Genome-wide meta-analysis identifies 22 loci for normal tension glaucoma with significant overlap with high tension glaucoma
Bibliographic record
Abstract
Primary open-angle glaucoma typically presents as two subtypes. This study aimed to elucidate the shared and distinct genetic architectures of normal-tension (NTG) and high-tension glaucoma (HTG), motivated by the need to develop intraocular pressure (IOP)-independent drug targets for the disease. We conducted a comprehensive multi-ethnic meta-analysis, prioritized variants based on functional annotation, and explored drug-gene interactions. We further assessed the genetic overlap between NTG and HTG using pairwise GWAS analysis. We identified 22 risk loci associated with NTG, 17 of which have not previously been reported for NTG. Two loci, BMP4 and TBKBP1, have not previously been associated with glaucoma at the genome-wide significance level. Our results indicate that while there is a significant overlap in risk loci between tension subtypes, the magnitude of the effect tends to be lower in NTG compared to HTG, particularly for IOP-related loci. Additionally, we identified a potential role for biologic immunomodulatory treatments as neuroprotective agents. This study investigates the genetic similarities and differences between two subtypes of glaucoma (normal tension and high tension). Multi-ethnic meta-analysis reveals overlapping risk loci, with a lower effect magnitude in normal tension glaucoma. The authors also use their gene discovery approach to highlight possible neuro-protective drug targets for glaucoma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".