KCNQ2 mutations: genotype-phenotype association beyond epilepsy
Bibliographic record
Abstract
Background: KCNQ2 abnormalities were described in infants with benign familial neonatal seizures (BFNS) and epileptic encephalopathy (EE). Associated features possibly include abnormal neuroimaging findings such as hypomyelination and/or T2 high signal of basal ganglia. Methods: This report describes 4 infants carrying different heterozygous KCNQ2 variants and 2 infants with 20q13.33 deletions encompassing KCNQ2 gene. Results: The different KCNQ2 mutations led to EE in 3 patients and included a novel de novo missense variant, p.Arg201Cys/c.601C>T, in an infant with severe EE and global developmental delay, hyperkinetic movement disorder, autonomic dysfunction with chronic hypoventilation, apnea, low GABA levels in CSF, and hypomyelination. She died at age 3 years of respiratory failure. One patient with BFNS and normal MRI has a previously reported c.508delG frame shift mutation in KCNQ2. Of the two de novo 22q13.33 deletions (1.2Mb versus 254.1 Kb) the larger caused a more severe phenotype, including focal epilepsy from infancy until 4 years, moderate developmental delay and diffuse brain volume loss. Conclusions: Along with varied epilepsy phenotypes and neuroimaging findings KCNQ abnormalities were associated with severe autonomic dysfunction and reduced CSF GABA levels. This might have further treatment implications, besides that the altered potassium channel function itself presents a therapeutic target.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".