The Ethnic/Racial Variations of Intracerebral Hemorrhage Genetics (ERICH-GENE) Study Protocol

The Ethnic/Racial Variations of Intracerebral Hemorrhage Genetics (ERICH-GENE) Study Protocol | 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 The Ethnic/Racial Variations of Intracerebral Hemorrhage Genetics (ERICH-GENE) Study Protocol View ORCID Profile Guido J. Falcone , Stacey Q. Wolfe , Marialuisa Zedde , Rosario Pascarella , Jordi Jimenez-Conde , Marta Vallverdu Prats , Joan Jimenez-Balado , Alessandro Pezzini , Sandra Rossi , Rustam Al-Shahi Salman , Neshika Samarasekera , Ramin Zand , Jinag Li , Christina Jern , Daniel Strbian , Liisa Tomppo , Hanne Sallinen , Mar Hernandez Guillamon , Magdy Selim , Mayowa Owolabi , Rufus Akinyemi , Gregory Fakunle , Tsong-Hai Lee , David Werring , Isabel C. Hostettler , Henry Houlden , Pankaj Sharma , Isaac John , Gie Ken-Dror , Wendy Jenkins , Kevin N. Sheth , Lauren H. Sansing , Dharambir K. Sanghera , Evgeny Sidorov , Israel Fernandez-Cadenas , Jara Cárcel-Márquez , Ching-Jen Chen , Andrea Becerril-Gaitan , Keon-Joo Lee , Hee-Joon Bae , Martin Dichgans , Rainer Malik , Stephanie Debette , Aniket Mishra , Guillaume Pare , Michael Chong , Yoichiro Kamatani , Zhengming Chen , Robin G. Walters , Sudha Seshadri , Myriam Fornage , Catherine Sudlow , Lee A. Gilkerson , Vivek J. Khandwala , Thomas C. Maloney , Stacie Demel , Livia Parodi , Ramin Zand , Paul Nyquist , Wendy Ziai , Bradford Worrall , Vagal M Achala , Carl D. Langefeld , Jonathan Rosand , Christopher D. Anderson , Daniel Woo , the ERICH-Gene Investigators doi: https://doi.org/10.1101/2025.06.11.25329301 Guido J. Falcone 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA 2 Yale Center for Brain and Mind Health, Yale School of Medicine , New Haven, CT, USA MD ScD MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guido J. Falcone Stacey Q. Wolfe 3 Department of Neurosurgery, Wake Forest School of Medicine , Winston-Salem, NC, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marialuisa Zedde 4 Neurology Unit, Stroke Unit, Azienda Unita Sanitaria Locale IRCCS di Reggio Emilia , Reggio Emilia, Italy MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rosario Pascarella 4 Neurology Unit, Stroke Unit, Azienda Unita Sanitaria Locale IRCCS di Reggio Emilia , Reggio Emilia, Italy MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jordi Jimenez-Conde 5 Department of Neurology, IMIM-Hospital del Mar; Neurovascular Research Group, Institut Hospital del Mar d’Investigacions Mèdiques (IMIM) , Barcelona, Spain MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marta Vallverdu Prats 5 Department of Neurology, IMIM-Hospital del Mar; Neurovascular Research Group, Institut Hospital del Mar d’Investigacions Mèdiques (IMIM) , Barcelona, Spain MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joan Jimenez-Balado 5 Department of Neurology, IMIM-Hospital del Mar; Neurovascular Research Group, Institut Hospital del Mar d’Investigacions Mèdiques (IMIM) , Barcelona, Spain MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alessandro Pezzini 6 Department of Medicine and Surgery, University of Parma and Stroke Care Program, Department of Emergency, Parma University Hospital , Parma, Italy MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sandra Rossi 6 Department of Medicine and Surgery, University of Parma and Stroke Care Program, Department of Emergency, Parma University Hospital , Parma, Italy MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rustam Al-Shahi Salman 7 Centre for Clinical Brain Sciences, University of Edinburgh , Edinburgh, UK FMedSci Find this author on Google Scholar Find this author on PubMed Search for this author on this site Neshika Samarasekera 7 Centre for Clinical Brain Sciences, University of Edinburgh , Edinburgh, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ramin Zand 8 Department of Neurology, College of Medicine, The Pennsylvania State University , Hershey, PA, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jinag Li 8 Department of Neurology, College of Medicine, The Pennsylvania State University , Hershey, PA, USA MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christina Jern 9 Department of Neurology, Sahlgrenska University Hospital , Region Västra Götaland, Gothenburg, Sweden MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Strbian 10 Department of Neurology, Helsinki University Hospital and University of Helsinki , Helsinki, Finland MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Liisa Tomppo 10 Department of Neurology, Helsinki University Hospital and University of Helsinki , Helsinki, Finland MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hanne Sallinen 10 Department of Neurology, Helsinki University Hospital and University of Helsinki , Helsinki, Finland MD PhD MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mar Hernandez Guillamon 11 Neurovascular Research Laboratory, Vall d’Hebron Research Institute , Universitat Autònoma de Barcelona, Barcelona, Spain MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Magdy Selim 12 Department of Neurology, Beth Israel Deaconess Medical Center/Harvard Medical School , Boston, MA, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mayowa Owolabi 13 Department of Medicine, University College Hospital , Ibadan, Nigeria MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rufus Akinyemi 13 Department of Medicine, University College Hospital , Ibadan, Nigeria MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gregory Fakunle 13 Department of Medicine, University College Hospital , Ibadan, Nigeria MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tsong-Hai Lee 14 Stroke Center and Department of Neurology, Linkou Chang Gung Memorial Hospital , Taoyuan, Taiwan MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site David Werring 15 UCL Queen Square Institute of Neurology , London, UK MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isabel C. Hostettler 15 UCL Queen Square Institute of Neurology , London, UK MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henry Houlden 15 UCL Queen Square Institute of Neurology , London, UK MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pankaj Sharma 16 Institute of Cardiovascular Research, Royal Holloway University of London , Egham, TW20 0EX, UK MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isaac John 16 Institute of Cardiovascular Research, Royal Holloway University of London , Egham, TW20 0EX, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gie Ken-Dror 16 Institute of Cardiovascular Research, Royal Holloway University of London , Egham, TW20 0EX, UK MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wendy Jenkins 3 Department of Neurosurgery, Wake Forest School of Medicine , Winston-Salem, NC, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kevin N. Sheth 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA 2 Yale Center for Brain and Mind Health, Yale School of Medicine , New Haven, CT, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lauren H. Sansing 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA MD MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dharambir K. Sanghera 17 Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center , Oklahoma