{"paper_id":"3da46c2a-23f2-42f1-86f4-196dbe1cb18b","body_text":"Multi-Ancestry Genome-Wide Association Study in All of Us for Primary Open- Angle Glaucoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multi-Ancestry Genome-Wide Association Study in All of Us for Primary Open- Angle Glaucoma Kiana Tavakoli, Bonnie B. Huang, Tara Mirmira, Nichole Ma, Robert N. Weinreb, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7754041/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract This study aims to identify new genetic loci associated with primary open-angle glaucoma (POAG) and explore shared genetic risk factors across African, European, and Admixed American/Latino populations. Genome-wide Association Study (GWAS) utilizing data from the All of Us Research Program. The study included 374,254 participants, with 4,305 individuals diagnosed with POAG and 369,949 controls. Participants were categorized by ancestry: European, African, and Admixed American/Latino. We used short-read sequencing data and applied strict quality control measures (MAF > 0.01, INFO > 0.8). GWAS were conducted for each ancestry group using a logistic mixed model, adjusting for age, sex, and the top 11 principal components. A fixed-effect meta-analysis combined the results across ancestries. Genome-wide significance was set at p<5×10 -8 . The primary outcome measures were the identification of genetic loci associated with POAG, and the analysis of transcription factors linked to these loci in relevant tissues. In the European cohort, we identified four novel loci associated with POAG, linked to the TUT4, RYK, MOXD1, and UBAP2 genes, as well as the previously known TMCO1 locus. In the African cohort, we found five new loci, including TSPAN17, SLC16A7, LOC100506869, LINC02388, and LOC107984606 . For the Admixed American/Latino cohort, we identified GATA5, FAM135B, and LINC00871 genes as novel loci. Our analysis identified three novel loci in individuals of European ancestry, mapped to the genes TUT4, RYK, and MOXD1 . In addition, five novel loci were detected in the GWAS of African ancestry participants, and four novel loci were identified in individuals of Admixed American/Latino ancestry. These findings indicate that the genetic determinants contributing to POAG may differ across populations, underscoring the importance of accounting for population-specific genetic architectures in the study of complex traits. Given the substantial variation in POAG prevalence among ancestries, it is plausible that certain genetic variants exert ancestry-specific effects. Consequently, conducting ancestry-stratified GWAS is essential for elucidating these unique genetic contributions. Health sciences/Diseases Biological sciences/Genetics Glaucoma Open-Angle Genetic Association Studies Genetic Loci Figures Figure 1 Figure 2 Figure 3 Introduction Primary Open Angle Glaucoma (POAG) is the leading cause of irreversible blindness globally. 1 It is a degenerative disease of the optic nerve that leads to progressive vision loss. 2 The transferability of genetic findings between populations is understood to be limited by ancestry-specific differences in linkage disequilibrium, minor allele frequency, and potentially differences in causal variants, which pose significant limitations to our understanding of the genetic architecture of POAG in non-European populations. This disparity may result in unequal benefits among different populations from precision medicine, as genetic risk models derived from large-scale studies conducted in European populations exhibit high predictive power in European samples but demonstrate poor predictive accuracy in non-European samples. 3 Consequently, enhancing ethnic and ancestral diversity among study participants is crucial for identifying understudied mechanisms of disease and ultimately ensuring equitable genetic findings. 4,5 Specifically, in large studies that focus only on people of European ancestry, disease-critical genetic variants may be missed because they are either rare or completely absent. In this study, we report a genome-wide association study (GWAS) of POAG utilizing the All of Us Research Program dataset, a diverse nationwide database in the United States that emphasizes the recruitment of populations historically underrepresented in biomedical research. 6 Our analysis includes individuals of European, African, and admixed American/Latino ancestries. We provide a comprehensive discussion on the identification of novel loci associated with POAG and examine the extent to which genetic signals are shared across ancestries, as well as the presence of ancestry-specific genetic signals. Our findings offer valuable insights into the etiology of POAG and underscore the importance of conducting genetic studies within non-European populations. Methods Study cohort: Data were sourced from the All of Us Research Program, a landmark research initiative aimed at advancing precision medicine by collecting and analyzing health data from diverse populations. 6 The program encompasses demographic, geographic, and medical diversity, including historically underrepresented populations such as ethnic minorities and individuals from underserved communities. All participants provided written informed consent, demonstrating their voluntary participation in the study and understanding of its purpose. Data sources for the All of Us Research Program include electronic health records, physical measurements, surveys, biospecimens, and wearable technology data.Prospective enrollment and data collection were approved by an independent institutional review board, with written informed consent obtained from all participants. The All of Us Data Research Center harmonized the data into the Observational Medical Outcomes Partnership (OMOP) common data model, a standardized framework for representing observational health data from diverse sources. To protect participant privacy, the Data Research Center applied measures such as deidentification and date shifting before making the data available on the All of Us Researcher Workbench. Secondary analyses of these deidentified datasets were classified as not involving human subjects research by the University of California San Diego Institutional Review Board. This study was conducted in accordance with the Declaration of Helsinki and followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational research.. 7 Phenotyping: There were 403,916 individuals who were enrolled in All of Us and had short-read sequencing data available on the All of Us Researcher Workbench Controlled Tier dataset version 8. Participants diagnosed with POAG were identified using SNOMED concept ID 77075001 (“Primary open angle glaucoma”) derived from electronic health record data. Individuals with normal-tension glaucoma, a subtype of glaucoma where optic nerve damage occurs despite normal intraocular pressure (IOP), were excluded from the study to ensure a homogeneous cohort. Participants lacking age, sex, or genotype data were excluded as these individuals would not possess the expected covariates for the association analysis. Categories of genetically-determined ancestry in All of Us corresponded directly to categorical ancestry definitions used within gnomAD, 8 the Human Genome Diversity Project, 9 and 1000 Genomes: 10 African/African American, Admixed American/Latino, East Asian, European, Middle Eastern, South Asian, and Other (meaning an individual’s predominant ancestry is < 50% of their total ancestral composition). Genotyping: Details of genotyping procedures used by All of Us have been described previously 11 . We excluded samples with a variant call rate below 99% or fewer than five heterozygotes. No imputation was required in the All of Us research dataset, as data was generated from short-read whole genome sequencing across 403,916 individuals. Genomic analysis used the GRCh38 reference genome. 12 We utilized the Allele Count Allele Frequency (ACAF) data in the All of Us Researcher Workbench. The ACAF threshold callset includes variants with a population-specific allele frequency (AF) greater than 1% or a population-specific allele count over 100 in any ancestral subpopulations. Quality control measures ensured genotype data reliability, including filtering out variants with an allele frequency less than 1% and Hardy-Weinberg Equilibrium outliers (p < 1×10 − 10 ) 13 . Logistic regression analysis was conducted separately for each autosome, considering covariates such as the top 11 genotyping principal components, sex, and age. Related individuals were excluded based on available relatedness data, including first-degree relatives (parents, siblings, cousins) to minimize confounding; specifically, one individual was retained randomly from each family. Our analysis utilized Hail 14 for scalable genomic data analysis, Bokeh 15 for interactive visualization, Pandas 16 for data manipulation, and NumPy 17 for numerical computing. Genome-wide association study: To account for potential population stratification amongst our study participants within ancestry categories, we projected everyone’s genotype by principal components using cohort-wide standardized genotypes. We performed ancestry-specific GWAS analyses for each group of European, African, and Admixed American/Latino ancestries to explore genetic associations unique to each ancestry. For each population, we separately computed the top 11 genotyping PCs. To this end, we performed a logistic regression Wald test. Manhattan and quantile-quantile plots were generated to visualize the GWAS results and compute the genomic inflation factor, which could reveal unaccounted population stratification (lambda = 1.00). Genome-wide significant single nucleotide polymorphisms (SNPs) were identified at a threshold of p < 5×10 − 8 for each ancestry group and separately for the cross-ancestry meta-analysis. 18 We defined a POAG-associated locus as a genomic region within ± 1 Mb of the lead variant. A locus was considered novel if it did not include any previously reported variants with a p-value < 5×10 − 8 in previous GWAS nor was in high linkage disequilibrium (r² >0.1) with genome-wide significant POAG variants from previous GWAS. 18 We employed the GWAS Catalog 19 and Litvar 20 databases to account for previous GWAS. If a genome-wide significant SNP landed in a protein-coding region of a gene, we also searched the GWAS Catalog to identify if this gene was associated with any potential comorbidities which may be physiologically connected with POAG. Fixed-effect meta-analysis and multi-ancestry GWAS: We conducted a fixed-effect meta-analysis across three ancestry groups (European, African, and Admixed American/Latino) by integrating summary statistics from separate GWAS for each group. The remaining ancestry groups available in All of Us were not included in these analyses due to prohibitively small sample sizes. We applied an inverse-variance-weighted fixed-effect meta-analysis to these ancestry-specific results, which enhanced our overall statistical power to identify POAG-associated variants. We estimated meta-analyzed effect sizes and standard errors for each variant and calculated p-values based on a normal distribution. This method integrated data from multiple ancestry groups, providing a comprehensive view of genetic associations that may be shared across populations. Study cohort characteristics: We identified 4,305 cases of POAG and 369,949 controls without POAG. Among these participants, there were 2,302 cases of European ancestry, 1,339 cases of African ancestry, and 465 cases of Admixed American/Latino ancestry. (Table 1) Table 1. Characteristics of All of Us participants with and without POAG with genotype data available for analysis. POAG Cases (N = 4,305) Controls (N = 369,949) Mean (Standard Deviation) Age 73.9 (10.6) years 56.17 (17.0) years No. (%) Hispanic 572 (13.3% of cases) 70,329 (19% of controls) No. (%) Male 2,195 (51% of cases) 146,223 (39.5% of controls) No. (%) Female 2,110 (49% of cases) 223,726 (60.5% of controls) No. (%) African ancestry 1,339 (31.1% of cases) 69,491 (18.8% of controls) No. (%) Admixed American/Latino ancestry 465 (10.9% of cases) 67,875 (18.3% of controls) No. (%) European ancestry 2,302 (53.5% of cases) 213,774 (57.8% of controls) No. (%) Other ancestry 199 (4.5% of cases) 18,809 (5.1% of controls) Results European POAG GWAS identifies newly associated variants near genes with known roles in eye development and function : The analysis of individuals of European ancestry (2,302 POAG cases, 213,774 controls) identified 52 genome-wide significant variants and five distinct loci associated with POAG, consistent with previous GWAS findings related to POAG and visual field loss. 21 Notably, four of these loci were novel and have not been previously reported for POAG or glaucoma in general.(Fig. 1 )( Table S1 (available at https://www.aaojournal.org )) On chromosome 1, we observed a significant number of associated variants near the TMCO1 gene, which has been implicated in various disorders, including POAG, craniofacial dysmorphism, skeletal anomalies, and impaired intellectual development syndrome. 22 TMCO1 plays a crucial role in regulating intraocular pressure (IOP), a key factor in the development of POAG. Dysregulation of TMCO1 may hinder the outflow of aqueous humor potentially resulting in elevated IOP levels. 