Hereditary haemorrhagic telangiectasia: mutation detection, test sensitivity and novel mutations
Bibliographic record
Abstract
BACKGROUND: Hereditary haemorrhagic telangiectasia (HHT) is a genetic disorder present in 1 in 8000 people and associated with arteriovenous malformations. Genetic testing can identify individuals at risk of developing the disease and is a useful diagnostic tool. OBJECTIVE: To present a strategy for mutation detection in families clinically diagnosed with HHT. METHODS: An optimised strategy for detecting mutations that predispose to HHT is presented. The strategy includes quantitative multiplex polymerase chain reaction, sequence analysis, RNA analysis, validation of missense mutations by amino acid conservation analysis for the ENG (endoglin) and ACVRL1 (ALK1) genes, and analysis of an ACVRL1 protein structural model. If no causative ENG or ACVRL1 mutation is found, proband samples are referred for sequence analysis of MADH4 (associated with a combined syndrome of juvenile polyposis and HHT). RESULTS: Data obtained over the past eight years were summarised and 16 novel mutations described. Mutations were identified in 155 of 194 families with a confirmed clinical diagnosis (80% sensitivity). Of 155 mutations identified, 94 were in ENG (61%), 58 in ACVRL1 (37%), and three in MADH4 (2%). CONCLUSIONS: For most missense variants of ENG and ACVRL1 reported to date, study of amino acid conservation showed good concordance between prediction of altered protein function and disease occurrence. The 39 families (20%) yet to be resolved may carry ENG, ACVRL1, or MADH4 mutations too complex or difficult to detect, or mutations in genes yet to be identified.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".