Genotype-phenotype association study conducted on LARGE-PD reveals novel loci associated with Parkinson’s Disease

Genotype-phenotype association study conducted on LARGE-PD reveals novel loci associated with Parkinson’s Disease | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Genotype-phenotype association study conducted on LARGE-PD reveals novel loci associated with Parkinson’s Disease Thiago P Leal , Emily Waldo , Felipe Duarte-Zambrano , Miguel Inca-Martinez , Janvi Ramchandra , Henry Mauricio Chaparro-Solano , Anna E Anello , Victor Borda , Mateus Henrique Gouveia , Daniel Teixeira-dos-Santos , Paula Reyes-Pérez , Emilia Mabel Gatto , View ORCID Profile Bruno Lopes Santos-Lobato , Gracivane Eufraseo , Grace Helena Letro , Gonzalo Arboleda , Oscar Bernal-Pacheco , Jorge L Orozco , Beatriz Munoz , Pedro Chana-Cuevas , David Aguillon , Sonia Moreno , Gabriel Torrealba-Acosta , Tanya Lobo-Prada , Valentina Muller , C Matias Lopez Razquin , Pedro Braga-Neto , Reyna M Durón , View ORCID Profile Mayela Rodríguez-Violante , Ana Jimena Hernández-Medrano , Amin Cervantes-Arriaga , Daniel Martinez-Ramirez , View ORCID Profile Artur F S Schuh , View ORCID Profile Carlos Roberto de Mello Rieder , Mario Cornejo-Olivas , Julia Rios-Pinto , Angel C Medina , Ivan Cornejo-Herrera , Koni Mejia-Rojas , Angel Vinuela , Vitor Tumas , Angela Vieira Pimentel , Vanderci Borges , Cesar L Avila , Patricio Olguin , Alicia Colombo , Juan Cristobal Nuñez , View ORCID Profile Alejandra Medina-Rivera , View ORCID Profile Alejandra E. Ruiz-Contreras , View ORCID Profile Sarael Alcauter , Elena Dieguez , Karen Nuytemans , Global Parkinson’s Genetics Program , View ORCID Profile Ignacio F Mata the Latin American Research Consortium on the Genetics of Parkinson’s Disease (LARGE-PD) doi: https://doi.org/10.1101/2025.07.18.25331793 Thiago P Leal 1 Genomic Medicine Institute, Cleveland Clinic Research, Cleveland Clinic , 9500 Euclid Ave, Cleveland, OH, 44195, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emily Waldo 1 Genomic Medicine Institute, Cleveland Clinic Research, Cleveland Clinic , 9500 Euclid Ave, Cleveland, OH, 44195, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Felipe Duarte-Zambrano 1 Genomic Medicine Institute, Cleveland Clinic Research, Cleveland Clinic , 9500 Euclid Ave, Cleveland, OH, 44195, United States of America 2 Instituto de Genética , Facultad de Medicina sede Bogotá, Universidad Nacional de Colombia , Calle 53 #37a-47, Bogotá, Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Miguel Inca-Martinez 1 Genomic Medicine Institute, Cleveland Clinic Research, Cleveland Clinic , 9500 Euclid Ave, Cleveland, OH, 44195, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Janvi Ramchandra 1 Genomic Medicine Institute, Cleveland Clinic Research, Cleveland Clinic , 9500 Euclid Ave, Cleveland, OH, 44195, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henry Mauricio Chaparro-Solano 1 Genomic Medicine Institute, Cleveland Clinic Research, Cleveland Clinic , 9500 Euclid Ave, Cleveland, OH, 44195, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna E Anello 1 Genomic Medicine Institute, Cleveland Clinic Research, Cleveland Clinic , 9500 Euclid Ave, Cleveland, OH, 44195, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Victor Borda 3 Institute for Health Computing, University of Maryland , 6116 Executive Boulevard North, Bethesda, MD, 20852, United States of America 4 Institute for Genome Sciences, University of Maryland , 670 W Baltimore St, Baltimore, MD, 21201, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mateus Henrique Gouveia 5 National Human Genome Research Institute, National Human Genome Research Institute , Bethesda, MD, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Teixeira-dos-Santos 6 Department of Neurology, Neurological Institute, Cleveland Clinic , 9500 Euclid Ave, Cleveland, OH, 44195, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paula Reyes-Pérez 7 Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México , Blvd. 3001, Campus UNAM 3001, Juriquilla, QRO, 76230, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emilia Mabel Gatto 8 Neurology, Sanatorio de la Trinidad Mitre , Bartolome Mitre 2553, Buenos Aires, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bruno Lopes Santos-Lobato 9 Laboratório de Neuropatologia Experimental, Universidade Federal do Pará , Rua dos Mundurucus, 4487, Belém, PA, 66073-005, Brazil 10 Hospital Ophir Loyola , Av. Magalhães Barata, 992, Belém, PA, 66073-240, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bruno Lopes Santos-Lobato Gracivane Eufraseo 11 Nucelo de Pesquisa em Oncologia, Universidade Federal do Pará , Rua dos Mundurucus, 4487, Belém, PA, 66073-005, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Grace Helena Letro 12 Hospital PUC-Campinas , Avenida John Boyd Dunlop s/n, Campinas, SP, 13034-685, Brazil 13 Departamento de Neurologia Clínica, Pontifícia Universidade de Campinas , Avenida John Boyd Dunlop s/n, Campinas, SP, 13034-685, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gonzalo Arboleda 14 Facultad de Medicina, Universidad Nacional de Colombia , Ave Cra 30 N° 45-03, Bogotá, DC, 111321, Colombia 15 Instituto de Genética, Universidad Nacional de Colombia , Ave Cra 30 N° 45-03, Bogotá, DC, 111321, Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Oscar Bernal-Pacheco 16 Hospital Militar, Universidad Militar Nueva Granada , Tv 3 49-00, Bogotá, DC, 110221, Colombia 17 Instituto Ortopédico Roosevelt, Universidad Militar Nueva Granada , Tv 4 17-00, Bogotá, DC, 110221, Colombia 18 Fundación Santa Fe, Universidad Militar Nueva Granada , Cra 7 116-50, Bogotá, DC, 110221, Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jorge L Orozco 19 Neurology Department , Fundación Valle del Lili, Carrera 98 No. 18-49, Calle 18 No. 122-135, Cali, UVAC, 760032, Colombia 20 Human Sciences Department, Universidad Icesi , Calle 18 No. 122-135, Cali, UVAC, 760031, Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Beatriz Munoz 21 Adults Neuropsychology , Fundación Valle del Lili, Carrera 98 No. 18-49, Calle 18 No. 122-135, Cali, UVAC, 760032, Colombia 20 Human Sciences Department, Universidad Icesi , Calle 18 No. 122-135, Cali, UVAC, 760031, Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pedro Chana-Cuevas 22 Centro de Trastonos del Movimiento, Universidad de Santiago de Chile , Av Jose Joaquin Prieto Vial 8170, Lo espejo, Santiago, 9120000, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site David Aguillon 23 Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia , St. 64 No. 51–31, Medellín, ANT, 050010, Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sonia Moreno 23 Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia , St. 64 No. 51–31, Medellín, ANT, 050010, Colombia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gabriel Torrealba-Acosta 24 Department of Neurology, Duke University, 311 Research Drive , Room 201-G, Durham, NC, 27710, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tanya Lobo-Prada 25 Rare Disease Unit, Hospital San Juan de Dios , P.° Colón, San Jose, 10103, Costa Rica Find this author on Google Scholar Find this author on PubMed Search for this author on this site Valentina Muller 26 Servicio de Neurologia, Hospital Interzonal General de Agudos (HIGA) "Gral. San Martin" , 1st Ave. & 70 St. B1900, La Plata, Buenos Aires, Argentina 27 Alianza de Pacientes , Avda Callao 648 piso 11 #H, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site C Matias Lopez Razquin 26 Servicio de Neurologia, Hospital Interzonal General de Agudos (HIGA) "Gral. San Martin" , 1st Ave. & 70 St. B1900, La Plata, Buenos Aires, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pedro Braga-Neto 28 Department of Clínical Medicine, Universidade Federal do Ceará , 1608 Prof. Costa Mendes , Rodolfo Teófilo, Fortaleza, CE, 60430-140, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Reyna M Durón 29 Instituto de Investigaciones One Health, Universidad Tecnológica Centroamericana , Blvd. Kennedy, V-782, Tegucigalpa, Francisco Morazán, 11101, Honduras Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mayela Rodríguez-Violante 30 Laboratorio Clínico de Enfermedades Neurodegenerativas, Instituto Nacional de Neurología y Neurocirugía , Insurgentes Sur 3877, Tlalpan, Ciudad de México, 14269, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mayela Rodríguez-Violante Ana Jimena Hernández-Medrano 30 Laboratorio Clínico de Enfermedades Neurodegenerativas, Instituto Nacional de Neurología y Neurocirugía , Insurgentes Sur 3877, Tlalpan, Ciudad de México, 14269, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amin Cervantes-Arriaga 30 Laboratorio Clínico de Enfermedades Neurodegenerativas, Instituto Nacional de Neurología y Neurocirugía , Insurgentes Sur 3877, Tlalpan, Ciudad de México, 14269, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Martinez-Ramirez 31 