In Silico Predictions of KCNQ Variant Pathogenicity in Epilepsy.
In Silico Predictions of KCNQ Variant Pathogenicity in Epilepsy.
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DOI：
10.1016/j.pediatrneurol.2021.01.006
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发表时间：
2021-05
影响因子：
3.8
通讯作者：
Holland KD
中科院分区：
文献类型：
--
作者：
Ritter DM;Horn PS;Holland KD
KCNQ 2和KCNQ 3的变体可能会导致良性新生儿家族性癫痫发作（BNFS）和早期婴儿癫痫性脑病（EIEE）。先前的报告表明，计算机模型不能足够准确地预测致病性以供临床使用。在这里，我们试图建立一个模型，以准确地预测KCNQ2和KCNQ3错义变体的致病性的基础上，在硅片预测模型。对新生儿癫痫患者中报告的KCNQ2和KCNQ3错义变体的ClinVar和gnomAD数据库进行访问，并将其分类为良性、致病性或意义不确定。测定了10种广泛使用的预测算法程序预测致病性的灵敏度、特异性和分类准确度，并进行了比较。使用其氨基酸位置和预测算法得分创建变体的数学模型（KCNQ指数）以提高预测准确性。使用临床特征的变异，免费在线工具PROVEAN准确预测致病性的时间为92%，KCNQ指数的准确率为96%。然而，当包括gnomAD数据库作为良性变体时，只有KCNQ指数能够预测致病性，准确度>90%（灵敏度= 93%，特异性= 98%）。没有模型可以准确地预测变异的表型。我们发现，KCNQ通道变异的致病性可以预测一种新的KCNQ指数在新生儿癫痫。然而，需要更多的工作来准确地预测患者的癫痫表型从计算机算法。
Variants in KCNQ2 and KCNQ3 may cause benign neonatal familial seizures (BNFS) and early infantile epileptic encephalopathy (EIEE). 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. 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 ten 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. 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 >90% (sensitivity = 93% and specificity = 98%). No model could accurately predict the phenotype of variants. 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.
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