Endophenotype Guided Genome-Wide Association Study to Enhance Genetic Risk Prediction of Primary Open-Angle Glaucoma in African Ancestry Populations

Endophenotype Guided Genome-Wide Association Study to Enhance Genetic Risk Prediction of Primary Open-Angle Glaucoma in African Ancestry Populations | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Endophenotype Guided Genome-Wide Association Study to Enhance Genetic Risk Prediction of Primary Open-Angle Glaucoma in African Ancestry Populations Aude Benigne Ikuzwe Sindikubwabo , Lannawill Caruth , Yan Zhu , Yuki Bradford , Rebecca Salowe , Marylyn D. Ritchie , Marijana Vujković , Joan O’Brien , Shefali Setia-Verma doi: https://doi.org/10.1101/2025.09.29.25336758 Aude Benigne Ikuzwe Sindikubwabo 1 Department of Pathology and Laboratory Medicine, University of Pennsylvania Philadelphia , PA 19104 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lannawill Caruth 1 Department of Pathology and Laboratory Medicine, University of Pennsylvania Philadelphia , PA 19104 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yan Zhu 2 Center for Genetics of Complex Disease, Department of Ophthalmology, University of Pennsylvania Philadelphia , PA 19104 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuki Bradford 3 Department of Genetics, University of Pennsylvania , Philadelphia, PA 19104 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rebecca Salowe 2 Center for Genetics of Complex Disease, Department of Ophthalmology, University of Pennsylvania Philadelphia , PA 19104 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marylyn D. Ritchie 3 Department of Genetics, University of Pennsylvania , Philadelphia, PA 19104 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marijana Vujković 4 Department of Medicine, University of Pennsylvania , Philadelphia, PA 19104 PhD, MSCE Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joan O’Brien 2 Center for Genetics of Complex Disease, Department of Ophthalmology, University of Pennsylvania Philadelphia , PA 19104 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: shefali.setiaverma{at}pennmedicine.upenn.edu Shefali Setia-Verma 1 Department of Pathology and Laboratory Medicine, University of Pennsylvania Philadelphia , PA 19104 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: shefali.setiaverma{at}pennmedicine.upenn.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Primary open angle glaucoma (POAG) is the leading cause of irreversible blindness, with genetic predisposition playing a major role. While most genetic studies have focused on variants directly associated with POAG, loci influencing POAG diagnosis and progression, through intermediate traits also known as endophenotypes, remain underexplored. These endophenotypes, including intraocular pressure (IOP), cup to disc ratio (CDR), and central corneal thickness (CCT), and more represent promising and potentially modifiable targets for intervention. Using the Primary Open Angle African American Glaucoma Genetics (POAAGG) cohort, which consist of we first conducted GWAS on POAG related endophenotypes. We then incorporated findings from a prior GWAS focused solely on POAG followed by mediation based GWAS adjusting POAG for key endophenotypes. This allowed us to quantify direct and indirect SNP effects on POAG risk. We then constructed mediator informed polygenic risk scores (PRS) combining both effects and evaluated predictive accuracy in 196 holdout samples (97 cases and 99 controls). Mediation based GWAS identified genome wide significant variants in NAT2 (for CCT) and MED16 (for CDR), while traditional POAG GWAS identified 46 loci most of which lost significance after mediation, highlighting strong indirect effects. Among PRS models, the CDR informed PRS achieved the highest predictive accuracy (AUC = 0.99), outperforming the traditional POAG PRS (AUC = 0.71). PRS informed by IOP and MD also showed improved performance (AUC = 0.98–0.87). These findings highlight the power of incorporating endophenotypes into genetic analyses of POAG advancing our understanding of disease mechanisms, enhancing the precision of risk prediction, and paving the way toward endophenotype guided therapeutic strategies in personalized glaucoma care. 1. Introduction Primary open-angle glaucoma (POAG) is a chronic eye disease characterized by progressive damage of the optic nerve and corresponding loss of visual fields 1 . POAG is the most prevalent glaucoma subtype, affecting 57.5 million individuals worldwide, with a global prevalence of 2.2% 2 . This heritable disease disproportionally affects individuals of African ancestry, with a four to five times increase in susceptibility compared to individuals of European ancestry 3 . Moreover, approximately half of individuals with POAG remain undiagnosed until significant vision loss has already occurred 3 . In addition, POAG prevalence is projected to increase by 79.8% globally by 2040, with individuals of African ancestry contributing a disproportionately high 130.8% rise in cases 4 . Taken together, these findings emphasize the need for development of novel screening tools to capture at-risk patients before irreversible vision loss has occurred. In addition to African ancestry, POAG is influenced by several risk factors, including older age, positive family history with heritability estimates spanning 0.17 to 0.81 5 , and a range of traits. These POAG-associated traits, often referred to as endophenotypes, are continuous, heritable clinical measures, which mediate the genotype to POAG relationship and aid in the diagnosis and understanding of POAG 5 . Although these traits lie on the causal pathway to POAG, they are not independently causal, instead they are