23 , 24 Additionally, on chromosome 1, we replicated the association near the pseudogene LOC440700 and the TMCO-AS1 gene which have both been previously reported in POAG GWAS. 25 , 26 We identified several loci associated with POAG that have not been previously reported by existing GWAS. One such locus is centered at 52.5 Mb on chromosome 1 near the TUT4 gene, which is responsible for uridylating miRNAs. 27 This gene is related to glutathione peroxidase 7, where changes in enzyme activity may contribute to age-related macular degeneration (AMD). 28 , 29 Furthermore, TUT4 has been linked to height, 30 with studies suggesting that individuals who are taller or have lower body mass index tend to have a smaller neuroretina rim area and a larger optic cup-to-disc area ratio. 31 On chromosome 3, we discovered an associated locus consisting of intronic variants within the RYK gene. The RYK gene significantly influences eye development, particularly through its modulation of Wnt signaling pathways critical for eye organogenesis. 32 Additionally, RYK has been shown to affect systolic and diastolic blood pressure, 33 and numerous studies have demonstrated an association between blood pressure and POAG. 34 – 36 We identified another POAG-associated locus centered on the promoter region of the MOXD1 gene on chromosome 6 (at 132.2 Mb). MOXD1 has been implicated in the progression of AMD 37 and anemia. 38 MOXD1 is also known to affect tau protein levels, which may lead to modifications in neuronal injury associated with ocular hypertension. 39 , 40 Lastly we identified a POAG-associated intronic variant on chromosome 9 which encodes UBAP2 , a gene associated with the neurodegenerative disease amyotrophic lateral sclerosis 41 ,in which astrocytes play a role in both ALS disease and in changes to the optic nerve head in glaucoma. 42 African ancestry GWAS identifies new loci not previously identified with European GWAS data: During our investigation into the genetic factors contributing to POAG within the African ancestry group (1,339 POAG cases, 69,491 controls), we uncovered novel associations that highlight the intricate genetic complexity and remarkable diversity present in POAG genes in different populations. Our research identified seven genome-wide significant SNPs across five independent loci (as determined by distance and linkage disequilibrium) (Fig. 2 , Table S1 (available at https://www.aaojournal.org )). None of these associations have been previously identified in any GWAS related to POAG. Notably, this GWAS did not recapitulate the well-established TMCO1 locus found in European POAG GWAS, suggesting that this gene may not play as critical a role in POAG pathogenesis in non-European individuals. This is supported by our analysis which found that the associated variants in the TMCO1 locus are specifically common in the European population, but rare in the African and Latino populations. Here, we summarize these novel POAG-associated loci in order of genomic coordinates. First, we identified an associated locus on chromosome 5 centered on an intronic variant of the TSPAN17 gene. The expression of TSPAN17 in the neural tube and brain suggests a potential influence on neurological factors related to POAG. 43 Second, on chromosome 12, we identified another associated locus centered on the SLC16A7 gene, which has been implicated in age-related cataract and is expressed in retinal tissue. 44 Notably, it has been shown that AMD and POAG exhibit a positive genetic correlation. 45 Additionally, previous work indicates that SLC16A7 may affect alcohol consumption 46 , which has been shown to increase the risk of glaucoma. 47 Third, also on chromosome 12, but more than 1 Mb away, we identified a locus harboring two non-coding RNA genes: LOC100506869 and LINC02388 . The latter gene has been connected to cataract formation, 48 which may contribute to primary angle-closure glaucoma due to a narrower drainage angle in the eye. While cataracts do not directly cause glaucoma, there are rare instances where cataracts can lead to elevated IOP and damage to the optic nerve. 49 Lastly, we discovered an associated locus on chromosome 13 encoding the LOC107984606 gene with no immediate connection to POAG pathogenesis, as well as an association on chromosome 15 centered on a nonfunctional variant which does not encode any gene. All of Us cohort enables first POAG GWAS for individuals of Admixed American/Latino ancestry : The modest sample size of the Admixed American/Latino population in our cohort (465 POAG cases, 67,875 controls) has enabled us to conduct an ancestry-specific GWAS for this demographic, whereas previous studies suffered from small sample size and thus were only powered to perform cross-ancestry meta-analysis. 25 Our advance toward learning population-specific genetic susceptibility for POAG is critical as Latinos are approximately 5% more likely to be affected by POAG compared to other populations 50 . Our analysis led to the identification of five genome-wide significant variants, constituting independent loci. (Fig. 3 , Table S1 (available at https://www.aaojournal.org )). First, we recapitulated an associated locus found by previous European POAG GWAS 25 , 51 on chromosome 11 encoding the SLC22A20P gene. The genome-wide significant variants from the European GWAS 25 , 51 are in moderate linkage disequilibrium (r 2 > 0.1) with our lead variant. The SLC22A20P gene has been shown to influence mean corpuscular hemoglobin levels, 52 with higher levels correlating with a faster rate of retinal nerve fiber layer (RNFL) thinning. 52 , 53 Second, we identified a genome-wide significant locus on chromosome 22, centered on an intergenic variant near encoding intergenic the non-coding RNA gene LINC00895 . This locus is situated within ± 1 Mb of previously associated POAG variants 51 , although these variants were suggested to regulate different genes. Importantly, the variants identified in these previous studies exhibited low linkage disequilibrium (r² < 0.1) with our lead variant, potentially suggesting that this finding may represent an independent mechanism. The LINC00895 gene is known to affect platelet count, and lower platelet counts have been observed in individuals with POAG. 54 We also identified several loci that have not previously been implicated in POAG. First, on chromosome 8, we identified an associated locus centered on an intronic variant of FAM135B which is associated with smoking behavior. 46 Second, we identified a genome-wide significant intronic variant of LINC00871 on chromosome 14. Expression of this gene is observed in the basal ganglia, particularly within the caudate and putamen nuclei. Prior GWAS has reported the association of this variant with Sjögren's syndrome, which has implications for ocular dryness. 55 Moreover, LINC00871 has been associated with body mass index, 56 suggesting potential pleiotropy affecting obesity and the development of POAG, 57 as well as smoking status and initiation 58 highlighting the impact of tobacco use on POAG. 59 Third, on chromosome 20, we identified a POAG-associated variant 35 kb upstream of the GATA5 gene, which is associated with AMD. Others have hypothesized that the mechanisms underlying the associations at the GATA5 locus in neovascular AMD patients may be linked to retinoic acid signaling. 60 Furthermore, GATA5 has been shown to affect hematocrit levels, 61 potentially contributing to increased IOP. 62 Additionally, GATA5 influences lung function, 63 where reduced lung function has been associated with an increased risk of glaucoma. 64 Cross-ancestry GWAS meta-analysis: In our analysis, we identified 56 genome-wide significant variants, 6 of which were not identified in ancestry-specific POAG GWAS. All but five of the genome-wide significant ancestry-specific GWAS variants were additionally found to be significant in the cross-ancestry meta-analysis. The exceptions mostly included variants identified in the Admixed American/Latino GWAS, which has a substantially smaller sample size and thus lower contribution to the cross-ancestry meta-analysis. First, on chromosome 5, an intergenic variant was newly associated in the meta-analysis; the closest gene is ENSG00000286625 and is 10,000 Kb away. Second, we identified an intronic variant in the SGCZ gene on chromosome 8 that has previously been linked to BMI 65 , reinforcing the possible role of metabolic pathways in glaucoma development. Third, we detected an intronic variant on chromosome 12 ,the SLC16A7 gene influencing body weight and BMI 46 , suggesting a relationship between metabolic factors and POAG risk. 57 Fourth, we identified one intronic variant on chromosome 16 in the MAFTRR and LOC105371356 genes, both of which affect thyroid function, indicating a potential link between thyroid-related pathways and POAG susceptibility. 66 Fifth, on chromosome 20, we identified an intronic variant in the GGT7 gene, which is linked to chronic kidney disease (CKD), suggesting a potential association between glaucoma and CKD. 67 Lastly, we identified a intron variant on chromosome 21,The gene TRPM2 , which is a channel gene is associated with POAG, suggesting that TRPM2 may serve as a potential aqueous humor biomarker for glaucoma. 68 , 69 Discussion Our study highlights the benefits of conducting genetic research in non-European populations. LD often poses a significant challenge in identifying causal variants in GWAS. However, an analysis of GWAS results from different ancestries with diverse LD structures can enhance the precision of causal variant identification. We performed this analysis for African ancestry, as well as for European and admixed American Latino populations. While previous GWAS have included non-European populations, such as those studied in the DIGS/ADAGES and NEIGHBORHOOD consortia 3 , a large proportion of prior research has focused on European ancestry groups. 3 Based on our literature search, only one prior study has investigated POAG in admixed Latino populations 70 , highlighting the importance of exploring genetic contributions in these underrepresented groups. However, there remains a significant gap in our understanding of genetic risk factors in other admixed populations, despite the increasing incidence of POAG 71 in these diverse communities. Additionally, our study identified novel loci and variants that have not been reported in earlier GWAS. Our analysis revealed three new loci in European populations associated with genes TUT4, RYK , and MOXD1 . Additionally, we identified five new loci from the African ancestry GWAS, as well as four novel loci in Admixed American/Latino ancestry. These results suggest that the genetic effects contributing to POAG may vary between populations, highlighting the importance of considering population-specific genetic architectures in complex traits. Given the significant differences in POAG prevalence across ancestries, it is likely that certain variants have ancestry-specific effects. Therefore, it is crucial to conduct ancestry-specific GWAS to uncover these unique genetic contributions. In our study's limitations, we acknowledge the relatively modest sample sizes for African, East Asian, Admixed American/Latino, and Middle Eastern populations, which may hinder the robustness of our GWAS findings in diverse ancestries. Additionally, the lack of data in All of Us on visual field measurements and IOP restricts our ability to assess the effects of novel variants or loci on these established factors that are known to be associated with POAG. In addition, phenotyping using EHR diagnostic codes has known limitations, 72 but additional clinical data that may assist with more precise phenotyping, such as imaging, testing, and free-text notes, are currently not available in All of Us . Our study marks a significant advancement in understanding the genetic aspects of POAG across diverse populations. The findings provide insights into the genetic architecture of POAG, emphasizing the importance of genetic diversity in understanding disease susceptibility. Addressing challenges through more inclusive research that includes clinical, environmental, and genetic data is essential for developing effective, personalized interventions. Ongoing research is needed to validate these findings and clarify the functional consequences of identified genetic variations, ultimately aiming to improve early detection and management of this sight-threatening condition. Abbreviations and Acronyms POAG Primary open-angle glaucoma GWAS Genome-Wide Association Studies IOP Intraocular Pressure AMD Age-related Macular Degeneration Declarations Author Contribution Kiana Tavakoli, Bonnie B. Huang, Tara Mirmira, Nichole Ma, Robert N. Weinreb, and Sally L. Baxter contributed to the conception and design of the study, data collection, analysis, interpretation, drafting, and critical revision of the manuscript. All authors read and approved the final version of the manuscript. This article contains additional online-only material. The following should appear online only: Table S1. The data used in this study are available through the All of Us Researcher Workbench. Access requires registration in the controlled tier of the Workbench to ensure compliance with program policies. For inquiries regarding data access, please contact Kiana Tavakoli ( [email protected] ). Kiana Tavakoli, Bonnie B. Huang, Tara Mirmira, Nichole Ma, Robert N. Weinreb, and Sally L. Baxter contributed to the conception and design of the study, data collection, analysis, interpretation, drafting, and critical revision of the manuscript. All authors read and approved the final version of the manuscript. Additional Information Financial Support This study was supported by grants from the National Institutes of Health (Bethesda, MD, USA; grant numbers R03EY035824, DP5OD029610, P30EY022589) and an unrestricted departmental grant from Research to Prevent Blindness (New York, NY, USA). The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants. This research was supported in part by an award from the All of Us Research Program to the National Alliance for Hispanic Health, funded by the Division of Engagement and Outreach, All of Us Research Program, National Institutes of Health (Award Number OT2OD025277), and the Lucy Delgado Fund of the Healthy Americas Foundation. The All of Us Research Program would not be possible without the partnership of its participants to advance science and better health for all of us. Competing Interests The authors declare no competing financial or non-financial interests. Meeting Presentation: Paper presentation at the 2024 American Academy of Ophthalmology (AAO) Annual Meeting and poster presentation at 2024 American Society of Human Genetics (ASHG) Annual Meeting, Presentation at 2025 World Glaucoma Congress. Data Availability This study was conducted using data from the All of Us Research Program. The data are available to registered and contolled researchers through the All of Us Researcher Workbench. Access to individual-level data is controlled and requires approval by the All of Us Research Program. References Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology . 