Summary Background Missense variants in genes encoding GABAA receptors are involved in the pathophysiology of common and rare epilepsies. Variant effects on channel biophysical function are associated with key clinical characteristics and treatment response. Predicting variant effects is therefore key to improving care for individuals with GABAA receptor-related disorders. Methods We collected data from 505 affected individuals with 272 (likely) pathogenic GABAA receptor missense variants (GABRA1, GABRB2, GABRB3, GABRG2). All variants were evaluated with in-vitro electrophysiology. Variants were annotated with features based on sequence, structure, and phenotype. Model performance was estimated using cross-validation and external validation on a further 197 individuals with 138 (likely) pathogenic variants. Findings Our models enable highly accurate prediction of missense variant effects in GABAA (AU-ROC 0.862–0.946), outperforming state-of-the-art models (AU-ROC 0.495–0.756) and clinical decision-making. Model scores correlated with GABA sensitivity and were consistent with expert-based structure–function hypotheses, supporting plausibility. Predictions on population variants were similar to functionally neutral variants, while cases from ClinVar were similar to GOF/LOF variants. Our model may provide additional evidence for 5–25% of variants in ClinVar. Lastly, we show that we can predict likely clinical characteristics from variant information alone (median Lin similarity 0.754 IQR 0.161). Interpretation We demonstrate accurate missense variant effect prediction in GABAA receptors with rigorous validation across a large dataset of functionally tested variants. These predictions may facilitate timely diagnosis and precision treatment of individuals with GABAA receptor-related disorders, pending prospective clinical validation. A web interface, precomputed scores, and calibrated score thresholds for all possible variants are openly available. Funding Else Kröner-Fresenius-Stiftung; German Federal Ministry of Research, Technology and Space; German Research Foundation; Medical Faculty University of Tübingen; Lundbeck Foundation; Novo Nordisk Foundation.
C. Boßelmann, S. Ortiz, R. Dahl et al.· EBioMedicine· 0 citations
OBJECTIVE
The polygenic risk score (PRS) for individuals with genetic generalized epilepsy (GGE) quantifies the common risk variants in genes identified in genome-wide association studies. We hypothesized that the phenotype of GGE patients differs based on their GGE PRS.
METHODS
We identified participants with highest (n = 59) versus lowest (n = 48) PRS from the GGE patients (n = 2256) recruited through the Epi25 Collaborative for comparison. Detailed clinical data were acquired retrospectively for the 59 high PRS and 48 low PRS individuals with GGE from the Epi25 database and from the contributing centers. For validation, we accessed a larger cohort (n = 1175) of patients with GGE included in the Epi25 Collaborative.
RESULTS
This study found no difference in phenotypic features of patients between the high-PRS GGE and low-PRS GGE subgroups, including age at onset, family history, and specific GGE syndrome. However, more patients from the lowest compared to the highest PRS subgroup were pharmacoresistant (31.7% vs. 8.9%, p = .01). On validation in a larger cohort, the PRS did not differ in the group of pharmacoresistant compared to nonpharmacoresistant patients.
SIGNIFICANCE
No meaningful association between PRS and age at onset, history of febrile seizures, pre-/perinatal complications, epilepsy syndromes, seizure types, co-occurrence of functional/dissociative (nonepileptic) seizures, psychiatric comorbidities, electroencephalographic/magnetic resonance imaging findings, or drug response could be demonstrated in this study of people with GGE.
Sophie von Brauchitsch, Nils Hartung, R. Karge et al.· Epilepsia· 0 citations
The findings highlight genetic NDDs as genome-informed yet exposure-sensitive disorders and several external factors that both represent markers of epilepsy severity and may support early, genetically informed seizure management and careful stewardship of treatment exposures.
C. Boßelmann, Natasha N. Ludwig, C. Holingue et al.· Epilepsia· 0 citations
These prediction models demonstrate the feasibility of early prognostication in KCNQ2-RD and support future prospective external validation and enable more accurate individualised counselling by integrating clinical and genetic information readily available at time of genetic diagnosis.
E. Van Boxstael, C. Millevert, M. Hairabedian et al.· medRxiv· 0 citations
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