informative markers of disease risk and progression. These traits also demonstrate significant heritability, particularly intraocular pressure (IOP) (0.43 [0.38– 0.48]), cup-to-disc ratio (CDR) (0.56 [0.44–0.68]), and retinal nerve fiber layer (RNFL) thickness (0.73 [0.42–0.91]) 5 . Notably, genome-wide association studies (GWAS) have shown that up to 72% of loci significantly associated with POAG also show associations with IOP 6 , and approximately 40% of loci linked to CDR are also associated with POAG 7 . This pattern of shared genetic architecture highlights the clinical relevance of these endophenotypes in understanding the underlying biology of POAG. Cross-phenotype associations are frequently observed in complex diseases like POAG, where genetic variants are often associated with both the disease and related endophenotypes 8 . However, interpreting these associations remains challenging, as it is often unclear if the same genetic variants influence multiple traits independently or through causal intermediate pathways. These relationships can arise through biological pleiotropy, where a single genetic variant has independent effects on multiple phenotypes, or mediated pleiotropy 8 , where the effect of a genetic variant on disease is transmitted through an intermediate phenotype. Disentangling these mechanisms is critical for understanding disease biology. Importantly, mediated pleiotropy offers a unique opportunity for clinical translation: while genetic variants themselves are not easily modifiable, their mediators often can be targeted. Identifying genetic effects that act indirectly through modifiable traits can enable the discovery of actionable biological pathways and inform the development of targeted interventions. GWAS have been instrumental in identifying genetic variants associated with POAG and its endophenotypes, providing the foundation for constructing polygenic risk scores (PRS) 3 .These scores aggregate the effects of many variants to estimate an individual’s genetic predisposition to disease. However, traditional GWAS do not distinguish between direct and indirect genetic effects, potentially limiting the biological interpretability and clinical utility of the resulting PRS. Mediated GWAS approaches address this gap by incorporating information about intermediate traits, enabling the decomposition of variant effects into direct and indirect components. This distinction enhances the ability to build more informative and mechanism-aware genetic risk models—tools that can better reflect underlying disease pathways and guide personalized diagnostic or therapeutic strategies. To disentangle the mechanisms underlying shared genetic associations between POAG and its endophenotypes, we leveraged the Primary Open-Angle African American Glaucoma Genetics (POAAGG) study cohort. Using a mediated GWAS framework, we assessed the association between genetic variants and POAG while adjusting for each relevant endophenotype, such as IOP and CDR. The findings from this mediated GWAS were then used to build an endophenotype informed PRS, which was compared to a traditional POAG PRS. Our overreaching goal is to better understand the biological pathways linking genetic variations to POAG, to quantify the direct and indirect effects of these variants, and to develop a more accurate and interpretable genetic risk model. Ultimately, this approach aims to identify modifiable, endophenotype-informed targets and enhance early detection strategies bringing us closer to more precise and effective therapeutic interventions for those affected by POAG. 2. Methods 2.1. Study Dataset The POAAGG study population consists of self-identified Blacks (African American, African descent, or African Caribbean), aged 35 years or older, recruited from the Philadelphia region between 2010 and 2019. Subjects were enrolled during regularly scheduled appointments at the Ophthalmology Department at University of Pennsylvania and other sites 9 . At the time of enrollment, each subject was classified as a POAG case, suspect, or control by a glaucoma specialist or ophthalmologist based on previously published criteria 10 . All subjects provided written informed consent for their participation in the study. The POAAGG study was approved by the University of Pennsylvania Institutional Review Board under protocol #812036 and followed the tenets of the Declaration of Helsinki. In this study, we included 2696 POAG cases and 3533 controls. Participants were included if they had case-control status and available phenotype measurements for one of the endophenotypes. During the study visit, subjects underwent an onsite interview and examination, which included the collection of baseline endophenotypes. These measures included IOP_baseline, CDR, mean RNFL thickness, visual acuity (VA), central corneal thickness (CCT), mean deviation (MD), and pattern standard deviation (PSD). For subjects with prior IOP-lowering medication or a history of glaucoma surgery, pre-treatment IOP was adjusted by dividing by IOP_baseline by 0.7. This particular estimate was based on the reduction of IOP levels by 30% following IOP-lowering medications 11 and 32% following trabeculotomy 12 . IOP_max was defined as the highest IOP value from all IOP measurements, from baseline to most recent visit, for each subject. All ocular characteristics were stored in Research Electronic Data Capture 13 , 14 ( Table 1 ). In each of the endophenotypes, the more severely affected eye was used. View this table: View inline View popup Table 1: Description of endophenotypes, corresponding sample sizes, and demographics 2.2. Statistical Analysis To dissect the pathways through which genetic variants influence POAG, we implemented