2014;121(11):2081-2090. Weinreb RN, Leung CKS, Crowston JG, et al. Primary open-angle glaucoma. Nat Rev Dis Primers . 2016;2:16067. Verma SS, Gudiseva HV, Chavali VRM, et al. A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma. Cell . 2024;187(2):464-480.e10. Gibson J, Griffiths H, De Salvo G, et al. Genome-wide association study of primary open angle glaucoma risk and quantitative traits. Mol Vis . 2012;18:1083-1092. Zhou T. Exploring the Genetics of Primary Open-Angle Glaucoma with Next Generation Sequencing .; 2021. All of Us Research Program Investigators, Denny JC, Rutter JL, et al. The “All of Us” Research Program. N Engl J Med . 2019;381(7):668-676. Delavar A, Radha Saseendrakumar B, Weinreb RN, Baxter SL. Racial and Ethnic Disparities in Cost-Related Barriers to Medication Adherence Among Patients With Glaucoma Enrolled in the National Institutes of Health All of Us Research Program. JAMA Ophthalmol . 2022;140(4):354-361. gnomAD. Accessed August 27, 2024. https://gnomad.broadinstitute.org/help/ancestry Cavalli-Sforza LL. The Human Genome Diversity Project: past, present and future. Nat Rev Genet . 2005;6(4):333-340. A global reference for human genetic variation. Nature . 2015;526(7571):68-74. Genomic data in the All of Us Research Program. Nature . 2024;627(8003):340-346. Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis. Genomics . 2017;109(2):83-90. Marees AT, de Kluiver H, Stringer S, et al. A tutorial on conducting genome-wide association studies: Quality control and statistical analysis. International Journal of Methods in Psychiatric Research . 2018;27(2):e1608. Hail. Accessed September 5, 2024. https://hail.is/ Van de Ven B. Bokeh. Accessed September 5, 2024. https://bokeh.org/ pandas. Accessed September 5, 2024. https://pandas.pydata.org/ NumPy. Accessed September 5, 2024. https://numpy.org/ Ishigaki K, Akiyama M, Kanai M, et al. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat Genet . 2020;52(7):669-679. Sollis E, Mosaku A, Abid A, et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res . 2022;51(D1):D977-D985. Allot A, Wei CH, Phan L, et al. Tracking genetic variants in the biomedical literature using LitVar 2.0. Nat Genet . 2023;55(6):901-903. Burdon KP, Macgregor S, Hewitt AW, et al. Genome-wide association study identifies susceptibility loci for open angle glaucoma at TMCO1 and CDKN2B-AS1. Nat Genet . 2011;43(6):574-578. Abdelrazek IM, Holling T, Harms FL, et al. Craniofacial dysmorphism, skeletal anomalies, and impaired intellectual development syndrome-1 in two new patients with the same homozygous TMCO1 variant and review of the literature. Eur J Med Genet . 2023;66(3):104715. Ojha P, Wiggs JL, Pasquale LR. The genetics of intraocular pressure. Semin Ophthalmol . 2013;28(5-6):301-305. Scheetz TE, Faga B, Ortega L, et al. Glaucoma Risk Alleles in the Ocular Hypertension Treatment Study. Ophthalmology . 2016;123(12):2527-2536. Choquet H, Paylakhi S, Kneeland SC, et al. A multiethnic genome-wide association study of primary open-angle glaucoma identifies novel risk loci. Nat Commun . 2018;9(1):2278. Sharma S, Burdon KP, Chidlow G, et al. Association of genetic variants in the TMCO1 gene with clinical parameters related to glaucoma and characterization of the protein in the eye. Invest Ophthalmol Vis Sci . 2012;53(8):4917-4925. Yang A, Bofill-De Ros X, Stanton R, Shao TJ, Villanueva P, Gu S. TENT2, TUT4, and TUT7 selectively regulate miRNA sequence and abundance. Nat Commun . 2022;13(1):1-15. Preedy VR, Watson RR. Handbook of Nutrition, Diet, and the Eye . Academic Press; 2019. Pietzner M, Wheeler E, Carrasco-Zanini J, et al. Mapping the proteo-genomic convergence of human diseases. Science . 2021;374(6569):eabj1541. Yengo L, Vedantam S, Marouli E, et al. A saturated map of common genetic variants associated with human height. Nature . 2022;610(7933):704-712. Zheng Y, Cheung CYL, Wong TY, Mitchell P, Aung T. Influence of Height, Weight, and Body Mass Index on Optic Disc Parameters. Invest Ophthalmol Vis Sci . 2010;51(6):2998-3002. Wang Z, Liu CH, Huang S, Chen J. Wnt Signaling in vascular eye diseases. Prog Retin Eye Res . 2019;70:110-133. Plotnikov D, Huang Y, Khawaja AP, et al. High Blood Pressure and Intraocular Pressure: A Mendelian Randomization Study. Invest Ophthalmol Vis Sci . 2022;63(6):29. Melgarejo JD, Van Eijgen J, Wei D, et al. Effect of 24-h blood pressure dysregulations and reduced ocular perfusion pressure in open-angle glaucoma progression. J Hypertens . 2023;41(11):1785-1792. Baxter SL, Saseendrakumar BR, Paul P, et al. Predictive Analytics for Glaucoma Using Data From the All of Us Research Program. Am J Ophthalmol . 2021;227:74-86. Amariuta T, Siewert-Rocks K, Price AL. Modeling tissue co-regulation estimates tissue-specific contributions to disease. Nat Genet . 2023;55(9):1503-1511. Wang JH, Wong RCB, Liu GS. Retinal Aging Transcriptome and Cellular Landscape in Association With the Progression of Age-Related Macular Degeneration. Invest Ophthalmol Vis Sci . 2023;64(4):32. Firat PG, Demirel EE, Dikci S, Kuku I, Genc O. Evaluation of Iron Deficiency Anemia Frequency as a Risk Factor in Glaucoma. Anemia . 2018;2018:1456323. Passaro ML, Matarazzo F, Abbadessa G, et al. Glaucoma as a Tauopathy-Is It the Missing Piece in the Glaucoma Puzzle? J Clin Med Res . 2023;12(21). doi:10.3390/jcm12216900 Wang H, Yang J, Schneider JA, De Jager PL, Bennett DA, Zhang HY. Genome-wide interaction analysis of pathological hallmarks in Alzheimer’s disease. Neurobiol Aging . 2020;93:61-68. Rizzuti M, Melzi V, Gagliardi D, et al. Insights into the identification of a molecular signature for amyotrophic lateral sclerosis exploiting integrated microRNA profiling of iPSC-derived motor neurons and exosomes. Cellular and Molecular Life Sciences: CMLS . 2022;79(3):189. Carreras FJ. Glaucoma and amyotrophic lateral sclerosis, two kindred diseases? Neural Regeneration Research . 2016;11(9):1415. Becic A, Leifeld J, Shaukat J, Hollmann M. Tetraspanins as Potential Modulators of Glutamatergic Synaptic Function. Front Mol Neurosci . 2021;14:801882. Felmlee MA, Jones RS, Rodriguez-Cruz V, Follman KE, Morris ME. Monocarboxylate Transporters (SLC16): Function, Regulation, and Role in Health and Disease. Pharmacol Rev . 2020;72(2):466-485. Cuellar-Partida G, Craig JE, Burdon KP, et al. Assessment of polygenic effects links primary open-angle glaucoma and age-related macular degeneration. Sci Rep . 2016;6:26885. Saunders GRB, Wang X, Chen F, et al. Genetic diversity fuels gene discovery for tobacco and alcohol use. Nature . 2022;612(7941):720-724. Stuart KV, Madjedi K, Luben RN, et al. Alcohol, Intraocular Pressure, and Open-Angle Glaucoma: A Systematic Review and Meta-analysis. Ophthalmology . 2022;129(6):637-652. Sakaue S, Kanai M, Tanigawa Y, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet . 2021;53(10):1415-1424. Ou Y. Glaucoma and Cataracts. Accessed August 8, 2024. https://www.brightfocus.org/glaucoma/article/glaucoma-and-cataracts# Gharahkhani P, Jorgenson E, Hysi P, et al. Genome-wide meta-analysis identifies 127 open-angle glaucoma loci with consistent effect across ancestries. Nature Communications . 2021;12(1):1-16. Han X, Gharahkhani P, Hamel AR, et al. Large-scale multitrait genome-wide association analyses identify hundreds of glaucoma risk loci. Nat Genet . 2023;55(7):1116-1125. Kanai M, Akiyama M, Takahashi A, et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet . 2018;50(3):390-400. Yoon JS, Kim YE, Lee EJ, Kim H, Kim TW. Systemic factors associated with 10-year glaucoma progression in South Korean population: a single center study based on electronic medical records. Sci Rep . 2023;13(1):1-10. Ma Y, Han J, Li S, Zhang A, Cao W, Sun X. Association between Platelet Parameters and Glaucoma Severity in Primary Open-Angle Glaucoma. J Ophthalmol . 2019;2019:3425023. Taylor KE, Wong Q, Levine DM, et al. Genome-Wide Association Analysis Reveals Genetic Heterogeneity of Sjögren’s Syndrome According to Ancestry. Arthritis Rheumatol . 2017;69(6):1294-1305. Zhu Z, Guo Y, Shi H, et al. Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. J Allergy Clin Immunol . 2020;145(2):537-549. Jung Y, Han K, Park HYL, Lee SH, Park CK. Metabolic Health, Obesity, and the Risk of Developing Open-Angle Glaucoma: Metabolically Healthy Obese Patients versus Metabolically Unhealthy but Normal Weight Patients. Diabetes Metab J . 2020;44(3):414-425. Liu M, Jiang Y, Wedow R, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet . 2019;51(2):237-244. Mahmoudinezhad G, Nishida T, Weinreb RN, et al. Associations of smoking and alcohol consumption with the development of open angle glaucoma: a retrospective cohort study. BMJ Open . 2023;13(10):e072163. Fan Q, Li H, Wang X, et al. Contribution of common and rare variants to Asian neovascular age-related macular degeneration subtypes. Nat Commun . 2023;14(1):1-14. Chen MH, Raffield LM, Mousas A, et al. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. Cell . 2020;182(5):1198-1213.e14. Cohen E, Kramer M, Shochat T, Goldberg E, Krause I. Relationship between hematocrit levels and intraocular pressure in men and women: A population-based cross-sectional study. Medicine . 2017;96(41):e8290. Barton AR, Sherman MA, Mukamel RE, Loh PR. Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses. Nat Genet . 2021;53(8):1260-1269. Lee JS, Kim YJ, Kim SS, et al. Increased risk of open-angle glaucoma in non-smoking women with obstructive pattern of spirometric tests. Sci Rep . 2022;12(1):16915. Huang J, Huffman JE, Huang Y, et al. Genomics and phenomics of body mass index reveals a complex disease network. Nat Commun . 2022;13(1):7973. Peng H, Ding X, Xu J, et al. Elevated Expression of the Long Noncoding RNA MAFTRR in Patients with Hashimoto’s Thyroiditis. J Immunol Res . 2021;2021:3577011. Co-occurrence of chronic kidney disease and glaucoma: Epidemiology and etiological mechanisms. Survey of Ophthalmology . 2023;68(1):1-16. Giblin JP, Comes N, Strauss O, Gasull X. Ion Channels in the Eye: Involvement in Ocular Pathologies. Adv Protein Chem Struct Biol . 2016;104:157-231. Okumus S, Demiryürek S, Gürler B, et al. Association transient receptor potential melastatin channel gene polymorphism with primary open angle glaucoma. Mol Vis . 2013;19:1852-1858. Nannini DR, Kim H, Fan F, Gao X. Genetic Risk Score Is Associated with Vertical Cup-to-Disc Ratio and Improves Prediction of Primary Open-Angle Glaucoma in Latinos. Ophthalmology . 2018;125(6):815-821. Varma R, Wang D, Wu C, et al. Four-year incidence of open-angle glaucoma and ocular hypertension: the Los Angeles Latino Eye Study. Am J Ophthalmol . 2012;154(2):315-325.e1. Pendergrass SA, Crawford DC. Using Electronic Health Records To Generate Phenotypes For Research. Curr Protoc Hum Genet . 2019;100(1):e80. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers invited by journal 11 Oct, 2025 Editor assigned by journal 11 Oct, 2025 Editor invited by journal 08 Oct, 2025 Submission checks completed at journal 07 Oct, 2025 First submitted to journal 07 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7754041\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":533086377,\"identity\":\"d7cd7325-cc66-4895-8122-6e74902f069c\",\"order_by\":0,\"name\":\"Kiana Tavakoli\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of California, San Diego\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kiana\",\"middleName\":\"\",\"lastName\":\"Tavakoli\",\"suffix\":\"\"},{\"id\":533086379,\"identity\":\"314060d9-6724-4e65-9e6c-dc20855d1541\",\"order_by\":1,\"name\":\"Bonnie B. Huang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of California, San Diego\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bonnie\",\"middleName\":\"B.