Multimodal Analysis of SCN1A Missense Variants Improves Interpretation of Clinically Relevant Variants in Dravet Syndrome
Multimodal Analysis of SCN1A Missense Variants Improves Interpretation of Clinically Relevant Variants in Dravet Syndrome
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DOI：
10.3389/fneur.2019.00289
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发表时间：
2019-03-28
影响因子：
3.4
通讯作者：
Lopes-Cendes, Iscia
中科院分区：
文献类型：
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作者：
Gonsales, Marina C.;Montenegro, Maria Augusta;Lopes-Cendes, Iscia
目的：我们的目的是改善Dravet综合征（DS）患者的SCN 1A错义变异的分类，通过结合和修改目前的变异分类标准，以尽量减少不确定的测试结果。方法：我们建立了一个评分分类工作流程的基础上的证据的致病性，以适应DS相关的SCN 1A错义变异的分类。此外，我们汇编了文献和我们的队列中报告的变异，并评估了拟议的致病性分类标准。我们结合了以前建立的致病性氨基酸变化，遗传方式，人群特异性等位基因频率，蛋白质结构域内的定位和有害效应预测analysis.Results：我们的荟萃分析显示，46%（506/1,101）的DS相关SCN 1A变异是错义的。我们应用了评分分类工作流程，56.5%（286/506）的变异体的分类从VUS变为：17.8%（90/506）变为“致病性”，38.7%（196/506）变为“可能致病性”。“结论：我们的研究结果表明，使用多模态分析似乎是最好的方法来解释的致病影响的SCN 1A错义的变化与DS患者的分子诊断。通过应用所提出的工作流程，大多数DS相关SCN 1A变体的分类得到了改进。
Objective: We aimed to improve the classification of SCN1A missense variants in patients with Dravet syndrome (DS) by combining and modifying the current variants classification criteria to minimize inconclusive test results.Methods: We established a score classification workflow based on evidence of pathogenicity to adapt the classification of DS-related SCN1A missense variants. In addition, we compiled the variants reported in the literature and our cohort and assessed the proposed pathogenic classification criteria. We combined information regarding previously established pathogenic amino acid changes, mode of inheritance, population-specific allele frequencies, localization within protein domains, and deleterious effect prediction analysis.Results: Our meta-analysis showed that 46% (506/1,101) of DS-associated SCN1A variants are missense. We applied the score classification workflow and 56.5% (286/506) of the variants had their classification changed from VUS: 17.8% (90/506) into "pathogenic" and 38.7% (196/506) as "likely pathogenic."Conclusion: Our results indicate that using multimodal analysis seems to be the best approach to interpret the pathogenic impact of SCN1A missense changes for the molecular diagnosis of patients with DS. By applying the proposed workflow, most DS related SCN1A variants had their classification improved.