Effects of MECP2 Mutation Type, Location and X-inactivation in Modulating Rett Syndrome Phenotype
Bibliographic record
Abstract
Rett syndrome (RTT) is a clinically defined disorder that describes a subset of patients with mutations in the X-linked MECP2 gene. However, there is a high degree of variability in the clinical phenotypes produced by mutations in MECP2, even amongst classical RTT patients. In a large-scale screening project, this variability has been examined by looking at the effects of mutation type, functional domain affected and X-inactivation. Mutations have been identified in 60% of RTT patients in this study (25% of whom were atypical), including 23 novel mutations and polymorphisms. More mutations were found in classical patients (63%) compared to atypical patients (44%). All of the pathogenic mutations were de novo in patients for whom parent DNA was available for screening. A composite phenotype score was developed, based on the recommendations for reporting clinical features in RTT of an international collaborative group. This score proved useful for summarising phenotypic severity, but did not correlate with mutation type, domain affected or X-inactivation, probably due to complex interactions between all three. Other correlations suggested that truncating mutations and mutations affecting the methyl-CpG-binding domain tend to lead to a more severe phenotype. Skewed X-inactivation was found in a large proportion (43%) of our patients, particularly in those with truncating mutations and mutations affecting the MBD. It is therefore likely that X-inactivation does modulate the phenotype in RTT. © 2003 Wiley-Liss, Inc.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".