\",\"lastName\":\"Huang\",\"suffix\":\"\"},{\"id\":533086382,\"identity\":\"d94f1f58-dc97-41b7-afd1-fd9dead1444c\",\"order_by\":2,\"name\":\"Tara Mirmira\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of California, San Diego\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tara\",\"middleName\":\"\",\"lastName\":\"Mirmira\",\"suffix\":\"\"},{\"id\":533086386,\"identity\":\"f74484c9-f8f9-4fa9-9bdd-4fa472129d79\",\"order_by\":3,\"name\":\"Nichole Ma\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of California, San Diego\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Nichole\",\"middleName\":\"\",\"lastName\":\"Ma\",\"suffix\":\"\"},{\"id\":533086391,\"identity\":\"b391c33a-1533-4822-a6b9-75edf77fa2f7\",\"order_by\":4,\"name\":\"Robert N. Weinreb\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of California, San Diego\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Robert\",\"middleName\":\"N.\",\"lastName\":\"Weinreb\",\"suffix\":\"\"},{\"id\":533086392,\"identity\":\"6dcc546f-b51c-498a-9706-04395faf4a21\",\"order_by\":5,\"name\":\"Sally L. Baxter\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie2SMYvCMBTHXynoEts1HTy/woNAUSz3WZ4U4lJwdXAQCrfemk9ysxLQxdsDOtwhOBdcOt1dap3EQMcb8hvyEsiP938hAB7PfyRkbY25Xb5gczvYfehW+o1CAIlqKnVR4ruCpqsyKgfb66DOFuJYbqtZfQLc778NLKeztUNBHeWJIjn5OO1yTnQBPBRiDIe5WwkZ8oo0pqZAq2ibkPV48Kadyqhkoib6RaEW17pV+hce/LgV0Cy1XTaIvOjdu0DKg7VbsbPIiZI5ciPTMUnNkmYW2s2FM9j7pz6y7BVjlZ9NlemXqHmxajUdOoM90n4G6nrd4/F4PE/5A72UWr1V8oAcAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"University of California, San Diego\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Sally\",\"middleName\":\"L.\",\"lastName\":\"Baxter\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-09-30 17:53:24\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7754041/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7754041/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1038/s41598-026-43993-9\",\"type\":\"published\",\"date\":\"2026-03-17T15:58:32+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":94398102,\"identity\":\"1a8d7382-c1cc-4621-a0c0-6b1be381f083\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:56:59\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":49073,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Manuscriptofthepaper1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/9de0d406840877fa604b480e.docx\"},{\"id\":94399498,\"identity\":\"dcbf16e7-8bd2-4f39-ae82-f9ef60e53733\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:57:36\",\"extension\":\"tiff\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":319398,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure1.tiff\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/944e2e293e160bdfc55f271e.tiff\"},{\"id\":94398137,\"identity\":\"a5df3322-0f6c-4120-862c-0880e1603c4f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:57:00\",\"extension\":\"tiff\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":408388,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure2.tiff\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/7dfaebc0e93cf0c93de3f114.tiff\"},{\"id\":94397748,\"identity\":\"9252bcdb-0458-451e-ab05-c62b0dd53681\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:56:47\",\"extension\":\"tiff\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":366734,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure3.tiff\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/bddfe1d2392cea30eef3c776.tiff\"},{\"id\":94399461,\"identity\":\"53374226-8652-431c-88dc-63083beeab23\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:57:34\",\"extension\":\"json\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":9476,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"4d0d436bfc144cd4be9eac385c671a5d.json\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/ff10345bfbde924a2c1bc578.json\"},{\"id\":94399284,\"identity\":\"6c7d70d3-3eb1-44fe-8c4a-9ab807e227a0\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:57:26\",\"extension\":\"pdf\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":303296,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SNPtable.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/7b42c3f1a15f8db639b40d67.pdf\"},{\"id\":94397156,\"identity\":\"b09abc5a-87f6-4594-bc03-9b93710b2b06\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:56:30\",\"extension\":\"xml\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":129627,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"4d0d436bfc144cd4be9eac385c671a5d1enriched.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/d86c4e9e660b6feb0d06d3cb.xml\"},{\"id\":94398404,\"identity\":\"ecae9591-28e8-4df8-abba-5dce77cd66d8\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:57:04\",\"extension\":\"tiff\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":319398,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure1.tiff\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/5aa6fa8bb0b85c9507f6f398.tiff\"},{\"id\":94396583,\"identity\":\"08f4f21a-f31a-44fa-8210-396d4c37c914\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:56:05\",\"extension\":\"tiff\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":408388,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure2.tiff\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/bf92bcd06f79dabc1c352c73.tiff\"},{\"id\":94398390,\"identity\":\"c21bef2c-dc39-4777-af05-3225b338fb2f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:57:03\",\"extension\":\"tiff\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":366734,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Figure3.tiff\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/a0d5dd009bffd9bbcd784fa6.tiff\"},{\"id\":94397018,\"identity\":\"fa268c24-7fe4-468d-893e-b271d675893b\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:56:24\",\"extension\":\"png\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":50620,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"OnlineFigure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/7a1e62cd8be0fc939f5d31bd.png\"},{\"id\":94396650,\"identity\":\"ae983efa-7544-4038-a989-307f71a17b54\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:56:08\",\"extension\":\"png\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":71187,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"OnlineFigure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/286122a0a0b326adabdadcaf.png\"},{\"id\":94396577,\"identity\":\"b5c2e96a-480c-434d-9776-5564331da112\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:56:05\",\"extension\":\"png\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":58905,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"OnlineFigure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/82afb4bacf9d690eb858f635.png\"},{\"id\":94396967,\"identity\":\"bfbce69f-166b-48c3-86d1-47a8af327f7b\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:56:22\",\"extension\":\"xml\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":123825,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"4d0d436bfc144cd4be9eac385c671a5d1structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/91cf1f12f7f3eced93d32936.xml\"},{\"id\":94398883,\"identity\":\"5ead21f7-8a1d-4dcb-a09d-8ecfd97213d9\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:57:14\",\"extension\":\"html\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":142178,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/f2b8d49f8be6e4a3112864a0.html\"},{\"id\":94398310,\"identity\":\"30ef2114-dc62-4483-8b7a-572b8fa19b40\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:57:03\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":513085,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eManhattan plot demonstrating variants associated with POAG among All of Us participants of European ancestry.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.tiff.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/35ad2a95d581ee4989272c45.jpg\"},{\"id\":94399194,\"identity\":\"78e5b571-9f51-4bee-91cb-759ef93f9ff5\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:57:23\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":706850,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eManhattan plot demonstrating variants associated with POAG among All of Us participants of African ancestry.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.tiff.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/8546a4a709f3c7d841f319cd.jpg\"},{\"id\":94398959,\"identity\":\"296bcf89-110e-4004-9071-1b764ef41c0e\",\"added_by\":\"auto\",\"created_at\":\"2025-10-27 13:57:17\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":669213,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eManhattan plot demonstrating variants associated with POAG among All of Us participants of Admixed American/Latino ancestry.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.tiff.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/ca8687ac56d6816677cb0acd.jpg\"},{\"id\":105224921,\"identity\":\"0a86706a-f329-4151-8fe2-a8f0b54d4682\",\"added_by\":\"auto\",\"created_at\":\"2026-03-23 16:17:10\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2511169,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7754041/v1/2b2901df-0bab-4505-be95-29dccf7f01b3.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Multi-Ancestry Genome-Wide Association Study in All of Us for Primary Open- Angle Glaucoma\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003ePrimary Open Angle Glaucoma (POAG) is the leading cause of irreversible blindness globally.\\u003csup\\u003e1\\u003c/sup\\u003e It is a degenerative disease of the optic nerve that leads to progressive vision loss.\\u003csup\\u003e2\\u003c/sup\\u003e The transferability of genetic findings between populations is understood to be limited by ancestry-specific differences in linkage disequilibrium, minor allele frequency, and potentially differences in causal variants, which pose significant limitations to our understanding of the genetic architecture of POAG in non-European populations. This disparity may result in unequal benefits among different populations from precision medicine, as genetic risk models derived from large-scale studies conducted in European populations exhibit high predictive power in European samples but demonstrate poor predictive accuracy in non-European samples.\\u003csup\\u003e3\\u003c/sup\\u003e Consequently, enhancing ethnic and ancestral diversity among study participants is crucial for identifying understudied mechanisms of disease and ultimately ensuring equitable genetic findings.\\u003csup\\u003e4,5\\u003c/sup\\u003e Specifically, in large studies that focus only on people of European ancestry, disease-critical genetic variants may be missed because they are either rare or completely absent.\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, we report a genome-wide association study (GWAS) of POAG utilizing the \\u003cem\\u003eAll of Us\\u003c/em\\u003e Research Program dataset, a diverse nationwide database in the United States that emphasizes the recruitment of populations historically underrepresented in biomedical research.\\u003csup\\u003e6\\u003c/sup\\u003e Our analysis includes individuals of European, African, and admixed American/Latino ancestries. We provide a comprehensive discussion on the identification of novel loci associated with POAG and examine the extent to which genetic signals are shared across ancestries, as well as the presence of ancestry-specific genetic signals. Our findings offer valuable insights into the etiology of POAG and underscore the importance of conducting genetic studies within non-European populations.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec2\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStudy cohort:\\u003c/h2\\u003e\\u003cp\\u003eData were sourced from the \\u003cem\\u003eAll of Us\\u003c/em\\u003e Research Program, a landmark research initiative aimed at advancing precision medicine by collecting and analyzing health data from diverse populations.\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e The program encompasses demographic, geographic, and medical diversity, including historically underrepresented populations such as ethnic minorities and individuals from underserved communities. All participants provided written informed consent, demonstrating their voluntary participation in the study and understanding of its purpose. Data sources for the \\u003cem\\u003eAll of Us\\u003c/em\\u003e Research Program include electronic health records, physical measurements, surveys, biospecimens, and wearable technology data.Prospective enrollment and data collection were approved by an independent institutional review board, with written informed consent obtained from all participants. The \\u003cem\\u003eAll of Us\\u003c/em\\u003e Data Research Center harmonized the data into the Observational Medical Outcomes Partnership (OMOP) common data model, a standardized framework for representing observational health data from diverse sources. To protect participant privacy, the Data Research Center applied measures such as deidentification and date shifting before making the data available on the \\u003cem\\u003eAll of Us\\u003c/em\\u003e Researcher Workbench. Secondary analyses of these deidentified datasets were classified as not involving human subjects research by the University of California San Diego Institutional Review Board. This study was conducted in accordance with the Declaration of Helsinki and followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational research..