Functional consequence of pathogenic GABRA3 variants determines whether X-linked inheritance is dominant or recessive
Bibliographic record
Abstract
Disorders of GABRA3, the only epilepsy-associated GABAA receptor subunit gene on the X chromosome, have eluded clinical clarity due to ambiguous inheritance patterns and variable phenotypes. The long-standing assumption that all pathogenic variants cause loss of function further obscured genotype-phenotype relationships and hindered progress. Here, we curated a cohort of individuals with a GABRA3 variant, integrating deep phenotyping, genotyping, family history, and electrophysiology with a targeted mouse model. Among 43 individuals with 19 GABRA3 variants, functional analyses revealed gain- and loss-of-function effects, each linked to distinct clinical profiles. Gain-of-function variants were associated with severe, treatment-resistant epilepsy and profound intellectual disability, disproportionately affecting males, who were often nonambulant and had cortical visual impairment. Loss-of-function variants produced milder phenotypes, with epilepsy rarely observed; affected males showed behavioral issues and language delay, while females were unaffected carriers. Our gain-of-function (Gabra3Q242L/+) mouse model mirrored these sex-specific differences, showing increased seizure susceptibility, early death, and marked cortical hyperexcitability. These insights resolve longstanding uncertainties surrounding GABRA3 and redefine how X-linked disorders are interpreted. They demonstrate that it is the functional impact of a variant, not its mere presence, that determines whether a condition manifests dominantly or recessively. This distinction carries important implications for genetic counseling, precision medicine, and the broader interpretation of X-linked neurodevelopmental disorders.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".