Rare variants in γ‐aminobutyric acid type <scp>A</scp> receptor genes in rolandic epilepsy and related syndromes
Bibliographic record
Abstract
OBJECTIVE: To test whether mutations in γ-aminobutyric acid type A receptor (GABAA -R) subunit genes contribute to the etiology of rolandic epilepsy (RE) or its atypical variants (ARE). METHODS: We performed exome sequencing to compare the frequency of variants in 18 GABAA -R genes in 204 European patients with RE/ARE versus 728 platform-matched controls. Identified GABRG2 variants were functionally assessed for protein stability, trafficking, postsynaptic clustering, and receptor function. RESULTS: Of 18 screened GABAA -R genes, we detected an enrichment of rare variants in the GABRG2 gene in RE/ARE patients (5 of 204, 2.45%) in comparison to controls (1 of 723, 0.14%; odds ratio = 18.07, 95% confidence interval = 2.01-855.07, p = 0.0024, pcorr = 0.043). We identified a GABRG2 splice variant (c.549-3T>G) in 2 unrelated patients as well as 3 nonsynonymous variations in this gene (p.G257R, p.R323Q, p.I389V). Functional assessment showed reduced surface expression of p.G257R and decreased GABA-evoked currents for p.R323Q. The p.G257R mutation displayed diminished levels of palmitoylation, a post-translational modification crucial for trafficking of proteins to the cell membrane. Enzymatically raised palmitoylation levels restored the surface expression of the p.G257R variant γ2 subunit. INTERPRETATION: The statistical association and the functional evidence suggest that mutations of the GABRG2 gene may increase the risk of RE/ARE. Restoring the impaired membrane trafficking of some GABRG2 mutations by enhancing palmitoylation might be an interesting therapeutic approach to reverse the pathogenic effect of such mutants.
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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 | 0.001 |
| 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) | 0.003 | 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".