\\u003csup\\u003e7\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003ePhenotyping:\\u003c/h3\\u003e\\n\\u003cp\\u003eThere were 403,916 individuals who were enrolled in \\u003cem\\u003eAll of Us\\u003c/em\\u003e and had short-read sequencing data available on the \\u003cem\\u003eAll of Us\\u003c/em\\u003e Researcher Workbench Controlled Tier dataset version 8. Participants diagnosed with POAG were identified using SNOMED concept ID 77075001 (\\u0026ldquo;Primary open angle glaucoma\\u0026rdquo;) derived from electronic health record data. Individuals with normal-tension glaucoma, a subtype of glaucoma where optic nerve damage occurs despite normal intraocular pressure (IOP), were excluded from the study to ensure a homogeneous cohort. Participants lacking age, sex, or genotype data were excluded as these individuals would not possess the expected covariates for the association analysis. Categories of genetically-determined ancestry in \\u003cem\\u003eAll of Us\\u003c/em\\u003e corresponded directly to categorical ancestry definitions used within gnomAD,\\u003csup\\u003e8\\u003c/sup\\u003e the Human Genome Diversity Project,\\u003csup\\u003e9\\u003c/sup\\u003e and 1000 Genomes:\\u003csup\\u003e10\\u003c/sup\\u003e African/African American, Admixed American/Latino, East Asian, European, Middle Eastern, South Asian, and Other (meaning an individual\\u0026rsquo;s predominant ancestry is \\u0026lt;\\u0026thinsp;50% of their total ancestral composition).\\u003c/p\\u003e\\n\\u003ch3\\u003eGenotyping:\\u003c/h3\\u003e\\n\\u003cp\\u003eDetails of genotyping procedures used by \\u003cem\\u003eAll of Us\\u003c/em\\u003e have been described previously\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e. We excluded samples with a variant call rate below 99% or fewer than five heterozygotes. No imputation was required in the \\u003cem\\u003eAll of Us\\u003c/em\\u003e research dataset, as data was generated from short-read whole genome sequencing across 403,916 individuals. Genomic analysis used the GRCh38 reference genome.\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e We utilized the Allele Count Allele Frequency (ACAF) data in the \\u003cem\\u003eAll of Us\\u003c/em\\u003e Researcher Workbench. The ACAF threshold callset includes variants with a population-specific allele frequency (AF) greater than 1% or a population-specific allele count over 100 in any ancestral subpopulations. Quality control measures ensured genotype data reliability, including filtering out variants with an allele frequency less than 1% and Hardy-Weinberg Equilibrium outliers (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;1\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;10\\u003c/sup\\u003e)\\u003csup\\u003e13\\u003c/sup\\u003e. Logistic regression analysis was conducted separately for each autosome, considering covariates such as the top 11 genotyping principal components, sex, and age. Related individuals were excluded based on available relatedness data, including first-degree relatives (parents, siblings, cousins) to minimize confounding; specifically, one individual was retained randomly from each family.\\u003c/p\\u003e\\u003cp\\u003eOur analysis utilized \\u003cem\\u003eHail\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/em\\u003e\\u003c/sup\\u003e for scalable genomic data analysis, \\u003cem\\u003eBokeh\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/em\\u003e\\u003c/sup\\u003e for interactive visualization, \\u003cem\\u003ePandas\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/em\\u003e\\u003c/sup\\u003e for data manipulation, and \\u003cem\\u003eNumPy\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/em\\u003e\\u003c/sup\\u003e for numerical computing.\\u003c/p\\u003e\\n\\u003ch3\\u003eGenome-wide association study:\\u003c/h3\\u003e\\n\\u003cp\\u003eTo account for potential population stratification amongst our study participants within ancestry categories, we projected everyone\\u0026rsquo;s genotype by principal components using cohort-wide standardized genotypes.\\u003c/p\\u003e\\u003cp\\u003eWe performed ancestry-specific GWAS analyses for each group of European, African, and Admixed American/Latino ancestries to explore genetic associations unique to each ancestry. For each population, we separately computed the top 11 genotyping PCs. To this end, we performed a logistic regression Wald test. Manhattan and quantile-quantile plots were generated to visualize the GWAS results and compute the genomic inflation factor, which could reveal unaccounted population stratification (lambda\\u0026thinsp;=\\u0026thinsp;1.00). Genome-wide significant single nucleotide polymorphisms (SNPs) were identified at a threshold of p\\u0026thinsp;\\u0026lt;\\u0026thinsp;5\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;8\\u003c/sup\\u003e for each ancestry group and separately for the cross-ancestry meta-analysis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eWe defined a POAG-associated locus as a genomic region within \\u0026plusmn;\\u0026thinsp;1 Mb of the lead variant. A locus was considered novel if it did not include any previously reported variants with a p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;5\\u0026times;10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;8\\u003c/sup\\u003e in previous GWAS nor was in high linkage disequilibrium (r\\u0026sup2; \\u0026gt;0.1) with genome-wide significant POAG variants from previous GWAS.\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e We employed the GWAS Catalog\\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u003c/sup\\u003e and Litvar\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e databases to account for previous GWAS. If a genome-wide significant SNP landed in a protein-coding region of a gene, we also searched the GWAS Catalog to identify if this gene was associated with any potential comorbidities which may be physiologically connected with POAG.\\u003c/p\\u003e\\n\\u003ch3\\u003eFixed-effect meta-analysis and multi-ancestry GWAS:\\u003c/h3\\u003e\\n\\u003cp\\u003eWe conducted a fixed-effect meta-analysis across three ancestry groups (European, African, and Admixed American/Latino) by integrating summary statistics from separate GWAS for each group. The remaining ancestry groups available in \\u003cem\\u003eAll of Us\\u003c/em\\u003e were not included in these analyses due to prohibitively small sample sizes.\\u003c/p\\u003e\\u003cp\\u003eWe applied an inverse-variance-weighted fixed-effect meta-analysis to these ancestry-specific results, which enhanced our overall statistical power to identify POAG-associated variants. We estimated meta-analyzed effect sizes and standard errors for each variant and calculated p-values based on a normal distribution. This method integrated data from multiple ancestry groups, providing a comprehensive view of genetic associations that may be shared across populations.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStudy cohort characteristics:\\u003c/h2\\u003e\\u003cp\\u003eWe identified 4,305 cases of POAG and 369,949 controls without POAG. Among these participants, there were 2,302 cases of European ancestry, 1,339 cases of African ancestry, and 465 cases of Admixed American/Latino ancestry. (Table\\u0026nbsp;1)\\u003c/p\\u003e\\u003cp\\u003eTable 1. Characteristics of \\u003cem\\u003eAll of Us\\u0026nbsp;\\u003c/em\\u003eparticipants with and without POAG with genotype data available for analysis. \\u0026nbsp;\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Taba\\\" border=\\\"1\\\"\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePOAG Cases (N\\u0026thinsp;=\\u0026thinsp;4,305)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eControls (N\\u0026thinsp;=\\u0026thinsp;369,949)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMean (Standard Deviation) Age\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e73.9 (10.6) years\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e56.17 (17.0) years\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo. (%) Hispanic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e572 (13.3% of cases)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e70,329 (19% of controls)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo. (%) Male\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2,195 (51% of cases)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e146,223 (39.5% of controls)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo. (%) Female\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2,110 (49% of cases)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e223,726 (60.5% of controls)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo. (%) African ancestry\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1,339 (31.1% of cases)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e69,491 (18.8% of controls)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo. (%) Admixed American/Latino ancestry\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e465 (10.9% of cases)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e67,875 (18.3% of controls)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo. (%) European ancestry\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2,302 (53.5% of cases)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e213,774 (57.8% of controls)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo. (%) Other ancestry\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e199 (4.5% of cases)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18,809 (5.1% of controls)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eEuropean POAG GWAS identifies newly associated variants near genes with known roles in eye development and function\\u003c/span\\u003e:\\u003c/p\\u003e\\u003cp\\u003eThe analysis of individuals of European ancestry (2,302 POAG cases, 213,774 controls) identified 52 genome-wide significant variants and five distinct loci associated with POAG, consistent with previous GWAS findings related to POAG and visual field loss.\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e Notably, four of these loci were novel and have not been previously reported for POAG or glaucoma in general.(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e)( Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e (available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.aaojournal.org\\u003c/span\\u003e\\u003cspan address=\\\"https://www.aaojournal.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e))\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eOn chromosome 1, we observed a significant number of associated variants near the \\u003cem\\u003eTMCO1\\u003c/em\\u003e gene, which has been implicated in various disorders, including POAG, craniofacial dysmorphism, skeletal anomalies, and impaired intellectual development syndrome.\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e \\u003cem\\u003eTMCO1\\u003c/em\\u003e plays a crucial role in regulating intraocular pressure (IOP), a key factor in the development of POAG. Dysregulation of \\u003cem\\u003eTMCO1\\u003c/em\\u003e may hinder the outflow of aqueous humor potentially resulting in elevated IOP levels.\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e Additionally, on chromosome 1, we replicated the association near the pseudogene \\u003cem\\u003eLOC440700\\u003c/em\\u003e and the \\u003cem\\u003eTMCO-AS1\\u003c/em\\u003e gene which have both been previously reported in POAG GWAS.\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eWe identified several loci associated with POAG that have not been previously reported by existing GWAS. One such locus is centered at 52.5 Mb on chromosome 1 near the \\u003cem\\u003eTUT4\\u003c/em\\u003e gene, which is responsible for uridylating miRNAs.\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e This gene is related to glutathione peroxidase 7, where changes in enzyme activity may contribute to age-related macular degeneration (AMD).\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e Furthermore, \\u003cem\\u003eTUT4\\u003c/em\\u003e has been linked to height,\\u003csup\\u003e30\\u003c/sup\\u003e with studies suggesting that individuals who are taller or have lower body mass index tend to have a smaller neuroretina rim area and a larger optic cup-to-disc area ratio.\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eOn chromosome 3, we discovered an associated locus consisting of intronic variants within the \\u003cem\\u003eRYK\\u003c/em\\u003e gene. The \\u003cem\\u003eRYK\\u003c/em\\u003e gene significantly influences eye development, particularly through its modulation of Wnt signaling pathways critical for eye organogenesis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e Additionally, \\u003cem\\u003eRYK\\u003c/em\\u003e has been shown to affect systolic and diastolic blood pressure,\\u003csup\\u003e33\\u003c/sup\\u003e and numerous studies have demonstrated an association between blood pressure and POAG.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR35\\\" citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eWe identified another POAG-associated locus centered on the promoter region of the \\u003cem\\u003eMOXD1\\u003c/em\\u003e gene on chromosome 6 (at 132.2 Mb). \\u003cem\\u003eMOXD1\\u003c/em\\u003e has been implicated in the progression of AMD\\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e and anemia.