Genotype-Phenotype correlation in Dravet Syndrome with SCN1A mutation increase efficiency of molecular diagnosis

• Autor(a) principal: Gonsales,MC
• Data de Publicação: 2012
• Outros Autores: Preto,P, Montenegro,MA, Guerreiro,MM, Lopes-Cendes,I
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Journal of epilepsy and clinical neurophysiology (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1676-26492012000200009
• topic: genetic testing; mutations; sodium channel

OBJECTIVES: The purpose of this study was to advance the knowledge on the clinical use of SCN1A testing for severe epilepsies within the spectrum of generalized epilepsy with febrile seizures plus by performing genetic screening in patients with Dravet and Doose syndromes and establishing genotype-phenotype correlations. METHODS: Mutation screening in SCN1A was performed in 15 patients with Dravet syndrome and 13 with Doose syndrome. Eight prediction algorithms were used to analyze the impact of the mutations in putative protein function. Furthermore, all SCN1A mutations previously published were compiled and analyzed. In addition, Multiplex Ligation-Dependent Probe Amplification (MLPA) technique was used to detect possible copy number variations within SCN1A. RESULTS: Twelve mutations were identified in patients with Dravet syndrome, while patients with Doose syndrome showed no mutations. Our results show that the most common type of mutation found is missense, and that they are mostly located in the pore region and the N- and C-terminal of the protein. No copy number variants in SCN1A were identified in our cohort. CONCLUSIONS: SCN1A testing is clinically useful for patients with Dravet syndrome, but not for those with Doose syndrome, since both syndromes do not seem to share the same genetic basis. Our results indicate that indeed missense mutations can cause severe phenotypes depending on its location and the type of amino-acid substitution. Moreover, our strategy for predicting deleterious effect of mutations using multiple computation algorithms was efficient for most of the mutations identified.