Rapid determination of COL2A1 mutations in individuals with Stickler syndrome: Analysis of potential premature termination codons
Bibliographic record
Abstract
Stickler syndrome is one of the milder phenotypes resulting from mutations in the gene that encodes type-II collagen, COL2A1. All COL2A1 mutations known to cause Stickler syndrome result in the formation of a premature termination codon within the type-II collagen gene. COL2A1 has 10 in-frame CGA codons, which can mutate to TGA STOP codons via a methylation-deamination mechanism. We have analyzed these sites in genomic DNA from a panel of 40 Stickler syndrome patients to test the hypothesis that mutations that cause Stickler syndrome preferentially occur at these bases. Polymerase chain reaction (PCR) amplification of genomic DNA containing each of the in-frame CGA codons was done by one of two methods: either using primers that amplify DNA that includes the CGA codon, or using allele-specific primers that either amplify normal sequence containing a CGA codon or amplify a mutant sequence containing a TGA codon. Analysis of PCR products by restriction endonuclease digestion or sequencing demonstrated the presence of a normal or mutated codon. TGA mutations were identified in eight patients, at five of the 10 in-frame CGA codons. The identification of these mutations in eight of 40 patients demonstrates that these sites are common sites for mutations in individuals with Stickler syndrome and, we propose, should be analyzed as a first step in the search for mutations that result in this disorder.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".