Escuela de Medicina y Ciencias de la Salud , Tecnologico de Monterrey, Av. Ignacio Morones Prieto 3000, Sertoma, 64710 Monterrey, N.L., Monterrey, NL, 64710, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Artur F S Schuh 32 Department of Pharmacology, Universidade Federal do Rio Grande do Sul , Rua Ramiro Barcelos, 2350, Porto Alegre, RS, 90035-003, Brazil 33 Department of Neurology, Hospital de Clínicas de Porto Alegre , Rua Ramiro Barcelos, 2350, Porto Alegre, RS, 90035-003, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Artur F S Schuh Carlos Roberto de Mello Rieder 34 Department of Clinical Medicine, Universidade Federal de Ciências da Saúde de Porto Alegre , R. Sarmento Leite, 245 - Centro Histórico, Porto Alegre, RS, 90050-170, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carlos Roberto de Mello Rieder Mario Cornejo-Olivas 35 Neurogenetics Working Group, Universidad Cientifica del Sur , Panamerica Sur Km 19, Lima, Lima, 15067, Peru 36 Neurogenetics Research Center, Instituto Nacional de Ciencias Neurologicas , Jr. Áncash 1271, Lima, Lima, 15003, Peru , Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julia Rios-Pinto 37 Universidad Peruana Los Andes , Av. Giráldez 230, Huancayo, Junín, 12001, Peru Find this author on Google Scholar Find this author on PubMed Search for this author on this site Angel C Medina 38 National University the Altiplano Puno , Av. Floral 1153, Puno, Puno, 21001, Peru 39 Regional Public Health Reference Laboratory , Regional Health Directorate of Puno, Jirón José Antonio Encinas 145, Puno, Puno, 21002, Peru Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ivan Cornejo-Herrera 40 Neurology, Hospital Hipólito Unanue de Tacna , Calle Federico Barreto s/n, Tacna, Tacna, 23001, Peru Find this author on Google Scholar Find this author on PubMed Search for this author on this site Koni Mejia-Rojas 41 EDMECON Continuing Medical Education , C. Los Antares 308, Santiago de Surco, Lima, 15039, Peru 42 Department of Neurology, Daniel Alcides Carrion National Hospital , Av. Guardia Chalaca 2176, Bellavista, Callao, 07016, Peru Find this author on Google Scholar Find this author on PubMed Search for this author on this site Angel Vinuela 43 Neurosciences Department , Fundacion Parkinson Puerto Rico, Calle Hernandez Carrion, Manatí, PR, 00674, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vitor Tumas 44 Department of Neurosciences and Behavior Sciences, Ribeirão Preto Medical School, Universidade de São Paulo , Av. Bandeirantes, 3900, Ribeirão Preto, SP, 14049-900, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Angela Vieira Pimentel 44 Department of Neurosciences and Behavior Sciences, Ribeirão Preto Medical School, Universidade de São Paulo , Av. Bandeirantes, 3900, Ribeirão Preto, SP, 14049-900, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vanderci Borges 45 Department of Neurology and Neurosurgery , Escola Paulista de Medicina UNIFESP-EPM, Universidade Federal de São Paulo , Rua Botucatu, 740, São Paulo, SP, 04023-060, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cesar L Avila 46 Universidad Nacional de Tucuman , Pasaje Manuel Dorrego 1080, San Miguel de Tucumán, Tucuman, Argentina 47 Instituto de Investigación en Biologia Molecular y Celular Aplicada , Consejo Nacional de Investigaciones Científicas y Técnicas, Batalla de Ayacucho 491, T4000, San Miguel de Tucumán, Tucuman, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Patricio Olguin 48 Instituto de Ciencias Biomédicas, Universidad de Chile , Avenida Independencia 1027, Santiago de Chile, Chile 49 Departamento de Neurociencias, Universidad de Chile , Avenida Independencia 1027, Santiago de Chile, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alicia Colombo 50 Departament of Pathological Anatomy, Facultad de Medicina, Universidad de Chile , Avenida Independencia 1027, Santiago de Chile, Chile 51 Pathological Anatomy Service, Hospital Clínico de la Universidad de Chile , Dr. Carlos Lorca Tobar 999, Santiago de Chile, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site Juan Cristobal Nuñez 52 Departamento de Neurología-Neurocirugía, Hospital Clínico de la Universidad de Chile , Dr. Carlos Lorca Tobar 999, Santiago de Chile, Chile 53 Departamento de Neurología-Psiquiatría , Clínica Alemana Santiago, Av. Manquehue Nte. 1410, Santiago de Chile, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alejandra Medina-Rivera 54 Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México , Blvd Juriquilla 3001, Santiago de Querétaro, 76230, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alejandra Medina-Rivera Alejandra E. Ruiz-Contreras 55 Laboratorio de Neurogenómica Cognitiva, Unidad de Psicobiología y Neurociencias, Facultad de Psicología, Universidad Nacional Autónoma de México, Av. Universidad 3004 , Ciudad de México, 04510, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alejandra E. Ruiz-Contreras Sarael Alcauter 56 Instituto de Neurobiología, Universidad Nacional Autónoma de México , Blvd Juriquilla 3001, Santiago de Querétaro, 76230, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sarael Alcauter Elena Dieguez 57 Trastornos de movimiento, Universidad de la Republica , Av. 18 de Julio 1824-1850, Montevideo, 11200, Uruguay 58 Hospital de Clinicas , Av. Italia s/n, Montevideo, 11600, Uruguay Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karen Nuytemans 59 John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , 1501 NW 10 Ave, Biomedical Research Building , Miami, FL, 33136, United States of America 60 Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine , 1501 NW 10 Ave, Biomedical Research Building , Miami, FL, 33136, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ignacio F Mata 1 Genomic Medicine Institute, Cleveland Clinic Research, Cleveland Clinic , 9500 Euclid Ave, Cleveland, OH, 44195, United States of America 61 Department of Molecular Medicine, Case Western Reserve University , 10900 Euclid Ave, OH, 44106, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ignacio F Mata For correspondence: matai{at}ccf.org Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background The Latin American Research Consortium on the Genetics of Parkinson’s Disease (LARGE-PD) is a multicenter collaboration aimed at understanding the genetic architecture of Parkinson’s disease (PD) in this underrepresented population using data from 15 countries across the Americas and the Caribbean. In this study, we conducted the largest genome-wide association studies (GWAS) for PD susceptibility in Latin Americans. Methods We analyzed genotype data from LARGE-PD Phase 1 (n = 1,498) and Phase 2 (n = 4,401) using multiple GWAS approaches: SAIGE, which incorporates a genetic relationship matrix in the model; ATT, which includes global ancestry on the model; TRACTOR, which splits allele dosages by ancestry to detect ancestry-specific risk loci; and admixture mapping. We also assessed linkage disequilibrium (LD) patterns and performed Meta-Regression of Multi-AncEstry Genetic Association (MR-MEGA), integrating data from both LARGE-PD phases and two South Asian GWAS. Results We identified PD-associated loci on chromosomes 1 and 4. Our results replicated previous findings, including the well-established SNCA variant rs356182-A (OR = 1.517, p = 1.62×10 −16 ). Notably, we identified a locus in ITPKB (rs117185933-A, OR = 1.75, p = 3.8×10 −12 ), which had the highest CADD Phred score (17.92, top ∼3% most deleterious) among all candidate variants, suggesting strong functional relevance. Functional annotation predicted that this variant may create a premature start codon in the 5′ UTR of ITPKB . Although rs117185933-A is in high LD (r 2 > 0.8) with a variant previously reported by Kishore et al., our LD analysis and MR-MEGA results indicate that this signal is correlated with ancestry heterogeneity and likely represents an independent PD risk locus and a novel putative causal variant. This variant is most frequent in Peruvians from the 1000 Genomes Project (MAF = 0.20) and more common in admixed American populations in gnomAD (MAF = 0.0835), but nearly absent in non-Finnish Europeans (MAF = 0.0002). Conclusion We identified PD-associated variants in SNCA and ITPKB, the latter not previously reported in European-ancestry studies. The ITPKB variant may lead to a start codon gain in a gene with known protective effects against α-synuclein aggregation in vivo and in vitro models. These findings underscore the critical importance of including underrepresented populations in genetic research to uncover ancestry-specific risk loci and advance precision medicine for Parkinson’s disease. Introduction Studies indicate that more than 8.5 million people live with Parkinson’s disease (PD) worldwide, and this number could rise to 25.2 million by 2050, with Latin America being one of the populations experiencing the highest increase 1 – 4 . Genetics plays