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. 2025 Oct 30;14(10):38. doi: 10.1167/tvst.14.10.38

Druggable Genome Mendelian Randomization and GWAS-sceQTLs MR Analysis Reveal Genetic Associations Between Open-Angle Glaucoma and Immune Cells, and Identify Potential Drugs

Kunmao Ke 1,2,✉, Qing Zhou 1, Jingxian Zhong 2
PMCID: PMC12584030  PMID: 41165397

Abstract

Purpose

The purpose of this study was to identify therapeutic targets and repurposable drugs for primary open-angle glaucoma (POAG) by investigating its immunometabolic mechanisms using druggable genomic and single-cell transcriptomic approaches.

Methods

We integrated druggable genome-wide and single-cell Mendelian randomization (MR) using POAG genome-wide association study (GWAS) data, blood and single-cell expression quantitative trait loci (eQTL) datasets. Causal genes were identified via colocalization and MR inference (inverse-variance weighted [IVW]). Drug-gene interactions were predicted using molecular docking (DSigDB/CB-Dock2), and safety was assessed via phenome-wide association studies (PheWAS).

Results

(1) Identified two POAG causal genes: risk genes YWHAG (odds ratio [OR] = 1.207, 95% confidence interval [CI] = 1.131–1.288) and protective genes GFPT1 (OR = 0.874, 95% CI = 0.840–0.910). (2) Cell-type-specific paradoxical effect: In CD4+KLRB1-T cells, high GFPT1 expression increased POAG risk (OR = 1.448, 95% CI = 1.241–1.690, P = 2.545 × 10−6), suggesting its role in driving immunometabolic reprogramming via the hexosamine biosynthesis pathway (HBP). (3) Drug screening: Molecular docking confirmed strong binding of trimipramine, desipramine, and cyclosporin to GFPT1 (Vina score < −5), with PheWAS indicating no significant off-target effects.

Conclusions

GFPT1 in CD4+ memory T cells contributes to POAG pathogenesis through immunometabolic dysregulation. Three existing drugs identify potential for therapeutic repurposing.

Translational Relevance

This study identifies GFPT1-driven immunometabolic dysfunction as a novel target in POAG and nominates three US Food and Drug Administration (FDA)-approved drugs for immediate clinical translation, accelerating the path to trials.

Keywords: primary open-angle glaucoma (POAG), Mendelian randomization, druggable genome, single-immune cells analysis, drug repositioning, molecular docking

Introduction

Primary open-angle glaucoma (POAG) is a chronic neurodegenerative disorder characterized by progressive optic nerve damage and visual field defects, representing a leading global cause of irreversible blindness.1 Although elevated intraocular pressure (IOP) is a primary risk factor, approximately 30% of patients present with normal-tension glaucoma, suggesting complex interactions between genetic predisposition and microenvironmental factors in disease pathogenesis.1 First-line therapy primarily relies on topical prostaglandin analogs (e.g. latanoprost), reducing IOP by 5 to 8 millimeters of mercury (mm Hg). However, limitations exist: 20% to 30% of patients exhibit an inadequate response,2 and long-term use may cause adverse effects, such as conjunctival hyperemia and ocular surface disease.3 Furthermore, current drugs predominantly target aqueous humor outflow pathways, offering insufficient coverage of parallel mechanisms like neuroprotection and vascular regulation.1 Therefore, integrating multi-omic strategies to identify novel targets and repurpose oral drugs is critical for overcoming current therapeutic bottlenecks.4

Over the past decade, the immune microenvironment has emerged as a prominent focus in biological research, encompassing immune infiltration, antigen presentation, immune cell exhaustion, and intercellular communication. Composed of diverse lymphocytes (e.g. T cells, B cells, and macrophages), this microenvironment plays a pivotal role in POAG. Prior studies indicate that neuroinflammation in patients with POAG may stem from dysregulated immune responses.5 For instance, M1-polarized macrophages exhibit enhanced antigen-presenting capacity and drive tissue inflammation.6 Thus, delineating the immune landscape of POAG is essential to elucidate its neuroinflammatory mechanisms. Given the functional heterogeneity of immune cells and their unique gene expression profiles—factors contributing to clinical trial failures—single-cell analysis offers deeper mechanistic insights and enables precise cell-targeted therapies.

