Deleterious Rare Variants Reveal Risk for Loss of GABAA Receptor Function in Patients with Genetic Epilepsy and in the General Population
Bibliographic record
Abstract
Genetic epilepsies (GEs) account for approximately 50% of all seizure disorders, and familial forms include mutations in single GABAA receptor subunit genes (GABRs). In 144 sporadic GE cases (GECs), exome sequencing of 237 ion channel genes identified 520 GABR variants. Among these variants, 33 rare variants in 11 GABR genes were present in 24 GECs. To assess functional risk of variants in GECs, we selected 8 variants found in GABRA, 3 in GABRB, and 3 in GABRG and compared them to 18 variants found in the general population for GABRA1 (n = 9), GABRB3 (n = 7), and GABRG2 (n = 2). To identify deleterious variants and gain insight into structure-function relationships, we studied the gating properties, surface expression and structural perturbations of the 32 variants. Significant reduction of GABAA receptor function was strongly associated with variants scored as deleterious and mapped within the N-terminal and transmembrane domains. In addition, 12 out of 17 variants mapped along the β+/α- GABA binding interface, were associated with reduction in channel gating and were predicted to cause structural rearrangements of the receptor by in silico simulations. Missense or nonsense mutations of GABRA1, GABRB3 and GABRG2 primarily impair subunit biogenesis. In contrast, GABR variants affected receptor function by impairing gating, suggesting that different mechanisms are operating in GABR epilepsy susceptibility variants and disease-causing mutations. The functional impact of single GABR variants found in individuals with sporadic GEs warrants the use of molecular diagnosis and will ultimately improve the treatment of genetic epilepsies by using a personalized approach.
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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 | 0.001 | 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".