a mediation framework integrating GWAS of POAG and its related endophenotypes. Traditional GWAS identify associations between genetic variants and disease status but do not distinguish whether these effects are direct or operate through intermediate traits. By incorporating well-characterized POAG endophenotypes—such as intraocular pressure (IOP), cup-to-disc ratio (CDR), and central corneal thickness (CCT)—as potential mediators, our approach enables decomposition of total genetic effects into direct effects on POAG, and indirect effects transmitted through these measurable traits. This framework provides a biologically informative lens through which to interpret genetic associations, offering the potential to uncover modifiable targets along the causal pathway to disease. Our analysis was conducted in three main stages: (1) GWAS of each individual endophenotype to estimate SNP-mediator associations, (2) mediated based GWAS of POAG adjusting separately for each mediator to estimate SNP-outcome associations conditional on the mediator, and (3) construction of mediation-informed polygenic risk scores (PRS) to evaluate the predictive utility of direct vs. total genetic effects. Each of these stages are described below. This approach enables decomposition of genetic effects into direct effects on disease and indirect effects mediated through intermediate phenotypes. 2.2.1. Quality Control and Imputation The POAAGG dataset was imputed using the TOPMed reference panel 15 . Post-imputation quality control was applied, excluding SNPs with an average imputation R 2 <0.3, minor allele frequency (MAF) <1%, and/or significant deviation from Hardy–Weinberg equilibrium (p < 1 × 10 −8 ). Principal component analysis (PCA) was conducted with the EIGENSOFT package, projecting the PCs onto the 1000 Genomes dataset 16 , 17 . 2.2.2. Traditional GWAS To contextualize and evaluate the findings from our mediation framework, we used the previously published POAG GWAS [Reference 3] as a baseline reference. This study, conducted in the same POAAGG cohort and other similar cohorts, totaling 11,275 individuals of African ancestry (6,003 cases; 5,272 controls), identified 46 genome-wide significant loci associated with POAG using a traditional case-control GWAS approach without adjusting for endophenotypes 3 . We compared the results of our mediation-informed GWAS including both endophenotype-specific SNP associations and mediated POAG associations to this baseline GWAS to assess whether accounting for intermediate phenotypes affects the number, strength, and interpretation of genetic associations. This comparative analysis provided insight into the extent to which POAG-associated loci may operate through indirect, endophenotype-mediated pathways, as well as which loci are more likely to reflect direct effects on disease risk. 2.2.3. GWAS on POAG-Related Mediators We performed a GWAS using mixed linear regression model for each of the eight POAG-related endophenotypes (also referred to as mediators), as described in Eq. (1) 18 using SAIGE. For these analyses, the measurement from the more severely affected eye was used for each subject. SNP-level association tests were conducted to quantify the genetic influence on each mediator, fitting the following model, age, sex and the first five principal components were used as covariate. SAIGE applies a mixed linear regression model while also accounting for sample relatedness. For continuous traits such as these endophenotypes, SAIGE also performs transformations to ensure a normal distribution. Association of Endophenotypes/Mediator with SNPs Where: ‐ i denotes an individual, ‐ G i ∈ {0, 1, 2} is the genotype dosage at the variant of interest, ‐ α G quantifies the effect of genotype on the mediator, ‐ PC i is the vector of top genetic principal components(PC), ‐ u i ∼ N(0, τK) is a random effect accounting for sample relatedness, with K denoting the genetic relationship matrix (GRM). 2.2.4. Mediation-Based GWAS Next, to evaluate mediation, we conducted a series of POAG case-control regression GWAS analyses, adjusting individually for each endophenotype. This allows estimation of SNP effects on POAG conditional on the mediator, as described in Eq. (2) 18 . We compared SNP effect sizes from the mediated GWAS to those from our traditional POAG GWAS without mediator adjustment 3 . This comparison distinguished direct genetic effects from those potentially operating through the mediator. age, sex and the first five principal components were used as covariates. The binary outcome Y i ∈ {0,1} was modeled using a logistic mixed model, as implemented in SAIGE: Association of Outcome (POAG) with SNPs adjusted by mediator/endophenotypes: Where: ‐ i denotes an individual, ‐ β G represents the direct effect of genotype on the outcome, adjusting for the mediator, ‐ β M captures the effect of the mediator on the outcome, ‐ PC i is the vector of top genetic principal components(PC), ‐ u i ∼ N(0, τK) is a random effect accounting for sample relatedness, with K denoting the genetic relationship matrix (GRM). SAIGE estimates the null model (excluding G i and performs a saddlepoint approximation– corrected score test for β G , conditioning on all covariates, including M i ). All association analyses were run using SAIGE version 1.2.0 18 , which implements a generalized mixed model that efficiently accounts for sample relatedness and case-control imbalance. Covariates included age, sex, and the first five genetic PCs. This mediation-informed GWAS framework provides a rigorous strategy to unravel the complex genetic architecture of POAG, enabling more precise biological interpretation and improving subsequent risk prediction modeling. 