Mendelian randomization (MR) is a method for evaluating causal relationships between modifiable exposures or risk factors and clinically relevant outcomes.7 Widely applied in drug repurposing,8 MR integrates summary data from disease GWAS and expression quantitative trait loci (eQTL) studies to discover novel therapeutic targets.9,10 Gene expression levels can be considered lifelong exposures, with eQTLs in druggable genomic regions serving as instrumental variables.11,12 Large-scale human genetic studies provide opportunities for drug development in complex diseases, as genetically validated targets show higher success rates in discovery pipelines.13,14 In essence, “druggable” genes encoding proteins or regulating expression offer robust clues for target identification.15 Although numerous GWAS have identified POAG-associated SNPs, the relationship between the druggable genome and POAG remains unexplored.

This study innovatively integrates druggable genome-wide MR analysis,16 single-cell immune MR, and molecular docking to identify potential POAG therapeutics from genetic and single-cell perspectives. First, we constructed an MR framework using large-scale POAG GWAS and eQTL data to prioritize high-confidence causal targets. Second, druggable genome annotation identified repositionable approved drugs. Finally, molecular docking validated binding efficacy between lead compounds and target proteins.17

Methods

The study roadmap is illustrated in Figure 1. Summary-level datasets were utilized, with all informed consent and ethical approvals obtained in original studies. This research workflow commenced with the identification of the druggable genome to systematically screen for genes with targeting potential. Subsequently, genetic approaches were used to identify druggable genes significantly associated with POAG, pinpointing potential therapeutic targets. To further investigate their cell-specific mechanisms, a single-cell cis-eQTL analysis of the prior candidate druggable genes was conducted to evaluate their regulatory effects within specific cell types. To assess the potential risks of targeting these genes, the study also identified the side effects associated with the previously shortlisted druggable genes. Finally, based on these findings, the research focused on the identification of actionable, marketed drugs and molecular docking for the candidate genes, providing a rationale for drug repurposing and guiding clinical translation for POAG.

Figure 1.

Figure 1.

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Study workflow.

Identification of Druggable Genes

Druggable genes were sourced from Finan et al.18 (Table 1). This resource links GWAS-identified loci of complex diseases to druggable genes, facilitating drug target identification and validation.18

Table 1.

Data Sources for Mendelian Randomization Analysis

Type of Dataset Data Subtype Source Sample Size Population Download Site
Druggable genome Prior druggable gene Finan C, et al. 2017 – – Finan C, et al. PMID: 28356508.
QTL datasets Blood cis‐eQTL eQTLGen Consortium (Võsa U, et al. 2021) 31,684 European https://www.eqtlgen.org/cis-eqtls.html34475573
QTL datasets sc‐eQTL OneK1K 982 European https://onek1k.org/
GWAS summary Primary open-angle glaucoma, strict FinnGen R12 484,589 European https://www.finngen.fi/en/access_results36653562
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eQTL Data

Given the more direct and specific biological effects of cis-regulatory elements compared with trans-acting factors,19 we used blood cis-eQTL data for variants within ±1 Mb of druggable gene coding sequences (see Table 1). Blood cis-eQTLs were obtained from the eQTLGen Consortium, covering 19,250 gene transcripts in 31,684 individuals.20 Single-cell eQTLs for immune cells were derived from the OneK1K database, comprising scRNA-seq data from 1.27 million peripheral blood mononuclear cells (PBMCs) across 982 donors.21

POAG GWAS Dataset

Summary statistics for POAG were acquired from FinnGen (https://www.finngen.fi/en; accessed January 1, 2025), including 10,832 patients and 473,757 healthy controls.22

