In Silico Predictions of KCNQ Variant Pathogenicity in Epilepsy

Background: Variants in KCNQ2 and KCNQ3 may cause benign neonatal familial seizures and early infantile epileptic encephalopathy. Previous reports suggest that in silico models cannot predict pathogenicity accurately enough for clinical use. Here we sought to establish a model to accurately predict the pathogenicity of KCNQ2 and KCNQ3 missense variants based on available in silico prediction models. Methods: ClinVar and gnomAD databases of reported KCNQ2 and KCNQ3 missense variants in patients with neonatal epilepsy were accessed and classified as benign, pathogenic, or of uncertain significance. Sensitivity, specificity, and classification accuracy for prediction of pathogenicity were determined and compared for 10 widely used prediction algorithms program. A mathematical model of the variants (KCNQ Index) was created using their amino acid location and prediction algorithm scores to improve prediction accuracy. Results: Using clinically characterized variants, the free online tool PROVEAN accurately predicted pathogenicity 92% of the time and the KCNQ Index had an accuracy of 96%. However, when including the gnomAD database as benign variants, only the KCNQ Index was able to predict pathogenicity with an accuracy greater than 90% (sensitivity - 93% and specificity - 98%). No model could accurately predict the phenotype of variants. Conclusion: We show that KCNQ channel variant pathogenicity can be predicted by a novel KCNQ Index in neonatal epilepsy. However, more work is needed to accurately predict the patient's epilepsy phenotype from in silico algorithms. (C) 2021 Elsevier Inc. All rights reserved.