Structural mapping of <i>GABRB3</i> variants reveals genotype-phenotype correlations
Bibliographic record
Abstract
Abstract Purpose Pathogenic variants in GABRB3 have been associated with a spectrum of phenotypes from severe developmental disorders and epileptic encephalopathies to milder epilepsy syndromes and mild intellectual disability. In the present study, we analyzed a large cohort of individuals with GABRB3 variants to deepen the phenotypic understanding and investigate genotype-phenotype correlations. Methods Through an international collaboration, we analyzed electro-clinical data of unpublished individuals with variants in GABRB3 and we reviewed previously published cases. All missense variants were mapped onto the 3D structure of the GABRB3 subunit and clinical phenotypes associated with the different key structural domains were investigated. Results We characterize 71 individuals with GABRB3 variants, including 22 novel subjects, expressing a wide spectrum of phenotypes. Interestingly, phenotypes correlated with structural locations of the variants. Generalized epilepsy, with a median age at onset of 10.5 months, and mild-to-moderate intellectual disability were associated with variants in the extracellular domain. Focal epilepsy with early onset (median: 2.75 months of age) and severe intellectual disability were associated with variants in the pore-lining helical transmembrane domain. Conclusion These genotype/phenotype correlations will aid the genetic counseling and treatment of individuals affected by GABRB3 -related disorders. Future studies may reveal whether functional differences underlie the phenotypic differences. Key points Pathogenic variants in GABRB3 cause a wide range of phenotypes Missense variants in the ECD have generalized epilepsy with later onset and non-severe ID Missense variants in the TMD have focal epilepsy with early onset and severe ID Behavioral issues are common features of GABRB3 disease Precision medicine approaches for GABRB3 disease is limited
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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 | 0.000 |
| 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) | 0.002 | 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".