Instrumental Variable Selection

MR uses single-nucleotide polymorphisms (SNPs) strongly associated with exposures as instrumental variables (IVs) to infer causal effects. Valid IVs must satisfy three assumptions23: (1) direct association with the exposure and independence from confounders; (2) no direct effect on the outcome; and (3) we first intersected 4463 druggable genes with 19,127 blood eQTL genes to obtain druggable gene-specific eQTLs (see Fig. 1). Genetic variants significantly associated with gene expression (±1 Mb window) were extracted. To minimize pleiotropy and ensure IV strength: genome-wide significance threshold: P < 5 × 10−8; minimum F-statistic: 10.24 Linkage disequilibrium (LD) refers to the non-random association of alleles at two or more loci in a genome; independent IVs were clumped (LD r2 < 0.01 and window = 10 Mb) using the TwoSampleMR package.25 For single-cell reverse-MR, the significance threshold was relaxed to P < 5 × 10−6 due to limited power.24

Mendelian Randomization and Steiger Filtering

Causal effects were estimated using Wald ratio (single IV); and the inverse-variance weighted (IVW) method (multiple IVs; fixed-effects).26

Sensitivity analyses included MR-Egger, weighted mode, and weighted median.27–29 Significance was defined by Bonferroni correction (threshold = 0.05/2,888). Heterogeneity (IVW Q-statistic) and pleiotropy (MR-Egger intercept) were assessed.30 Outliers were detected via Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO).31 Significant heterogeneity (P < 0.05) prompted random-effects IVW analysis.32 Steiger filtering ensured correct causal direction by excluding SNPs explaining more outcome than exposure variance.33 Analyses were performed in R software version 4.4.3.

Bayesian Colocalization

We evaluated whether POAG and druggable gene associations shared causal variants using five hypotheses34: H0 = no association for either trait; H1 = association only with druggable gene; H2 = association only with POAG; H3 = separate causal variants for each trait; and H4 = shared causal variant. A posterior probability H4 > 80% indicated colocalization.35 Implemented via the coloc R software package.

SMR Analysis and HEIDI Test

Summary-data-based Mendelian randomization (SMR) integrated GWAS and eQTL data to distinguish pleiotropy from linkage.36 SMR analysis (version 1.3.1) tested POAG-gene expression causality,37 with HEIDI testing excluding linkage effects (HEIDI > 0.05).37 False discovery rate (FDR) correction was applied (significant threshold = FDR < 0.05).

Phenome-Wide Association studies

Phenome-wide association studies (PheWAS) assessed target pleiotropy and adverse effect risks using the Atlas PheWAS portal (https://azphewas.com/).38 Data covered approximately 15,500 binary and 1500 continuous traits from UK Biobank exome sequencing.

Candidate Drug Prediction and Molecular Docking

Target genes were submitted to the Drug Signatures Data Base (DSigDB; http://dsigdb.tanlab.org/DSigDBv1.0/) to identify protein-drug interactions.39 This database links 22,527 gene sets to 17,389 compounds targeting 19,531 genes. Potential drugs were predicted using the enrichplot R software package. Protein structures were retrieved from RCSB Protein Data Bank (PDB; (https://www.rcsb.org/),40 and drug 3D structures from PubChem (https://pubchem.ncbi.nlm.nih.gov/).41 Molecular docking was performed via CB-Dock2 (https://cadd.labshare.cn/cb-dock2/index.php), with Vina scores < −5 indicating high binding affinity.41,43

Results

Selection of Instrumental Variables

Intersection of druggable genes from Finan et al. and significant cis-eQTL genes from the eQTLGen consortium yielded 2888 druggable genes (Fig. 2). Applying IV selection criteria, we identified 37,672 cis-eQTLs for 2534 druggable genes as IVs in the discovery analysis.

Figure 2.

Figure 2.

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Venn diagram of two datasets.

MR Analysis of Gene Expression and POAG

A two-sample MR analysis was performed for POAG. Genetic variants of eQTLs are listed in the file called “eqtlclump.zip.” After Steiger filtering (to ensure causal directionality), Bonferroni correction (P < 0.05/2888), and exclusion of variants with heterogeneity (P > 0.05) or horizontal pleiotropy (P > 0.05), 14 genes showed significant associations with POAG (Fig. 3; Supplementary Table S2).

Figure 3.

Figure 3.

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Forest plot of MR results for 14 significant genes.