a crucial role in PD etiology. Genome-wide association studies (GWAS) and GWAS meta-analyses identified ∼130 variants associated with PD with an estimated heritability of up to 28% depending on the studied population 5 – 9 . Despite these advances, most of our knowledge about PD genetics is based on studies conducted with individuals of European or Asian ancestry, limiting understanding of the genetic architectures of PD in other ancestral populations 10 . This lack of genetic diversity poses a significant barrier to genetic diagnosis and treatment, as different populations may have other risk loci and/or tag SNPs 11 . An African GWAS, for example, conducted by researchers from the Global Parkinson’s Genetics Program (GP2) identified a novel PD risk variant in the GBA1 gene (rs3115534-G), which is relatively common within African ancestry populations but rare in other ancestries 12 . Addressing these issues, the Latin American Research Consortium on the Genetics of PD (LARGE-PD) was established. This ongoing effort involves over 45 institutions across 15 American and Caribbean countries. LARGE-PD Phase 1 (P1) explored the genetics of PD by genotyping ∼1,500 samples from five South American countries. This dataset was previously used in the first Latin American PD GWAS, as well as in genome-wide analyses of copy number variation, X-chromosome-wide association (XWAS), and polygenic risk score (PRS) inferences based on European GWAS summary statistics 13 – 16 . LARGE-PD Phase 2 (P2) is dedicated to advancing the understanding of PD within the Latin American and Caribbean (LAC) populations. This work involves recruiting and genotyping additional samples to use as replication cohorts and increasing the sample size to identify new susceptibility loci for developing PD in Latin American populations. In this study, we performed a comprehensive association analysis, including different GWAS approach and admixture mapping, of PD susceptibility in Latin American populations, using data from LARGE-PD P1 and P2. Methods Samples Discovery cohort: LARGE-PD Phase 1 We analyzed data from 1,498 individuals (∼53.9% cases) enrolled in the LARGE-PD P1, recruited from Brazil, Chile, Colombia, Peru, and Uruguay, genotyped with the Multi-Ethnic Genotyping Array (Illumina Inc., Chicago, IL, USA). After exclusion of 17 individuals due to missing data, the final dataset included 798 PD cases and 683 controls. A detailed description of the LARGE-PD P1 cohort has been previously reported 14 . Replication cohort: LARGE-PD Phase 2 The 4,401 samples from LARGE-PD P2 were collected between 2019 and 2025 and genotyped using the NeuroBooster Array (NBA) from Illumina. The NBA includes ∼2M variants from Infinium Global Diversity Array and ∼95k variants associated with more than 70 neurological conditions or traits 17 . Our data includes samples from 10 countries, enrolled across 23 sites. All PD patients were assessed by a local movement disorders specialist and diagnosed using the UK PD Society Brain Bank and/or MDS Clinical Diagnostic Criteria 18 , 19 . Controls were individuals without major neurological conditions. All participants provided written informed consent according to their respective regional IRB requirements. Additional cohort details are available in the Supporting Information (SI) Section Cohort description (Table S1). The LARGE-PD P1 and P2 datasets were genotyped with different arrays, so we performed all analyses independently to keep the maximum number of variants and ensure the independence of both datasets. We re-ran the analysis on LARGE-PD P1 to eliminate any potential biases introduced by methodological differences between this study and the approach used by Loesch et al. 14 . Genotyping Calling and Genetic Quality Control Genotyping calling and quality control The Illumina signal intensity data files from LARGE-PD P2 were converted to PLINK format using the Illumina Array Analysis Platform (v1.1) using the GP2 cluster file and default parameters. Subsequently, we removed unaligned and problematic variants (see SI section Genotyping Calling). We applied our quality control (QC) pipeline to LARGE-PD datasets. This pipeline is focused on admixed populations. The pipeline removes individuals and variants based on the following criteria: (i) individuals missing sex and/or status, (ii) individuals with discrepancies between declared and genetic sex (calculated using PLINK v1.90 20 ), (iii) ambiguous genotypes, (iv) individuals and variants with more than 5% missing data, (v) duplicated variants (keeping the variant with lower missing rate), (vi) samples with heterozygosity rate ±3 standard deviation (SD) from the mean, (vii) variants failing Hardy-Weinberg Equilibrium (HWE) in controls (p < 1e-6) and cases (p < 1e-10). This process resulted in the related QCed dataset, which was imputed using the TOPMed imputation server panel r3 21 . The number of samples and SNPs is described in Table S2 We generated the list of samples to be removed due to genetic relationships from the QCed dataset using NAToRA 22 selecting the optimal mode that guarantees minimal sample loss (see SI section on relationship control). All QC analyses were performed using PLINK v2.00a5.10LM 23 , except where a specific tool was mentioned. Ancestry analysis Parental Reference dataset The ancestry analyses were performed using a reference panel of non-admixed samples from the 1000 Genomes Project (1KGP) 24 . The reference panel includes non-admixed samples from African (AFR), East Asian (EAS), European (EUR), Native American (NAT), and South Asian (SAS) metapopulations 25 (see SI section Ancestry Analysis). Principal Component Analysis In this study, we employed three approaches of Principal Component Analysis (PCA). For association analyses using all samples, we performed unsupervised PCA on the full dataset to be used as covariates (Figures S1–S4). For analyses incorporating ancestry into the statistical model, we first conducted a supervised PCA using the reference dataset to identify ancestry outliers (Figures S5–S8). After excluding ancestry outliers (samples more than ±3 SD from the mean in the first 10 PCs) and related individuals, we created the unrelated and non-outlier (UNO) dataset. We then performed unsupervised PCA on the UNO dataset to be used as covariates (Figures S9–S12) (see SI section Ancestry Analysis). Ancestry inferences In genetic studies, ancestry inference refers to the inference of the contribution of each parental population to an individual. In this work, we performed global ancestry (GA) and local ancestry (LA) inference. GA estimates the proportion of genetic ancestry from each ancestral population in an individual. LA identifies the ancestral population of chromosomal segments across an individual’s genome. GA was inferred using supervised ADMIXTURE 26 , with the previously described 1KGP panel as the reference. LA was inferred using G-nomix [32]. We conducted tests to determine the optimal parameters for performing LA analysis. Our analyses were performed using a three-way admixed reference panel, consisting of EUR and AFR from 1KGP, and a NAT reference panel composed of 1KGP NAT samples and non-admixed controls from LARGE-PD (see SI Section Ancestry Analysis). Association studies Genome-Wide Association Studies Conducting a GWAS in an admixed population presents unique challenges. The genetic heterogeneity within such populations can reduce statistical power and increase susceptibility to false positives and false negatives. In this work, we performed GWAS using three methods: SAIGE 27 , ATT 28 , and TRACTOR 29 . SAIGE (Scalable and Accurate Implementation of Generalized mixed model) controls for population stratification and relatedness, incorporating the genetic relationship matrix (GRM), allowing all samples to be included. ATT is a Cochran–Armitage trend test that corrects for GA. TRACTOR is a method that splits the dosage based on LA, assuming the possibility of independent genetic effects across different ancestries. TRACTOR and SAIGE were selected based on a study 30 demonstrating that SAIGE was the best method to control the type 1 error rate in admixed cohorts. At the same time, TRACTOR was the best model for identifying ancestry-specific risk loci. ATT was chosen because it exhibits greater statistical power than TRACTOR when the risk allele is common across ancestries 31 . For SAIGE, we used all samples with age, sex, and the first 10 PCs (Figures S1–S4) as covariates. For TRACTOR and ATT, we used the UNO dataset with sex, age and the 10 PCs (Figures S9–S12) from UNO dataset as covariates. Meta-analysis After performing GWAS, we meta-analyzed data from LARGE-PD P1 and P2 for each method using GWAMA (v2.2.2) 32 and Meta-Regression of Multi-AncEstry Genetic Association (MR-MEGA) with both phases of LARGE-PD and two SAS GWAS 33 , 34 . In both we performed under random and