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

Authors:
Marina C Gonsales
Marina C Gonsales
Instituto Brasileiro de Neurociências e Neurotecnologia
Maria Augusta Montenegro
Maria Augusta Montenegro
University of Campinas
Marilisa M Guerreiro
Marilisa M Guerreiro
Departamento de Neurologia
Ana Carolina Coan
Ana Carolina Coan
State University of Campinas
Brazil
Benilton S Carvalho
Benilton S Carvalho
The Walter and Eliza Hall Institute of Medical Research
Australia
Iscia Lopes-Cendes
Iscia Lopes-Cendes
University of Campinas
Brazil

Front Neurol 2019 28;10:289. Epub 2019 Mar 28.

Department of Medical Genetics and Genomic Medicine, School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotecnology, University of Campinas, Campinas, Brazil.

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.

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Source
http://dx.doi.org/10.3389/fneur.2019.00289DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6455056PMC

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March 2019

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