Where are the missing gene defects in inherited retinal disorders? Intronic and synonymous variants contribute at least to 4% of <i>CACNA1F</i> ‐mediated inherited retinal disorders
Bibliographic record
Abstract
Inherited retinal disorders (IRD) represent clinically and genetically heterogeneous diseases. To date, pathogenic variants have been identified in ~260 genes. Albeit that many genes are implicated in IRD, for 30-50% of the cases, the gene defect is unknown. These cases may be explained by novel gene defects, by overlooked structural variants, by variants in intronic, promoter or more distant regulatory regions, and represent synonymous variants of known genes contributing to the dysfunction of the respective proteins. Patients with one subgroup of IRD, namely incomplete congenital stationary night blindness (icCSNB), show a very specific phenotype. The major cause of this condition is the presence of a hemizygous pathogenic variant in CACNA1F. A comprehensive study applying direct Sanger sequencing of the gene-coding regions, exome and genome sequencing applied to a large cohort of patients with a clinical diagnosis of icCSNB revealed indeed that seven of the 189 CACNA1F-related cases have intronic and synonymous disease-causing variants leading to missplicing as validated by minigene approaches. These findings highlight that gene-locus sequencing may be a very efficient method in detecting disease-causing variants in clinically well-characterized patients with a diagnosis of IRD, like icCSNB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".