fixed-effect models. While the fixed-effect model assumes one true effect size across studies, the random-effects model assumes that true effect sizes can vary between studies, computing more conservative p-values and wider confidence intervals when heterogeneity is present 35 . Considering the admixture in LAC populations, the random-effects model is more suitable. Finally, heterogeneity was assessed using Cochrane Q statistics and I 2 calculations (SI section Meta-Analysis). Replication of previously identified PD risk loci To assess the consistency between our findings and previous results, we extracted the beta and p-value from associations reported in Kim et al. 2024 (KIM) and Nalls et al. 2019 (NALLS) 7 , 8 studies(GWAS Catalog accession IDs: GCST90275127 and GCST009325) and compared them with the results from the LARGE-PD meta-analysis (SAIGE, ATT, and TRACTOR-NAT random effects). We also selected variants that achieved a suggestive p-value in any LARGE-PD meta-analysis and searched for the reported p-values from both studies. Case-control admixture mapping Case-control admixture mapping (AM) is a LA-phenotype association study that tests the association between LA windows and the phenotype. Since AM involves fewer independent tests than a GWAS, it requires a less stringent p-value threshold 36 . We performed the AM using GENESIS package 37 using sex, age, and the first 10 PCs as covariates. After this, we extracted variants within the statistically significant regions, as well as those 1 MB flanking these regions, from the UNO dataset and performed association studies using PLINK2 using the same covariates used on AM. We considered statistically significant if p < 1.673x10 −5 (see SI section Admixture Mapping). Functional annotation analysis After the meta-analysis, we uploaded the summary statistics to the Functional Mapping and Annotation (FUMA) portal 38 . Using FUMA, we identified independent and lead signals within the genomic risk loci based on double clumping criteria. All the analyses used the 1KGP Phase 3 AMR panel as the reference population. Furthermore, MAGMA (Multi-marker Analysis of GenoMic Annotation) 39 function was used for gene-set analysis using ontology terms from the MSigDB v7.0 40 , setting the default parameters used by FUMA. Additionally, we investigated the functional impact of the identified variants using the Combined Annotation Dependent Depletion (CADD) score 41 , FORGEdb 42 score, and SnpEff 43 . While the CADD Phred score estimates the variant’s potential deleteriousness, SnpEff helps interpret the biological impact. The FORGEdb score was created to identify candidate regulatory variants for functional experiments (see SI section Functional annotation). Gene expression studies After obtaining the list with lead and independent variants, we proceeded with the identification of expression quantitative trait loci (eQTLs) using the Genotype-Tissue Expression (GTEx) portal 44 . We compiled a list of replicated variants and queried their rsIDs on the GTEx portal. All genes returned under the “Single-Tissue eQTLs” section were added to our list of candidate genes. Results Cohorts Description and Ancestry Characterization P1 includes individuals from five South American countries. P2 expands the cohort to include additional samples from South, Central, and North America, encompassing participants from a total of ten countries (Table 1, Table S1). In terms of demographic composition, the P1 cohort comprises 797 cases (46.9% females) and 681 control (66.4% females). The P2 cohort includes 2,744 cases (42.8% females) and 1,559 controls (67.7% females). The mean age of cases is higher than controls on both phases. The females also had a higher mean age of onset on P2 (56.63 on females vs 55.71 on males). Our ancestry analysis showed that our samples are primarily 3-way admixed, with contributions of Africans, Europeans, and Native Americans. Considering the minimal contribution of Asian ancestries ( Figure 1 , Figures S1–S4), our analyses were conducted using a three-way admixed model. To reduce potential bias, ancestry-informed methods were applied after removing outliers identified through projected PCA, thereby preventing misclassification of Asian ancestries in our dataset. Download figure Open in new tab Figure 1: Global ancestry analysis of LARGE-PD Phase 2 using supervised ADMIXTURE. (A) Bar plot for LARGE-PD Phase 1 with the proportions of African (AFR), European (EUR), East Asian (EAS), South Asian (SAS), and Native American (NAT) ancestry clusters. Percentages of continental ancestry stratified by cases (solid lines) and controls (dashed lines) for samples from Phase 1 and 2 for (B) LARGE-PD South America, (C) LARGE-PD North America, (D) LARGE-PD Central America and Caribbean. Arrows on the map indicate the geographic locations of the cohorts from which the samples were collected. (E) Bar plot for LARGE-PD Phase 2 with the proportions of AFR, EUR, EAS, SAS, and NAT ancestry clusters. Site names are listed in the Supplementary Information, Section Cohort Description. Cohort sample sizes are provided in Supplementary Table S1. Genotype-phenotype association study Genome-wide association studies Using SAIGE, three variants reached statistical significance on chromosome (chr) 4 in LARGE-PD P1, which replicated in P2 (Figure S13 (A), SI Table S4), with rs356183-G (inside the SNCA gene) emerging as the top SNP (P1 OR: 1.642, SE: 0.090, p-value: 3.45 × 10 −8 ; P2 OR: 1.452, SE: 0.058, p-value: 1.27 × 10 −10 , Figure S14). For ATT, we identified statistically significant associations in chr 3, 4, 11, and 13 in P1 (SI Figure S13(B), SI Table S5). However, only the chr 4 signals, which were observed in two different LD blocks (SI Figure S15), were replicated in P2. The top SNP in the first LD block was rs356182-G, the same variant reported on Loesch et al. (P1 OR: 1.736, SE: 0.082, p-value: 8.53 × 10 −12 , P2 OR: 1.467, SE: 0.057, p-value: 8.29 × 10 −12 ). In the second block, the top SNP was rs6830166-C (P1 OR: 1.793, SE: 0.089, p-value: 2.06 × 10 −11 , P2 OR: 1.280, SE: 0.060, p-value: 3.14 × 10 −5 ). Both SNPs are intronic variants inside the SNCA gene. Among the other regions, nine variants near the NRROS (chr 3) gene achieved statistical significance on P1, but exhibited an opposite effect in P2. Signals detected in other chr were not replicated. For TRACTOR, we observed signals on NRROS and SNCA genes associated only with NAT ancestry (Figure S16(D), SI Table S6). Chr 4 exhibited two LD blocks (Figure S17), one led by rs3857049-T (P1 OR: 2.075, SE: 0.124, p-value: 1.70 × 10 −9 ; P2 OR: 1.390, p-value: 7.52 × 10 −6 ) and the other led by rs2737021-T (P1 OR: 0.484,SE: 0.128 , p-value: 7.75 × 10 −9 ; P2 OR:0.632, SE: 0.088, p-value: 8.60 × 10 −8 ). No variants on chr 3 were replicated. Meta-analysis Random-effects meta-analyses were performed separately using SAIGE, ATT, and TRACTOR-NAT results (Table S7-S12). Across these methods, two genomic risk loci were consistently identified, mapped to ITPKB and SNCA genes ( Figure 2 ). Download figure Open in new tab Figure 2: Manhattan plots from random-effects genome-wide association meta-analysis (GWAMA) of LARGE-PD Phase 1 and 2 cohorts for (A ) SAIGE, (B ) ATT, and ( C) TRACTOR results for Native American ancestry meta-analysis. Each plot shows –log 10 (p) values across the genome; horizontal lines indicate genome-wide suggestive (1e-5) and statistical significance (5e-8) thresholds. The SAIGE meta-analysis identified rs117185933-A as the top SNP on chr 1 (OR: 1.75, SE: 0.131, p-value: 3.80 × 10 −12 ). On chr 4, two top SNPs were detected on SNCA gene: rs10027842-C (OR: 0.73, SE: 0.039, p-value: 1.76 × 10 −8 ) and rs356223-G (OR: 0.664, SE: 0.031, p-value: 3.05 × 10 −17 ). The ATT meta-analysis identified rs16846413-T (OR: 1.679, SE: 0.118, p-value: 8.81 × 10 −12 ) as the top variant on chr 1, and rs10027842-C (OR: 0.754, SE: 0.037, p-value: 4.17 × 10 −8 ), also detected by SAIGE, along with rs356211-T (OR: 0.663, SE: 0.029, p-value: 9.83 × 10 −19 ) on chr 4. In the TRACTOR-NAT meta-analysis, the top variants were rs4653463-T (OR: 1.60, SE: 0.127, p-value: 4.09 × 10 −8 ) on chr 1 and rs6532202-C (OR: 0.661, SE: 0.103, p-value: 1.62 × 10 −8 ) and rs356181-A (OR: 0.6, SE: 0.047, p-value: 2.01 × 10 −9 ) on chr 4. No variants reached statistical significance in the TRACTOR for EUR or AFR ancestry-specific meta-analyses (Figure S18). The ITPKB loci (rs117185933-A) was previously associated with PD, being in LD with the lead SNP (rs74990530-T) in a recent SAS GWAS (figure S21). However, in our dataset both variants are independent lead signals (being s117185933-A the top hit in SAIGE meta-analysis) (table S17). Likewise, our meta-analysis with meta-regression of SAS and AMR cohorts indicate that rs117185933-A association is lost within random-effects meta-analysis, and is the lead MR-MEGA