Refined genotype–phenotype correlations in neurofibromatosis type 1 patients with <i>NF1</i> point variants
Bibliographic record
Abstract
Background Neurofibromatosis type 1 (NF1) is one of the most frequent genetic disorders. NF1 is caused by dominant loss-of-function pathogenic variants (PVs) of the tumour-suppressor gene NF1 , which encodes neurofibromin, a negative regulator of rat sarcoma proteins. NF1 is an autosomal dominant disorder with complete penetrance, but a highly variable expression. Identification of genotype–phenotype correlations is challenging because of the wide clinical variability, the progressive nature of the disorder and the extreme diversity of the mutation spectrum. Only a few NF1 point variants have been associated with a specific phenotype in NF1 patients. Methods We investigated a large, well-phenotyped NF1 cohort. Results We report analyses of genotype-phenotype correlations in 112 NF1 patients with specific NF1 point variants: p.Arg1809 missense variants were associated with a mild form of NF1 (n=24), while a more severe phenotype was associated with codons 844–848 (n=27), p.Arg1276 (n=25) and p.Lys1423 (n=35) missense variants. We describe a new correlation for p.Arg1204 missense variants (n=11), with no neurofibroma observed in patients. Functional studies will be critical for drawing conclusions on the potential hypomorphic or dominant-negative effects of these variants. Conclusion The current data confirms several genotype-phenotype correlations in NF1, which may be relevant to the management and surveillance of NF1 patients with specific NF1 PVs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".