The Genetic Landscape of Epilepsy of Infancy with Migrating Focal Seizures
Bibliographic record
Abstract
OBJECTIVE: Epilepsy of infancy with migrating focal seizures (EIMFS) is one of the most severe developmental and epileptic encephalopathies. We delineate the genetic causes and genotype-phenotype correlations of a large EIMFS cohort. METHODS: Phenotypic and molecular data were analyzed on patients recruited through an international collaborative study. RESULTS: We ascertained 135 patients from 128 unrelated families. Ninety-three of 135 (69%) had causative variants (42/55 previously reported) across 23 genes, including 9 novel EIMFS genes: de novo dominant GABRA1, GABRB1, ATP1A3; X-linked CDKL5, PIGA; and recessive ITPA, AIMP1, KARS, WWOX. The most frequently implicated genes were KCNT1 (36/135, 27%) and SCN2A (10/135, 7%). Mosaicism occurred in 2 probands (SCN2A, GABRB3) and 3 unaffected mothers (KCNT1). Median age at seizure onset was 4 weeks, with earlier onset in the SCN2A, KCNQ2, and BRAT1 groups. Epileptic spasms occurred in 22% patients. A total of 127 patients had severe to profound developmental impairment. All but 7 patients had ongoing seizures. Additional features included microcephaly, movement disorders, spasticity, and scoliosis. Mortality occurred in 33% at median age 2 years 7 months. INTERPRETATION: We identified a genetic cause in 69% of patients with EIMFS. We highlight the genetic heterogeneity of EIMFS with 9 newly implicated genes, bringing the total number to 33. Mosaicism was observed in probands and parents, carrying critical implications for recurrence risk. EIMFS pathophysiology involves diverse molecular processes from gene and protein regulation to ion channel function and solute trafficking. ANN NEUROL 2019;86:821-831.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".