Voltage-gated sodium channels have been implicated in numerous inherited paroxysmal disorders of the nervous system, muscle, and heart. Our goal is to provide a framework that helps neurologists understand the clinical and treatment implications of sodium channel variants they encounter in clinical practice. This will be accomplished through our objectives of (1) recognizing the relationship between location of a missense sodium channel gene variant and its effect on channel function, and (2) categorizing clinical phenotype based on functional effect of a variant. The relationship between location, function, and treatment response is also discussed. These interactions can be illustrated by the sodium channelopathies seen in people with epilepsy but generalize beyond that disorder. Ann Neurol 2018;83:1-9.

SUMMARY Objective Variants in neuronal voltage gated sodium channel α-subunits genes SCN1A, SCN2A, and SCN8A are common in early-onset epileptic encephalopathies and other autosomal dominant childhood epilepsy syndromes. However, in clinical practice missense variants are often classified as variants of uncertain significance when missense variants are identified but heritability cannot be determined. Genetic testing reports often include results of computational tests to estimate pathogenicity and the frequency of that variant in population-based databases. The objective of this work was to enhance clinicians’ understanding of results by (1) determining how effectively computational algorithms predict epileptogenicity of sodium channel (SCN) missense variants; (2) optimizing their predictive capabilities; and (3) determining if epilepsy-associated SCN variants are present in population based databases. This will help clinicians better understand results of indeterminate SCN test results in people with epilepsy. Methods Pathogenic, likely pathogenic, and benign variants in SCNs were identified using databases of sodium channel variants. Benign variants were also identified from population-based databases. Eight algorithms commonly used to predict pathogenicity were compared. In addition logistic regression was used to determine if a combination of algorithms could better predict pathogenicity. Results Based on American College of Medical Genetic Criteria, 440 variants were classified as pathogenic or likely pathogenic and 84 were classified as benign or likely benign. Twenty-eight variants previously associated with epilepsy were present in population-based gene databases. The output provided by most computational algorithms had a high sensitivity but low specificity with an accuracy of 0.52–0.77. Accuracy could be improved by adjusting the threshold for pathogenicity. Using this adjustment, the M-CAP algorithm had an accuracy of 0.90 and a combination of algorithms increased the accuracy to 0.92. Significance Potentially pathogenic variants are present in population-based sources. Most computational algorithms overestimate pathogenicity; however, a weighted combination of several algorithms increased classification accuracy to over 0.90.