signal, with heterogeneity significantly correlated with ancestry (table S18). Furthermore, Bayesian fine mapping implemented by MR-MEGA nominates rs117185933-A as a putative causal variant being the single member of the 99% credible set for this genomic region (table S19), likely mapping an independent PD risk locus. Replication of previously identified PD risk loci We investigated the beta coefficients and p-values for 78 and 90 GWAS hits reported by KIM and NALLS, respectively 7 , 8 . Among these, we identified 57 and 56 variants from the SAIGE (KIM: n=27; NALLS: n=30) and ATT (KIM: n=28; NALLS: n=28) meta-analyses, respectively, that had p < 0.05 and the same effect direction. One variant from KIM, rs79333841, had p 0.05 were excluded from further consideration. We also conducted a complementary analysis by selecting variants with p-values < 1 × 10 −5 in our meta-analyses (SAIGE, ATT, and TRACTOR–NAT) and comparing their effect directions with variants showing p-values < 0.05 in KIM and/or NALLS. All overlapping variants from SAIGE (KIM n=13; NALLS: n=18), ATT (KIM: n=20; NALLS: n=36), and TRACTOR-NAT (KIM: n=3; NALLS: n=9) exhibited consistent effect directions (Figure S24). Admixture mapping After performing AM, two significant regions were detected: one on chr 1, associated with EUR ancestry, and one on chr 6, associated with AFR ancestry (Figure S19). We then identified the genomic regions on chr1:242,089,864 - 243,560,064 and chr6:165,474,043 - 167,351,763. After performing the association study at the variant level, no variants on chr 1 achieved statistical significance, while 3 variants on chr 6 achieved statistical significance in P1, but were not replicated (SI Table S13). Functional annotation analysis After using the MAGMA function available in FUMA, 16,992 gene ontology sets in MSigDB v7.0 were tested for enrichment of SNP signals based on the summary statistics provided. The ATT GWAMA gene-set analysis identified two significantly enriched gene-sets, belonging to the collections of Curated Gene Sets (Reactome Interleukin 23 signaling; BETA: 1.141, p-value: 1.463e-6, p-bon: 0.0248) and Gene Ontology Biological Process (Growth Hormone Receptor Signaling Pathway Via JAK-STAT; BETA 1.35, p-value 2.334e-6, p-bon: 0.0396). After the replication and meta-analysis, we selected 15 variants identified as top or independent signals by FUMA (Table 2). For these variants, we retrieved CADD Phred scores, FORGEDB score, and SnpEff annotations. Most variants showed low CADD Phred scores and were annotated as intronic or intergenic according to SnpEff. However, the variant rs117185933 had a CADD Phred score of 17.92 (top ∼3% most deleterious variants) and FORGEdb 8, and was classified as a potential “5’ UTR premature start codon gain variant” according SnpEff (Table S14). Located 80 nucleotides upstream of the coding sequence in the 5′ untranslated region (UTR), this variant may introduce a premature start codon, potentially altering the translation initiation site. Such a change could result in a truncated or non-functional protein. Gene expression studies We queried the rsIDs for the top or independent signals (Table 2) in the GTEx portal. No significant eQTL associations were identified for variants on chr 1. In contrast, eight variants on chr 4 showed significant associations with gene expression in multiple tissues. These included associations with the protein-coding genes SNCA, SNCA-AS1 , and MMRN1 , besides some long non-coding RNAs (Table S15). Notably, eQTL associations were identified in several brain regions, including the amygdala, anterior cingulate cortex (BA24), caudate (basal ganglia), cerebellar hemisphere, frontal cortex (BA9), hippocampus, putamen (basal ganglia), and nucleus accumbens (basal ganglia). All regions showed associations with the MMRN1 gene, with additional associations observed for SNCA-AS1 in the anterior cingulate cortex and SNCA in the cerebellar hemisphere. Associations were also observed in non-brain tissues, such as adipose tissue (subcutaneous and visceral - omentum), arteries (aorta and tibial), EBV-transformed lymphocytes, esophagus (mucosa), heart (atrial appendage and left ventricle), liver, skeletal muscle, tibial nerve, skin (not sun-exposed - suprapubic and sun-exposed - lower leg), and whole blood. Discussion In this work we conducted the largest genotype-phenotype association study in Latin American populations. Our dataset is composed of data from two phases of LARGE-PD, the P1 with ∼1,500 samples and P2 with ∼4400. We performed GWAS with three different methods: i) SAIGE, which includes the GRM on the logistic regression; ii) ATT, which incorporates the GA on the statistical model; and iii) TRACTOR, which splits the dosage by ancestry based on LA information. Besides that, we also performed AM. In our discovery cohort, we identified association signals on chr 3 (ATT and TRACTOR-NAT) and 4 (SAIGE, ATT, and TRACTOR-NAT); however, only the variants on chr 4 were successfully replicated. ATT and TRACTOR identified and replicated additional independent regions on chr 4 that SAIGE did not find. These findings show the importance of including ancestry information in our association models. Looking at previous studies, our effect directions were well correlated (r>0.69) with previous GWAS (Figure S23-S24), and most of our findings were also replicated in these prior studies (r>0.95). The conducted meta-analysis using P1 and P2 data identified two genomic regions significantly associated with PD, located on chr 1 and 4. Notably, the region on chromosome 1 reached statistical significance in P2, but it did not achieve statistical significance in P1 (Table 2). Across meta-analyses, specific top SNPs and independent signals differed (SI Table S6-11). This likely reflects variations in analytical assumptions across the methods, while the consistent identification of ITPKB and SNCA underscores their robustness. Furthermore, random-effects meta-analysis found no evidence of between-cohort heterogeneity for the lead variants (I 2 = 0%), except for TRACTOR-NAT on chromosome 4, with low-to-moderate I 2 values (I 2 = 11–37%) (Table 2). Because TRACTOR splits the genotypes by LA, differences in array content, LA inference, imputation methods, or gene-environment interactions may explain the observed heterogeneity. Looking for the functional annotation in our study, we observed that the variant with the lowest p-value is inside the SNCA gene (Figure S20), which encodes Alpha-synuclein (α-Syn). These results are consistent with those of Loesch et al. GWAS 14 and PRS 16 findings, where rs365182 accounted for most of the trait variance in the PRS. α-Syn is a small protein (140 amino acids) involved in several key processes at the synapse, including modulation of the dopamine transporter 45 , promotion of the dilation of the exocytotic fusion pore 45 , 46 , and others 47 , 48 . Notably, α-Syn has long been implicated in PD even before the advent of modern genetic studies 49 , 50 . In the GWAS era, the association became more evident 7 , 8 , 51 , with in vitro and in vivo models 52 , 53 contributing to a deeper understanding of the role of α-Syn in PD. In PD cases, α-Syn accumulates in the substantia nigra, leading to the loss of dopaminergic neurons and the formation of Lewy bodies 54 . Interestingly, the SNCA gene was not associated with PD in the African GWAS 12 , highlighting the potential impact of genetic ancestry on PD genetic architecture. We also identified variants in chr 1 within the ITPKB gene, which encodes the 3-kinase B of inositol 1,4,5-triphosphate (IP3). It influences key cellular processes, including calcium dynamics and immune function, and has been associated with neurodegenerative diseases, including Alzheimer’s disease 55 and PD 55 – 58 . Some studies suggest that ITPKB may exert a protective effect against α-Syn aggregation 59 , and its expression levels are positively correlated with those of α-Syn 60 . Among the variants identified inside the ITPKB gene, rs117185933-A stands out with a high CADD Phred score. The functional annotation using SnEff classified this variant as a potential 5’ UTR premature start codon gain variant . This variant shows its highest allele frequency in admixed American populations (8.35%), according to the gnomAD browser 61 . The rs117185933-A was previously associated with PD, being in LD with the lead SNP (rs74990530). Given that rs117185933-A was previously associated with PD, being in LD with the lead SNP (rs74990530-T) in a recent SAS PD GWAS 33 , and that both signals are independent in our SAIGE meta-analysis results (figure S21). A meta-analysis with meta-regression as implemented in Kim et al., 2024 8 was performed to validate the independence of rs117185933-A across populations. Using both LARGE-PD cohorts and the two available SAS PD GWASs 33 , 34 , a random-effects meta-analysis and