Gonsales Marina C, Montenegro Maria Augusta, Preto Paula, Guerreiro Marilisa M, Coan Ana Carolina, Quast Monica Paiva, Carvalho Benilton S, Lopes-Cendes Iscia
Department of Medical Genetics and Genomic Medicine, School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotecnology, University of Campinas, Campinas, Brazil.
Department of Neurology, School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotecnology, University of Campinas, Campinas, Brazil.
Front Neurol. 2019 Mar 28;10:289. doi: 10.3389/fneur.2019.00289. eCollection 2019.
We aimed to improve the classification of missense variants in patients with Dravet syndrome (DS) by combining and modifying the current variants classification criteria to minimize inconclusive test results. We established a score classification workflow based on evidence of pathogenicity to adapt the classification of DS-related 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. Our meta-analysis showed that 46% (506/1,101) of DS-associated 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." Our results indicate that using multimodal analysis seems to be the best approach to interpret the pathogenic impact of missense changes for the molecular diagnosis of patients with DS. By applying the proposed workflow, most DS related variants had their classification improved.
我们旨在通过合并和修改当前的变异分类标准，以尽量减少不确定的检测结果，从而改进对Dravet综合征（DS）患者错义变异的分类。我们基于致病性证据建立了一个评分分类工作流程，以适应与DS相关的错义变异的分类。此外，我们汇总了文献和我们队列中报告的变异，并评估了所提出的致病性分类标准。我们综合了有关先前确定的致病性氨基酸变化、遗传模式、特定人群的等位基因频率、在蛋白质结构域内的定位以及有害效应预测分析的信息。我们的荟萃分析表明，46%（506/1101）的与DS相关的变异是错义变异。我们应用了评分分类工作流程，56.5%（286/506）的变异分类发生了变化，其中17.8%（90/506）变为“致病性”，38.7%（196/506）变为“可能致病性”。我们的结果表明，使用多模式分析似乎是解释错义变化对DS患者分子诊断的致病影响的最佳方法。通过应用所提出的工作流程，大多数与DS相关的变异的分类得到了改进。
