Biallelic loss-of-function variants in <i>C19orf44</i> lead to retinal degeneration
Bibliographic record
Abstract
BACKGROUND: Inherited retinal diseases (IRDs) are a group of disorders often resulting in progressive vision loss, ultimately leading to blindness. A significant portion of their genetic causes remain unresolved, partly due to undiscovered disease-associated genes or variants. This study aimed to identify novel genetic links to IRDs. METHODS: All patients underwent comprehensive ophthalmological evaluation, including retinal imaging (fundus autofluorescence and macular optical coherence tomography) and electroretinogram testing. Whole exome sequencing and whole genome sequencing were performed on patients with clinically unsolved IRD, and data were analysed using an in-house pipeline to identify causal variants. Subsequently, Sanger sequencing was performed to confirm identified variants. RESULTS: (HGNC: 26141), a gene of unknown function. The homozygous variant NM_032207.2:c.549_550del;p.Ser185Profs*2 was identified in two unrelated patients (European and Middle Eastern). Moreover, an East Asian patient had likely compound heterozygous LoF variants (NM_032207.2:c.1168C>T;p.Gln390*/c.976_977del;p.Leu326Lysfs*15). CONCLUSIONS: as a novel disease-causing gene for IRD with Stargardt-like phenotype, expanding the genetic landscape of retinal degeneration.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".