Sex-stratified analysis of the potential association between PGLYRP2 rs892145 variant and Parkinson’s disease across diverse ancestral populations

Sex-stratified analysis of the potential association between PGLYRP2 rs892145 variant and Parkinson’s disease across diverse ancestral populations | 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 Sex-stratified analysis of the potential association between PGLYRP2 rs892145 variant and Parkinson’s disease across diverse ancestral populations View ORCID Profile Cesar Luis Avila , View ORCID Profile Henry Mauricio Chaparro-Solano , View ORCID Profile Valentina Quintana-Peña , Kajsalisa Åberg , the Global Parkinson’s Genetics Program (GP2) , View ORCID Profile Kajsa Atterling Brolin doi: https://doi.org/10.1101/2025.07.23.25331993 Cesar Luis Avila 1 Instituto de Investigación en Medicina Molecular y Celular Aplicada (IMMCA) (CONICET-UNT-SIPROSA) , Pasaje Dorrego 1080, San Miguel de Tucumán 4000, Argentina PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cesar Luis Avila Henry Mauricio Chaparro-Solano 2 Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University , 44195, Cleveland, OH, United States 3 Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic , 44195, Cleveland, OH, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Henry Mauricio Chaparro-Solano Valentina Quintana-Peña 4 Universidad Icesi . Cali, Colombia 5 Hospital Universitario Fundación Valle del Lili . Cali, Colombia MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Valentina Quintana-Peña Kajsalisa Åberg 6 Translational Neurogenetics Unit, Department of Experimental Medical Science, Lund University , Lund, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kajsa Atterling Brolin 6 Translational Neurogenetics Unit, Department of Experimental Medical Science, Lund University , Lund, Sweden 7 Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University of London , EC1M 6BQ, London, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kajsa Atterling Brolin For correspondence: kajsa.atterling-brolin{at}med.lu.se Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Variants in PGLYRP2 , particularly rs892145-T, have been suggested as Parkinson’s disease (PD) risk factors. We analyzed data from 31,334 PD patients and 17,772 controls across diverse ancestries. A significant sex-dependent effect of rs892145-T was observed in African (AFR) ancestry males (OR=0.73, 95%CI: 0.57-0.94, p=0.014). Gene-based analyses identified another variant, rs7251871-A, as significantly associated with PD in AFR males (OR=1.34, 95%CI:1.13-1.59, p=6.65E-04, Bonferroni p=0.0432). No associations were observed in other ancestries. Further studies are needed to understand PGLYRP2’s role in PD. Plain language summary (PLS) Genetic variations in the PGLYRP2 gene have been linked to the risk of developing Parkinson’s disease (PD), but the research reports have been inconclusive. In this study, we therefore investigated whether genetic variants in this gene are associated with PD. We analyzed data from 32,334 people with PD and 17,772 people without the disease, all from diverse ancestral backgrounds. We found that a previously reported genetic variant, called rs892145-T, appeared to have a possible protective effect against PD specifically in males of African descent. We also identified a different variant, rs7251871-A, which had not been linked to PD before, that seemed to increase PD risk in the same group. No significant results were found in other ancestry groups or for the females group. These findings need to be confirmed in future studies with larger numbers of participants to better understand the role of the gene PGLYRP2 in PD. Introduction The etiopathology of Parkinson’s disease (PD) remains unresolved 1 . Studies have implicated the innate immune response in the gut as a potential risk factor for PD, 2 such as the case of genetic variants in peptidoglycan recognition proteins (PGRPs) genes 3 . Specifically, the PGLYRP2 protein is involved in pro-inflammatory responses as well as immune-mediated diseases such as rheumatoid arthritis, and tuberculosis, among others 4 – 8 . Animal