Comparison and optimization of in silico algorithms for predicting the pathogenicity of sodium channel variants in epilepsy

Abstract

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.

Structural Biology Helps Interpret Variants of Uncertain Significance in Genes Causing Endocrine and Metabolic Disorders

“…In silico predictions tools thus appear to have a high sensitivity and a low specificity, at least for variants occurring in genes known to cause endocrine and metabolic disorders. Although our results cannot at present be generalized to the whole proteome, our findings confirm what was previously reported on a smaller dataset of variants in genes causing epilepsy syndromes [ 27 ].…”

“…Other authors have reported a high sensitivity but low specificity for in silico tools used to assess the effect of variants in disease-causing genes involved in the pathogenesis of epileptic encephalopathies [ 27 ]. Recently, in vitro testing of variants identified in the cancer gene TP53 and predicted to be damaging by in silico tools revealed that only half of the variants affected TP53 activity [ 6 ].…”

SCN1A variants from bench to bedside—improved clinical prediction from functional characterization

“…The majority are based on bioinformatic algorithms considering conservation across species, AA characteristics, and previous reports, with the aim to differentiate pathogenic from benign variants. A common problem is that most computational algorithms have high sensitivity but low specificity and therefore overestimate disease pathogenicity (Holland et al, ). With in vitro functional characterization used as gold standard comparator, our results show that conventional pathogenicity software classified nearly 90% of the here examined variants correctly as disease‐causing, regardless of the underlying phenotype.…”

SCN1A/NaV1.1 channelopathies: Mechanisms in expression systems, animal models, and human iPSC models

“…These puzzling findings highlighted the importance of functional studies for the identification of pathologic mechanisms, which are still essential. In fact, although there has been progress in the development of algorithms for in silico prediction of effects of mutations, in general, based on conservation of protein's physicochemical properties and of amino acid sequence within and across species, they cannot reliably disclose the detailed effect on protein's functions, and there are no tools for predicting overall effects on phenotypes.…”