City, OK, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Evgeny Sidorov 17 Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center , Oklahoma City, OK, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Israel Fernandez-Cadenas 18 Stroke Pharmacogenomics and genetics, Department of Neurology, Institut d’Investigació Biomedica Sant Pau , IIB-Sant Pau, Barcelona, Spain PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jara Cárcel-Márquez 18 Stroke Pharmacogenomics and genetics, Department of Neurology, Institut d’Investigació Biomedica Sant Pau , IIB-Sant Pau, Barcelona, Spain PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ching-Jen Chen 19 Department of Neurosurgery, University of Texas Health Science Center , Houston, TX, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrea Becerril-Gaitan 19 Department of Neurosurgery, University of Texas Health Science Center , Houston, TX, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Keon-Joo Lee 20 Department of Neurology, Korea University Guro Hospital , Seoul, Republic of Korea MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hee-Joon Bae 20 Department of Neurology, Korea University Guro Hospital , Seoul, Republic of Korea MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Dichgans 21 Institute for Stroke and Dementia Research (ISD), University Hospital , LMU Munich, Germany MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rainer Malik 21 Institute for Stroke and Dementia Research (ISD), University Hospital , LMU Munich, Germany PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephanie Debette 22 University of Bordeaux, INSERM, Bordeaux Population Health research center , UMR1219, Bordeaux, France MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aniket Mishra 22 University of Bordeaux, INSERM, Bordeaux Population Health research center , UMR1219, Bordeaux, France PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Guillaume Pare 23 Population Health Research Institute, Hamilton Health Sciences and McMaster University , Hamilton, ON, Canada MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael Chong 23 Population Health Research Institute, Hamilton Health Sciences and McMaster University , Hamilton, ON, Canada PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yoichiro Kamatani 24 Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo , Tokyo, Japan MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhengming Chen 25 Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford , Oxford, UK DPhil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Robin G. Walters 25 Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford , Oxford, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sudha Seshadri 26 Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio , San Antonio, Texas, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Myriam Fornage 27 Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, Texas, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Catherine Sudlow 28 Centre for Medical Informatics, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh , United Kingdom MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lee A. Gilkerson 29 Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine , Cincinnati, OH, USA BSN Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vivek J. Khandwala 30 Department of Radiology, University of Cincinnati College of Medicine , Cincinnati, OH, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas C. Maloney 30 Department of Radiology, University of Cincinnati College of Medicine , Cincinnati, OH, USA MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stacie Demel 29 Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine , Cincinnati, OH, USA DO PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Livia Parodi 31 Department of Neurology, Massachusetts General Hospital , Boston, MA, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ramin Zand 32 Department of Neurology, Penn State College of Medicine , Hershey, PA, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul Nyquist 33 Department of Neurology, Johns Hopkins School of Medicine , Baltimore, MA, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wendy Ziai 33 Department of Neurology, Johns Hopkins School of Medicine , Baltimore, MA, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bradford Worrall 34 Department of Neurology, University of Virginia School of Medicine , Charlottesville, VA, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vagal M Achala 30 Department of Radiology, University of Cincinnati College of Medicine , Cincinnati, OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Carl D. Langefeld 35 Department of Biostatistics and Data Science, Wake Forest University School of Medicine , Winston-Salem, NC, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jonathan Rosand 36 Department of Neurology, Massachusetts General Hospital , Boston, MA, USA 37 Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital , Boston, MA, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher D. Anderson 38 Department of Neurology, Mass General Brigham , Boston, MA, USA MD MMSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: cdanderson{at}mgb.org dwoo3{at}buffalo.edu Daniel Woo 39 Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo , Buffalo, NY MD MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: cdanderson{at}mgb.org dwoo3{at}buffalo.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Background Spontaneous, non-traumatic intracranial hemorrhage (ICH) is highly heritable disease. However, the identification of the genetic risk factors driving this high genetic predisposition has been limited by small sample sizes and underrepresentation of non-European populations. The ERICH-GENE study will gather and harmonize clinical, neuroimaging and genomic data on the largest and more diverse collection of ICH cases assembled to date. Methods ERICH-GENE is an NIH-funded, multi-center, international, genetic and neuroimaging study that aims to achieve the necessary sample size and diversity required to accurately describe the genetic architecture and trans-ethnic variation of ICH. ERICH-GENE will collect and harmonize clinical, neuroimaging and genomic data at least 10,000 multi-ethnic ICH cases. These data will be aggregated with 20,000 existing ICH cases and 600,000 ICH-free controls available through completed studies by the International Stroke Genetics Consortium. To ensure validity, data will undergo extensive harmonization, including expert review of neuroimages to ensure spontaneous etiology and hemorrhage location. We will conduct genome-wide association studies of risk, severity and outcome of ICH, testing for effect modification by race/ethnicity, sex and hemorrhage location. We will also conduct pathway, polygenic risk score and Mendelian randomization analyses. Results This study will include whole genome sequencing data from 10,850 spontaneous ICH samples, including clinical and radiographic phenotypic data to ensure reliability of true non-traumatic, non-lesional ICH and lobar vs nonlobar location. Of these, 1,497 have already been genotyped using genome-wide arrays, 3,753 have undergone whole genome sequencing, and 5,600 will undergo genome-wide genotyping through ERICH-GENE. There are currently 42 contributing sites exceeding study milestone enrollments. 