<i>SCN1A</i> duplications and deletions detected in Dravet syndrome: Implications for molecular diagnosis
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine the type, frequency, and size of microchromosomal copy number variations (CNVs) affecting the neuronal sodium channel α 1 subunit gene (SCN1A) in Dravet syndrome (DS), other epileptic encephalopathies, and generalized epilepsy with febrile seizures plus (GEFS+). METHODS: Multiplex ligation-dependent probe amplification (MLPA) was applied to detect SCN1A CNVs among 289 cases (126 DS, 97 GEFS+, and 66 with other phenotypes). CNVs extending beyond SCN1A were further characterized by comparative genome hybridization (array CGH). RESULTS: Novel SCN1A CNVs were found in 12.5% of DS patients where sequence-based mutations had been excluded. We identified the first partial SCN1A duplications in two siblings with typical DS and in a patient with early-onset symptomatic generalized epilepsy. In addition, a patient with DS had a partial SCN1A amplification of 5-6 copies. The remaining CNVs abnormalities were four partial and nine whole SCN1A deletions involving contiguous genes. Two CNVs (a partial SCN1A deletion and a duplication) were inherited from a parent, in whom there was mosaicism. Array CGH showed intragenic deletions of 90 kb and larger, with the largest of 9.3 Mb deleting 49 contiguous genes and extending beyond SCN1A. DISCUSSION: Duplication and amplification involving SCN1A are now added to molecular mechanisms of DS patients. Our findings showed that 12.5% of DS patients who are mutation negative have MLPA-detected SCN1A CNVs with an overall frequency of about 2-3%. MLPA is the established second-line testing strategy to reliably detect all CNVs of SCN1A from the megabase range down to one exon. Large CNVs extending outside SCN1A and involving contiguous genes can be precisely characterized by array CGH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".