MR-MEGA were employed to uncover homogeneous and heterogeneous effects respectively. The rs117185933-A signal lost genome-wide significance under the random-effects model, showing high heterogeneity across cohorts that conditioned wider confidence intervals (table S18). However, MR-MEGA rescued rs117185933-A genome-wide significance while highlighting signal heterogeneity correlated with ancestry (p<0.05) and not residual heterogeneity (table S18), suggesting differences in population genetic structure drive the heterogeneity. Since MR-MEGA results identified rs117185933-A as the lead signal among other six independent SNPs (table S17), we leveraged the Bayes Factors provided to assemble credible sets as described previously 8 . Subsequently, rs117185933-A was identified as the single member of the 99% credible set, emerging as a novel putative causal variant that was not previously reported in the most recent multi-ancestry PD meta-analysis 8 . Besides the single variant testing, gene-set-based MAGMA analysis through FUMA, based on ATT fixed and random-effects meta-analysis, identified two novel pathways associated with PD risk. Reactome Interleukin 23 signaling and Growth Hormone Receptor Signaling Pathway Via JAK-STAT were not previously reported among significant gene-sets in the most recent multi-ancestry meta-analysis for PD 8 . However, a signal near the JAK1 gene was reported to be close to genome-wide significance under their meta-analysis 8 . The biological role of JAK/STAT signaling has been highlighted in microglial activation and expression of pro-inflammatory cytokines, with therapeutic potential for its inhibition to halt neurodegeneration in PD 62 . Hence, variants within members of JAK/STAT signaling-related pathways appear relevant for PD risk in LAC populations. Further studies to unveil their genomic significance are needed. Our study has limitations. The sample size combined with the high genetic heterogeneity of Latin American populations, reduce statistical power to detect associations, especially for variants with small effect sizes. Additionally, the case-control imbalance in P2 (64% cases overall, 76% among males) may introduce bias. The cohorts include a high proportion of individuals from Peru (48% in P1 and 30% in P2). This uneven geographic representation may affect the generalizability of our findings to the broader LAC region, hence the relevance of continuing current efforts in the region. In terms of ancestry analysis, a major limitation is the underrepresentation of NAT individuals in reference panels. To compensate, we included non-admixed LARGE-PD controls, although this approach may introduce bias. Moreover, AFR ancestry is underrepresented in our dataset (5% in P1 and 2% in P2), reflecting a well-documented gap in PD research 63 . Regarding the meta-analysis with meta-regression and fine mapping approaches, variation in allele frequencies among SAS and LARGE-PD cohorts are taken as a proxy of ancestry to include in the meta-regression and different imputation panels were used by each study. Further validation with ancestry-matched LD estimates are needed, although the lack of robust panels for underrepresented and admixed populations will remain as a challenge. Likewise, MR-MEGA and random-effects meta-analysis of SAS and LARGE-PD cohorts outlined multiple independent signals besides the lead variants addressed. Hence, future follow up must assess the validity of rs117185933-A as a standalone PD putative causal variant within ITPKB and its biological mechanism. Thus, our work contributes to elucidating the genetic architecture of PD in LAC populations by leveraging ancestry-based analyses. We successfully replicated findings from previous GWAS and identified novel variants associated with PD in this population, including rs117185933-A within the ITPKB gene. While these findings are promising, further investigation and validation are needed. Data Availability Data used in the preparation of this article were obtained from the Global Parkinson's Genetics Program (GP2; https://gp2.org) and from the Latin American Research Consortium on the Genetics of Parkinson's Disease (LARGE-PD) project. Specifically, we used Tier 2 data from GP2 release 10 (DOI 10.5281/zenodo.15748014, release 10). Tier 1 data can be accessed by completing a form on the Accelerating Medicines Partnership in Parkinson's Disease (AMP-PD) website (https://amp-pd.org/register-for-amp-pd). Tier 2 data access requires approval and a Data Use Agreement signed by your institution. Conflict of interest Mayela Rodríguez-Violante has received honoraria from Ferrer and Zydus, and educational grants from Medtronic and Boston Scientific. All other authors declare that there are no conflicts of interest. Data and code availability Data used in the preparation of this article were obtained from the Global Parkinson’s Genetics Program (GP2; https://gp2.org ) and from the Latin American Research Consortium on the Genetics of Parkinson’s Disease (LARGE-PD) project. Specifically, we used Tier 2 data from GP2 release 10 (DOI 10.5281/zenodo.15748014, release 10). Tier 1 data can be accessed by completing a form on the Accelerating Medicines Partnership in Parkinson’s Disease (AMP®-PD) website ( https://amp-pd.org/register-for-amp-pd ). Tier 2 data access requires approval and a Data Use Agreement signed by your institution. All code generated for this article, and the identifiers for all software programs and packages used, are available on GitHub ( https://github.com/GP2code/LARGE-PD_Phase2 ) and were given a persistent identifier via Zenodo (DOI: 10.5281/zenodo.15864760). Summary statistics from every ancestry-level meta-analysis are available on NDPK ( https://ndkp.hugeamp.org/research.html?pageid=a2f_downloads_280 ). LARGE-PD consortium Argentina: Emilia Mabel Gatto, Claudia Perandones, Martin Radrizani, Gustavo DaPrat, Natalia Gonzalez Rojas, Melisa Espindola, Martin Cesarini, Maria Valentina Muller, Carlos Matias López Razquin, Bibiana Pizarro, Lucia Wang, Clarisa Marchetti, Cesar Avila, Griselda Alvarado, Luciana Rojas-Vazquez, Juan Pablo Diaz-Rearte, Marcelo Kauffman, Sergio Rodriguez, Dolores Gonzales, Pavel Alejandro Hernandez, Belen Ceballos, Florencia Echeverria, Gabriela Costa, Marcelo Merello, Federico Capparelli, Florencia Wainberg, Tomas Poklepovich, Florencia Mallou, Denise De Belder Bolivia: Erick Gonzalez, Enrique Wagner, Robin Rodriguez, Alexander Quecana Janco Brazil: Bruno Lopes Santos-Lobato, Gracivane Lopes Eufraseo, Juliana Duarte, Marcella Montenegro, Tatiane Souza, Camille Sena, Ândrea Ribeiro-dos-Santos, Pedro Braga-Neto, Deborah Rangel, Marconny Cavalcante, Mateus Balsells, Vitor Tumas, Angela Vieira Pìmentel, Vanderci Borges, Carolina Candeias da Silva, Henrique Ballali Ferraz, Dayany Leonel Boone, Mariana Cavalcanti Costa, Egberto Reis Barbosa, Grace Letro, Artur F S Schuh, Carlos R M Rieder, Gabriela Magalhães Pereira, Deise Cristine Friedrich, Thalya Osmilda Alves de Carvalho, Isabella Fonseca Benati, Jullivan Käfer Pasin, Vitor Picanço Lima Gomes, Marcelo Somma Tessari, Ingrid Lorena da Silva Gomes, Juan Sebastián Sánchez León, Paula Saffie-Awad, Gabriel Alves Marconi, Manoella Guatimuzim Testa da Silva Colombia: Gonzalo Arboleda, Oscar Bernal Pacheco, Oscar Bernal Pacheco, Tatiana Lopez Gonzalez, Humberto Arboleda, Carlos Eduardo Arboleda Bustos, Hebert Bernal Castro, Juan David Caicedo Narvaez, Kelly Bonilla Vargas, Jorge Orozco, Beatriz Munoz Ospina, Harold Londono, David Aguillon, Sonia Moreno, Omar Buritica, David Pineda, Marlene Jimenez-Del-Rio, Carlos Vélez-Pardo, Sarita Firstman Chile: Pedro Chana-Cuevas, Ximena Pizarro Correa, Consuelo Moos, Natalia Rojas, Patricio Olguin, Alicia Colombo, Juan Cristobal Nuñez, Andres De la Cerda, María Francisca Canals, Gonzalo Farías, Valentina Bessa, Mérida Terán, Pen Cheng Zhongxomg, Paula Saffie, Eduardo Perez, Dominga Berrios, Elías Fernandez, Marlene Valenzuela Valenzuela, Mario Fuentealba Sandoval, Susana Pineda, Floria Pancetti, Maria Eugenia Contreras Pinto, Benjamin Soto Flores, Costa Rica: Gabriel Torrealba-Acosta, Tanya Lobo-Prada, Jaime Fornaguera-Trías, Álvaro Hernandez-Guillen, Roger Rodríguez Dominican Republic: Rossy Cruz Vicioso, Ernestina Castro, Alpher Perez, Sergio Mosquera, Cesarina Torres, Janfreisy Carbonell Honduras: Reyna M. Durón, Alex Medina, Heike Hesse, Evelin Alvarez Herrera, Eduardo P. Murillo, Glenda Oliva El Salvador: Susana Pena, Tatiana Ascencio, Oscar Peña Rodas, Ecuador: Faryd Llerena Toro, Michael Castelo, Carlos Rodriguez Mexico: Mayela Rodríguez-Violante, Ana Jimena Hernández-Medrano, Amin Cervantes-Arriaga, Daniel Martinez Ramirez, Sarael Alcauter, Alejandra Medina-Rivera, Alejandra E. Ruiz-Contreras, Miguel E. Rentería, Alejandra Lázaro-Figueroa, Juan Manuel Esquivias-Farias, Andrés Morales-de-Arcia, Alejandra Zayas-Del Moral, Damaris Vazquez-Guevara, Yamil Matuk-Pérez , Carlos Manuel Guerra-Galicia, Ildefonso Rodriguez-Leyva, Karla Salinas-Barboza, Eugenia Morelos-Figaredo, Omar Cardenas-Saenz, Nadia A Gandarilla-Martínez, Sara Isais-Millán , Teresa Pérez-Torres, Domingo Martinez, Ingrid Estada-Bellmann, Roberto Trejo-Ayala, Carlos Alberto Ponce-Fernández, Dane Oropeza Peru: Mario Cornejo-Olivas, Julia Rios Pinto, Angel Medina, Ivan Cornejo-Herrera, Koni Mejia-Rojas, Cintia Armas Puente, Edward Ochoa-Valle, Marcela Alvarado Morales, Elison Sarapura Castro, Andrea Rivera-Valdivia, Maryenela Illanes Manrique, Carla Manrique Enciso, Victoria Marca Ysabel, Olimpio Ortega Dávila, Freddy Requejo-Navarro, Alid Manrique Palomino, Gabriela Gushiken Oshiro, Laura Zelada Rios Uruguay: Elena Dieguez, Victor Raggio, Andres Lescano USA-Puerto Rico: Angel Vinuela, Esther Colon USA: Thiago P Leal, Emily Waldo, Felipe Duarte-Zambrano, Miguel Inca-Martinez, Janvi Ramchandra, Kamilah Stark, Henry Mauricio Chaparro-Solano, Daniel Teixeira-dos-Santos, Emily Leininger, Nicolas Gutierrez, Valerie Rico, Paula Reyes-Pérez, Maria Rivera Paz, Ignacio F Mata, Karen Nuytemans, Anisley Martinez, Liena Infante Acknowledgments This work was supported by the National Institutes of Health (NIH) grants (R01 1R01NS112499-01A1 to T.P.L, M.I-M, A.A., J.R., M.C-O and I.F.M.), Veterans Affair Puget Sound Healthcare System (5I01ABX005978-2 to T.P.L, V.B.P. and I.F.M.), ASAP-GP2 (M.C-O and I.F.M.), Michael J. Fox Foundation (MJFF-026283 for E.W., M.C-O and I.F.M.), Alzheimer’s Disease Sequencing Project (ADSP) (5U01AG076482-03 to E.W. and M.I-M), Vicedecanatura de Investigación y Extensión, Facultad de Medicina, Universidad Nacional de Colombia (for F.D.Z.), American Parkinson Disease Association (APDA) (1282087 for M.I-M and I.F.M; K.N.), Parkinson’s Foundation (PDGENE-1333334 M.I-M. and I.F.M; K.N.), National Council for Scientific and Technological Development (CNPq Brazil) (for B.L.S-L, STP, AFS, PBN and F.T.), DIEB-Universidad Nacional de Colombia (for G.A.), FAP-UNIFESP (for V.B.), Universidad Nacional de Tucumán (PIUNT D711 for C.A.), Vicerrectoría de Investigación, Universidad de Costa Rica (JFT), Centro de Investigaciones Clinicas (CIC) de la Fundación Valle del Lili (BMO), CONACYT-FORDECYT-PRONACES (11311 and 6390 for A.M-R), Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica–Universidad Nacional Autónoma de México (IN218023 for A.M-R and IN208622 for S.A.),Chan Zuckerberg Initiative Ancestry Network (2021-240438 for A.M-R), Secretaría de Ciencia, Humanidades, Tecnología e Innovación (Secihti) (CF-2019/6390. For S.A., and 1222481 for ALF), Margaret Q. Landenberger Foundation (for K.N.). This project was supported by the Global Parkinson’s Genetics Program (GP2). GP2 is funded by the Aligning Science Across Parkinson’s (ASAP) initiative and implemented by The Michael J. Fox Foundation for Parkinson’s Research ( https://gp2.org ). For a complete list of GP2 members see https://gp2.org . The authors thank Emily Mason for her assistance with DNA extraction during the project, Dr. Daniel Shriner for his support on ancestry-related topics, and Sara Bandres Ciga with eQTLs and co-localization analysis. This work received technical support from Luis Aguilar, Alejandro León, and Jair García of the Laboratorio Nacional de Visualización Científica Avanzada at the Universidad Nacional Autónoma de México. We also thank Carina Uribe Díaz and Alejandra Castillo Carbajal for their technical support at the Universidad Nacional Autónoma de México. We also acknowledge the technical and professional staff of the Biobank of Tissue and Fluids at the University of Chile for their commitment and support. We are grateful to the DNA-Neurogenetics Bank of the Instituto Nacional de Ciencias Neurológicas (INCN – Peru) for their support in the collection of DNA samples and associated data used in this publication. Finally, we are deeply grateful to all the individuals who participated in LARGE-PD. References 1. ↵ GBD 2016 Parkinson’s Disease Collaborators . Global, regional, and national burden of Parkinson’s disease, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016 . Lancet Neurol 17 , 939 – 953 ( 2018 ). OpenUrl CrossRef PubMed 2. Dorsey , E. R. , Sherer , T. , Okun , M. S. & Bloem , B. R . The Emerging Evidence of the Parkinson Pandemic . Journal of Parkinson’s Disease 8 , S3 – S8 ( 2018 ). OpenUrl CrossRef 3. Su , D. et al. Projections for prevalence of Parkinson’s disease and its driving factors in 195 countries and territories to 2050: modelling study of Global Burden of Disease Study 2021 . BMJ 388 , e080952 ( 2025 ). OpenUrl Abstract / FREE Full Text 4. ↵ Pereira , G. M. et al. A systematic review and meta-analysis of the prevalence of Parkinson’s disease in lower to upper-middle-income countries . NPJ Parkinsons Dis 10 , 181 ( 2024 ). 5. ↵ Bandres-Ciga , S. , Diez-Fairen , M. , Kim , J. J. & Singleton , A. B . Genetics of Parkinson’s disease: An introspection of its journey towards precision medicine . Neurobiol Dis 137 , 104782 ( 2020 ). 6. Foo , J. N. et al. Identification of Risk Loci for Parkinson Disease in Asians and Comparison of Risk Between Asians and Europeans: A Genome-Wide Association Study . JAMA Neurol 77 , 746 – 754 ( 2020 ). OpenUrl CrossRef PubMed 7. ↵ Nalls , M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies . Lancet Neurol 18 , 1091 – 1102 ( 2019 ). OpenUrl CrossRef PubMed 8. ↵ Kim , J. J. et al. Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease . Nat Genet 56 , 27 – 36 ( 2024 ). OpenUrl CrossRef PubMed 9. ↵ Global Parkinson’s Genetics Program (GP2) & Leonard , H. L. Novel Parkinson’s Disease Genetic Risk Factors Within and Across European Populations . medRxiv ( 2025 ) doi: 10.1101/2025.03.14.24319455 . OpenUrl Abstract / FREE Full Text 10. ↵ Schumacher-Schuh , A. F. et al. Diversity in Parkinson’s disease genetics research: current landscape and future directions . ( 2021 ) doi: 10.1101/2021.12.07.21266995 . OpenUrl Abstract / FREE Full Text 11. ↵ The Missing Diversity in Human Genetic Studies . Cell 177, 26 – 31 ( 2019 ). 12. ↵ Rizig , M. et al. Identification of genetic risk loci and causal insights associated with Parkinson’s disease in African and African admixed populations: a genome-wide association study . Lancet Neurol 22 , 1015 – 1025 ( 2023 ). OpenUrl CrossRef PubMed 13. ↵ Leal , T. P. et al. X-Chromosome Association Study in Latin American Cohorts Identifies New Loci in Parkinson’s Disease . Mov. Disord . 38 , 1625 – 1635 ( 2023 ). OpenUrl PubMed 14. ↵ Loesch , D. P. et al. Characterizing the Genetic Architecture of Parkinson’s Disease in Latinos . Ann. Neurol . 90 , 353 – 365 ( 2021 ). OpenUrl CrossRef PubMed 15. Sarihan , E. I. et al. Genome-Wide Analysis of Copy Number Variation in Latin American Parkinson’s Disease Patients . Mov. Disord . 36 , 434 – 441 ( 2021 ). OpenUrl CrossRef PubMed 16. ↵ Loesch , D. P. et al. Polygenic risk prediction and SNCA haplotype analysis in a Latino Parkinson’s disease cohort . Parkinsonism Relat. Disord . 102 , 7 – 15 ( 2022 ). OpenUrl PubMed 17. ↵ Bandres-Ciga , S. et al. NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations . Mov Disord 39 , 2039 – 2048 ( 2024 ). OpenUrl PubMed 18. ↵ Gibb , W. R. & Lees , A. J . The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson’s disease . J Neurol Neurosurg Psychiatry 51 , 745 – 752 ( 1988 ). OpenUrl Abstract / FREE Full Text 19. ↵ Postuma , R. B. et al. MDS clinical diagnostic criteria for Parkinson’s disease . Mov Disord 30 , 1591 – 1601 ( 2015 ). OpenUrl CrossRef PubMed 20. ↵ Purcell , S. et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses . American Journal of Human Genetics 81 , 559 ( 2007 ). 21. ↵ Das , S. et al. Next-generation genotype imputation service and methods . Nature Genetics 48 , 1284 – 1287 ( 2016 ). OpenUrl CrossRef PubMed 22. ↵ Leal , T. P. et al. NAToRA, a relatedness-pruning method to minimize the loss of dataset size in genetic and omics analyses . Computational and structural biotechnology journal 20 , ( 2022 ). 23. ↵ Chang , C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets . Gigascience 4 , 7 ( 2015 ). 24. ↵ Byrska-Bishop , M. et al. High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios . Cell 185 , 3426 – 3440 .e19 ( 2022 ). OpenUrl CrossRef PubMed 25. ↵ Shriner , D. et al. Universal genome-wide association studies: Powerful joint ancestry and association testing . HGG Adv 4 , 100235 ( 2023 ). 26. ↵ Alexander , D. H. , Novembre , J. & Lange , K . Fast model-based estimation of ancestry in unrelated individuals . Genome Res 19 , 1655 – 1664 ( 2009 ). OpenUrl Abstract / FREE Full Text 27. ↵ Zhou , W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies . Nat Genet 50 , 1335 – 1341 ( 2018 ). OpenUrl CrossRef PubMed 28. ↵ Hou , K. et al. Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations . Bioinformatics 40 , ( 2024 ). 