16,175 radiographic studies from 4,974 patients have been uploaded for harmonization to date, including 26% lobar and 64% nonlobar hemorrhages. Neuroimaging assessment will also include grading for white matter hyperintensities, cerebral atrophy, and presence and severity of IVH. Nearly 6,000 ICH cases will complete genotyping by August 2025. Data/material transfer agreements for summary statistics as well as additional samples are on target to meet the study’s objectives. Conclusion ERICH-GENE is the largest trans-ethnic genetic study of ICH conducted to date. Combining a diverse patient population with expert adjudication of neuroimaging data, ERICH-GENE will identify genetic risk loci that drive the high heritability observed for this disease and make a significant contribution to the understanding of the trans-ethnic variation of its genetic architecture. INTRODUCTION Spontaneous, non-traumatic intracerebral hemorrhage (ICH) has the highest morbidity and mortality rate of any stroke subtype with about 79,000 new cases in the United States per year. 1 About a third of ICH patients die during the hospital admission and another third remain severely disabled. 2 There are clear disparities in ICH incidence, with higher risk of first-ever and recurrent ICH observed in Blacks, Hispanics and Asians. 3 – 5 The societal burden of ICH is likely to rise in coming years, based off clear trends over the previous two decades as well as increasing age and use of anticoagulant treatment. 1 – 3 , 6 Unlike ischemic stroke, ICH has not shown any decline in observed incidence rate. 7 – 9 However, ICH shares clinical, histopathologic and radiographic features within the spectrum of cerebral small vessel disease (CSVD), which contributes to stroke, depression, and Alzheimer’s Disease and related dementias. 10 – 14 We now know that ICH is a complex disease with an estimated heritable component of 30%. 15 Importantly, genetic variation appears to influence not only risk but also the acute severity and long-term clinical outcomes of ICH. We have previously identified novel genetic risk loci for ICH that have proven crucial to identifying underlying biological links between ICH, small vessel stroke, and CSVD. Our prior genome-wide association studies (GWAS) of ICH, some of which have combined ICH with related CSVD phenotypes, have found associations at APOE , PMF1 , ICA1L , and COL4A2 , providing insights into the roles of endothelial cell proliferation, basement membrane maintenance, and beta-amyloid in ICH risk. 14 , 16 – 18 While initial discoveries were from predominantly White/Caucasian populations, subsequent analyses indicate substantial variability for associations across ethnicities. 16 , 19 Black, Asian, and Hispanic populations suffer from ICH approximately 10 years earlier in life than whites, with disproportionate burden and effects of risk factors such as hypertension. 19 Maximizing sample size as well as ethnic diversity when studying ICH genetics will enable the identification of new loci both within and between ethnicities, elucidating causal mechanisms and providing tools for genetic risk stratification in diverse patient populations. As an example of the additional scientific value provided by studies with appropriate trans-ethnic representation, a recent meta-analysis confirmed the previously reported effect of APOE ε2 and APOE ε4 on ICH risk. 16 However, when restricting the analysis to only self-identified Hispanics and blacks, attenuated effects were found, despite adequate power to detect an effect lower than that seen in whites. 16 These results illustrate the variability of genetic effects in ICH by race and ethnicity and demonstrate the need for multi-ethnic samples to better understand how genetic risk interacts with other exposures. In this setting, the scientific goals of Ethnic/Racial Variations of ICH (ERICH)-GENE are to: 1) harmonize ICH case and exposure phenotypes, and perform a GWAS of ICH risk, CSVD imaging features, and outcome in a multi-ethnic dataset; 2) identify genetic risk factors for ICH that vary by sex or ethnicity, 3) build and validate genetic risk prediction tools for ICH risk and outcome, and 4) develop a data sharing platform incorporating whole genome sequencing and neuroimaging data. METHODS Following recent guidelines of research terminology describing population descriptors, 20 we will refrain from usage of the term “race”, which was originally included in the title of the ERICH-GENE study. To facilitate understanding, we will refer to “ethnicity” as a sociopolitical approximation of genetic ancestry, to which we here refer to as “ancestry”. The ERICH-GENE Study Group and objectives The ERICH-GENE Study Group leverages existing and new collaborations to maximize efficiency and diversity in patient representation. ERICH-GENE combines ICH cases and controls that have already been genotyped with ICH cases and controls that will be newly genotyped. One batch of already genotyped participants is contributed by the ERICH study, that enrolled 3,000 ICH cases (1000 white, 1000 black and 1000 Hispanic) and 3000 matched controls at 41 sites across the US whose samples completed whole genome sequencing (WGS) as part of the NHGRI-sponsored Centers for Common Disease Genomics (CCDG) initiative 21 A second batch of already genotyped cases is contributed by the Women’s Health Initiative (WHI), including an additional 753 ICH patients whose samples completed whole genome sequencing through the NHLBI-supported TOPMed initiative. In addition, 5,600 ICH cases are being newly enrolled and genotyped as part of ERICH-GENE. Importantly, these cases undergo expert review, harmonization and blinded case adjudication to assign hemorrhage location and exclude secondary causes of bleeding (e.g., tumors and vascular malformations). This process will allow us to better capture the two major pathophysiologic ICH mechanisms (hypertension-related vasculopathy and cerebral amyloid angiopathy) that underlie ICH and usually correspond to ICH location (nonlobar versus lobar, respectively). Finally, over 20,000 spontaneous ICH cases and 600,000 controls will be aggregated through summary statistics from various biobanks, including the UK Biobank, China Kadoorie Biobank, and Biobank Japan, and additional previously-genotyped cases and controls available through the Cerebrovascular Disease Knowledge Portal in association with the International Stroke Genetics Consortium. Rather than pursuing underpowered and unharmonized GWAS, we are performing a coordinated approach to maximize power for discovery of new loci for ICH. Definition of ICH The primary phenotype is spontaneous, non-traumatic ICH, meeting the following eligibility criteria: 1) age ≥18 years, 2) associated acute symptoms (abrupt onset of severe headache, altered level of consciousness and/or focal neurologic deficit), 3) neuroimaging confirmation (head CT or brain MRI), and 4) no evidence of arteriovenous malformation, aneurysm, hemorrhagic conversion of ischemic stroke, trauma or brain tumor as a cause of the hemorrhage. ICH location, demographic and medical covariates, will be cataloged and harmonized by the Phenotyping Committee. On intake, phenotype libraries will