High‐resolution molecular genomic autopsy reveals complex sudden unexpected death in epilepsy risk profile
Bibliographic record
Abstract
Advanced variant detection in genes underlying risk of sudden unexpected death in epilepsy (SUDEP) can uncover extensive epistatic complexity and improve diagnostic accuracy of epilepsy-related mortality. However, the sensitivity and clinical utility of diagnostic panels based solely on established cardiac arrhythmia genes in the molecular autopsy of SUDEP is unknown. We applied the established clinical diagnostic panels, followed by sequencing and a high density copy number variant (CNV) detection array of an additional 253 related ion channel subunit genes to analyze the overall genomic variation in a SUDEP of the 3-year-old proband with severe myoclonic epilepsy of infancy (SMEI). We uncovered complex combinations of single nucleotide polymorphisms and CNVs in genes expressed in both neurocardiac and respiratory control pathways, including SCN1A, KCNA1, RYR3, and HTR2C. Our findings demonstrate the importance of comprehensive high-resolution variant analysis in the assessment of personally relevant SUDEP risk. In this case, the combination of de novo single nucleotide polymorphisms (SNPs) and CNVs in the SCN1A and KCNA1 genes, respectively, is suspected to be the principal risk factor for both epilepsy and premature death. However, consideration of the overall biologically relevant variant complexity with its extensive functional epistatic interactions reveals potential personal risk more accurately.
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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 | 0.000 |
| 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".