Biallelic Mutations in<i>CRB1</i>Underlie Autosomal Recessive Familial Foveal Retinoschisis
Bibliographic record
Abstract
PURPOSE: To identify the genetic cause of autosomal recessive familial foveal retinoschisis (FFR). METHODS: A female sibship with FFR was identified (Family-A; 17 and 16 years, respectively); panel based genetic sequencing (132 genes) and comparative genome hybridization (142 genes) were performed. Whole-exome sequencing (WES) was performed on both siblings using the Illumina-HiSeq-2500 platform. A sporadic male (Family-B; 35 years) with FFR underwent WES using Illumina NextSeq500. All three affected subjects underwent detailed ophthalmologic evaluation including fundus photography, autofluorescence imaging, spectral-domain optical coherence tomography (SD-OCT), and full-field electroretinogram (ERG). RESULTS: Panel-based genetic testing identified two presumed disease causing variants in CRB1 (p.Gly123Cys and p.Cys948Tyr) in Family-A sibship; no deletion or duplication was detected. WES analysis in the sibship identified nine genes with two or more shared nonsynonymous rare coding sequence variants; CRB1 remained a strong candidate gene, and CRB1 variants segregated with the disease. WES in Family-B identified two presumed disease causing variants in CRB1 (p.Ile167_Gly169del and p.Arg764Cys) that segregated with the disease phenotype. Distance visual acuity was 20/40 or better in all three affected except for the left eye of the older subject (Family-B), which showed macular atrophy. Fundus evaluation showed spoke-wheel appearance at the macula in five eyes. The SD-OCT showed macular schitic changes in inner and outer nuclear layers in all cases. The ERG responses were normal in all subjects. CONCLUSIONS: This is the first report to implicate CRB1 as the underlying cause of FFR. This phenotype forms the mildest end of the spectrum of CRB1-related diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".