be recoded using Common Data Elements to prevent phenotype misclassification and account for missing or discordant data elements between studies. Controls Control subjects without ICH will be ethnically matched and randomly selected from the populations that gave rise to the cases. Both publicly available cohorts and ICH-free controls will be utilized from publicly available cohorts as well as from certain sites providing both ICH and ICH-free controls identified independently of the exposure. In total, the expected combined sample size of our analysis is 21,375 cases and 616,951 ICH-free controls, of which 8,953 cases and 30,971 controls from ERICH, WHI and ISGC will be included in the primary analysis stratified by location (lobar vs. nonlobar ICH, see Table 2 ). View this table: View inline View popup Download powerpoint Table 1: GWAS and Monogenic loci in ICH and CSVD View this table: View inline View popup Download powerpoint Table 2: Overview of cohorts and sample sizes for ICH cases and controls. This table shows the sample sizes of cases and controls present or expected from each of the participating studies and their subdivision into discovery cohort, meta-analysis I and II. Neuroimaging and Imaging Validation For validation of ICH as the qualifying event and reduction of heterogeneity in the ICH case population due to contamination with secondary causes of ICH, all collected neuroimaging will undergo central harmonization. All imaging will be submitted through Ambra (Intelerad, Montreal, Canada), a secure deidentified web-based portal, and reviewed by a panel of experts in ICH neuroimaging that includes stroke neurologists and neuroradiologist to confirm location and etiology. The location of ICH (nonlobar vs. lobar) is a crucial phenotype to ascertain, as hemorrhage location contains information about the underlying pathophysiology (i.e. hypertensive lipohyalinotic disease in the setting of nonlobar ICH, and cerebral amyloid deposition in the setting of lobar ICH) as well as the overall genetic architecture of the ICH trait. Additional CT-based measurements to be obtained include of ICH volume, presence and volume of intraventricular hemorrhage using the Graeb score, leukoaraiosis using the van Swieten scale, cerebral atrophy using the Global Atrophy Score. Where possible, we will adhere to NINDS Common Data Elements for collection of neuroimaging phenotypes. MRI will be solicited and available on a subset of patients for central collection and storage for confirmatory phenotyping and future initiatives. Clinical Data Acquisition ERICH-GENE will collect core and secondary non-genetic variables. The group of core variables, were selected to facilitate the collection and harmonization of data across a large number of sites, include age, date of hemorrhage, sex, ancestry, ethnicity and whether admission CT is available. Secondary non-genetic variables will be collected when able, though not crucial for GWAS analyses, and include vascular risk factors (hypertension, diabetes, hypercholesterolemia, obesity, smoking), comorbidities (previous ICH or stroke and dementia), medications (antithrombotics and statins), and clinical variables and outcomes (admission NIHSS, Glasgow Coma Scale, and baseline, discharge, and follow-up mRS with emphasis at 3 months are collected). Selection of Genotypes of Analysis Genotypes for ICH cases will be ascertained using common variant data extracted from WGS in CCDG and TOPMed as well as a dedicated common variant GWAS in newly-genotyped individuals, with pre- and post-imputation harmonization. For all individuals with WGS-based genotyping, only common variants with minor allele frequency > 1% will be included in GWAS analyses. New GWAS genotyping in ERICH-GENE will be performed through the genotyping core at MGH with the Infinium Global Screening Array (GSA, Illumina, San Diego CA USA), performed at the Broad Institute. The GSA has 642,824 markers and was designed to accommodate multi-ethnic samples. At the conclusion of the study, all genotyped cases will be shared through the NIH-supported Genotypes and Phenotypes (dbGaP) platform as well as the ISGC-supported Cerebrovascular Disease Knowledge Portal (CDKP, https://cd.hugeamp.org/ ), in order to maximize the scientific impact across the research community. Statistical Methods Quality Control Pipeline Common variant QC approaches have become largely standardized and will be implemented pre- and post-imputation, 22 – 24 including screening for batch effects and sex-inconsistency, and filtering for individual and variant missingness by study, differential missingness by case/control status, heterozygosity, Hardy-Weinberg Equilibrium, and identity by descent. Aligning strands across cohorts is critical and special attention will be given to single nucleotide polymorphisms (SNPs) that are A/T and C/G with minor allele frequency near 0.5. Population structure will be assessed at this step, using Eigenstrat, ADMIXTURE, and PC-AiR, and genetic outliers and those inconsistent with self-reported ethnicity will be removed. 25 , 26 Variant imputation and harmonization Using WGS and GWAS genotyping data, we will perform imputation to the 1000 genomes reference panel using IMPUTE2 within each dataset. Variants with high imputation quality (INFO > 0.5; confidence > 0.9) will be retained for analysis, resulting in a harmonized pool of common variants across included studies for uniform and unconfounded association testing. Common variants identified via WGS in ERICH and WHI but not included in the final imputed dataset from the new ISGC GWAS dataset will be excluded from further analysis. Downstream analyses will account for imputation uncertainty. Single variant analysis We will use logistic regression as well as logistic mixed modeling for single variant analyses using SNPTEST, PLINK and GMMAT in R, 27 adjusting for covariates and accounting for imputation uncertainty. GMMAT incorporates population structure and a relatedness matrix, reducing false positive associations due to between-sample heterogeneity and ethnicity. 12 Analyses will be performed within each self-reported ethnicity and within study, followed by meta-analysis via METAL. 28 Benjamini-Hochberg false discovery rate adjusted p-values will be computed. 29 Chromosome X variants will be tested. Meta-analysis We will employ a two-stage meta-analysis to account for heterogeneity in multi-ethnic results from primary association testing. We will cluster population subgroups with similar ancestral backgrounds prior to meta-analysis based on admixture estimates referent to anchoring populations (rather than a traditional cohort-based approach), followed by fixed-effects modeling using METAL. 28 This approach has been shown to maximize statistical power while controlling Type I error due to heterogeneity in ethnically stratified datsets. 31 Gene-based testing Aggregation of variants by gene or other functional unit carries the advantages of limiting the number of statistical tests as well as combining the information from neighboring variants in a single gene into a variable with greater power. We will include both collapsing methods and sequence kernel association tests. 30 Additional gene-set tools will also be employed. 