Novel and de novo mutations in pediatric refractory epilepsy
Bibliographic record
Abstract
Pediatric refractory epilepsy is a broad phenotypic spectrum with great genetic heterogeneity. Next-generation sequencing (NGS) combined with Sanger sequencing could help to understand the genetic diversity and underlying disease mechanisms in pediatric epilepsy. Here, we report sequencing results from a cohort of 172 refractory epilepsy patients aged 0-14 years. The pathogenicity of identified variants was evaluated in accordance with the American College of Medical Genetics and Genomics (ACMG) criteria. We identified 43 pathogenic or likely pathogenic variants in 40 patients (23.3%). Among these variants, 74.4% mutations (32/43) were de novo and 60.5% mutations (26/43) were novel. Patients with onset age of seizures ≤12 months had higher yields of deleterious variants compared to those with onset age of seizures > 12 months (P = 0.006). Variants in ion channel genes accounted for the greatest functional gene category (55.8%), with SCN1A coming first (16/43). 81.25% (13/16) of SCN1A mutations were de novo and 68.8% (11/16) were novel in Dravet syndrome. Pathogenic or likely pathogenic variants were found in the KCNQ2, STXBP1, SCN2A genes in Ohtahara syndrome. Novel deleterious variants were also found in West syndrome, Doose syndrome and glucose transporter type 1 deficiency syndrome patients. One de novo MECP2 mutation were found in a Rett syndrome patient. TSC1/TSC2 variants were found in 60% patients with tuberous sclerosis complex patients. Other novel mutations detected in unclassified epilepsy patients involve the SCN8A, CACNA1A, GABRB3, GABRA1, IQSEC2, TSC1, VRK2, ATP1A2, PCDH19, SLC9A6 and CHD2 genes. Our study provides novel insights into the genetic origins of pediatric epilepsy and represents a starting-point for further investigations into the molecular pathophysiology of pediatric epilepsy that could eventually lead to better treatments.
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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 | 0.001 | 0.001 |
| 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".