29. ↵ Atkinson , E. G. et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power . Nat Genet 53 , 195 – 204 ( 2021 ). OpenUrl CrossRef PubMed 30. ↵ Yang , Z. et al. A benchmark study on current GWAS models in admixed populations . Brief. Bioinform . 25 , ( 2023 ). 31. ↵ Hou , K. , Bhattacharya , A. , Mester , R. , Burch , K. S. & Pasaniuc , B . On powerful GWAS in admixed populations . Nature Genetics 53 , 1631 – 1633 ( 2021 ). OpenUrl CrossRef PubMed 32. ↵ Mägi , R. & Morris , A. P . GWAMA: software for genome-wide association meta-analysis . BMC Bioinformatics 11 , 288 ( 2010 ). 33. ↵ Kishore , A. et al. Deciphering the Genetic Architecture of Parkinson’s Disease in India . medRxiv ( 2025 ) doi: 10.1101/2025.02.17.25322132 . OpenUrl Abstract / FREE Full Text 34. ↵ Andrews , S. V. et al. The Genetic Drivers of Juvenile, Young, and Early-Onset Parkinson’s Disease in India . Mov Disord 39 , 339 – 349 ( 2024 ). OpenUrl PubMed 35. ↵ Ioannidis , J. P. A. , Patsopoulos , N. A. & Evangelou , E . Heterogeneity in meta-analyses of genome-wide association investigations . PLoS One 2 , e841 ( 2007 ). OpenUrl CrossRef PubMed 36. ↵ Shriner , D . Overview of Admixture Mapping . Current Protocols in Human Genetics 76 , 1.23.1–1.23.8 ( 2013 ). 37. ↵ Gogarten , S. M. et al. Genetic association testing using the GENESIS R/Bioconductor package . Bioinformatics 35 , 5346 – 5348 ( 2019 ). OpenUrl CrossRef PubMed 38. ↵ Watanabe , K. , Taskesen , E. , van Bochoven , A. & Posthuma , D . Functional mapping and annotation of genetic associations with FUMA . Nat Commun 8 , 1826 ( 2017 ). OpenUrl CrossRef PubMed 39. ↵ de Leeuw , C. A. , Mooij , J. M. , Heskes , T. & Posthuma , D . MAGMA: generalized gene-set analysis of GWAS data . PLoS Comput. Biol . 11 , e1004219 ( 2015 ). OpenUrl CrossRef PubMed 40. ↵ Liberzon , A. et al. Molecular signatures database (MSigDB) 3.0 . Bioinformatics 27 , 1739 – 1740 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 41. ↵ Schubach , M. , Maass , T. , Nazaretyan , L. , Röner , S. & Kircher , M . CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions . Nucleic Acids Res 52 , D1143 – D1154 ( 2024 ). OpenUrl CrossRef PubMed 42. ↵ Breeze , C. E. et al. FORGEdb: a tool for identifying candidate functional variants and uncovering target genes and mechanisms for complex diseases . Genome Biol 25 , 3 ( 2024 ). 43. ↵ Cingolani , P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3 . Fly (Austin) 6 , 80 – 92 ( 2012 ). OpenUrl PubMed 44. ↵ Lonsdale , J. et al. The Genotype-Tissue Expression (GTEx) project . Nature Genetics 45 , 580 – 585 ( 2013 ). OpenUrl CrossRef PubMed 45. ↵ Butler , B. et al. Dopamine Transporter Activity Is Modulated by α-Synuclein . J Biol Chem 290 , 29542 – 29554 ( 2015 ). OpenUrl Abstract / FREE Full Text 46. ↵ Logan , T. , Bendor , J. , Toupin , C. , Thorn , K. & Edwards , R. H . α-Synuclein promotes dilation of the exocytotic fusion pore . Nat Neurosci 20 , 681 – 689 ( 2017 ). OpenUrl CrossRef PubMed 47. ↵ Huang , C.-C. et al. Soluble α-synuclein facilitates priming and fusion by releasing Ca from the thapsigargin-sensitive Ca pool in PC12 cells . J Cell Sci 131 , ( 2018 ). 48. ↵ Burré , J. et al. Alpha-synuclein promotes SNARE-complex assembly in vivo and in vitro . Science 329 , 1663 – 1667 ( 2010 ). OpenUrl Abstract / FREE Full Text 49. ↵ Polymeropoulos , M. H. et al. Mutation in the alpha-synuclein gene identified in families with Parkinson’s disease . Science 276 , 2045 – 2047 ( 1997 ). OpenUrl Abstract / FREE Full Text 50. ↵ Spillantini , M. G. et al. Alpha-synuclein in Lewy bodies . Nature 388 , 839 – 840 ( 1997 ). OpenUrl CrossRef PubMed Web of Science 51. ↵ Edwards , T. L. et al. Genome-wide association study confirms SNPs in SNCA and the MAPT region as common risk factors for Parkinson disease . Ann Hum Genet 74 , 97 – 109 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 52. ↵ Price , D. L. et al. In vivo effects of the alpha-synuclein misfolding inhibitor minzasolmin supports clinical development in Parkinson’s disease . NPJ Parkinsons Dis 9 , 114 ( 2023 ). 53. ↵ Calabresi , P. et al. Alpha-synuclein in Parkinson’s disease and other synucleinopathies: from overt neurodegeneration back to early synaptic dysfunction . Cell Death Dis 14 , 176 ( 2023 ). 54. ↵ Wong , Y. C. & Krainc , D . α-synuclein toxicity in neurodegeneration: mechanism and therapeutic strategies . Nat Med 23 , 1 – 13 ( 2017 ). OpenUrl CrossRef PubMed 55. ↵ Stygelbout , V. et al. Inositol trisphosphate 3-kinase B is increased in human Alzheimer brain and exacerbates mouse Alzheimer pathology . Brain 137 , 537 – 552 ( 2014 ). OpenUrl CrossRef PubMed 56. Fang , H.-S. et al. Association of ITPKB, IL1R2 and COQ7 with Parkinson’s disease in Taiwan . J Formos Med Assoc 121 , 679 – 686 ( 2022 ). OpenUrl PubMed 57. Gao , T. et al. Association of ZNF184, IL1R2, LRRK2, ITPKB, and PARK16 with sporadic Parkinson’s disease in Eastern China . Neurosci Lett 735 , 135261 ( 2020 ). OpenUrl PubMed 58. ↵ Chew , E. G. Y. et al. ITPKB and ZNF184 are associated with Parkinson’s disease risk in East Asians . Neurobiol Aging 86 , 201 . e15 – 201 .e17 ( 2020 ). OpenUrl 59. ↵ Apicco , D. J. et al. The Parkinson’s disease-associated gene ITPKB protects against α-synuclein aggregation by regulating ER-to-mitochondria calcium release . Proc Natl Acad Sci U S A 118 , ( 2021 ). 60. ↵ Di Leva , F. et al. Increased Levels of the Parkinson’s Disease-Associated Gene ITPKB Correlate with Higher Expression Levels of α-Synuclein, Independent of Mutation Status . Int J Mol Sci 24 , ( 2023 ). 61. ↵ Chen , S. et al. A genomic mutational constraint map using variation in 76,156 human genomes . Nature 625 , 92 – 100 ( 2024 ). OpenUrl CrossRef PubMed 62. ↵ Qin , H. et al. Inhibition of the JAK/STAT pathway protects against α-synuclein-induced neuroinflammation and dopaminergic neurodegeneration . J. Neurosci . 36 , 5144 – 5159 ( 2016 ). OpenUrl Abstract / FREE Full Text 63. ↵ Schumacher-Schuh , A. F. et al. Underrepresented Populations in Parkinson’s Genetics Research: Current Landscape and Future Directions . Mov Disord 37 , 1593 – 1604 ( 2022 ). OpenUrl PubMed 64. Mills , M. C. & Rahal , C . The GWAS Diversity Monitor tracks diversity by disease in real time . Nat Genet 52 , 242 – 243 ( 2020 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted July 18, 2025. 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Share Genotype-phenotype association study conducted on LARGE-PD reveals novel loci associated with Parkinson’s Disease Thiago P Leal , Emily Waldo , Felipe Duarte-Zambrano , Miguel Inca-Martinez , Janvi Ramchandra , Henry Mauricio Chaparro-Solano , Anna E Anello , Victor Borda , Mateus Henrique Gouveia , Daniel Teixeira-dos-Santos , Paula Reyes-Pérez , Emilia Mabel Gatto , Bruno Lopes Santos-Lobato , Gracivane Eufraseo , Grace Helena Letro , Gonzalo Arboleda , Oscar Bernal-Pacheco , Jorge L Orozco , Beatriz Munoz , Pedro Chana-Cuevas , David Aguillon , Sonia Moreno , Gabriel Torrealba-Acosta , Tanya Lobo-Prada , Valentina Muller , C Matias Lopez Razquin , Pedro Braga-Neto , Reyna M Durón , Mayela Rodríguez-Violante , Ana Jimena Hernández-Medrano , Amin Cervantes-Arriaga , Daniel Martinez-Ramirez , Artur F S Schuh , Carlos Roberto de Mello Rieder , Mario Cornejo-Olivas , Julia Rios-Pinto , Angel C Medina , Ivan Cornejo-Herrera , Koni Mejia-Rojas , Angel Vinuela , Vitor Tumas , Angela Vieira Pimentel , Vanderci Borges , Cesar L Avila , Patricio Olguin , Alicia Colombo , Juan Cristobal Nuñez , Alejandra Medina-Rivera , Alejandra E. Ruiz-Contreras , Sarael Alcauter , Elena Dieguez , Karen Nuytemans , Global Parkinson’s Genetics Program , Ignacio F Mata medRxiv 2025.07.18.25331793; doi: https://doi.org/10.1101/2025.07.18.25331793 Share This Article: Copy Citation Tools Genotype-phenotype association study conducted on LARGE-PD reveals novel loci associated with Parkinson’s Disease Thiago P Leal , Emily Waldo , Felipe Duarte-Zambrano , Miguel Inca-Martinez , Janvi Ramchandra , Henry Mauricio Chaparro-Solano , Anna E Anello , Victor Borda , Mateus Henrique Gouveia , Daniel Teixeira-dos-Santos , Paula Reyes-Pérez , Emilia Mabel Gatto , Bruno Lopes Santos-Lobato , Gracivane Eufraseo , Grace Helena Letro , Gonzalo Arboleda , Oscar Bernal-Pacheco , Jorge L Orozco , Beatriz Munoz , Pedro Chana-Cuevas , David Aguillon , Sonia Moreno , Gabriel Torrealba-Acosta , Tanya Lobo-Prada , Valentina Muller , C Matias Lopez Razquin , Pedro Braga-Neto , Reyna M Durón , Mayela Rodríguez-Violante , Ana Jimena Hernández-Medrano , Amin Cervantes-Arriaga , Daniel Martinez-Ramirez , Artur F S Schuh , Carlos Roberto de Mello Rieder , Mario Cornejo-Olivas , Julia Rios-Pinto , Angel C Medina , Ivan Cornejo-Herrera , Koni Mejia-Rojas , Angel Vinuela , Vitor Tumas , Angela Vieira Pimentel , Vanderci Borges , Cesar L Avila , Patricio Olguin , Alicia Colombo , Juan Cristobal Nuñez , Alejandra Medina-Rivera , Alejandra E. 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