Colocalization and SMR Validation

Bayesian colocalization provided strong evidence for shared causal variants between gene expression and POAG risk: GFPT1 (PPH4 = 0.9405), YWHAG (PPH4 = 0.8103; Table 2), indicating that linkage disequilibrium did not drive the MR associations. SMR analysis validated four genes (FDR < 0.05 and HEIDI > 0.05), whereas ACP2 showed unreliable results (HEIDI ≤ 0.05; see Table 2).

Table 2.

Bayesian Colocalization Results for 14 Genes With POAG

ID Nsnps PP.H4.abf SMR-FDR SMR-HEIDI
GFPT1 47 0.940539 8.61E-05 0.211
YWHAG 19 0.810306 0.000371 0.198
PIK3C2A 31 0.508874 \ \
CLEC3B 29 0.486347 \ \
SLC22A4 42 0.353355 \ \
COL5A2 5 0.31385 \ \
FAAH 30 0.222195 \ \
ADAM12 9 0.210002 \ \
TNXB 23 0.186696 \ \
DAPK2 11 0.12744 \ \
NENF 18 0.038625 \ \
ADCK3 83 0.019805 \ \
FDFT1 113 0.013529 \ \
BCAT1 66 0.01177 \ \
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Single-Cell MR in Immune Cells

Genetic variants of single cell-eQTLs are listed in the file called “sceqtlclump.zip.” Extracting cis-eQTLs for 5 candidate targets from 14 immune cell types revealed GFPT1-associated IVs in CD4+ KLRB1– T cells. Wald ratio analysis demonstrated a positive association between GFPT1 expression and POAG risk in this subset (odds ratio [OR] = 1.448, 95% confidence interval [CI] = 1.241–1.690, P = 2.545 × 10−6; Supplementary Table S3). Colocalization supported shared causal variants (PPH4 = 0.998), and reverse MR analysis excluded reverse causation (P = 0.584; Supplementary Table S5).

Phenome-Wide Association Studies

PheWAS of GFPT1 using the Atlas portal showed no significant associations with other phenotypes at genome-wide significance (P < 5 × 10−8; Fig. 4), suggesting low risk of adverse effects or horizontal pleiotropy if targeted therapeutically.

Figure 4.

Figure 4.

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PheWAS associations for GFPT1 (binary traits).

Candidate Drug Prediction

Drug enrichment analysis identified GFPT1-targeting compounds (adjusted P ≤ 0.05; Fig. 5; Supplementary Table S6).

Figure 5.

Figure 5.

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Drug enrichment bubble plot for GFPT1.

Molecular Docking

The top five clinically approved candidates underwent molecular docking with GFPT1 protein (PDB = 6 × 7T). Vina scores < −5 indicated strong binding affinity for trimipramine, desipramine, and cyclosporin (Table 3), with cyclosporin showing the highest affinity. Docking poses are visualized in Figure 6.

Table 3.

Molecular Docking Results for GFPT1 and Candidate Drugs

Drug Target Vina Score
Trimipramine GFPT1 −7.8
Desipramine GFPT1 −7.6
Cyclosporin GFPT1 −14.2
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Figure 6.

Figure 6.

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Docking conformations of FDA-approved drugs with GFPT1. (A) Trimipramine-GFPT1, (B) Desipramine-GFPT1, and (C) Cyclosporin-GFPT1.

Discussion

This study identified four significant therapeutic targets through druggable genome cis-eQTL MR analysis with POAG, validated by colocalization and SMR. Notably, YWHAG (FDR = 4.44 × 10−5; OR = 1.207, 95% CI: 1.131–1.288) increased POAG risk; GFPT1 (FDR = 8.22 × 10−8; OR = 0.874, 95% CI: 0.840–0.910) conferred protective effects.

Given the cell-type specificity of gene expression, single-cell MR across 14 immune cell subtypes revealed that elevated GFPT1 expression in CD4+ KLRB1– T cells was significantly associated with increased POAG risk (OR = 1.448, 95% CI: 1.241–1.690; P = 2.545 × 10−6), contrasting with its protective role in bulk tissue analysis.

