Whole‐exome sequencing broadens the phenotypic spectrum of rare pediatric epilepsy: a retrospective study
Bibliographic record
Abstract
Whole-exome sequencing (WES) has transformed our ability to detect mutations causing rare diseases. FORGE (Finding Of Rare disease GEnes) and Care4Rare Canada are nation-wide projects focused on identifying disease genes using WES and translating this technology to patient care. Rare forms of epilepsy are well-suited for WES and we retrospectively selected FORGE and Care4Rare families with clinical descriptions that included childhood-onset epilepsy or seizures not part of a recognizable syndrome or an early-onset encephalopathy where standard-of-care investigations were unrevealing. Nine families met these criteria and a diagnosis was made in seven, and potentially eight, of the families. In the eight families we identified mutations in genes associated with known neurological and epilepsy disorders: ASAH1, FOLR1, GRIN2A (two families), SCN8A, SYNGAP1 and SYNJ1. A novel and rare mutation was identified in KCNQ2 and was likely responsible for the benign seizures segregating in the family though additional evidence would be required to be definitive. In retrospect, the clinical presentation of four of the patients was considered atypical, thereby broadening the phenotypic spectrum of these conditions. Given the extensive clinical and genetic heterogeneity associated with epilepsy, our findings suggest that WES may be considered when a specific gene is not immediately suspected as causal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".