\\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e \\u003cem\\u003eMOXD1\\u003c/em\\u003e is also known to affect tau protein levels, which may lead to modifications in neuronal injury associated with ocular hypertension.\\u003csup\\u003e\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e Lastly we identified a POAG-associated intronic variant on chromosome 9 which encodes \\u003cem\\u003eUBAP2\\u003c/em\\u003e, a gene associated with the neurodegenerative disease amyotrophic lateral sclerosis\\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e ,in which astrocytes play a role in both ALS disease and in changes to the optic nerve head in glaucoma.\\u003csup\\u003e\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eAfrican ancestry GWAS identifies new loci not previously identified with European GWAS data:\\u003c/h3\\u003e\\n\\u003cp\\u003eDuring our investigation into the genetic factors contributing to POAG within the African ancestry group (1,339 POAG cases, 69,491 controls), we uncovered novel associations that highlight the intricate genetic complexity and remarkable diversity present in POAG genes in different populations. Our research identified seven genome-wide significant SNPs across five independent loci (as determined by distance and linkage disequilibrium) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e (available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.aaojournal.org\\u003c/span\\u003e\\u003cspan address=\\\"https://www.aaojournal.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e)).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eNone of these associations have been previously identified in any GWAS related to POAG. Notably, this GWAS did not recapitulate the well-established \\u003cem\\u003eTMCO1\\u003c/em\\u003e locus found in European POAG GWAS, suggesting that this gene may not play as critical a role in POAG pathogenesis in non-European individuals. This is supported by our analysis which found that the associated variants in the TMCO1 locus are specifically common in the European population, but rare in the African and Latino populations.\\u003c/p\\u003e\\u003cp\\u003eHere, we summarize these novel POAG-associated loci in order of genomic coordinates. First, we identified an associated locus on chromosome 5 centered on an intronic variant of the \\u003cem\\u003eTSPAN17\\u003c/em\\u003e gene. The expression of \\u003cem\\u003eTSPAN17\\u003c/em\\u003e in the neural tube and brain suggests a potential influence on neurological factors related to POAG.\\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u003c/sup\\u003e Second, on chromosome 12, we identified another associated locus centered on the \\u003cem\\u003eSLC16A7\\u003c/em\\u003e gene, which has been implicated in age-related cataract and is expressed in retinal tissue.\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u003c/sup\\u003e Notably, it has been shown that AMD and POAG exhibit a positive genetic correlation.\\u003csup\\u003e\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e Additionally, previous work indicates that \\u003cem\\u003eSLC16A7\\u003c/em\\u003e may affect alcohol consumption\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e, which has been shown to increase the risk of glaucoma.\\u003csup\\u003e\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u003c/sup\\u003e Third, also on chromosome 12, but more than 1 Mb away, we identified a locus harboring two non-coding RNA genes: \\u003cem\\u003eLOC100506869\\u003c/em\\u003e and \\u003cem\\u003eLINC02388\\u003c/em\\u003e. The latter gene has been connected to cataract formation,\\u003csup\\u003e48\\u003c/sup\\u003e which may contribute to primary angle-closure glaucoma due to a narrower drainage angle in the eye. While cataracts do not directly cause glaucoma, there are rare instances where cataracts can lead to elevated IOP and damage to the optic nerve.\\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eLastly, we discovered an associated locus on chromosome 13 encoding the \\u003cem\\u003eLOC107984606\\u003c/em\\u003e gene with no immediate connection to POAG pathogenesis, as well as an association on chromosome 15 centered on a nonfunctional variant which does not encode any gene.\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan type=\\\"ItalicUnderline\\\" class=\\\"ItalicUnderline\\\" name=\\\"Emphasis\\\"\\u003eAll of Us\\u003c/span\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003ecohort enables first POAG GWAS for individuals of Admixed American/Latino ancestry\\u003c/span\\u003e:\\u003c/p\\u003e\\u003cp\\u003eThe modest sample size of the Admixed American/Latino population in our cohort (465 POAG cases, 67,875 controls) has enabled us to conduct an ancestry-specific GWAS for this demographic, whereas previous studies suffered from small sample size and thus were only powered to perform cross-ancestry meta-analysis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e Our advance toward learning population-specific genetic susceptibility for POAG is critical as Latinos are approximately 5% more likely to be affected by POAG compared to other populations\\u003csup\\u003e\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e. Our analysis led to the identification of five genome-wide significant variants, constituting independent loci. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e(available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.aaojournal.org\\u003c/span\\u003e\\u003cspan address=\\\"https://www.aaojournal.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e)).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eFirst, we recapitulated an associated locus found by previous European POAG GWAS\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e on chromosome 11 encoding the \\u003cem\\u003eSLC22A20P\\u003c/em\\u003e gene. The genome-wide significant variants from the European GWAS \\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e are in moderate linkage disequilibrium (r\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.1) with our lead variant. The \\u003cem\\u003eSLC22A20P\\u003c/em\\u003e gene has been shown to influence mean corpuscular hemoglobin levels,\\u003csup\\u003e52\\u003c/sup\\u003e with higher levels correlating with a faster rate of retinal nerve fiber layer (RNFL) thinning.\\u003csup\\u003e\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e\\u003c/sup\\u003e Second, we identified a genome-wide significant locus on chromosome 22, centered on an intergenic variant near encoding intergenic the non-coding RNA gene \\u003cem\\u003eLINC00895\\u003c/em\\u003e. This locus is situated within \\u0026plusmn;\\u0026thinsp;1 Mb of previously associated POAG variants\\u003csup\\u003e\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e, although these variants were suggested to regulate different genes. Importantly, the variants identified in these previous studies exhibited low linkage disequilibrium (r\\u0026sup2; \\u0026lt; 0.1) with our lead variant, potentially suggesting that this finding may represent an independent mechanism. The \\u003cem\\u003eLINC00895\\u003c/em\\u003e gene is known to affect platelet count, and lower platelet counts have been observed in individuals with POAG.\\u003csup\\u003e\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eWe also identified several loci that have not previously been implicated in POAG. First, on chromosome 8, we identified an associated locus centered on an intronic variant of \\u003cem\\u003eFAM135B\\u003c/em\\u003e which is associated with smoking behavior.\\u003csup\\u003e\\u003cem\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/em\\u003e\\u003c/sup\\u003e Second, we identified a genome-wide significant intronic variant of \\u003cem\\u003eLINC00871\\u003c/em\\u003e on chromosome 14. Expression of this gene is observed in the basal ganglia, particularly within the caudate and putamen nuclei. Prior GWAS has reported the association of this variant with Sj\\u0026ouml;gren's syndrome, which has implications for ocular dryness.\\u003csup\\u003e\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e\\u003c/sup\\u003e Moreover, \\u003cem\\u003eLINC00871\\u003c/em\\u003e has been associated with body mass index,\\u003csup\\u003e56\\u003c/sup\\u003e suggesting potential pleiotropy affecting obesity and the development of POAG,\\u003csup\\u003e57\\u003c/sup\\u003e as well as smoking status and initiation\\u003csup\\u003e\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e\\u003c/sup\\u003e highlighting the impact of tobacco use on POAG.\\u003csup\\u003e\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eThird, on chromosome 20, we identified a POAG-associated variant 35 kb upstream of the \\u003cem\\u003eGATA5\\u003c/em\\u003e gene, which is associated with AMD. Others have hypothesized that the mechanisms underlying the associations at the \\u003cem\\u003eGATA5\\u003c/em\\u003e locus in neovascular AMD patients may be linked to retinoic acid signaling.\\u003csup\\u003e\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e\\u003c/sup\\u003e Furthermore, \\u003cem\\u003eGATA5\\u003c/em\\u003e has been shown to affect hematocrit levels,\\u003csup\\u003e61\\u003c/sup\\u003e potentially contributing to increased IOP.\\u003csup\\u003e\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e\\u003c/sup\\u003e Additionally, \\u003cem\\u003eGATA5\\u003c/em\\u003e influences lung function,\\u003csup\\u003e63\\u003c/sup\\u003e where reduced lung function has been associated with an increased risk of glaucoma.\\u003csup\\u003e\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eCross-ancestry GWAS meta-analysis:\\u003c/h2\\u003e\\u003cp\\u003eIn our analysis, we identified 56 genome-wide significant variants, 6 of which were not identified in ancestry-specific POAG GWAS. All but five of the genome-wide significant ancestry-specific GWAS variants were additionally found to be significant in the cross-ancestry meta-analysis. The exceptions mostly included variants identified in the Admixed American/Latino GWAS, which has a substantially smaller sample size and thus lower contribution to the cross-ancestry meta-analysis.\\u003c/p\\u003e\\u003cp\\u003eFirst, on chromosome 5, an intergenic variant was newly associated in the meta-analysis; the closest gene is ENSG00000286625 and is 10,000 Kb away. Second, we identified an intronic variant in the \\u003cem\\u003eSGCZ\\u003c/em\\u003e gene on chromosome 8 that has previously been linked to BMI \\u003csup\\u003e\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e\\u003c/sup\\u003e, reinforcing the possible role of metabolic pathways in glaucoma development. Third, we detected an intronic variant on chromosome 12 ,the \\u003cem\\u003eSLC16A7\\u003c/em\\u003e gene influencing body weight and BMI\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e, suggesting a relationship between metabolic factors and POAG risk.\\u003csup\\u003e\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u003c/sup\\u003e Fourth, we identified one intronic variant on chromosome 16 in the \\u003cem\\u003eMAFTRR\\u003c/em\\u003e and \\u003cem\\u003eLOC105371356\\u003c/em\\u003e genes, both of which affect thyroid function, indicating a potential link between thyroid-related pathways and POAG susceptibility.\\u003csup\\u003e\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e\\u003c/sup\\u003e Fifth, on chromosome 20, we identified an intronic variant in the \\u003cem\\u003eGGT7\\u003c/em\\u003e gene, which is linked to chronic kidney disease (CKD), suggesting a potential association between glaucoma and CKD.\\u003csup\\u003e\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e\\u003c/sup\\u003e Lastly, we identified a intron variant on chromosome 21,The gene \\u003cem\\u003eTRPM2\\u003c/em\\u003e, which is a channel gene is associated with POAG, suggesting that TRPM2 may serve as a potential aqueous humor biomarker for glaucoma.\\u003csup\\u003e\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eOur study highlights the benefits of conducting genetic research in non-European populations. LD often poses a significant challenge in identifying causal variants in GWAS. However, an analysis of GWAS results from different ancestries with diverse LD structures can enhance the precision of causal variant identification. We performed this analysis for African ancestry, as well as for European and admixed American Latino populations. While previous GWAS have included non-European populations, such as those studied in the DIGS/ADAGES and NEIGHBORHOOD consortia\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e, a large proportion of prior research has focused on European ancestry groups.\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e Based on our literature search, only one prior study has investigated POAG in admixed Latino populations\\u003csup\\u003e\\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e70\\u003c/span\\u003e\\u003c/sup\\u003e, highlighting the importance of exploring genetic contributions in these underrepresented groups. However, there remains a significant gap in our understanding of genetic risk factors in other admixed populations, despite the increasing incidence of POAG\\u003csup\\u003e\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e\\u003c/sup\\u003e in these diverse communities. Additionally, our study identified novel loci and variants that have not been reported in earlier GWAS.