<i>PRRT2</i> links infantile convulsions and paroxysmal dyskinesia with migraine
Bibliographic record
Abstract
OBJECTIVE: Whole genome sequencing and the screening of 103 families recently led us to identify PRRT2 (proline-rich-transmembrane protein) as the gene causing infantile convulsions (IC) with paroxysmal kinesigenic dyskinesia (PKD) (PKD/IC syndrome, formerly ICCA). There is interfamilial and intrafamilial variability and the patients may have IC or PKD. Association of IC with hemiplegic migraine (HM) has also been reported. In order to explore the mutational and clinical spectra, we analyzed 34 additional families with either typical PKD/IC or PKD/IC with migraine. METHODS: We performed Sanger sequencing of all PRRT2 coding exons and of exon-intron boundaries in the probands and in their relatives whenever appropriate. RESULTS: Two known and 2 novel PRRT2 mutations were detected in 18 families. The p.R217Pfs*8 recurrent mutation was found in ≈50% of typical PKD/IC, and the unreported p.R145Gfs*31 in one more typical family. PRRT2 mutations were also found in PKD/IC with migraine: p.R217Pfs*8 cosegregated with PKD associated with HM in one family, and was also detected in one IC patient having migraine with aura, in related PKD/IC familial patients having migraine without aura, and in one sporadic migraineur with abnormal MRI. Previously reported p.R240X was found in one patient with PKD with migraine without aura. The novel frameshift p.S248Afs*65 was identified in a PKD/IC family member with IC and migraine with aura. CONCLUSIONS: We extend the spectrum of PRRT2 mutations and phenotypes to HM and to other types of migraine in the context of PKD/IC, and emphasize the phenotypic pleiotropy seen in patients with PRRT2 mutations.
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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) | 0.002 | 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".