHC) 64 . Significant eQTLs were determined based on Bonferroni adjusted significance threshold (0.05/ n of transcripts tested for the locus). Colocalization of any validated novel variants in the CCHC cohort was performed using the coloc software 65 with a posterior probability >75% supporting colocalization from a single causal variant. Detailed method descriptions on CCHC study population, genotyping and eQTL mapping are provided in Chen et al . 2022 43 Comparison of Replicated SNPs With Previous Publication We tested the direction of association of in our discovery analysis for Obesity Class III with 813 known BMI-associated variants (based on HapMap imputed data) that met a significance threshold of P0.8) with SevO traits within this cohort to assess consistency of associations in both the meta-analysis of GIANT and UKBB 12 and the UKBB trans-ancestry meta-analysis of BMI. SNP based heritability’s and genetic correlation between SevO and BMI Using GWAS summary statistics, we calculated single nucleotide polymorphisms (SNP)-based heritability for SevO traits and the genetic correlation between each trait with BMI using linkage disequilibrium score regression (LDSC) 70 , 71 . Heritability, ranging from 0 to 1, measures the proportion of variation in a phenotype accounted for by genetic factors. SNP-based genetic correlations, ranging from −1 to +1, measure the extent to which two phenotypes share common genetic variation. Analyses were conducted in the ancestry -combined samples and in European -combined samples only. Polygenic Risk Score (PRS) Polygenic risk scores (PRS) were generated using GWAS summary statistics from three comparative sets: a) the significant lead independent SNPs from each SevO trait all ancestry GWAS, b) the HapMap 3 SNPs in the all ancestry GWAS of SevO traits from the current study, and c) the HapMap 3 SNPs in the BMI GWAS from the Locke et al (2015) paper in the GIANT consortium 14 . Both the UKBB and CCHC test cohorts were not included in the generation of the PRS. We utilized the PRS-continuous shrinkage (CS) method which applies a Bayesian regression framework and places conceptually CS priors on variant effect estimates 72 . Pairwise LD matrices within pre-defined LD blocks 73 (using European LD blocks for LD calculations were calculated using PLINK and converted to HDF5 format 74 ). PRS for SevO or BMI was calculated for each ancestral group across all SevO classes. Multivariable logistic regression was used to test the association of PRS SevO or BMI with SevO classes adjusted for age, sex, 10 PCs and ancestry in the UKBB. A partial Nagelkerke R 2 was used to estimate the proportion of variance for SevO classes explained by the PRS. PheWAS using the UKBB Using the weighted Obesity Class III PRS-BMI we performed a PheWAS of 19 clinical classes of traits in the UKBB for each ancestry (African, European, South Asian, and East Asian). Detailed description of the classes of clinical traits are described in Supplementary Table 16. Regression modelling with Obesity Class III PRS-BMI as the independent variable, phecodes as the dependent variables, and age, sex, the first 10 PCs as covariates, were used to identify phenotypic associations. An FDR of 0.05 was applied to account for multiple testing. Measures of Diagnostic Accuracy Sensitivity, Specificity, Positive Predictive Values (PPV), Negative Predictive Values (NPV) and area under the curve (AUC) was applied in evaluating the effectiveness of SevO PRS only, covariates only and combined SevO PRS and covariates with Obesity Class III across European, African, and South Asian participants from the UKBB and Hispanic participants from the CCHC cohort. PRS deciles and assessment of lifestyle factors We stratified UKBB participants by their Class III SevO PRS-BMI according to deciles (1-10) with high PRS-BMI comprising >90 th percentile (decile 10) and low PRS-BMI the ≤10 