31 , 32 As with single variants, meta-analysis will be performed across ethnic groups and between studies, with multiple testing burden corrected using false discovery rate-adjusted p-values. Outcome phenotypes Prior ICH GWAS have successfully identified genetic loci associated with outcome, 33 and modified Rankin Scale (mRS) and mortality at 3 months will be available for approximately 6,654 ICH cases in the present proposal. 3-month mRS will be binarized into good outcome (mRS 0-3) and poor outcome (4-6) in available participants. 1-year and further analyses of mRS will be converted to averaged z-scores to reduce bias due to floor/ceiling artifacts and tested using proportional hazards controlled for age, sex, and education. The ongoing Recurrent Hemorrhagic Stroke in Minority Populations study program is collecting long-term functional, cognitive, and psychiatric outcome data on 937 ERICH participants with > 4 years of average follow-up which will become available for genetic association testing during ERICH-GENE. Genetic associations with neuroimaging phenotypes in ICH cases Genetic variants will be tested for association with relevant neuroimaging phenotypes, including hemorrhage location l(lobar and nonlobar), hemorrhage volume, intraventricular hemorrhage, leukoaraiosis, and cerebral atrophy. Polygenic risk scores (PRS) for ICH risk and outcomes Using summary statistics from GWAS of ICH risk, we will extract ancestry-specific beta-coefficients, allele frequencies, and linkage disequilibrium (LD) information to construct PRS. This will maximize the predictive utility of the included variants based on regional associations rather than specific SNPs, which are more likely to vary across ethnicities. 34 For each method, beta coefficients for all included variants are summed into a score by individual. Individuals with the greatest number of high-risk effect variants, in the tail of the distribution, represent the highest risk group for ICH risk or poor outcome compared with the rest of the population. This analysis will not only increase our understanding of the specific genetic locations that increase ICH risk across ethnicities, furthermore, it will provide a powerful clinical stratification tool ( Figure 3 ). Testing and validation of PRS performance PRS for ICH risk and outcome will be validated in the meta-analysis and biobank datasets. Test characteristics for these PRS will be calculated in a minimal model, followed by inclusion of clinical and radiographic traits associated with ICH risk and outcome, to determine whether the PRS capture risk information orthogonal to classical risk factors. Risk and outcome PRS will also be applied within each representative ethnicity to determine utility across ancestries. Future analyses will include PRS generation within each ethnic strata combined with regional fine-mapping to optimize ICH PRS predictive utility for the broadest possible populations, a unique aspect to this application which addresses a key limitation in efforts to derive clinically meaningful PRS. Data sharing platform incorporating WGS and neuroimaging data ERICH-GENE will support back-end development of WGS integration to update the CDKP, ensuring the NIH investment in this well-utilized resource remains modern and relevant. 35 To date, the CDKP has been limited to GWAS statistics for case-control studies of stroke, as well as continuous phenotype GWAS of related traits such as BMI. We will integrate the ERICH genotypes and association results shared through the CDKP with CT-based phenotypes shared through the newly introduced, NIH-supported I-CDKP resource for neuroimage data sharing, in order to allow external investigators to access our adjudicated CT-based phenotypes, as well as to derive new image-based endophenotypes for further studies. Individuals in both portals will be given matching subject IDs to allow joint genetic-imaging analyses. STUDY UPDATE ERICH-GENE received funding September 2021 and is currently in Year 4. A total of 42 institutions worldwide are enrolling in this study. Figure 1 provides an overview of the study timeline. In Years 1-3, the Ambra radiographic database has been up and running, and a total of 16,175 images from 4,974 patients with ∼ 3,000 MRIs are uploaded for expert review by the neuroimaging core, which is close to completion. In Year 4, an additional 4,000 cases will undergo phenotypic harmonization at the Cincinnati core. In Years 1-3, in addition to the 3,000 samples from ERICH and the 753 samples from WHI, ∼1,600 new ICH cases have received WGS, and an additional ∼2100 cases have been genotyped using the GSA. In Year 4, an additional 1,800 cases are pending genotyping from Asia, Europe and the US, followed by another estimated 2,000 cases from contributing sites in Africa, Europe, US and Asia. Download figure Open in new tab Figure 1. Study Organization and Contributors Download figure Open in new tab Figure 2. Study Timeline and Milestones Download figure Open in new tab Figure 3: Design of Polygenic Risk Scores DISCUSSION Despite current knowledge about the high heritability of ICH and the growing disease disparities across ancestries, research investigating the clinical and genetic predictors of ICH and outcomes remains limited by small study sizes and non-representative samples, primarily focused on white populations. In this study protocol, we introduce a collaboration of several of the largest studies and biobanks involving ICH patients, namely the ERICH study, WHI, ISCG contributors, China Kadoorie Biobank, Biobank Japan, and the UK Biobank. With the addition of 5,600 newly acquired ICH genotype and phenotype samples, all with centrally adjudicated neuroimaging, these combined data across many ethnicities will be used to perform the largest and most diverse harmonized GWAS to determine genetic risk in ICH to date, and to develop more specific and precise downstream genomic tools for clinical applications. ICH is a devastating disease for patients, families and communities. It has the highest morbidity and mortality of all stroke subtypes, long hospital and rehabilitation length of stays, and significant loss of functional independence. 6 There are remarkable differences in ICH risk across ethnic and socioeconomic strata. ICH incidence is 1.5-2-fold higher among African Americans and Hispanic Americans compared to White Americans. 21 , 36 CSVD is highly linked to spontaneous ICH, with commonly found arteriolosclerosis (or lipohyalinosis) and cerebral CAA as underlying etiologies. Lipohyalinosis creates concentric hyalinized vascular wall thickening in the penetrating arterioles of the basal ganglia, thalamus, brainstem, and deep cerebellar nuclei with risk factors of hypertension, diabetes mellitus, and age. CAA is more often lobar and defined by arteriolar deposition of amyloid-β peptide. With approximately 30% heritability, associations at APOE (involved in the metabolism of lipids and implicated in both Alzheimer’s and cardiovascular disease), PMF1 (involved in polyamine metabolism) , ICA1L (likely an autoantigen in diabetes and amyotrophic lateral sclerosis) , and COL4A2 (found in collagen with the C-terminal portion an inhibitor of angiogenesis) have been found. 