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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.

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