th percentile (decile 1). Normal weight (≤25 kg/m 2 ), overweight (>25 kg/m 2 ≤30 kg/m 2 ) obesity (>30 kg/m 2 ≤40 kg/m 2 ) and SevO Class 3 (>40 kg/m 2 ) was determined within each decile for ancestry -stratified and -combined groups. We explored the predictive power of our PRS-BMI on severe obesity (≥ 40 kg/m 2 ) between our > 90 th percentile ( n= 42,844) and ≤10 th percentile ( n= 42,856) in the ancestry -combined samples within UKBB. We then assessed whether patterns of lifestyle factor associations, including physical activity, dietary patterns, alcohol/smoke servings, sleep behaviors and self-reported “body size” at age 10, were differentially associated in individuals across the PRS-BMI deciles. Those with a PRS in the ≥ 90 th percentile and a BMI 90 th percentile for PRS and SevO Class III; 3) < 10 th percentile for PRS and SevO and 4) <10 th percentile and BMI <25 kg/m 2 . Data availability The datasets generated and/or analyzed during the current study will be available in the GWAS Catalog repository. Footnotes ↵ ¶ These authors jointly supervised this work: Kari E North, Mariaelisa Graff References 1. ↵ Finkelstein , E.A. et al. Obesity and severe obesity forecasts through 2030 . 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Share Genome-wide association study provides novel insight into the genetic architecture of severe obesity Mohanraj Krishnan , Mohammad Yaser Anwar , Anne E Justice , Geetha Chittoor , Hung-Hsin Chen , Rashedeh Roshani , Roelof A.J Smit , Michael H Preuss , Nathalie Chami , Benjamin S Hadad , Esteban J Parra , Miguel Cruz , Qin Hui , Peter W.F Wilson , Yan V Sun , Xiaoyu Zhang , Gregorio V Linchangco , Sharon L.R Kardia , Jessica D Faul , David R Weir , Lawrence F Bielak , Heather M Highland , Kristin L Young , Baiyu Qi , Yujie Wang , Myriam Fornage , Christopher Haiman , Iona Cheng , Ulrike Peters , Charles Kooperberg , Steven Buyske , Joseph B McCormick , Susan P Fisher-Hoch , Frida Lona-Durazo , Jesus Peralta , Jamie Gomez-Zamudio , Stephen S Rich , Kendra R Ferrier , Ethan M Lange , Christopher G Gignoux , Eimear E Kenny , Genevieve L Wojcik , Kelly Cho , Michael J Gaziano , Luc Djousse , Shuwei Liu , Dhananjay Vaidya , Renée de Mutsert , Navya S Josyula , Christopher R Bauer , Wei Zhao , Ryan W Walker , Jennifer A Smith , Leslie A Lange , Mariah C Meyer , Ching-Ti Liu , Lisa R Yanek , Miryoung Lee , Laura M Raffield , Ruth J.F Loos , Penny Gordon-Larsen , Jennifer E Below , Kari E North , Mariaelisa Graff medRxiv 2025.02.25.25322870; doi: https://doi.org/10.1101/2025.02.25.25322870 Share This Article: Copy Citation Tools Genome-wide association study provides novel insight into the genetic architecture of severe obesity Mohanraj Krishnan , Mohammad Yaser Anwar , Anne E Justice , Geetha Chittoor , Hung-Hsin Chen , Rashedeh Roshani , Roelof A.J Smit , Michael H Preuss , Nathalie Chami , Benjamin S Hadad , Esteban J Parra , Miguel Cruz , Qin Hui , Peter W.F Wilson , Yan V Sun , Xiaoyu Zhang , Gregorio V Linchangco , Sharon L.R Kardia , Jessica D Faul , David R Weir , Lawrence F Bielak , Heather M Highland , Kristin L Young , Baiyu Qi , Yujie Wang , Myriam Fornage , Christopher Haiman , Iona Cheng , Ulrike Peters , Charles Kooperberg , Steven Buyske , Joseph B McCormick , Susan P Fisher-Hoch , Frida Lona-Durazo , Jesus Peralta , Jamie Gomez-Zamudio , Stephen S Rich , Kendra R Ferrier , Ethan M Lange , Christopher G Gignoux , Eimear E Kenny , Genevieve L Wojcik , Kelly Cho , Michael J Gaziano , Luc Djousse , Shuwei Liu , Dhananjay Vaidya , Renée de Mutsert , Navya S Josyula , Christopher R Bauer , Wei Zhao , Ryan W Walker , Jennifer A Smith , Leslie A Lange , Mariah C Meyer , Ching-Ti Liu , Lisa R Yanek , Miryoung Lee , Laura M Raffield , Ruth J.F Loos , Penny Gordon-Larsen , Jennifer E Below , Kari E North , Mariaelisa Graff medRxiv 2025.02.25.25322870; 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