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Multimodal Analysis of SCN1A Missense Variants Improves Interpretation of Clinically Relevant Variants in Dravet Syndrome.


ABSTRACT: 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.

SUBMITTER: Gonsales MC 

PROVIDER: S-EPMC6455056 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Multimodal Analysis of <i>SCN1A</i> Missense Variants Improves Interpretation of Clinically Relevant Variants in Dravet Syndrome.

Gonsales Marina C MC   Montenegro Maria Augusta MA   Preto Paula P   Guerreiro Marilisa M MM   Coan Ana Carolina AC   Quast Monica Paiva MP   Carvalho Benilton S BS   Lopes-Cendes Iscia I  

Frontiers in neurology 20190328


<b>Objective:</b> We aimed to improve the classification of <i>SCN1A</i> missense variants in patients with Dravet syndrome (DS) by combining and modifying the current variants classification criteria to minimize inconclusive test results. <b>Methods:</b> We established a score classification workflow based on evidence of pathogenicity to adapt the classification of DS-related <i>SCN1A</i> missense variants. In addition, we compiled the variants reported in the literature and our cohort and asse  ...[more]

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