2.2.5. Proportion mediated To estimate the extent to which an endophenotype mediates the relationship between a genetic variant and a disease, we used the Proportion Mediated formula, shown in Eq. (3) . This formula quantifies the proportion of the total genetic effect on disease risk that is mediated through a specific intermediate trait (i.e., an endophenotype). It is expressed as : Where: β t represents the total effect of genotype to the outcome (before adjustment) β G represents the direct effect of genotype on the outcome, adjusting for the mediator This expression captures how much of the SNP’s impact on disease risk is explained indirectly through the mediator rather than directly. A detailed explanation of this formula is provided in Reference 22. 2.2.6. Traditional PRS A traditional PRS was developed using results from GWAS analyses performed solely on POAG 3 . This approach calculated polygenic risk scores by weighting genetic variants according to their effect sizes identified in the discovery cohort, then summing these weighted contributions across all included SNPs to generate individual risk predictions. 2.2.7. Mediator-Informed PRS A mediator-informed PRS was constructed for each endophenotype using a counterfactual-based mediation framework 19 , 20 , 21 to quantify the direct and indirect effects of genetic variants on POAG. This approach models the hypothetical (or “counterfactual”) outcome that would occur under different levels of the mediator, enabling decomposition of total SNP effects into components mediated by endophenotypes versus those acting directly on disease. In our study, these decomposed effects were then incorporated into the construction of mediation-informed polygenic risk scores (PRS), allowing us to assess whether using direct-effect estimates improves prediction of POAG status incorporating both direct and indirect effect estimates derived from the mediation based GWAS Eq. (3) .The complete derivation of the mediation formula leading to Equation (3) is detailed in Vujković et al . 22 Briefly, this framework extends standard regression-based mediation to the high-dimensional, genome-wide setting by using effect estimates from GWAS of both the mediator and the outcome, along with joint modeling of covariates. Our implementation leverages these estimates to isolate the direct (non-mediated) SNP effects, which form the basis of the mediation-informed PRS. Mediator-Informed PRS: where θ 1 is the direct effect from SNP to POAG, β 1 is the direct effect from SNP to mediator, and θ 2 is the indirect effect of SNP to POAG through the mediator, M is the value of the mediator. We evaluated both approaches using the same set of 219 SNPs, previously linked to POAG in multiple studies 3 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 6 . The list of these SNPs can be found in Supplementary table 1. Performance was assessed using 196 well-characterized 97 cases and 99 control samples that were held out datasets from our GWAS analyses in the POAAGG study 9 . We measured predictive accuracy using the area under the receiver operating characteristic curve (AUC-ROC), which indicates how well the model distinguishes between cases and controls. 3. Results 3.1. GWAS on POAG-Related Mediators A total of eight significant loci (p-value <5×10-8) were identified from POAG-related endophenotypes GWASs, highlighting distinct genetic architectures underlying these intermediate traits. We identified one significant locus associated with CCT (mapping to the nearest gene of ZNF646) , one with CDR ( LOC110120806) , one with IOP_baseline ( LYPLAL1) , one with IOP_max ( LOC105374760) , 1 with PSD ( LENG8) , and 3 with VA ( LOC107984525, SLC12A2 , and between LOC124903246 / RPL35P9) ( Table 2 ). Of particular interest, LYPLAL1 has previously been implicated in POAG 6 and suggested as a potential drug target due to its protective effects on the disease 34 . Supporting this protective role, the variant that mapped on LYPLAL1 in our GWAS also showed a negative effect estimate (p-value: 4.10×10 −08 , Beta: -1.1065, SE: 0.201679), indicating potential protecting effect in POAG patients. View this table: View inline View popup Table 2: Genome-wide significant loci from endophenotype/mediators GWAS Distinct genetic loci were defined by grouping statistically significant variants located within 250kb of each other into single, independent genomic regions through a clumping procedure. Full table of results is available in Supplementary table 2. 3.2. Mediation-Based GWAS In the traditional POAG case-control GWAS reported elsewhere by previously 3 , we identified 46 genome-wide significant loci as significantly associated with POAG 3 . However, when we incorporated mediation into the analysis framework by adjusting for each endophenotype individually, the number of genome-wide significant loci dropped substantially. All previously identified loci from the non-mediated GWAS lost significance and had reduced effect sizes upon endophenotype adjustment. For instance, rs11824032 mapping to ARHGEF12 showed reduced effect sizes from β = 0.25 to β = 0.01 when adjusted for baseline IOP and from β = 0.25 to β = 0.06 when adjusted for CDR. Similarly, rs34957764 mapping to ROCK1P1 demonstrated effect size reductions from β = 0.56 to β = -0.24 with IOP baseline adjustment and from β = 0.56 to β = -0.22 with CDR adjustment ( Fig 1 ). Moreover, the rs11824032 locus showed 5% and 25% proportion mediated when adjusted for baseline IOP and CDR, respectively, while rs34957764 showed negative mediation (the direction of the mediated (indirect) effect is opposite to the direction of the total effect) of 70% and 66% when adjusted for baseline IOP and CDR, respectively ( Table 