studies have also suggested that PGRPs play a role in the gut microbiota and brain development 9 . The link between PGLYRP2 and PD is controversial. In 2023, Ran and collaborators conducted a meta-analysis of existing data on the rs892145-T variant (p.Met270Lys, chr19:15475861A>T [GRCh38]), and investigated its association with PD in a Swedish cohort of 508 PD patients and 585 controls. The authors did not find an association between PD risk and rs892145-T 10 . However, an interaction between sex and rs892145-T was described in which the T allele was found to be significantly less prevalent in female PD patients and more prevalent in male patients, compared to controls, suggesting that rs892145-T may be a risk factor for PD in males (odds ratio [OR] = 1.29, 95%CI: 1.01-1.64, nominal P = 0.04). Here we aim to provide further evidence of the potential association between PGLYRP2 and PD risk by comprehensively exploring this gene across diverse ancestries and in a sex-stratified manner. Methods We leveraged genotyping imputed data from the 9 th release of the Global Parkinson’s Genetics Program (GP2; https://gp2.org ) 11 , genotyped on the NeuroBooster Array 12 (NBA; v.1.0, Illumina, San Diego, CA). The dataset consisted of 31,334 PD patients and 17,772 controls after excluding related samples, split into ten ancestries: European [EUR], African American [AAC], African [AFR], Ashkenazi Jews [AJ], Admixed American/Latin American [AMR], Central Asian [CAS], East Asian [EAS], Middle Eastern [MDE], South Asian [SAS], and Complex Admixture History [CAH] (Supplementary table 1). All analyses were run separately in each ancestry group. Quality control and genetic ancestry prediction was done with GenoTools ( https://github.com/GP2code/GenoTools ) as previously described 13 . Additionally, variants with a minor allele count<2, minor allele frequency (MAF)<1%, and Hardy-Weinberg Equilibrium (HWE) p-value≤1×10 −4 were excluded. We also utilized whole genome sequencing (WGS) data from the Accelerating Medicines Partnership - Parkinson Disease (AMP-PD; https://amp-pd.org/ ) release 3, including 2,251 unrelated PD patients and 2,835 controls of European descent. PGLYRP2 gene boundaries (chr19:15,468,645-15,479,501 [GRCh38, NCBI]) were extracted using PLINK2.0 14 . Annotation was performed using ANNOVAR 15 utilizing RefSeq and ClinVar databases (ver. 20140902). Burden tests were assessed for coding (frameshift, nonframeshift, startloss, stoploss, stopgain, splicing, or missense), loss of function (frameshift, startloss, stopgain, or splicing), potentially functional variants (frameshift, nonframeshift, startloss, stoploss, stopgain, splicing, missense, exonic, untranslated region at the 5’ end [UTR5], UTR at the 3’ end [UTR3], upstream [-100bp], downstream [+100bp], or ncRNA) as well as low frequency variants (MAF < 3%) on PD risk using RVTESTS 16 . Sex, age (when available), and the first five genetic principal components (PCs) were used as covariates in all analyses. Association analyses for PGLYRP2 variants and sex-stratified analyses focused on rs892145 10 , were performed using logistic regressions in PLINK 2.0 14 . Bonferroni correction (Bonf.) was applied, accounting for all variants identified in the gene within each ancestry group independently. Furthermore, we included an interaction test between rs892145-T and sex utilizing R version 4.4.2, adjusting for PC1-5. Due to missing age data, we mainly report results without adjusting for age to maintain sample size and statistical power. Additionally, allele and genotype frequencies, and HWE (control group) were calculated using PLINK1.9 17 and PLINK 2.0 14 , respectively. For each ancestry group, power calculations were done using the Genetic Association Study (GAS) Power Calculator ( https://csg.sph.umich.edu/abecasis/cats/gas_power_calculator/ ). An additive model was used at a significance level p=0.05 with a disease prevalence of 0.5% at the OR=1.16 (meta-analysis) 10 and the highest reported OR=1.46 18 (Supplementary table 2 and 3). Results The total number of identified variants in the genotyping imputed data from GP2 within the PGLYRP2 