GFPT1 in Hexosamine Biosynthesis

As the rate-limiting enzyme of the hexosamine biosynthesis pathway (HBP), GFPT1 catalyzes fructose-6-phosphate conversion to glucosamine-6-phosphate.44 The end-product of the HBP, UDP-GlcNAc is required for the O-linked β-N-acetylglucosamine (O-GlcNAc) modification, a prevalent and often regulatory protein modification.45,46 Essential for cellular function,47,48 its activity is allosterically regulated by metabolites.49 Although GFPT1 upregulation promotes tumor progression in cervical cancer and hepatocellular carcinoma,50,51 its downregulation in osteoarthritis suggests context-dependent mechanisms.52 Intriguingly, UDP—an HBP metabolite—reduces IOP by 82.9 ± 2.6% in rabbits via P2Y6 receptors in ciliary processes53 and enhances retinal ganglion cell (RGC) axon growth via P2Y6 activation,54 implicating HBP modulation in glaucoma neuroprotection. However, at the single-cell level, we found that elevated GFPT1 expression in CD4+ KLRB1– T cells (a central memory T cell subset) was significantly associated with an increased risk of POAG (OR = 1.448). This apparent paradox highlights the complexity of gene function within cell-type-specific contexts.

Immunological Implications

O-GlcNAcylation is a post-translational protein modification that involves the enzymatic addition of a single O-GlcNAc molecule to serine or threonine residues of proteins. Accumulating evidence supports the importance of dynamic O-GlcNAc cycling in T cell activation and regulation, underscoring the significance of O-GlcNAc modification both in early T-cell receptor (TCR) signaling and in the subsequent modulation of T cell activation.55–60

Upon TCR activation, naive CD4+ T cells can differentiate into various effector lineages—including T helper (Th)1, Th2, Th17, and regulatory T (Treg) cells—depending on the microenvironment and proximal signals, such as cytokines and ligands from antigen-presenting cells.60 Each effector subtype performs distinct functions: Th1, Th2, and Th17 cells are responsible for eliminating various pathogens, whereas Treg cells suppress inflammatory immune responses. Interestingly, O-GlcNAcylation is required for both the differentiation and homeostasis of Th17 and Treg cells,61,62 highlighting its role in balancing opposing immune functions. Notably, production of IL-17A—a major pro-inflammatory cytokine secreted by Th17 cells—is significantly enhanced when splenic CD4+ T cells are treated with thiamet G (TMG), a highly selective OGA inhibitor.61

T cell subsets, particularly the balance between effector T cells and Treg cells, play a critical role in determining RGC survival during glaucoma progression.63 CD4+ KLRB1– T cells represent central memory T cells (Tm) capable of homing to lymph nodes. Lymphocytic infiltration has been shown to exacerbate RGC loss in glaucoma models,64,65 with CD4+ T cells directly promoting RGC death under ischemic injury.66 An imbalance in Treg/Th17 responses is observed in experimental autoimmune optic neuritis, which shares features with glaucoma pathology,67 and elevated IOP can trigger T-cell-mediated RGC degeneration.68,69

Therefore, we speculate that high expression of GFPT1 may promote the differentiation of naive CD4+ T cells into Th17 cells or cause an imbalance in Treg/Th17 cells, thereby exacerbating optic nerve inflammation in patients with POAG. In vivo, the flux through the HBP pathway is tightly regulated; however, under inflammatory conditions, this regulatory mechanism may become disrupted, leading to a substantial increase in intracellular HBP flux. Previous studies have shown that treating R28 cells (rat retinal precursor cells) with excessive glucosamine—an intermediate of the HBP—elevated the level of UDP-HexNAc, the end product of HBP, by up to 1600%, and induced apoptosis in a dose-dependent manner.70 In summary, we hypothesize that high GFPT1 expression promotes the differentiation of naive CD4+ T cells into Th17 cells or disrupts the Treg/Th17 balance, contributing to optic nerve inflammation in patients with POAG. This inflammatory microenvironment may further lead to dysregulation of HBP metabolism and a significant increase in metabolic flux, ultimately triggering RGC apoptosis.