3 ). These findings reinforce that these loci affect POAG indirectly through these endophenotypes. Two novel genome-wide significant variants emerged after endophenotype adjustments: rs116310605 (p = 3.13×10 −8 , β= –1.088) mapping to the NAT2 gene after adjusting for CCT, and rs12462823 (p = 1.59×10 −8 , β=1.725) mapping to MED16 after adjusting for CDR. No other endophenotypes yielded genome-wide significant variants in the mediation-based GWAS. View this table: View inline View popup Download powerpoint Table 3. Proportion of POAG Risk Mediated by Baseline IOP and CDR Download figure Open in new tab Fig 1: Synthesis plot comparing p-values and effect sizes of the non-mediated GWAS (46 significant GWAS signals) with the mediation-based GWAS. Summary statistics for variants reaching a p-value threshold of p < 5 ×10 −8 are provided in Supplementary Table 2. Table 3 presents two loci that were previously demonstrated by [Reference 3] to influence POAG through IOP and CDR pathways. The complete analysis of proportion mediated for all 219 POAG-associated loci identified in prior studies (identical to those used for polygenic risk score calculation) is provided in Supplementary Table 3. 3.3. Mediator-Informed PRS vs Traditional PRS We next aimed to assess the utility of mediation-informed genetic signals for risk prediction. We constructed PRS using SNP effect estimates from both the traditional GWAS and the mediation based GWAS. We aimed to determine whether incorporating the mediated (indirect) SNP effect estimates into the PRS would improve predictive performance, compared to the traditional PRS based on total genetic effects. Among the mediation-informed scores, CDR adjusted PRS demonstrated the strongest predictive performance (AUC = 0.99), which significantly surpassed the traditional POAG PRS (AUC = 0.71). PRS incorporating, IOP baseline (AUC =0.98) IOP max (AUC=0.96), and MD (AUC=0.87) also showed improvements over the traditional PRS. Table 4 summarizes AUROC, accuracy, sensitivity, and specificity, highlighting improved discriminatory power of mediator-informed scores in traits such as CDR, IOP baseline, and IOP max. Models corrected for the other endophenotypes did not yield enhanced predictive power. In addition, endophenotype-informed PRS models showed improved ability to distinguish between POAG case-control status compared to traditional PRS, highlighting the added value of accounting for intermediate phenotypes in genetic risk modeling ( Fig 2 ). View this table: View inline View popup Table 4: Performance metrics comparing Mediator-Informed PRS to traditional PRS across key POAG endophenotypes. Download figure Open in new tab Fig 2: Distribution of polygenic risk scores (PRS) among cases and controls, highlighting Mediator-Informed PRS and traditional POAG PRS. (a) Mediator informed PRS distribution between cases and control adjusted by CDR, (b) Mediator informed PRS distribution between cases and control adjusted by IOP baseline, (c) Mediator informed PRS distribution between cases and control adjusted by IOP max, (d) Traditional PRS 4. Discussion While POAG remains the leading cause of irreversible blindness worldwide, effective disease-modifying therapies are still limited. However, POAG endophenotypes have emerged as promising and potentially modifiable targets for intervention. Understanding the molecular mechanisms that underlie these intermediate traits offers a crucial opportunity to identify actionable pathways and to develop targeted therapeutics that could significantly improve glaucoma management at the population level. Here we implemented a novel mediation-informed GWAS framework to distinguish between direct and indirect genetic effects on disease risk. Using our mediation-informed approach, we demonstrated that adjusting for endophenotypes reduced previously identified POAG-associated loci from 46 to only 2 genome-wide significant variants, suggesting that many genetic associations operate indirectly through intermediate traits; we also developed mediator-informed polygenic risk scores that showed superior predictive performance compared to traditional approaches. In this study, the GWAS on each individual endophenotype revealed several notable findings. The GWAS of IOP baseline identified a variant 1:219125710 that mapped to LYPLAL1 gene, with a negative effect estimate indicating a potential protective effect in POAG patients. This gene, which is located on chromosome 1 and encodes lysophospholipase-like 1 protein, has previously been linked to POAG in a multi-ancestry GWAS 6 . Additionally, it also has been suggested as a potential POAG drug target due to its protective effects on the disease. Also of interest, the SLC12A2 gene mapped to a variant significantly associated with VA. This gene has been shown to be significantly reduced in glaucomatous optic nerve head astrocytes compared to control cells 35 . Although the other identified genes have not been specifically linked to POAG or glaucoma in prior research, the absence of evidence represents a promising opportunity to gain fresh insights into the genetic mechanisms underlying these conditions. Our mediation-informed GWAS framework allowed us to dissect the genetic architecture of POAG by distinguishing between direct and indirect effects of genetic variants. While our prior traditional GWAS identified 46 genome-wide significant loci associated with POAG (Verma et al., 2024), we observed that adjusting for endophenotypes reduced this number to only two loci. This result suggests that many previously identified associations may act indirectly through intermediate traits. For example, we previously reported that the chromosome 11 locus (rs11824032) located within