13 – 17 , 37 Only recently has there been a renewed interest in aggressive treatment and study of this disease to impact patient outcomes. Surgical treatment in clinical trials such as MISTIE III and ENRICH have shown promising results, 38 , 39 but treatments for prevention and secondary brain injury are clearly needed. Differences in outcomes amongst ethnicities, sexes, age and varying comorbidities must be addressed and understood. For example, mean age of ICH patients is lower among Black and Hispanic patients compared with White and over half of Black and Hispanic patients suffering ICH have untreated hypertension. 21 The estimated yearly incidence of delayed dementia after ICH is 5.8%, which does not appear to correlate with hematoma location or size. 10 Black and Hispanic ICH survivors had greater global small vessel disease on MRI as well as increased ICH recurrence. 40 These data must also be considered in light of epigenetic factors such as 20-30% of underrepresented patients with ICH are uninsured, compared to 5% of White patients. 21 The ERICH-GENE collaboration is an international commitment to analyze genomic risk factors which will impact and be key to both treatment development and outcomes. This effort will strive to overturn 25 years of failure for pharmacologic treatments in ICH and help identify and overcome treatment effect differences. Emphasizing the potential impact of genetic discoveries in terms of novel drug targets, recent findings indicate that pharmacological interventions with human genetic evidence supporting their mechanism of action have 2 times the chances of successfully moving from preclinical studies to FDA approval. Genomic studies carry potential pitfalls that must be addressed. ERICH-GENE has developed both a genome and phenotype core, with centralized adjudicated neuroimaging to help ensure harmonization. Despite training of site-PIs enrolling in ERICH, centralized review ultimately excluded 6% of cases as not spontaneous ICH. Some of the most common misclassifications include hemorrhagic conversion of ischemic infarct, hemorrhagic metastases, contusion, ruptured aneurysm or AVM ( Figure 4 ). In addition to case/not a case misclassification, ICH location determination errors were even more frequently identified with 211/3000 (9.3%) disagreement between the recruitment site and centralized reading and final adjudication. Because of the known differences in genetic architecture between lobar and nonlobar ICH, validation of neuroimaging is critical to minimize heterogeneity and enhance the potential to find true differences in a case-control study of validated spontaneous ICH. Download figure Open in new tab Figure 4. Examples of common ICH misclassifications. A) Submitted as lobar ICH, sharp delineation of hypodense borders extending to the cortex and less mass effect than expected for hemorrhage size indicate hemorrhagic conversion of ischemic infarct . B) Submitted as multiple amyloid ICH, however multiplicity and round shape of similar age bleeds indicate hemorrhagic metastases. C) Appearance consistent with contusion versus hemorrhagic infarct over spontaneous ICH. D) Submitted as deep ICH but location along the sylvian fissure is worrisome for a ruptured MCA aneurysm. Novel genetic associations arising from ERICH-GENE carry great potential to refine the search for new therapeutic approaches, and to prioritize potential mechanisms for further study. Existing GWAS data in ICH has been crucial in identifying the pathobiological links between nonlobar ICH, small vessel ischemic stroke, and CSVD phenotypes such as white matter hyperintensities on MRI. These phenotypes have been epidemiologically linked for many years, but genetics offers a key opportunity to demonstrate shared biological origins. Furthermore, methods that build on GWAS data, such as Mendelian Randomization, have provided valuable new information about the causality of risk factors that association with ICH risk, including lipid levels, chronic kidney disease, statin exposure, and obesity. Finally, the construction of polygenic risk scores using existing ICH GWAS data have recently shown great promise in identifying individuals at the greatest risk for ICH, even beyond traditional clinical risk factors, and the association with ethnicity and epigenetics. For example, unlike White patients, APOE alleles were not associated with lobar ICH among Black or Hispanic individuals. 21 Each of these efforts will be greatly strengthened through the influx of additional well-phenotyped ICH cases amassed under ERICH-GENE, which will drastically improve statistical power and permit new exploration of multi-ethnic models. Potential Limitations Our nested case-control study design allows us to maximize our sample size, while accounting for differences in the population by the above-mentioned harmonization strategies. However, some ethnicity-specific effects of small effect size may escape detection. Our expected power would allow us to detect some variants with modest effect sizes, while our trans-ethnic meta-analysis will allow for maximal power exploring novel ICH associations. The opposite, a lack of ethnicity-specific risk factors for ICH, might also be an obstacle. Given our preliminary data on APOE 16 and the success of trans-ethnic studies of related neurologic diseases of aging such as Alzheimer’s Disease 14 however, this is an unlikely risk. Furthermore, a lack of ethnicity-specific loci for ICH risk would provide new insight into the genetic architecture of ICH and provide broad support for an inclusive strategy for ICH case aggregation in future studies of ICH. GWAS identify genetic associations with disease, but assumptions about causality cannot be made. Non-causal genetic factors are still valid in predicting and stratifying risk in populations through PRS, and growing availability of WGS data will allow exploration of risk loci via fine-mapping and functional modeling. Given that WGS is available for the ERICH participants, we will lay the groundwork to perform multi-ethnic fine-mapping to detect causal loci. Protocols building on our results will be able to perform WGS or targeted sequencing on loci arising from our GWAS. The Multiplexed analysis of variant effects in cell and tissue models is already being leveraged in analyses of existing CSVD loci. Variants arising from our study will represent exciting new candidates for in vitro exploration using reporter assay and gene-editing studies. Furthermore, future clinical practice will increasingly incorporate PRS into medical decision-making. Because of known degradation of predictive utility of PRS outside of ethnic populations in which they are discovered, it is critical that PRS for ICH be constructed using the most ancestrally diverse population possible. Well-characterized PRS for ICH risk and outcome will require validation in prospective populations and determination of best practices for PRS measurement, disclosure of results, and integration into health records. Our study will advance the usage of genetic information into clinical care in ICH, in concert with mitigation of other risk factors such as hypertension, sleep apnea, and socioeconomic factors, 21 as well as concurrent efforts across other cardiometabolic diseases. 