genomic region ranged from 67 (CAS, 50 intronic variants) to 370 (EUR, 287 intronic variants). In the AMP-PD WGS data, a total of 150 variants were identified, including 115 intronic, 27 exonic (19 nonsynonymous), and 8 3’-UTR variants. Additional information on the identified variants can be found in Supplementary table 4. We evaluated the cumulative effect of PGLYRP2 variants on PD risk using burden analyses. Nominally significant associations were observed in some ancestry groups i.e., the AAC ancestry group between PD risk and potentially functional variants (N=27; SKAT p=0.01, SKAT-O p=0.01) (Supplementary table 5). However, none of the associations remained significant after multiple testing corrections (p=2.27E-03) and in burden testing a gene-wide significance p-value threshold of P < 2.5 × 10 −6 should be used 19 . The rs892145 variant, previously reported to be linked to PD, was in HWE in all groups except for the SAS ancestry group (p=0.039, Supplementary table 6). In the logistic regression analyses, no statistically significant association was observed for rs892145-T in any of the ancestry groups ( Figure 1 , Supplementary table 7). Download figure Open in new tab Figure 1: Forest plot showing the association between the PGLYRP2 rs892145 variant and Parkinson’s disease. Showing the results of the logistic regression analyses adjusted by sex and PC1-5 for the whole set (black line) or stratified by sex (blue and red lines) in the ancestry groups European [EUR], African American [AAC], African [AFR], Ashkenazi Jews [AJ], Admixed American/Latin American [AMR], Central Asian [CAS], East Asian [EAS], Middle Eastern [MDE], and South Asian [SAS] from the Global Parkinson’s Genetics Program (GP2, release 9) and the dataset from the Accelerating Medicines Partnership - Parkinson Disease (AMP-PD, release 3). The interaction test between sex and rs892145-T suggested a significant sex-dependent effect in males in the AFR ancestry group (OR=0.734, 95%CI: 0.57-0.94, p=0.014) (Supplementary table 8) with the T allele being less common in PD patients than controls among men (38.6% and 42.9%, respectively) (Supplementary table 9). None of the other ancestry groups showed evidence of a sex-dependent association. The sex-stratified logistic regression analysis between rs892145-T and PD showed a significant inverse association in the male AFR group (OR=0.791, 95%CI: 0.677-0.924, p=0.003). However, this did not remain significant following multiple test corrections (Bonf. p=0.20) (Supplementary table 9). Analysis of all variants in the gene revealed a significant association between the intronic variant rs7251871-A and PD (OR=1.34, 95%CI=1.13-1.59, p=6.65E-04, Bonf. p=0.0432) after adjusting for PC1-5 ( Table 1 ). However, after additionally adjusting for age, the association was lost (p=0.0394, Bonf. p=1.00), potentially due to a reduced sample size and the result should be interpreted with caution until replicated. No significant associations were detected in females across any ancestry for the variant (Supplementary table 10). View this table: View inline View popup Download powerpoint Table 1: PGLYRP2 variant rs7251871 associated with Parkinson’s disease among males in the GP2 African ancestry group (AFR) Discussion We investigated the potential relationship between variants in PGLYRP2 and PD in the largest and most ancestrally diverse dataset to date. No significant results were observed in the whole-gene regression or burden analyses. However, one variant (rs7251871) was significantly associated with PD among males in the AFR ancestry groups. The variant was in linkage disequilibrium (LD) with rs892145 (D’=1, r2=0.31) in the 1000 Genomes Project AFR sub-population and rs892145-T was nominally inversely associated with PD in AFR men in our study. However, the associations were lost when adjusting for age (rs7251871) or multiple test correction (rs892145). For the AFR ancestry, age was available in 274 of 988 PD patients (27.7%) and 950 of 1,667 controls (57.0%) which reduced the sample size when including age as a covariate. The findings should be interpreted with caution until further