the ARHGEF12 gene potentially influenced POAG development by increasing IOP and promoting CDR enlargement, with particularly pronounced effects observed in populations of African descent 3 . Although this variant did not reach statistical significance in our mediation-adjusted GWAS, its direct effect reduced when adjusted for IOP from β = 0.212 to β = 0.01, and CDR β = 0.25 to β = 0.06 with a proportion mediation of 5% and 25% respectively, indicating that this locus may exert its influence on POAG risk through IOP-mediated pathways, which in turn affects CDR. Another notable example is variant rs34957764 mapping to ROCK1P1 , which showed a drastic effect reversal change from β = 0.56 in unadjusted analyses to β = -0.24 and β = -0.22 when adjusted for IOP and CDR, respectively with a proportion mediation of 70% and 66% respectively. ROCK1P1 is a pseudogene on chromosome 18 arising from partial duplication of the functional ROCK1 gene, which regulates aqueous humor outflow through Rho GTPase signaling 36 , 3 . This effect reversal and negative proportion mediated suggests that the risk association of the variant on POAG is mediated through pressure-related pathways, since adjustment for IOP and CDR revealed an underlying protective effect. These results underscore the importance of mediation analysis in interpreting GWAS results. By accounting for biologically relevant mediators, we can more accurately trace the path from variant to disease and uncover mechanisms that may otherwise remain obscured. Building on these findings, we constructed mediation-informed PRS to account for the direct and indirect genetic effects on POAG susceptibility. Our endophenotype-informed PRS showed higher predictive performance compared to traditional PRS approaches. It also showed an increase in predictive power compared to traditional PRS and better risk stratifications between cases and controls. Notably, IOP and CDR, which are fundamental parameters in POAG diagnosis 37 , demonstrated the highest AUCs and enhanced predictive capacity compared to other endophenotypes. These results demonstrated that incorporating intermediate endophenotypes— such as CDR, IOP provides a more comprehensive understanding of POAG’s genetic architecture by capturing both direct disease pathways and those mediated through measurable traits. Second, the improved predictive accuracy enables more precise identification of high-risk individuals who would benefit from enhanced screening protocols or earlier therapeutic intervention. Despite these promising results, several limitations should be noted. First, while we adjusted for a panel of well-characterized POAG endophenotypes, it is possible that additional unmeasured or poorly characterized intermediates contribute to the observed associations. Second, the relatively modest sample size of our cohort may have limited our power to detect weaker associations, particularly in the endophenotype GWAS where quantitative trait variation is more subtle. This limitation likely contributed to the reduced number of genome-wide significant loci in the mediated analyses and may have led to underestimation of mediated effects. Third, although RNFL is a valuable POAG indicator, it is typically measured after diagnosis rather than serving as a primary screening endophenotype, making it unsuitable for our approach which requires measurement of endophenotypes which are commonly measured thereby making our method more applicable to larger populations. Moreover, The PRS models were evaluated on 196 carefully selected cases and controls, which may introduce overfitting bias due to the non-random sampling methodology employed in sample selection, in addition these samples were evaluated within the same cohort, external validation in independent datasets is needed to assess generalizability. The limited sample size also constrained our ability to perform stratified analyses by age, sex, or other relevant subgroups. The encouraging performance of our mediator-informed PRS opens several avenues for future research. We plan to extend this approach by first creating a multimodal mediator-informed PRS that incorporates multiple clinically relevant endophenotypes. Given that POAG is a complex disease, integrating additional clinically significant endophenotypes is expected to enhance the model performance and improve predictive accuracy. Secondly, this study was conducted only in individuals of the African ancestry, but we plan to validate our findings in diverse populations to ensure equity in genetic risk assessment. Additionally, we aim to develop clinical decision support tools integrating mediator-informed PRS and establish frameworks for quantifying the impact of targeted interventions on patient outcomes. In summary, our mediation-informed GWAS and PRS analyses provide new insight into the genetic architecture of POAG, highlighting the critical role of endophenotypes in shaping disease risk. By integrating genetic, clinical, and intermediate trait information, this approach moves us closer to developing more accurate, biologically grounded, and clinically useful models for risk prediction and therapeutic targeting in glaucoma. Data Availability Genotype and phenotype data for POAAGG subjects is available on dbGAP (accession phs001312). https://github.com/Setia-Verma-Lab/POAG_PSB_paper_supplementary_tables 5. Acknowledgements We greatly appreciate the work of the Clinical Trial Coordinators in the POAAGG study, who made it possible to enroll more than 10,200 individuals of African ancestry in Philadelphia. All groups appreciate the critical contribution made by the enrollees. The Primary Open-Angle African American Glaucoma Genetics (POAAGG) study was supported by the National Eye Institute, Bethesda, Maryland (grant #1R01EY023557-01) and the Vision Research Core Grant (P30 EY001583). Funds also come from the F.M. Kirby Foundation, Research to Prevent Blindness, The UPenn Hospital Board of Women Visitors, and The Paul and Evanina Bell Mackall Foundation Trust. Support also came from Regeneron Genetics Center, the Ophthalmology Department at the Perelman School of Medicine, the Genetics Department at the Perelman School of Medicine, and the VA Hospital in Philadelphia, PA. This work was supported by NIH grants R01DK134575 (PI: Dr. Vujkovic) and R01HD110567 (PI: Dr. Setia-Verma), which funded research underlying the development of the mediator-effect PRS model. 