Holland Katherine D, Bouley Thomas M, Horn Paul S
Departments of Pediatrics and Neurology, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A.
Division of Child Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A.
Epilepsia. 2017 Jul;58(7):1190-1198. doi: 10.1111/epi.13798. Epub 2017 May 18.
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 the results of indeterminate SCN test results in people with epilepsy.
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.
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 Mendelian Clinically Applicable Pathogenicity (M-CAP) algorithm had an accuracy of 0.90 and a combination of algorithms increased the accuracy to 0.92.
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 >0.90.
神经元电压门控钠通道α亚基基因SCN1A、SCN2A和SCN8A的变异在早发性癫痫性脑病和其他常染色体显性遗传性儿童癫痫综合征中很常见。然而，在临床实践中，当识别出错义变异但无法确定遗传力时，错义变异通常被归类为意义未明的变异。基因检测报告通常包括计算测试结果，以估计基于人群数据库中该变异的致病性和频率。这项工作的目的是通过以下方式增强临床医生对结果的理解：（1）确定计算算法预测钠通道（SCN）错义变异致痫性的有效性；（2）优化其预测能力；（3）确定基于人群的数据库中是否存在与癫痫相关的SCN变异。这将有助于临床医生更好地理解癫痫患者中不确定SCN检测结果的意义。
使用钠通道变异数据库识别SCN中的致病性、可能致病性和良性变异。良性变异也从基于人群的数据库中识别。比较了八种常用于预测致病性的算法。此外，使用逻辑回归来确定算法组合是否能更好地预测致病性。
根据美国医学遗传学学院标准，440个变异被归类为致病性或可能致病性，84个变异被归类为良性或可能良性。基于人群的基因数据库中存在28个先前与癫痫相关的变异。大多数计算算法提供的输出具有高敏感性但低特异性，准确率为0.52 - 0.77。通过调整致病性阈值可以提高准确率。使用这种调整，孟德尔临床适用致病性（M - CAP）算法的准确率为0.90，算法组合可将准确率提高到0.92。
基于人群的来源中存在潜在致病性变异。大多数计算算法高估了致病性；然而，几种算法的加权组合将分类准确率提高到>0.90。
