Accurate prediction of gain- and loss-of-function variants in GABAA receptors

GABAA receptors are critical for inhibitory neurotransmission. Variants in genes encoding these receptors are involved in the pathophysiology of both common and rare epilepsy syndromes. Variant effects on channel biophysical function, broadly classified as gain-of-function (GOF) or loss-of-function (LOF), are associated with key clinical characteristics and treatment response. Understanding and predicting variant effects is therefore essential to improve care for individuals with GABAA-related disorders. Here, we present GABAA receptor functional variant effect prediction using multi-task phenotypic learning (GENTLY). We collected clinical data from 505 affected individuals with 272 (likely) pathogenic GABAA receptor variants across GABRA1, GABRB2, GABRB3, and GABRG2. All variants were evaluated with in-vitro electrophysiology using receptor assemblies that reflect heteropentamer composition in heterozygous carriers. Variants were annotated with features based on sequence (e.g. physicochemical properties, conservation), structure (e.g. binding sites, domains), and phenotypes represented by 8185 HPO terms. We trained separate models on all features and without clinical features. Model performance was estimated using ablation, cross-validation, and external validation on a further 197 individuals with 138 (likely) pathogenic GABAA receptors variants. Our models enable highly accurate prediction of GOF/LOF in GABAA (AU-ROC 0.863-0.946), outperforming state-of-the-art genome-wide predictors (LoGoFunc: AU-ROC 0.495; evo2: AU-ROC 0.559-0.755) and clinical decision-making (decision tree: AU-ROC 0.823). Model scores correlated strongly with GABA sensitivity (r = -0.77, p < 0.001). Predictions were consistent with expert-based structure-function hypotheses: variants located in transmembrane domains were more likely GOF (p < 0.001), and variants in GABA binding sites were more likely LOF (p < 0.001). Predictions on variants from population databases behaved as expected: 13,389 population variants were similar to functionally neutral variants, and predictions from (likely) pathogenic ClinVar variants were similar to GOF/LOF variants. Our model may provide additional evidence for 10-29% of 2,295 variants in ClinVar. Lastly, we show that a simple k-nearest neighbour algorithm can predict likely clinical characteristics only from variant information (median Lin similarity 0.754 IQR 0.161). We demonstrate accurate variant effect prediction in GABAA receptors with rigorous validation across the largest dataset of functionally tested variants to date. Our predictions correlate with continuous electrophysiological measurements not directly used during training and conform to known structure-function relationships, supporting their biological plausibility. These predictions may facilitate timely diagnosis, precision treatment, and prognosis of individuals with GABAA receptor related disorders. A web interface, precomputed scores, and ACMG-calibrated score thresholds for all possible variants are openly available.