evidence is available with adjustment for age in larger sample sizes. We did not observe any significant association between PGLYRP2 rs892145-T and PD in any other ancestries and previous studies have shown conflicting results. No association was reported in the meta-analysis by Ran et al 10 (OR: 1.16, p=0.23). However, they suggested a potential sex-dependent role for this variant in PD with rs892145-T being described as a risk factor in males (OR = 1.29, 95% CI: 1.01–1.64, p=0.04) with the same direction of effect observed in Chinese and Australian studies 18 , 20 . We observed a potential interaction between rs892145-T and sex in the AFR ancestry group, but it did not pass multiple test corrections. Additionally, our findings of a nominal inverse association contradict previous reports and indicated that the rs892145-T would be a protective factor in AFR males. Heterogeneity between the studies in the previously published meta-analysis was observed, with one study reporting that homozygous carriers of the A allele had lower odds of PD (OR = 0.6, 95%CI: 0.40-1.0) in a pooled analysis of American cohorts 3 . However, the minor allele in their study population was the A allele whereas the T allele is reported as the minor allele in all ancestry populations according to the GnomAD browser 21 . None of the studies corrected for multiple testing, which could explain the discrepancies between previous reports and ours. Taking into account the effect size reported by Luan et al. (OR = 1.46) 18 , the datasets had sufficient statistical power (>0.8) across all ancestries except for the GP2 SAS ancestry at the observed MAF of ∼30-40% across populations. Nevertheless, no significant association was observed in EAS in contrast to that described by Luan et al for the Chinese Han population. Using the effect size reported by Ran et al. (OR = 1.16) 10 , only the EAS and EUR ancestry groups reached 80% power. This highlights the need for continued investigations on PGLYRP2 in large multi-ancestry datasets to improve representation and enable robust analyses of the potential association in diverse populations. Data Availability All GP2 data is hosted in collaboration with the Accelerating Medicines Partnership in Parkinson's disease and is available via application on the website (https://amp-pd.org/register-for-amp-pd;https://doi.org/10.5281/zenodo.7904832). Data used in the preparation of this article were obtained from the Global Parkinson's Genetics Program (GP2; https://gp2.org). Specifically, we used Tier 2 data from GP2 release 9 (DOI: 10.5281/zenodo.14510099). 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. https://github.com/GP2code/PGLYRP2_SexDifferences Authors contributions K.AB conceptualized the manuscript. All authors conducted the formal analysis. C.L.A., H.M.CS, V.Q.P, and K.AB. wrote the first draft of the manuscript. All authors reviewed, edited, and approved the final version of the manuscript for submission. Statements and declarations Ethical considerations and consent to participate/publication This study was approved by ethics committees or institutional review boards of all participating sites and conducted in accordance with their ethical standards. Informed consent for study participation was obtained from all participants. Individual study sites participating in GP2 were recruited and samples and data were collected as previously described 22 , 23 . Declaration of conflicting interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding statement This project was supported by the Global Parkinson’s Genetics Program (GP2; https://gp2.org ). GP2 is funded by the Aligning Science Across Parkinson’s (ASAP) initiative and implemented by The Michael J. Fox Foundation for Parkinson’s Research. Additional funding was provided by The Michael J. Fox Foundation for Parkinson’s Research through grant MJFF-009421/17483. The AMP® PD program is a public-private partnership managed by the Foundation for the National Institutes of Health and funded by the National Institute of Neurological Disorders and Stroke (NINDS) in partnership with the Aligning Science Across Parkinson’s (ASAP) initiative; Celgene Corporation, a subsidiary of Bristol-Myers Squibb Company; GlaxoSmithKline plc (GSK); The Michael J. Fox Foundation for Parkinson’s Research; Pfizer Inc.; Sanofi US Services Inc.; and Verily Life Sciences. For a complete list of GP2 members see doi.org/10.5281/zenodo.7904831 Data and code availability All GP2 data is hosted in collaboration with the Accelerating Medicines Partnership in Parkinson’s disease and is available via application on the website ( https://amp-pd.org/register-for-amp-pd ; https://doi.org/10.5281/zenodo.7904832 ). Data used in the preparation of this article were obtained from the Global Parkinson’s Genetics Program (GP2; https://gp2.org ). Specifically, we used Tier 2 data from GP2 release 9 (DOI: 10.5281/zenodo.14510099). 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. Genotyping imputation, quality control, ancestry prediction, and processing were performed using GenoTools v1.0, publicly available on GitHub ( https://github.com/GP2code/GenoTools ). All code generated for this article, and the identifiers for all software programs and packages used, are available on GitHub [ https://github.com/GP2code/PGLYRP2_SexDifferences ] and were given a persistent identifier via Zenodo [DOI: 10.5281/zenodo.15799510]. Acknowledgements This work was carried out with the support and guidance of the ‘GP2 Trainee Network’ which is part of the Global Parkinson’s Genetics Program and funded by the Aligning Science Across Parkinson’s (ASAP) initiative. Data used in the preparation of this article were obtained from Global Parkinson’s Genetics Program (GP2). For a complete list of GP2 members, see https://gp2.org . Data used in the preparation of this article were obtained from the Accelerating Medicines Partnership® (AMP®) Parkinson’s Disease (AMP® PD) Knowledge Platform. For up-to-date information on the study, visit https://www.amp-pd.org . ACCELERATING MEDICINES PARTNERSHIP and AMP are registered service marks of the US Department of Health and Human Services. References 1. ↵ Bandres-Ciga S , Diez-Fairen M , Kim JJ , et al. Genetics of Parkinson’s disease: An introspection of its journey towards precision medicine . Neurobiol Dis 2020 ; 137 : 104782 . OpenUrl CrossRef PubMed 2. ↵ Horsager J , Andersen KB , Knudsen K , et al. Brain-first versus body-first Parkinson’s disease: a multimodal imaging case-control study . Brain 2020 ; 143 : 3077 – 3088 . OpenUrl CrossRef PubMed 3. ↵ Goldman SM , Kamel F , Ross GW , et al. Peptidoglycan recognition protein genes and risk of Parkinson’s disease . Mov Disord 2014 ; 29 : 1171 – 1180 . OpenUrl CrossRef 4. ↵ Zulfiqar F , Hozo I , Rangarajan S , et al. Genetic association of peptidoglycan recognition protein variants with inflammatory bowel disease . PLoS One 2013 ; 8 : e67393 . OpenUrl CrossRef PubMed 5. Dziarski R , Gupta D. Review: Mammalian peptidoglycan recognition proteins (PGRPs) in innate immunity . Innate Immun 2010 ; 16 : 168 – 174 . OpenUrl CrossRef PubMed 6. Huang F , Liu X , Cheng Y , et al. Antibody to peptidoglycan recognition protein (PGLYRP)-2 as a novel biomarker in rheumatoid arthritis . Clin Exp Rheumatol 2021 ; 39 : 988 – 994 . OpenUrl 7. Li H , Meng D , Jia J , et al. PGLYRP2 as a novel biomarker for the activity and lipid metabolism of systemic lupus erythematosus . Lipids Health Dis 2021 ; 20 : 95 . OpenUrl 8. ↵ Chen J , Han Y-S , Yi W-J , et al. Serum sCD14, PGLYRP2 and FGA as potential biomarkers for multidrug-resistant tuberculosis based on data-independent acquisition and targeted proteomics . J Cell Mol Med 2020 ; 24 : 12537 – 12549 . OpenUrl 9. ↵ Arentsen T , Qian Y , Gkotzis S , et al. The bacterial peptidoglycan-sensing molecule Pglyrp2 modulates brain development and behavior . Mol Psychiatry 2017 ; 22 : 257 – 266 . OpenUrl CrossRef PubMed 10. ↵ Ran C , Wirdefeldt K , Sydow O , et al. Sex differences in the allele distribution of PGLYRP2 variant rs892145 in Parkinson’s disease . Parkinsons Dis 2023 ; 2023 : 6502727 . OpenUrl 11. ↵ Leonard H , Nalls M , Vitale D , et al. Global Parkinson’s Genetics Program data release 9 . Epub ahead of print 2024 . DOI: 10.5281/ZENODO.7904831 . OpenUrl CrossRef 12. ↵ Bandres-Ciga S , Faghri F , Majounie E , et al. NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations . Mov Disord 2024 ; 39 : 2039 – 2048 . OpenUrl PubMed 13. ↵ Vitale D , Koretsky M , Kuznetsov N , et al. GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis . bioRxiv . Epub ahead of print 3 July 2024 . DOI: 10.1101/2024.03.26.586362 . OpenUrl Abstract / FREE Full Text 14. ↵ Hill A , Loh P-R , Bharadwaj RB , et al. Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis . Gigascience 2017 ; 6 : 1 – 10 . OpenUrl CrossRef PubMed 15. ↵ Wang K , Li M , Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data . Nucleic Acids Res 2010 ; 38 : e164 . OpenUrl CrossRef PubMed 16. ↵ Zhan X , Hu Y , Li B , et al. RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data . Bioinformatics 2016 ; 32 : 1423 – 1426 . OpenUrl CrossRef PubMed 17. ↵ Purcell S , Neale B , Todd-Brown K , et al. PLINK: a tool set for whole-genome association and population-based linkage analyses . Am J Hum Genet 2007 ; 81 : 559 – 575 . OpenUrl CrossRef PubMed 18. ↵ Luan M , Jin J , Wang Y , et al. Association of PGLYRP2 gene polymorphism and sporadic Parkinson’s disease in northern Chinese Han population . Neurosci Lett 2022 ; 775 : 136547 . OpenUrl 19. ↵ de Leeuw CA , Mooij JM , Heskes T , et al. MAGMA: generalized gene-set analysis of GWAS data . PLoS Comput Biol 2015 ; 11 : e1004219 . OpenUrl CrossRef PubMed 20. ↵ Gorecki AM , Bakeberg MC , Theunissen F , et al. Single nucleotide polymorphisms associated with gut homeostasis influence risk and age-at-onset of Parkinson’s disease . Front Aging Neurosci 2020 ; 12 : 603849 . OpenUrl 21. ↵ Chen S , Francioli LC , Goodrich JK , et al. A genomic mutational constraint map using variation in 76,156 human genomes . Nature 2024 ; 625 : 92 – 100 . OpenUrl CrossRef PubMed 22. ↵ Towns C , Richer M , Jasaityte S , et al. Defining the causes of sporadic Parkinson’s disease in the global Parkinson’s genetics program (GP2) . NPJ Parkinsons Dis 2023 ; 9 : 131 . OpenUrl 23. ↵ Lange LM , Avenali M , Ellis M , et al. Elucidating causative gene variants in hereditary Parkinson’s disease in the Global Parkinson’s Genetics Program (GP2) . NPJ Parkinsons Dis 2023 ; 9 : 100 . OpenUrl View the discussion thread. Back to top Previous Next Posted July 24, 2025. Download PDF Supplementary Material 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 Sex-stratified analysis of the potential association between PGLYRP2 rs892145 variant and Parkinson’s disease across diverse ancestral populations 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. 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Share Sex-stratified analysis of the potential association between PGLYRP2 rs892145 variant and Parkinson’s disease across diverse ancestral populations Cesar Luis Avila , Henry Mauricio Chaparro-Solano , Valentina Quintana-Peña , Kajsalisa Åberg , the Global Parkinson’s Genetics Program (GP2) , Kajsa Atterling Brolin medRxiv 2025.07.23.25331993; doi: https://doi.org/10.1101/2025.07.23.25331993 Share This Article: Copy Citation Tools Sex-stratified analysis of the potential association between PGLYRP2 rs892145 variant and Parkinson’s disease across diverse ancestral populations Cesar Luis Avila , Henry Mauricio Chaparro-Solano , Valentina Quintana-Peña , Kajsalisa Åberg , the Global Parkinson’s Genetics Program (GP2) , Kajsa Atterling Brolin medRxiv 2025.07.23.25331993; doi: https://doi.org/10.1101/2025.07.23.25331993 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 (567) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4411) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6574) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1263) Infectious Diseases (except HIV/AIDS) (15903) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (666) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (968) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5422) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) 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:'9fe9381ebce606d3',t:'MTc3OTI1Nzg1NQ=='};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())}}}})();