\\u003c/p\\u003e\\u003cp\\u003eOur analysis revealed three new loci in European populations associated with genes \\u003cem\\u003eTUT4, RYK\\u003c/em\\u003e, and \\u003cem\\u003eMOXD1\\u003c/em\\u003e. Additionally, we identified five new loci from the African ancestry GWAS, as well as four novel loci in Admixed American/Latino ancestry. These results suggest that the genetic effects contributing to POAG may vary between populations, highlighting the importance of considering population-specific genetic architectures in complex traits. Given the significant differences in POAG prevalence across ancestries, it is likely that certain variants have ancestry-specific effects. Therefore, it is crucial to conduct ancestry-specific GWAS to uncover these unique genetic contributions.\\u003c/p\\u003e\\u003cp\\u003eIn our study's limitations, we acknowledge the relatively modest sample sizes for African, East Asian, Admixed American/Latino, and Middle Eastern populations, which may hinder the robustness of our GWAS findings in diverse ancestries. Additionally, the lack of data in \\u003cem\\u003eAll of Us\\u003c/em\\u003e on visual field measurements and IOP restricts our ability to assess the effects of novel variants or loci on these established factors that are known to be associated with POAG. In addition, phenotyping using EHR diagnostic codes has known limitations,\\u003csup\\u003e72\\u003c/sup\\u003e but additional clinical data that may assist with more precise phenotyping, such as imaging, testing, and free-text notes, are currently not available in \\u003cem\\u003eAll of Us\\u003c/em\\u003e.\\u003c/p\\u003e\\u003cp\\u003eOur study marks a significant advancement in understanding the genetic aspects of POAG across diverse populations. The findings provide insights into the genetic architecture of POAG, emphasizing the importance of genetic diversity in understanding disease susceptibility. Addressing challenges through more inclusive research that includes clinical, environmental, and genetic data is essential for developing effective, personalized interventions. Ongoing research is needed to validate these findings and clarify the functional consequences of identified genetic variations, ultimately aiming to improve early detection and management of this sight-threatening condition.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations and Acronyms\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003ePOAG\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003ePrimary open-angle glaucoma\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eGWAS\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eGenome-Wide Association Studies\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eIOP\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eIntraocular Pressure\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eAMD\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eAge-related Macular Degeneration\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eKiana Tavakoli, Bonnie B. Huang, Tara Mirmira, Nichole Ma, Robert N. Weinreb, and Sally L. Baxter contributed to the conception and design of the study, data collection, analysis, interpretation, drafting, and critical revision of the manuscript. All authors read and approved the final version of the manuscript.\\u003c/p\\u003e\\u003cp\\u003eThis article contains additional online-only material. The following should appear online only: Table S1.\\u003c/p\\u003e\\n\\u003cp\\u003eThe data used in this study are available through the \\u003cem\\u003eAll of Us\\u003c/em\\u003e Researcher Workbench. Access requires registration in the controlled tier of the Workbench to ensure compliance with program policies. For inquiries regarding data access, please contact Kiana Tavakoli (ktavakoli@health.ucsd.edu).\\u003c/p\\u003e\\n\\u003cp\\u003eKiana Tavakoli, Bonnie B. Huang, Tara Mirmira, Nichole Ma, Robert N. Weinreb, and Sally L. Baxter contributed to the conception and design of the study, data collection, analysis, interpretation, drafting, and critical revision of the manuscript. All authors read and approved the final version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003eAdditional Information\\u003c/p\\u003e\\n\\u003cp\\u003eFinancial Support\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by grants from the National Institutes of Health (Bethesda, MD, USA; grant numbers R03EY035824, DP5OD029610, P30EY022589) and an unrestricted departmental grant from Research to Prevent Blindness (New York, NY, USA). The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants. This research was supported in part by an award from the All of Us Research Program to the National Alliance for Hispanic Health, funded by the Division of Engagement and Outreach, All of Us Research Program, National Institutes of Health (Award Number OT2OD025277), and the Lucy Delgado Fund of the Healthy Americas Foundation. The All of Us Research Program would not be possible without the partnership of its participants to advance science and better health for all of us.\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting Interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing financial or non-financial interests.\\u003c/p\\u003e\\n\\u003cp\\u003eMeeting Presentation: Paper presentation at the 2024 American Academy of Ophthalmology (AAO) Annual Meeting and poster presentation at 2024 American Society of Human Genetics (ASHG) Annual Meeting, Presentation at 2025 World Glaucoma Congress.\\u003c/p\\u003e\\n\\u003cp\\u003eData Availability\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was conducted using data from the All of Us Research Program. The data are available to registered and contolled researchers through the All of Us Researcher Workbench. Access to individual-level data is controlled and requires approval by the All of Us Research Program.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eTham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. \\u003cem\\u003eOphthalmology\\u003c/em\\u003e. 2014;121(11):2081-2090.\\u003c/li\\u003e\\n\\u003cli\\u003eWeinreb RN, Leung CKS, Crowston JG, et al. Primary open-angle glaucoma. \\u003cem\\u003eNat Rev Dis Primers\\u003c/em\\u003e. 2016;2:16067.\\u003c/li\\u003e\\n\\u003cli\\u003eVerma SS, Gudiseva HV, Chavali VRM, et al. A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma. \\u003cem\\u003eCell\\u003c/em\\u003e. 2024;187(2):464-480.e10.\\u003c/li\\u003e\\n\\u003cli\\u003eGibson J, Griffiths H, De Salvo G, et al. Genome-wide association study of primary open angle glaucoma risk and quantitative traits. \\u003cem\\u003eMol Vis\\u003c/em\\u003e. 2012;18:1083-1092.\\u003c/li\\u003e\\n\\u003cli\\u003eZhou T. \\u003cem\\u003eExploring the Genetics of Primary Open-Angle Glaucoma with Next Generation Sequencing\\u003c/em\\u003e.; 2021.\\u003c/li\\u003e\\n\\u003cli\\u003eAll of Us Research Program Investigators, Denny JC, Rutter JL, et al. The \\u0026ldquo;All of Us\\u0026rdquo; Research Program. \\u003cem\\u003eN Engl J Med\\u003c/em\\u003e. 2019;381(7):668-676.\\u003c/li\\u003e\\n\\u003cli\\u003eDelavar A, Radha Saseendrakumar B, Weinreb RN, Baxter SL. Racial and Ethnic Disparities in Cost-Related Barriers to Medication Adherence Among Patients With Glaucoma Enrolled in the National Institutes of Health All of Us Research Program. \\u003cem\\u003eJAMA Ophthalmol\\u003c/em\\u003e. 2022;140(4):354-361.\\u003c/li\\u003e\\n\\u003cli\\u003egnomAD. Accessed August 27, 2024. https://gnomad.broadinstitute.org/help/ancestry\\u003c/li\\u003e\\n\\u003cli\\u003eCavalli-Sforza LL. The Human Genome Diversity Project: past, present and future. \\u003cem\\u003eNat Rev Genet\\u003c/em\\u003e. 2005;6(4):333-340.\\u003c/li\\u003e\\n\\u003cli\\u003eA global reference for human genetic variation. \\u003cem\\u003eNature\\u003c/em\\u003e. 2015;526(7571):68-74.\\u003c/li\\u003e\\n\\u003cli\\u003eGenomic data in the All of Us Research Program. \\u003cem\\u003eNature\\u003c/em\\u003e. 2024;627(8003):340-346.\\u003c/li\\u003e\\n\\u003cli\\u003eImprovements and impacts of GRCh38 human reference on high throughput sequencing data analysis. \\u003cem\\u003eGenomics\\u003c/em\\u003e. 2017;109(2):83-90.\\u003c/li\\u003e\\n\\u003cli\\u003eMarees AT, de Kluiver H, Stringer S, et al. A tutorial on conducting genome-wide association studies: Quality control and statistical analysis. \\u003cem\\u003eInternational Journal of Methods in Psychiatric Research\\u003c/em\\u003e. 2018;27(2):e1608.\\u003c/li\\u003e\\n\\u003cli\\u003eHail. Accessed September 5, 2024. https://hail.is/\\u003c/li\\u003e\\n\\u003cli\\u003eVan de Ven B. Bokeh. Accessed September 5, 2024. https://bokeh.org/\\u003c/li\\u003e\\n\\u003cli\\u003epandas. Accessed September 5, 2024. https://pandas.pydata.org/\\u003c/li\\u003e\\n\\u003cli\\u003eNumPy. Accessed September 5, 2024. https://numpy.org/\\u003c/li\\u003e\\n\\u003cli\\u003eIshigaki K, Akiyama M, Kanai M, et al. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. \\u003cem\\u003eNat Genet\\u003c/em\\u003e. 2020;52(7):669-679.\\u003c/li\\u003e\\n\\u003cli\\u003eSollis E, Mosaku A, Abid A, et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. \\u003cem\\u003eNucleic Acids Res\\u003c/em\\u003e. 2022;51(D1):D977-D985.\\u003c/li\\u003e\\n\\u003cli\\u003eAllot A, Wei CH, Phan L, et al. Tracking genetic variants in the biomedical literature using LitVar 2.0. \\u003cem\\u003eNat Genet\\u003c/em\\u003e. 2023;55(6):901-903.\\u003c/li\\u003e\\n\\u003cli\\u003eBurdon KP, Macgregor S, Hewitt AW, et al. Genome-wide association study identifies susceptibility loci for open angle glaucoma at TMCO1 and CDKN2B-AS1. \\u003cem\\u003eNat Genet\\u003c/em\\u003e. 2011;43(6):574-578.\\u003c/li\\u003e\\n\\u003cli\\u003eAbdelrazek IM, Holling T, Harms FL, et al. Craniofacial dysmorphism, skeletal anomalies, and impaired intellectual development syndrome-1 in two new patients with the same homozygous TMCO1 variant and review of the literature. \\u003cem\\u003eEur J Med Genet\\u003c/em\\u003e. 2023;66(3):104715.\\u003c/li\\u003e\\n\\u003cli\\u003eOjha P, Wiggs JL, Pasquale LR. The genetics of intraocular pressure. \\u003cem\\u003eSemin Ophthalmol\\u003c/em\\u003e. 2013;28(5-6):301-305.\\u003c/li\\u003e\\n\\u003cli\\u003eScheetz TE, Faga B, Ortega L, et al. Glaucoma Risk Alleles in the Ocular Hypertension Treatment Study. \\u003cem\\u003eOphthalmology\\u003c/em\\u003e. 2016;123(12):2527-2536.\\u003c/li\\u003e\\n\\u003cli\\u003eChoquet H, Paylakhi S, Kneeland SC, et al. A multiethnic genome-wide association study of primary open-angle glaucoma identifies novel risk loci. \\u003cem\\u003eNat Commun\\u003c/em\\u003e. 2018;9(1):2278.\\u003c/li\\u003e\\n\\u003cli\\u003eSharma S, Burdon KP, Chidlow G, et al. Association of genetic variants in the TMCO1 gene with clinical parameters related to glaucoma and characterization of the protein in the eye. \\u003cem\\u003eInvest Ophthalmol Vis Sci\\u003c/em\\u003e. 2012;53(8):4917-4925.\\u003c/li\\u003e\\n\\u003cli\\u003eYang A, Bofill-De Ros X, Stanton R, Shao TJ, Villanueva P, Gu S. TENT2, TUT4, and TUT7 selectively regulate miRNA sequence and abundance. \\u003cem\\u003eNat Commun\\u003c/em\\u003e. 2022;13(1):1-15.\\u003c/li\\u003e\\n\\u003cli\\u003ePreedy VR, Watson RR. \\u003cem\\u003eHandbook of Nutrition, Diet, and the Eye\\u003c/em\\u003e. Academic Press; 2019.\\u003c/li\\u003e\\n\\u003cli\\u003ePietzner M, Wheeler E, Carrasco-Zanini J, et al. Mapping the proteo-genomic convergence of human diseases. \\u003cem\\u003eScience\\u003c/em\\u003e. 2021;374(6569):eabj1541.\\u003c/li\\u003e\\n\\u003cli\\u003eYengo L, Vedantam S, Marouli E, et al. A saturated map of common genetic variants associated with human height. \\u003cem\\u003eNature\\u003c/em\\u003e. 2022;610(7933):704-712.\\u003c/li\\u003e\\n\\u003cli\\u003eZheng Y, Cheung CYL, Wong TY, Mitchell P, Aung T. Influence of Height, Weight, and Body Mass Index on Optic Disc Parameters. \\u003cem\\u003eInvest Ophthalmol Vis Sci\\u003c/em\\u003e. 2010;51(6):2998-3002.\\u003c/li\\u003e\\n\\u003cli\\u003eWang Z, Liu CH, Huang S, Chen J. Wnt Signaling in vascular eye diseases. \\u003cem\\u003eProg Retin Eye Res\\u003c/em\\u003e. 2019;70:110-133.\\u003c/li\\u003e\\n\\u003cli\\u003ePlotnikov D, Huang Y, Khawaja AP, et al. High Blood Pressure and Intraocular Pressure: A Mendelian Randomization Study. \\u003cem\\u003eInvest Ophthalmol Vis Sci\\u003c/em\\u003e. 2022;63(6):29.\\u003c/li\\u003e\\n\\u003cli\\u003eMelgarejo JD, Van Eijgen J, Wei D, et al. Effect of 24-h blood pressure dysregulations and reduced ocular perfusion pressure in open-angle glaucoma progression. \\u003cem\\u003eJ Hypertens\\u003c/em\\u003e. 2023;41(11):1785-1792.\\u003c/li\\u003e\\n\\u003cli\\u003eBaxter SL, Saseendrakumar BR, Paul P, et al. Predictive Analytics for Glaucoma Using Data From the All of Us Research Program. \\u003cem\\u003eAm J Ophthalmol\\u003c/em\\u003e. 2021;227:74-86.\\u003c/li\\u003e\\n\\u003cli\\u003eAmariuta T, Siewert-Rocks K, Price AL. Modeling tissue co-regulation estimates tissue-specific contributions to disease. \\u003cem\\u003eNat Genet\\u003c/em\\u003e. 2023;55(9):1503-1511.\\u003c/li\\u003e\\n\\u003cli\\u003eWang JH, Wong RCB, Liu GS. Retinal Aging Transcriptome and Cellular Landscape in Association With the Progression of Age-Related Macular Degeneration. \\u003cem\\u003eInvest Ophthalmol Vis Sci\\u003c/em\\u003e. 2023;64(4):32.