It is important to note that the single-cell data used in this study were primarily derived from peripheral blood. Whereas this provides a valuable window into the systemic immune state, the eye possesses a unique immune microenvironment where resident immune cells, such as microglia, play a more direct role in glaucoma progression.71,72 Future studies utilizing eye tissue-specific data (e.g. from aqueous humor, retina, or microglia) are needed to further validate the cell-specific functions of GFPT1 within the eye. This would enhance the tissue relevance of our findings and could reveal novel therapeutic targets.

Therapeutic Potential

In terms of drug repositioning, we identified three FDA-approved drugs: trimipramine, desipramine, and cyclosporin. Among these, cyclosporin, a well-established immunosuppressant, is already widely used in ophthalmology for treating dry eye disease.73 Its local administration (e.g. eye drops) has well-documented safety and tolerability profiles, providing a solid foundation for its direct repurposing for immunomodulatory therapy in POAG. The two tricyclic antidepressants (TCAs) present a scenario of concurrent opportunities and challenges. Although epidemiological studies suggest a link between depression/anxiety and glaucoma,74,75 the systemic side effects of TCAs (e.g. anticholinergic effects, sedation, and cardiovascular impacts) cannot be overlooked. This, however, underscores the necessity of developing topical ophthalmic formulations. Local delivery could bypass systemic side effects and directly target intraocular immune cells, potentially being the key to realizing their therapeutic potential.73

Furthermore, this study is the first to integrate a druggable genome-wide MR analysis for drug repositioning in the context of POAG. Although no previous POAG-specific studies of this kind exist, the druggable genome MR strategy has been successfully applied in other disease areas, such as identifying zinc supplements for sarcopenia and vitamin E for migraine.76,77 This methodology successfully validates the credibility and innovativeness of our current findings.

Strengths and Limitations

This study pioneers the integration of single-cell MR with druggable genomics in POAG, offering novel insights for targeted therapies. Rigorous methods addressed pleiotropy and confounding.

Limitations

Two key limitations warrant careful consideration when interpreting our results. First, the genetic datasets used are primarily from individuals of European ancestry. This limits the generalizability of our findings, as genetic architecture, including linkage disequilibrium patterns and allele frequencies, varies across populations. Consequently, the identified therapeutic targets may not be equally transferable or effective in non-European populations, potentially exacerbating health disparities. Second, whereas our single-cell MR analysis offers a novel, cell-type-specific perspective, the sample size for the single-cell eQTL dataset (n = 982) is modest. This may constrain the statistical power to detect anything but the strongest genetic effects and could increase the false negative rate. Therefore, our results, particularly the single-cell findings for GFPT1, should be viewed as robust but preliminary hypotheses that necessitate validation in larger, more diverse multi-ancestry cohorts with cell-type-specific data. Other limitations include potential unmeasured confounding in MR and untested nonlinearity in genetic effects.75

Conclusions

Integrating MR, colocalization, and SMR analyses, our results support an association between elevated GFPT1 expression in CD4+ KLRB1– T cells and an increased risk of POAG, suggesting a potential role in disease pathogenesis. Drug repositioning identified trimipramine, desipramine, and cyclosporin as potential GFPT1-targeting therapeutics, laying the groundwork for future studies to investigate their reuse in glaucoma management.

Supplementary Material

Supplement 1
tvst-14-10-38_s001.xlsx (62.6KB, xlsx)
Supplement 2
tvst-14-10-38_s002.zip (2.3MB, zip)
Supplement 3
tvst-14-10-38_s003.zip (1.6KB, zip)

Acknowledgments

Author Contributions: Conceptualization, K.M.K.; methodology, K.M.K.; data analysis and interpretation, K.M.K.; writing—original draft preparation, K.M.K.; writing—review and editing, Q.Z. and J.X.Z. All authors have read and agreed to the published version of the manuscript.

Data Availability Statements: FinnGen (https://www.finngen.fi/en; accessed January 1, 2025).

Disclosure: K. Ke, None; Q. Zhou, None; J. Zhong, None

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Associated Data

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Supplementary Materials

Supplement 1
tvst-14-10-38_s001.xlsx (62.6KB, xlsx)
Supplement 2
tvst-14-10-38_s002.zip (2.3MB, zip)
Supplement 3
tvst-14-10-38_s003.zip (1.6KB, zip)

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