Large-scale multitrait genome-wide association analyses identify hundreds of glaucoma risk loci
Bibliographic record
Abstract
Glaucoma, a leading cause of irreversible blindness, is a highly heritable human disease. Previous genome-wide association studies have identified over 100 loci for the most common form, primary open-angle glaucoma. Two key glaucoma-associated traits also show high heritability: intraocular pressure and optic nerve head excavation damage quantified as the vertical cup-to-disc ratio. Here, since much of glaucoma heritability remains unexplained, we conducted a large-scale multitrait genome-wide association study in participants of European ancestry combining primary open-angle glaucoma and its two associated traits (total sample size over 600,000) to substantially improve genetic discovery power (263 loci). We further increased our power by then employing a multiancestry approach, which increased the number of independent risk loci to 312, with the vast majority replicating in a large independent cohort from 23andMe, Inc. (total sample size over 2.8 million; 296 loci replicated at P < 0.05, 240 after Bonferroni correction). Leveraging multiomics datasets, we identified many potential druggable genes, including neuro-protection targets likely to act via the optic nerve, a key advance for glaucoma because all existing drugs only target intraocular pressure. We further used Mendelian randomization and genetic correlation-based approaches to identify novel links to other complex traits, including immune-related diseases such as multiple sclerosis and systemic lupus erythematosus.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".