<scp><i>SCN1A</i></scp> pathogenic variants do not have a distinctive blood‐derived <scp>DNA</scp> methylation signature
Bibliographic record
Abstract
DNA methylation signatures ("episignatures") can be used as biomarkers of genetic aberrations, clinical phenotypes, and environmental exposures in rare diseases. Episignatures are utilized in molecular diagnostics and can clarify variants of uncertain significance. A growing number of disease genes, including epilepsy genes, exhibit robust and reproducible episignatures. However, whether SCN1A, the most prominent epilepsy gene, has one or more episignatures has not yet been determined. We generated genome-wide DNA methylation data and performed episignature analysis on 64 individuals with Dravet syndrome due to pathogenic loss-of-function (LOF) variants in SCN1A and seven individuals with early infantile SCN1A developmental and epileptic encephalopathy due to pathogenic gain-of-function (GOF) variants in SCN1A, relative to a large reference database of controls and rare disease episignature-positive cohorts. We analyzed all samples with LOF variants together and performed separate analyses for missense, nonsense, and GOF variant cohorts. A reproducible blood-derived episignature was not evident in any of the cohorts using current analytical approaches and reference data.
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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 | 0.000 | 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) | 0.002 | 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".