6. Appendix Supplementary materials are available at https://github.com/Setia-Verma-Lab/POAG_PSB_paper_supplementary_tables The novel mediator informed methodology was originally developed under R01DK134575 and R01HD110567 grant support. 5. References 1. ↵ Weinreb , R. N. & Khaw , P. T. Primary open-angle glaucoma . The Lancet 363 , 1711 – 1720 ( 2004 ). OpenUrl CrossRef 2. ↵ Allison , K. , Patel , D. & Alabi , O. Epidemiology of Glaucoma: The Past, Present, and Predictions for the Future . Cureus 12 , e11686 ( 2020 ). OpenUrl 3. ↵ Verma , S. S. et al. A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma . Cell 187 , 464 - 480 .e10 ( 2024 ). OpenUrl CrossRef PubMed 4. ↵ Tham , Y.-C. et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis . Ophthalmology 121 , 2081 – 2090 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 5. ↵ Asefa , N. G. , Neustaeter , A. , Jansonius , N. M. & Snieder , H. Heritability of glaucoma and glaucoma-related endophenotypes: Systematic review and meta-analysis . Surv Ophthalmol 64 , 835 – 851 ( 2019 ). OpenUrl CrossRef PubMed 6. ↵ Gharahkhani , P. et al. Genome-wide meta-analysis identifies 127 open-angle glaucoma loci with consistent effect across ancestries . Nat Commun 12 , 1258 ( 2021 ). OpenUrl CrossRef PubMed 7. ↵ Fan , B. J. , Wang , D. Y. , Pasquale , L. R. , Haines , J. L. & Wiggs , J. L. Genetic variants associated with optic nerve vertical cup-to-disc ratio are risk factors for primary open angle glaucoma in a US Caucasian population . Invest Ophthalmol Vis Sci 52 , 1788 – 1792 ( 2011 ). OpenUrl Abstract / FREE Full Text 8. ↵ Solovieff , N. , Cotsapas , C. , Lee , P. H. , Purcell , S. M. & Smoller , J. W. Pleiotropy in complex traits: challenges and strategies . Nat Rev Genet 14 , 483 – 495 ( 2013 ). OpenUrl CrossRef PubMed 9. ↵ Salowe , R. J. et al. Recruitment strategies and lessons learned from a large genetic study of African Americans . PLOS Glob Public Health 2 , e0000416 ( 2022 ). OpenUrl 10. ↵ Charlson , E. S. et al. The primary open-angle african american glaucoma genetics study: baseline demographics . Ophthalmology 122 , 711 – 720 ( 2015 ). OpenUrl PubMed 11. ↵ van der Valk , R. et al. Intraocular pressure-lowering effects of all commonly used glaucoma drugs: a meta-analysis of randomized clinical trials . Ophthalmology 112 , 1177 – 1185 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 12. ↵ Deubel , C. , Böhringer , D. , Anton , A. , Reinhard , T. & Lübke , J. Long-term follow-up of intraocular pressure and pressure-lowering medication in patients following Excimer laser trabeculotomy . Graefes Arch Clin Exp Ophthalmol 259 , 957 – 962 ( 2021 ). OpenUrl PubMed 13. ↵ Harris , P. A. et al. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support . J Biomed Inform 42 , 377 – 381 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 14. ↵ Harris , P. A. et al. The REDCap consortium: Building an international community of software platform partners . J Biomed Inform 95 , 103208 ( 2019 ). OpenUrl CrossRef PubMed 15. ↵ Das , S. et al. Next-generation genotype imputation service and methods . Nat Genet 48 , 1284 – 1287 ( 2016 ). OpenUrl CrossRef PubMed 16. ↵ Galinsky , K. J. , Loh , P.-R. , Mallick , S. , Patterson , N. J. & Price , A. L. Population Structure of UK Biobank and Ancient Eurasians Reveals Adaptation at Genes Influencing Blood Pressure . Am J Hum Genet 99 , 1130 – 1139 ( 2016 ). OpenUrl CrossRef PubMed 17. ↵ Galinsky , K. J. et al. Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia . Am J Hum Genet 98 , 456 – 472 ( 2016 ). OpenUrl CrossRef PubMed 18. ↵ Zhou , W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies . Nat Genet 50 , 1335 – 1341 ( 2018 ). OpenUrl CrossRef PubMed 19. ↵ Robins , J. M. & Greenland , S. Identifiability and exchangeability for direct and indirect effects . Epidemiology 3 , 143 – 155 ( 1992 ). OpenUrl CrossRef PubMed Web of Science 20. ↵ MacKinnon , D. P. , Lockwood , C. M. , Hoffman , J. M. , West , S. G. & Sheets , V. A comparison of methods to test mediation and other intervening variable effects . Psychol Methods 7 , 83 – 104 ( 2002 ). OpenUrl CrossRef PubMed Web of Science 21. ↵ Valeri , L. & Vanderweele , T. J. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros . Psychol Methods 18 , 137 – 150 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 22. ↵ Marijana , V. & David E. Kaplan 4,1,*, Jonas Ghouse 5,6,7, Bao-Li Loza 8, Joseph Brancale 9,10, Adam Lewis 11, David Y. Zhang 12, Michael G. Levin 13,14,1, Olivia J. Veatch 15, Josephine P. Johnson 2, Carolin V. Schneider 16,17,2, Anurag Verma 2,1, Kirk J. Wangensteen 18, Eleonora Scorletti 2, Dipender Gill 19, Chigoziri Konkwo 9, Alexis M. Garófalo 2, Lindsay A. Guare 20, Tae-Wi Schwantes-An 21,22, Marco V. Abreu 21,22, Kyung Min Lee 23, Abraham Shaked 24, Kim M. Olthoff 24, Maarouf A. Hoteit 4, Elizabeth K. Speliotes 25,26, Yanhua Chen 25, Antonino Oliveri 25, Lishi Yin 25, Luca VC. Valenti 27,28, Francesco Malvestiti 27, Daniele Marchelli 27, Lorenzo Miano 27,28, Quentin M. Anstee 29,30, Ann K. Daly 29, Heather J. Cordell 31, Rebecca Darlay 31, Niek Verweij 32, George Hindy 32, Adam Locke 32, Kentaro Matsuura 33, Sumeet K. Asrani 34, James F. Trotter 34, Giuliano Testa 34, Luca A. Lotta 32, Marcus B. Jones 32, Daniel R. Dochtermann 35, Trina M. Norden-Krichmar 22,36, Craig C. Teerlink 23,37, Poornima Devineni 35, Saiju Pyarajan 35,38, Daniel J. Rader 2,12, Yasuhito Tanaka 39, Benjamin F. Voight 1,12,40, Silvia Vilarinho 9,10, Lisa A. Bastarache 11, Stefan Stender 41,42, Philip S. Tsao 43,44, Penn Medicine Biobank , Copenhagen Hospital Biobank and Danish Blood and Danish Blood Donor Study, BioVU Biobank, Michigan Genomics Initiative, Regeneron Genetics Center, Indiana Biobank, AllOfUs Biobank, Milano Biobank, LITMUS Consortium, VA Million Veteran Program , Timothy R. Morgan 22,45, Julie A. Lynch 23,37, Kyong-Mi Chang 4,1. Germline Variants Influence Chronic Liver Disease Progression through Distinct Pathways . 23. ↵ Zhou , W. et al. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease . Cell Genom 2 , 100192 ( 2022 ). OpenUrl PubMed 24. ↵ Burdon , K. P. et al. Genome-wide association study identifies susceptibility loci for open angle glaucoma at TMCO1 and CDKN2B-AS1 . Nat Genet 43 , 574 – 578 ( 2011 ). OpenUrl CrossRef PubMed 25. ↵ Gibson , J. et al. Genome-wide association study of primary open angle glaucoma risk and quantitative traits . Mol Vis 18 , 1083 – 1092 ( 2012 ). OpenUrl PubMed 26. ↵ Thorleifsson , G. et al. Common variants near CAV1 and CAV2 are associated with primary open-angle glaucoma . Nat Genet 42 , 906 – 909 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 27. ↵ Wiggs , J. L. et al. Common variants at 9p21 and 8q22 are associated with increased susceptibility to optic nerve degeneration in glaucoma . PLoS Genet 8 , e1002654 ( 2012 ). OpenUrl CrossRef PubMed 28. ↵ Nakano , M. et al. Three susceptible loci associated with primary open-angle glaucoma identified by genome-wide association study in a Japanese population . Proc Natl Acad Sci U S A 106 , 12838 – 12842 ( 2009 ). OpenUrl Abstract / FREE Full Text 29. ↵ Nakano , M. et al. Common variants in CDKN2B-AS1 associated with optic-nerve vulnerability of glaucoma identified by genome-wide association studies in Japanese . PLoS One 7 , e33389 ( 2012 ). OpenUrl CrossRef PubMed 30. ↵ Osman , W. , Low , S.-K. , Takahashi , A. , Kubo , M. & Nakamura , Y. A genome-wide association study in the Japanese population confirms 9p21 and 14q23 as susceptibility loci for primary open angle glaucoma . Hum Mol Genet 21 , 2836 – 2842 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 31. ↵ Takamoto , M. et al. Common variants on chromosome 9p21 are associated with normal tension glaucoma . PLoS One 7 , e40107 ( 2012 ). OpenUrl CrossRef PubMed 32. ↵ Writing Committee for the Normal Tension Glaucoma Genetic Study Group of Japan Glaucoma Society et al . Genome-wide association study of normal tension glaucoma: common variants in SRBD1 and ELOVL5 contribute to disease susceptibility . Ophthalmology 117 , 1331 - 1338 .e5 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 33. ↵ Wiggs , J. L. et al. Common variants at 9p21 and 8q22 are associated with increased susceptibility to optic nerve degeneration in glaucoma . PLoS Genet 8 , e1002654 ( 2012 ). OpenUrl CrossRef PubMed 34. ↵ Wang , Z. , Zhou , H. , Wang , F. & Huang , H. Exploration of potential drug targets for Glaucoma by plasma proteome screening . J Proteomics 310 , 105324 ( 2025 ). OpenUrl PubMed 35. ↵ Lukas , T. J. et al. Susceptibility to glaucoma: differential comparison of the astrocyte transcriptome from glaucomatous African American and Caucasian American donors . Genome Biol 9 , R111 ( 2008 ). OpenUrl CrossRef PubMed 36. ↵ Wang , J. , Liu , X. & Zhong , Y. Rho/Rho-associated kinase pathway in glaucoma (Review) . Int J Oncol 43 , 1357 – 1367 ( 2013 ). OpenUrl CrossRef PubMed 37. ↵ Bada , G. C. , Abid , A. R. & Patil , S. S. Correlative Study of Central Corneal Thickness and Intraocular Pressure with Optic Disc Changes in Primary Open-Angle Glaucoma . TNOA Journal of Ophthalmic Science and Research 62 , 346 – 349 ( 2024 ). OpenUrl View the discussion thread. Back to top Previous Next Posted October 07, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Endophenotype Guided Genome-Wide Association Study to Enhance Genetic Risk Prediction of Primary Open-Angle Glaucoma in African Ancestry Populations Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Endophenotype Guided Genome-Wide Association Study to Enhance Genetic Risk Prediction of Primary Open-Angle Glaucoma in African Ancestry Populations Aude Benigne Ikuzwe Sindikubwabo , Lannawill Caruth , Yan Zhu , Yuki Bradford , Rebecca Salowe , Marylyn D. Ritchie , Marijana Vujković , Joan O’Brien , Shefali Setia-Verma medRxiv 2025.09.29.25336758; doi: https://doi.org/10.1101/2025.09.29.25336758 Share This Article: Copy Citation Tools Endophenotype Guided Genome-Wide Association Study to Enhance Genetic Risk Prediction of Primary Open-Angle Glaucoma in African Ancestry Populations Aude Benigne Ikuzwe Sindikubwabo , Lannawill Caruth , Yan Zhu , Yuki Bradford , Rebecca Salowe , Marylyn D. Ritchie , Marijana Vujković , Joan O’Brien , Shefali Setia-Verma medRxiv 2025.09.29.25336758; doi: https://doi.org/10.1101/2025.09.29.25336758 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4436) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4538) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (542) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3333) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9232) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00e6ea779082571',t:'MTc3OTY0ODA2Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();