Comparison and optimization of in silico algorithms for predicting the pathogenicity of sodium channel variants in epilepsy.
Comparison and optimization of in silico algorithms for predicting the pathogenicity of sodium channel variants in epilepsy.
复制标题
用于预测癫痫钠通道变异致病性的计算机算法的比较和优化。
作者：
Holland KD;Bouley TM;Horn PS
神经元电压门控钠通道α亚基基因SCN 1A、SCN 2A和SCN 8A的变异在早发性癫痫性脑病和其他常染色体显性儿童癫痫综合征中很常见。然而，在临床实践中，当识别出错义变体但无法确定遗传性时，错义变体通常被归类为意义不确定的变体。基因检测报告通常包括计算测试的结果，以估计致病性和基于人群的数据库中该变体的频率。这项工作的目的是通过（1）确定计算算法如何有效预测钠通道（SCN）错义变体的致癫痫性;（2）优化其预测能力;（3）确定癫痫相关SCN变体是否存在于基于人群的数据库中，来增强临床医生对结果的理解。这将有助于临床医生更好地了解癫痫患者不确定的SCN测试结果。使用钠通道变体数据库鉴定SCN中的致病性、可能致病性和良性变体。还从基于人群的数据库中识别出良性变异。比较了8种常用的致病性预测算法。此外，使用逻辑回归来确定算法的组合是否可以更好地预测致病性。根据美国医学院遗传标准，440种变异被归类为致病性或可能致病性，84种被归类为良性或可能良性。28个以前与癫痫相关的变异存在于基于人群的基因数据库中。大多数计算算法提供的输出具有高灵敏度，但特异性低，准确度为0.52-0.77。通过调整致病性阈值可以提高准确性。使用这种调整，M-CAP算法的准确度为0.90，算法组合将准确度提高到0.92。潜在致病性变异存在于基于人群的来源中。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以上。
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. 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 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 over 0.90.
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