Genotype–phenotype correlations for a wide spectrum of mutations in the Wilson disease gene (<i>ATP7B</i>)
Bibliographic record
Abstract
Wilson disease (WND) is caused by mutations in the ATP7B gene and exhibits substantial allelic heterogeneity. In this study we report the results of molecular analyses of 20 WND families not described previously. When combined with our prior results, the cohort includes 93 index patients from 69 unrelated families. Twenty different mutations accounted for 86% of the WND chromosomes. The most frequent were p.H1069Q (35%), p.R969Q (12%), c.2530delA (7%), p.L936X (7%), p.Q289X (7%), and p.I1148T (3%). We also present here a detailed phenotypic assessment for patients whose molecular result was previously reported. Thirty cases were homozygous for 9 different mutations, 13 of which were homozygous for p.H1069Q, and 7 for p.R969Q. Mutations p.H1069Q and p.R969Q appeared to confer a milder disease as patients showed disease onset at a later age, and were associated with milder severity when found in trans with severe mutations. Predicted nonsense and frameshift mutations were associated with severe phenotypic expression with earlier disease onset and lower ceruloplasmin values. WND can be treated by copper-chelation therapy, particularly if the disease is diagnosed before irreversible tissue damage occurs. Our results on the effect of predicted nonsense and frameshift mutations are especially important for early medical intervention in presymptomatic infants and children with these genotypes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".