Genetic testing for inherited eye conditions in over 6,000 individuals through the <scp>eyeGENE</scp> network
Bibliographic record
Abstract
Genetic testing in a multisite clinical trial network for inherited eye conditions is described in this retrospective review of data collected through eyeGENE®, the National Ophthalmic Disease Genotyping and Phenotyping Network. Participants in eyeGENE were enrolled through a network of clinical providers throughout the United States and Canada. Blood samples and clinical data were collected to establish a phenotype:genotype database, biorepository, and patient registry. Data and samples are available for research use, and participants are provided results of clinical genetic testing. eyeGENE utilized a unique, distributed clinical trial design to enroll 6,403 participants from 5,385 families diagnosed with over 30 different inherited eye conditions. The most common diagnoses given for participants were retinitis pigmentosa (RP), Stargardt disease, and choroideremia. Pathogenic variants were most frequently reported in ABCA4 (37%), USH2A (7%), RPGR (6%), CHM (5%), and PRPH2 (3%). Among the 5,552 participants with genetic testing, at least one pathogenic or likely pathogenic variant was observed in 3,448 participants (62.1%), and variants of uncertain significance in 1,712 participants (30.8%). Ten genes represent 68% of all pathogenic and likely pathogenic variants in eyeGENE. Cross-referencing current gene therapy clinical trials, over a thousand participants may be eligible, based on pathogenic variants in genes targeted by those therapies. This article is the first summary of genetic testing from thousands of participants tested through eyeGENE, including reports from 5,552 individuals. eyeGENE provides a launching point for inherited eye research, connects researchers with potential future study participants, and provides a valuable resource to the vision community.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".