\\u003c/li\\u003e\\n\\u003cli\\u003eFirat PG, Demirel EE, Dikci S, Kuku I, Genc O. Evaluation of Iron Deficiency Anemia Frequency as a Risk Factor in Glaucoma. \\u003cem\\u003eAnemia\\u003c/em\\u003e. 2018;2018:1456323.\\u003c/li\\u003e\\n\\u003cli\\u003ePassaro ML, Matarazzo F, Abbadessa G, et al. Glaucoma as a Tauopathy-Is It the Missing Piece in the Glaucoma Puzzle? \\u003cem\\u003eJ Clin Med Res\\u003c/em\\u003e. 2023;12(21). doi:10.3390/jcm12216900\\u003c/li\\u003e\\n\\u003cli\\u003eWang H, Yang J, Schneider JA, De Jager PL, Bennett DA, Zhang HY. Genome-wide interaction analysis of pathological hallmarks in Alzheimer\\u0026rsquo;s disease. \\u003cem\\u003eNeurobiol Aging\\u003c/em\\u003e. 2020;93:61-68.\\u003c/li\\u003e\\n\\u003cli\\u003eRizzuti M, Melzi V, Gagliardi D, et al. Insights into the identification of a molecular signature for amyotrophic lateral sclerosis exploiting integrated microRNA profiling of iPSC-derived motor neurons and exosomes. \\u003cem\\u003eCellular and Molecular Life Sciences: CMLS\\u003c/em\\u003e. 2022;79(3):189.\\u003c/li\\u003e\\n\\u003cli\\u003eCarreras FJ. Glaucoma and amyotrophic lateral sclerosis, two kindred diseases? \\u003cem\\u003eNeural Regeneration Research\\u003c/em\\u003e. 2016;11(9):1415.\\u003c/li\\u003e\\n\\u003cli\\u003eBecic A, Leifeld J, Shaukat J, Hollmann M. Tetraspanins as Potential Modulators of Glutamatergic Synaptic Function. \\u003cem\\u003eFront Mol Neurosci\\u003c/em\\u003e. 2021;14:801882.\\u003c/li\\u003e\\n\\u003cli\\u003eFelmlee MA, Jones RS, Rodriguez-Cruz V, Follman KE, Morris ME. Monocarboxylate Transporters (SLC16): Function, Regulation, and Role in Health and Disease. \\u003cem\\u003ePharmacol Rev\\u003c/em\\u003e. 2020;72(2):466-485.\\u003c/li\\u003e\\n\\u003cli\\u003eCuellar-Partida G, Craig JE, Burdon KP, et al. Assessment of polygenic effects links primary open-angle glaucoma and age-related macular degeneration. \\u003cem\\u003eSci Rep\\u003c/em\\u003e. 2016;6:26885.\\u003c/li\\u003e\\n\\u003cli\\u003eSaunders GRB, Wang X, Chen F, et al. Genetic diversity fuels gene discovery for tobacco and alcohol use. \\u003cem\\u003eNature\\u003c/em\\u003e. 2022;612(7941):720-724.\\u003c/li\\u003e\\n\\u003cli\\u003eStuart KV, Madjedi K, Luben RN, et al. Alcohol, Intraocular Pressure, and Open-Angle Glaucoma: A Systematic Review and Meta-analysis. \\u003cem\\u003eOphthalmology\\u003c/em\\u003e. 2022;129(6):637-652.\\u003c/li\\u003e\\n\\u003cli\\u003eSakaue S, Kanai M, Tanigawa Y, et al. A cross-population atlas of genetic associations for 220 human phenotypes. \\u003cem\\u003eNat Genet\\u003c/em\\u003e. 2021;53(10):1415-1424.\\u003c/li\\u003e\\n\\u003cli\\u003eOu Y. Glaucoma and Cataracts. Accessed August 8, 2024. https://www.brightfocus.org/glaucoma/article/glaucoma-and-cataracts#\\u003c/li\\u003e\\n\\u003cli\\u003eGharahkhani P, Jorgenson E, Hysi P, et al. Genome-wide meta-analysis identifies 127 open-angle glaucoma loci with consistent effect across ancestries. \\u003cem\\u003eNature Communications\\u003c/em\\u003e. 2021;12(1):1-16.\\u003c/li\\u003e\\n\\u003cli\\u003eHan X, Gharahkhani P, Hamel AR, et al. Large-scale multitrait genome-wide association analyses identify hundreds of glaucoma risk loci. \\u003cem\\u003eNat Genet\\u003c/em\\u003e. 2023;55(7):1116-1125.\\u003c/li\\u003e\\n\\u003cli\\u003eKanai M, Akiyama M, Takahashi A, et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. \\u003cem\\u003eNat Genet\\u003c/em\\u003e. 2018;50(3):390-400.\\u003c/li\\u003e\\n\\u003cli\\u003eYoon JS, Kim YE, Lee EJ, Kim H, Kim TW. Systemic factors associated with 10-year glaucoma progression in South Korean population: a single center study based on electronic medical records. \\u003cem\\u003eSci Rep\\u003c/em\\u003e. 2023;13(1):1-10.\\u003c/li\\u003e\\n\\u003cli\\u003eMa Y, Han J, Li S, Zhang A, Cao W, Sun X. Association between Platelet Parameters and Glaucoma Severity in Primary Open-Angle Glaucoma. \\u003cem\\u003eJ Ophthalmol\\u003c/em\\u003e. 2019;2019:3425023.\\u003c/li\\u003e\\n\\u003cli\\u003eTaylor KE, Wong Q, Levine DM, et al. Genome-Wide Association Analysis Reveals Genetic Heterogeneity of Sj\\u0026ouml;gren\\u0026rsquo;s Syndrome According to Ancestry. \\u003cem\\u003eArthritis Rheumatol\\u003c/em\\u003e. 2017;69(6):1294-1305.\\u003c/li\\u003e\\n\\u003cli\\u003eZhu Z, Guo Y, Shi H, et al. Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. \\u003cem\\u003eJ Allergy Clin Immunol\\u003c/em\\u003e. 2020;145(2):537-549.\\u003c/li\\u003e\\n\\u003cli\\u003eJung Y, Han K, Park HYL, Lee SH, Park CK. Metabolic Health, Obesity, and the Risk of Developing Open-Angle Glaucoma: Metabolically Healthy Obese Patients versus Metabolically Unhealthy but Normal Weight Patients. \\u003cem\\u003eDiabetes Metab J\\u003c/em\\u003e. 2020;44(3):414-425.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu M, Jiang Y, Wedow R, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. \\u003cem\\u003eNat Genet\\u003c/em\\u003e. 2019;51(2):237-244.\\u003c/li\\u003e\\n\\u003cli\\u003eMahmoudinezhad G, Nishida T, Weinreb RN, et al. Associations of smoking and alcohol consumption with the development of open angle glaucoma: a retrospective cohort study. \\u003cem\\u003eBMJ Open\\u003c/em\\u003e. 2023;13(10):e072163.\\u003c/li\\u003e\\n\\u003cli\\u003eFan Q, Li H, Wang X, et al. Contribution of common and rare variants to Asian neovascular age-related macular degeneration subtypes. \\u003cem\\u003eNat Commun\\u003c/em\\u003e. 2023;14(1):1-14.\\u003c/li\\u003e\\n\\u003cli\\u003eChen MH, Raffield LM, Mousas A, et al. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. \\u003cem\\u003eCell\\u003c/em\\u003e. 2020;182(5):1198-1213.e14.\\u003c/li\\u003e\\n\\u003cli\\u003eCohen E, Kramer M, Shochat T, Goldberg E, Krause I. Relationship between hematocrit levels and intraocular pressure in men and women: A population-based cross-sectional study. \\u003cem\\u003eMedicine \\u003c/em\\u003e. 2017;96(41):e8290.\\u003c/li\\u003e\\n\\u003cli\\u003eBarton AR, Sherman MA, Mukamel RE, Loh PR. Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses. \\u003cem\\u003eNat Genet\\u003c/em\\u003e. 2021;53(8):1260-1269.\\u003c/li\\u003e\\n\\u003cli\\u003eLee JS, Kim YJ, Kim SS, et al. Increased risk of open-angle glaucoma in non-smoking women with obstructive pattern of spirometric tests. \\u003cem\\u003eSci Rep\\u003c/em\\u003e. 2022;12(1):16915.\\u003c/li\\u003e\\n\\u003cli\\u003eHuang J, Huffman JE, Huang Y, et al. Genomics and phenomics of body mass index reveals a complex disease network. \\u003cem\\u003eNat Commun\\u003c/em\\u003e. 2022;13(1):7973.\\u003c/li\\u003e\\n\\u003cli\\u003ePeng H, Ding X, Xu J, et al. Elevated Expression of the Long Noncoding RNA MAFTRR in Patients with Hashimoto\\u0026rsquo;s Thyroiditis. \\u003cem\\u003eJ Immunol Res\\u003c/em\\u003e. 2021;2021:3577011.\\u003c/li\\u003e\\n\\u003cli\\u003eCo-occurrence of chronic kidney disease and glaucoma: Epidemiology and etiological mechanisms. \\u003cem\\u003eSurvey of Ophthalmology\\u003c/em\\u003e. 2023;68(1):1-16.\\u003c/li\\u003e\\n\\u003cli\\u003eGiblin JP, Comes N, Strauss O, Gasull X. Ion Channels in the Eye: Involvement in Ocular Pathologies. \\u003cem\\u003eAdv Protein Chem Struct Biol\\u003c/em\\u003e. 2016;104:157-231.\\u003c/li\\u003e\\n\\u003cli\\u003eOkumus S, Demiry\\u0026uuml;rek S, G\\u0026uuml;rler B, et al. Association transient receptor potential melastatin channel gene polymorphism with primary open angle glaucoma. \\u003cem\\u003eMol Vis\\u003c/em\\u003e. 2013;19:1852-1858.\\u003c/li\\u003e\\n\\u003cli\\u003eNannini DR, Kim H, Fan F, Gao X. Genetic Risk Score Is Associated with Vertical Cup-to-Disc Ratio and Improves Prediction of Primary Open-Angle Glaucoma in Latinos. \\u003cem\\u003eOphthalmology\\u003c/em\\u003e. 2018;125(6):815-821.\\u003c/li\\u003e\\n\\u003cli\\u003eVarma R, Wang D, Wu C, et al. Four-year incidence of open-angle glaucoma and ocular hypertension: the Los Angeles Latino Eye Study. \\u003cem\\u003eAm J Ophthalmol\\u003c/em\\u003e. 2012;154(2):315-325.e1.\\u003c/li\\u003e\\n\\u003cli\\u003ePendergrass SA, Crawford DC. Using Electronic Health Records To Generate Phenotypes For Research. \\u003cem\\u003eCurr Protoc Hum Genet\\u003c/em\\u003e. 2019;100(1):e80.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Glaucoma, Open-Angle, Genetic Association Studies, Genetic Loci\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7754041/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7754041/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study aims to identify new genetic loci associated with primary open-angle glaucoma (POAG) and explore shared genetic risk factors across African, European, and Admixed American/Latino populations.\\u003cstrong\\u003e \\u003c/strong\\u003eGenome-wide Association Study (GWAS) utilizing data from the\\u003cem\\u003e All of Us\\u003c/em\\u003e Research Program.\\u003cstrong\\u003e \\u003c/strong\\u003eThe study included 374,254 participants, with 4,305 individuals diagnosed with POAG and 369,949 controls. Participants were categorized by ancestry: European, African, and Admixed American/Latino. We used short-read sequencing data and applied strict quality control measures (MAF \\u0026gt; 0.01, INFO \\u0026gt; 0.8). GWAS were conducted for each ancestry group using a logistic mixed model, adjusting for age, sex, and the top 11 principal components. A fixed-effect meta-analysis combined the results across ancestries. Genome-wide significance was set at p\\u0026lt;5×10\\u003csup\\u003e-8\\u003c/sup\\u003e.\\u003cstrong\\u003e \\u003c/strong\\u003eThe primary outcome measures were the identification of genetic loci associated with POAG, and the analysis of transcription factors linked to these loci in relevant tissues.\\u003cstrong\\u003e \\u003c/strong\\u003eIn the European cohort, we identified four novel loci associated with POAG, linked to the \\u003cem\\u003eTUT4, RYK, MOXD1, and UBAP2\\u003c/em\\u003e genes, as well as the previously known \\u003cem\\u003eTMCO1 \\u003c/em\\u003elocus. In the African cohort, we found five new loci, including \\u003cem\\u003eTSPAN17, SLC16A7, LOC100506869, LINC02388, and LOC107984606\\u003c/em\\u003e. For the Admixed American/Latino cohort, we identified \\u003cem\\u003eGATA5, FAM135B, and LINC00871\\u003c/em\\u003e genes as novel loci.\\u003cstrong\\u003e \\u003c/strong\\u003eOur analysis identified three novel loci in individuals of European ancestry, mapped to the genes \\u003cem\\u003eTUT4, RYK, and MOXD1\\u003c/em\\u003e. In addition, five novel loci were detected in the GWAS of African ancestry participants, and four novel loci were identified in individuals of Admixed American/Latino ancestry. These findings indicate that the genetic determinants contributing to POAG may differ across populations, underscoring the importance of accounting for population-specific genetic architectures in the study of complex traits. Given the substantial variation in POAG prevalence among ancestries, it is plausible that certain genetic variants exert ancestry-specific effects. Consequently, conducting ancestry-stratified GWAS is essential for elucidating these unique genetic contributions.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Multi-Ancestry Genome-Wide Association Study in All of Us for Primary Open- Angle Glaucoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-26 00:57:42\",\"doi\":\"10.21203/rs.3.rs-7754041/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-10-28T10:39:09+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-22T01:44:04+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-21T18:08:52+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-16T10:06:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"24670542580109430898559303552102809636\",\"date\":\"2025-10-14T06:45:57+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"149313631968893251079678637281115756746\",\"date\":\"2025-10-13T22:54:08+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"69925064635068018788250710048048060000\",\"date\":\"2025-10-13T19:32:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"136110784643709567662582318506378521945\",\"date\":\"2025-10-11T19:34:20+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-10-11T17:57:03+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-10-11T17:47:25+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-10-08T09:04:19+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-10-07T17:47:21+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2025-10-07T17:44:17+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"2fabc38f-adb3-42d6-865d-a519b9f90ddf\",\"owner\":[],\"postedDate\":\"October 26th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":56671553,\"name\":\"Health sciences/Diseases\"},{\"id\":56671554,\"name\":\"Biological sciences/Genetics\"}],\"tags\":[],\"updatedAt\":\"2026-03-23T16:16:41+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7754041\",\"link\":\"https://doi.org/10.1038/s41598-026-43993-9\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2026-03-17 15:58:32\",\"publishedOnDateReadable\":\"March 17th, 2026\"},\"versionCreatedAt\":\"2025-10-26 00:57:42\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-026-43993-9\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-026-43993-9\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7754041\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7754041\",\"identity\":\"rs-7754041\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}