41 , 42 CONCLUSION The ERICH-GENE study will provide the largest and ethnically most diverse dataset of genetic determinants of ICH. It will not only incorporate clinical and WGS data of ICH patients and controls, but also harmonized and adjudicated neuroimaging and clinical outcome data, that will be made openly accessible as an online data repository. Out study will address the increasing need of building diverse and comprehensive datasets as a basis for GWAS to provide information about genetic risk factors of ICH representative of a whole population and shed light on the genetic predictors of ICH and their clinical application. Data Availability All data produced in the present study are available upon reasonable request to the authors. SOURCES OF FUNDING This study is funded by the National Institutes of Health (U01NS069763). CONFLICTS OF INTEREST None. ACKNOWLEDGEMENTS We would like to thank the patients and families that agreed to participate in this study. We would also like to thank all the research teams from the numerous institutions around the world that have made this study possible. Footnotes ↵ * Co-leading authors ↵ ** Co-senior authors REFERENCES 1. ↵ Tsao CW , Aday AW , Almarzooq ZI , et al. Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association . Circulation . 2022 ; 145 ( 8 ). doi: 10.1161/CIR.0000000000001052 OpenUrl CrossRef PubMed 2. ↵ Greenberg SM , Ziai WC , Cordonnier C , et al. 2022 Guideline for the Management of Patients With Spontaneous Intracerebral Hemorrhage: A Guideline From the American Heart Association/American Stroke Association . Stroke . 2022 ; 53 ( 7 ): E282 – E361 . doi: 10.1161/STR.0000000000000407 OpenUrl CrossRef PubMed 3. ↵ Feigin VL , Stark BA , Johnson CO , et al. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019 . Lancet Neurol . 2021 ; 20 ( 10 ): 1 – 26 . doi: 10.1016/S1474-4422(21)00252-0 OpenUrl CrossRef PubMed 4. Flaherty ML , Woo D , Haverbusch M , et al. Racial Variations in Location and Risk of Intracerebral Hemorrhage . Stroke . 2005 ; 36 ( 5 ): 934 – 937 . doi: 10.1161/01.STR.0000160756.72109.95 OpenUrl Abstract / FREE Full Text 5. ↵ Leasure AC , King ZA , Torres-Lopez V , et al. Racial/ethnic disparities in the risk of intracerebral hemorrhage recurrence . Neurology . 2020 ; 94 ( 3 ): e314 – e322 . doi: 10.1212/WNL.0000000000008737 OpenUrl CrossRef PubMed 6. ↵ Jolink WMT , Klijn CJM , Brouwers PJAM , Kappelle LJ , Vaartjes I . Time trends in incidence, case fatality, and mortality of intracerebral hemorrhage . Neurology . 2015 ; 85 ( 15 ). doi: 10.1212/WNL.0000000000002015 OpenUrl Abstract / FREE Full Text 7. ↵ Lovelock C , Molyneux A , Rothwell P . Change in incidence and aetiology of intracerebral haemorrhage in Oxfordshire, UK, between 1981 and 2006: a population-based study . Lancet Neurol . 2007 ; 6 ( 6 ): 487 – 493 . doi: 10.1016/S1474-4422(07)70107-2 OpenUrl CrossRef PubMed Web of Science 8. Lu P , Cao Z , Gu H , et al. Association of sex and age with in-hospital mortality and complications of patients with intracerebral hemorrhage: A study from the Chinese Stroke Center Alliance . Brain Behav . 2023 ; 13 ( 1 ). doi: 10.1002/brb3.2846 OpenUrl CrossRef 9. ↵ Rincon F , Mayer SA . The epidemiology of intracerebral hemorrhage in the United States from 1979 to 2008 . Neurocrit Care . 2013 ; 19 ( 1 ): 95 – 102 . doi: 10.1007/s12028-012-9793-y OpenUrl CrossRef PubMed 10. ↵ Biffi A , Bailey D , Anderson CD , et al. Risk Factors Associated With Early vs Delayed Dementia After Intracerebral Hemorrhage . JAMA Neurol . 2016 ; 73 ( 8 ): 969 – 976 . doi: 10.1001/jamaneurol.2016.0955 OpenUrl CrossRef PubMed 11. Charidimou A , Boulouis G , Haley K , et al. White matter hyperintensity patterns in cerebral amyloid angiopathy and hypertensive arteriopathy . Neurology . 2016 ; 86 ( 6 ): 505 – 511 . doi: 10.1212/WNL.0000000000002362 OpenUrl Abstract / FREE Full Text 12. ↵ Haffner C , Malik R , Dichgans M . Genetic factors in cerebral small vessel disease and their impact on stroke and dementia . J Cereb Blood Flow Metab . 2016 ; 36 ( 1 ): 158 – 171 . doi: 10.1038/jcbfm.2015.71 OpenUrl CrossRef PubMed 13. ↵ Rannikmäe K , Davies G , Thomson PA , et al. Common variation in COL4A1/COL4A2 is associated with sporadic cerebral small vessel disease . Neurology . 2015 ; 84 ( 9 ): 918 – 926 . doi: 10.1212/WNL.0000000000001309 OpenUrl Abstract / FREE Full Text 14. ↵ Sawyer RP , Demel SL , Comeau ME , et al. Alzheimer’s disease related single nucleotide polymorphisms and correlation with intracerebral hemorrhage incidence . Medicine . 2022 ; 101 ( 39 ): E30782 . doi: 10.1097/MD.0000000000030782 OpenUrl CrossRef PubMed 15. ↵ Devan WJ , Falcone GJ , Anderson CD , et al. Heritability estimates identify a substantial genetic contribution to risk and outcome of intracerebral hemorrhage . Stroke . 2013 ; 44 ( 6 ): 1578 – 1583 . doi: 10.1161/STROKEAHA.111.000089 OpenUrl Abstract / FREE Full Text 16. ↵ Marini S , Crawford K , Morotti A , et al. Association of Apolipoprotein E With Intracerebral Hemorrhage Risk by Race/Ethnicity: A Meta-analysis . JAMA Neurol . 2019 ; 76 ( 4 ): 480 – 491 . doi: 10.1001/JAMANEUROL.2018.4519 OpenUrl CrossRef PubMed 17. ↵ Chung J , Marini S , Pera J , et al. Genome-wide association study of cerebral small vessel disease reveals established and novel loci . Brain . 2019 ; 142 ( 10 ): 3176 – 3189 . doi: 10.1093/BRAIN/AWZ233 OpenUrl CrossRef PubMed 18. ↵ Rannikme K , Sivakumaran V , Millar H , et al. COL4A2 is associated with lacunar ischemic stroke and deep ICH: Meta-analyses among 21,500 cases and 40,600 controls . Neurology . 2017 ; 89 ( 17 ): 1829 – 1839 . doi: 10.1212/WNL.0000000000004560 OpenUrl Abstract / FREE Full Text 19. ↵ Kittner SJ , Sekar P , Comeau ME , et al. Ethnic and Racial Variation in Intracerebral Hemorrhage Risk Factors and Risk Factor Burden . JAMA Netw Open . 2021 ; 4 ( 8 ). doi: 10.1001/JAMANETWORKOPEN.2021.21921 OpenUrl CrossRef 20. ↵ Using Population Descriptors in Genetics and Genomics Research . National Academies Press ; 2023 . doi: 10.17226/26902 OpenUrl CrossRef PubMed 21. ↵ Kittner SJ , Sekar P , Comeau ME , et al. Ethnic and Racial Variation in Intracerebral Hemorrhage Risk Factors and Risk Factor Burden . JAMA Netw Open . 2021 ; 4 ( 8 ): e2121921 . doi: 10.1001/jamanetworkopen.2021.21921 OpenUrl CrossRef 22. ↵ Winkler TW , Day FR , Croteau-Chonka DC , et al. Quality control and conduct of genome-wide association meta-analyses . Nat Protoc . 2014 ; 9 ( 5 ): 1192 – 1212 . doi: 10.1038/NPROT.2014.071 OpenUrl CrossRef PubMed 23. Truong VQ , Woerner JA , Cherlin TA , et al. Quality Control Procedures for Genome-Wide Association Studies . Curr Protoc . 2022 ; 2 ( 11 ). doi: 10.1002/CPZ1.603 OpenUrl CrossRef 24. ↵ Zuvich RL , Armstrong LL , Bielinski SJ , et al. Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality . Genet Epidemiol . 2011 ; 35 ( 8 ): 887 – 898 . doi: 10.1002/GEPI.20639 OpenUrl CrossRef PubMed 25. ↵ Conomos MP , Miller MB , Thornton TA . Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness . Genet Epidemiol . 2015 ; 39 ( 4 ): 276 – 293 . doi: 10.1002/GEPI.21896 OpenUrl CrossRef PubMed 26. ↵ Sankararaman S , Sridhar S , Kimmel G , Halperin E . Estimating local ancestry in admixed populations . Am J Hum Genet . 2008 ; 82 ( 2 ): 290 – 303 . doi: 10.1016/J.AJHG.2007.09.022 OpenUrl CrossRef PubMed Web of Science 27. ↵ Chen H , Wang C , Conomos MP , et al. Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models . Am J Hum Genet . 2016 ; 98 ( 4 ): 653 – 666 . doi: 10.1016/J.AJHG.2016.02.012 OpenUrl CrossRef PubMed 28. ↵ Willer CJ , Li Y , Abecasis GR . METAL: fast and efficient meta-analysis of genomewide association scans . Bioinformatics . 2010 ; 26 ( 17 ): 2190 – 2191 . doi: 10.1093/bioinformatics/btq340 OpenUrl CrossRef PubMed Web of Science 29. ↵ Green GH , Diggle PJ . On the operational characteristics of the Benjamini and Hochberg False Discovery Rate procedure . Stat Appl Genet Mol Biol . 2007 ; 6 :Article27. doi: 10.2202/1544-6115.1302 OpenUrl CrossRef 30. ↵ Lee S , Wu MC , Lin X . Optimal tests for rare variant effects in sequencing association studies . Biostatistics . 2012 ; 13 ( 4 ): 762 – 775 . doi: 10.1093/biostatistics/kxs014 OpenUrl CrossRef PubMed Web of Science 31. ↵ Hong J , Lunetta KL , Cupples LA , Dupuis J , Liu CT . Evaluation of a Two-Stage Approach in Trans-Ethnic Meta-Analysis in Genome-Wide Association Studies . Genet Epidemiol . 2016 ; 40 ( 4 ): 284 – 292 . doi: 10.1002/gepi.21963 OpenUrl CrossRef PubMed 32. ↵ de Leeuw CA , Mooij JM , Heskes T , Posthuma D . MAGMA: generalized gene-set analysis of GWAS data . PLoS Comput Biol . 2015 ; 11 ( 4 ): e1004219 . doi: 10.1371/journal.pcbi.1004219 OpenUrl CrossRef PubMed 33. ↵ Biffi A , Anderson CD , Jagiella JM , et al. APOE genotype and extent of bleeding and outcome in lobar intracerebral haemorrhage: a genetic association study . Lancet Neurol . 2011 ; 10 ( 8 ): 702 – 709 . doi: 10.1016/S1474-4422(11)70148-X OpenUrl CrossRef PubMed Web of Science 34. ↵ Márquez-Luna C , Loh PR , South Asian Type 2 Diabetes (SAT2D) Consortium, SIGMA Type 2 Diabetes Consortium, Price AL. Multiethnic polygenic risk scores improve risk prediction in diverse populations . Genet Epidemiol . 2017 ; 41 ( 8 ): 811 – 823 . doi: 10.1002/gepi.22083 OpenUrl CrossRef PubMed 35. ↵ Crawford KM , Gallego-Fabrega C , Kourkoulis C , et al. Cerebrovascular disease knowledge portal an open-access data resource to accelerate genomic discoveries in stroke . Stroke . 2018 ; 49 ( 2 ): 470 – 475 . doi: 10.1161/STROKEAHA.117.018922 OpenUrl FREE Full Text 36. ↵ Morgenstern LB , Smith MA , Lisabeth LD , et al. Excess stroke in Mexican Americans compared with non-Hispanic Whites: the Brain Attack Surveillance in Corpus Christi Project . Am J Epidemiol . 2004 ; 160 ( 4 ): 376 – 383 . doi: 10.1093/AJE/KWH225 OpenUrl CrossRef PubMed Web of Science 37. ↵ Parodi L , Comeau ME , Georgakis MK , et al. Deep Resequencing of the 1q22 Locus in Nonlobar Intracerebral Hemorrhage . Ann Neurol . 2024 ; 95 ( 2 ). doi: 10.1002/ANA.26814 OpenUrl CrossRef 38. ↵ Ratcliff JJ , Hall AJ , Porto E , et al. Early Minimally Invasive Removal of Intracerebral Hemorrhage (ENRICH): Study protocol for a multi-centered two-arm randomized adaptive trial . Front Neurol . 2023 ; 14 . doi: 10.3389/FNEUR.2023.1126958 OpenUrl CrossRef 39. ↵ Hanley DF , Thompson RE , Rosenblum M , et al. Efficacy and safety of minimally invasive surgery with thrombolysis in intracerebral haemorrhage evacuation (MISTIE III): a randomised, controlled, open-label, blinded endpoint phase 3 trial . Lancet . 2019 ; 393 ( 10175 ): 1021 – 1032 . doi: 10.1016/S0140-6736(19)30195-3 OpenUrl CrossRef PubMed 40. ↵ Castello JP , Pasi M , Abramson JR , et al. Contribution of Racial and Ethnic Differences in Cerebral Small Vessel Disease Subtype and Burden to Risk of Cerebral Hemorrhage Recurrence . Neurology . 2021 ; 96 ( 20 ): e2469 – e2480 . doi: 10.1212/WNL.0000000000011932 OpenUrl Abstract / FREE Full Text 41. ↵ Khera A V. , Chaffin M , Aragam KG , et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations . Nat Genet . 2018 ; 50 ( 9 ): 1219 – 1224 . doi: 10.1038/S41588-018-0183-Z OpenUrl CrossRef PubMed 42. ↵ Khera A V. , Emdin CA , Drake I , et al. Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease . N Engl J Med . 2016 ; 375 ( 24 ): 2349 – 2358 . doi: 10.1056/NEJMOA1605086 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted June 13, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following The Ethnic/Racial Variations of Intracerebral Hemorrhage Genetics (ERICH-GENE) Study Protocol Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share The Ethnic/Racial Variations of Intracerebral Hemorrhage Genetics (ERICH-GENE) Study Protocol Guido J. Falcone , Stacey Q. Wolfe , Marialuisa Zedde , Rosario Pascarella , Jordi Jimenez-Conde , Marta Vallverdu Prats , Joan Jimenez-Balado , Alessandro Pezzini , Sandra Rossi , Rustam Al-Shahi Salman , Neshika Samarasekera , Ramin Zand , Jinag Li , Christina Jern , Daniel Strbian , Liisa Tomppo , Hanne Sallinen , Mar Hernandez Guillamon , Magdy Selim , Mayowa Owolabi , Rufus Akinyemi , Gregory Fakunle , Tsong-Hai Lee , David Werring , Isabel C. Hostettler , Henry Houlden , Pankaj Sharma , Isaac John , Gie Ken-Dror , Wendy Jenkins , Kevin N. Sheth , Lauren H. Sansing , Dharambir K. Sanghera , Evgeny Sidorov , Israel Fernandez-Cadenas , Jara Cárcel-Márquez , Ching-Jen Chen , Andrea Becerril-Gaitan , Keon-Joo Lee , Hee-Joon Bae , Martin Dichgans , Rainer Malik , Stephanie Debette , Aniket Mishra , Guillaume Pare , Michael Chong , Yoichiro Kamatani , Zhengming Chen , Robin G. Walters , Sudha Seshadri , Myriam Fornage , Catherine Sudlow , Lee A. Gilkerson , Vivek J. Khandwala , Thomas C. Maloney , Stacie Demel , Livia Parodi , Ramin Zand , Paul Nyquist , Wendy Ziai , Bradford Worrall , Vagal M Achala , Carl D. Langefeld , Jonathan Rosand , Christopher D. Anderson , Daniel Woo , the ERICH-Gene Investigators medRxiv 2025.06.11.25329301; doi: https://doi.org/10.1101/2025.06.11.25329301 Share This Article: Copy Citation Tools The Ethnic/Racial Variations of Intracerebral Hemorrhage Genetics (ERICH-GENE) Study Protocol Guido J. Falcone , Stacey Q. Wolfe , Marialuisa Zedde , Rosario Pascarella , Jordi Jimenez-Conde , Marta Vallverdu Prats , Joan Jimenez-Balado , Alessandro Pezzini , Sandra Rossi , Rustam Al-Shahi Salman , Neshika Samarasekera , Ramin Zand , Jinag Li , Christina Jern , Daniel Strbian , Liisa Tomppo , Hanne Sallinen , Mar Hernandez Guillamon , Magdy Selim , Mayowa Owolabi , Rufus Akinyemi , Gregory Fakunle , Tsong-Hai Lee , David Werring , Isabel C. Hostettler , Henry Houlden , Pankaj Sharma , Isaac John , Gie Ken-Dror , Wendy Jenkins , Kevin N. Sheth , Lauren H. Sansing , Dharambir K. Sanghera , Evgeny Sidorov , Israel Fernandez-Cadenas , Jara Cárcel-Márquez , Ching-Jen Chen , Andrea Becerril-Gaitan , Keon-Joo Lee , Hee-Joon Bae , Martin Dichgans , Rainer Malik , Stephanie Debette , Aniket Mishra , Guillaume Pare , Michael Chong , Yoichiro Kamatani , Zhengming Chen , Robin G. Walters , Sudha Seshadri , Myriam Fornage , Catherine Sudlow , Lee A. Gilkerson , Vivek J. Khandwala , Thomas C. Maloney , Stacie Demel , Livia Parodi , Ramin Zand , Paul Nyquist , Wendy Ziai , Bradford Worrall , Vagal M Achala , Carl D. Langefeld , Jonathan Rosand , Christopher D. Anderson , Daniel Woo , the ERICH-Gene Investigators medRxiv 2025.06.11.25329301; doi: https://doi.org/10.1101/2025.06.11.25329301 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9219) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) 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:'9ffc546bcbab3fe2',t:'MTc3OTQ1ODI0NA=='};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())}}}})();