PubMed 24667783

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Automatically associated channels: KCNQ1 , Kv11.1 , Kv7.1 , Nav1.5

Title: Genetic analysis, in silico prediction, and family segregation in long QT syndrome.

Authors: Helena Riuró, Oscar Campuzano, Paola Berne, Elena Arbelo, Anna Iglesias, Alexandra Pérez-Serra, Mònica Coll-Vidal, Sara Partemi, Irene Mademont-Soler, Ferran Picó, Catarina Allegue, Antonio Oliva, Edward Gerstenfeld, Georgia Sarquella-Brugada, Víctor Castro-Urda, Ignacio Fernández-Lozano, Lluis Mont, Josep Brugada, Fabiana S Scornik, Ramon Brugada

Journal, date & volume: Eur. J. Hum. Genet., 2015 Jan , 23, 79-85

PubMed link:

The heritable cardiovascular disorder long QT syndrome (LQTS), characterized by prolongation of the QT interval on electrocardiogram, carries a high risk of sudden cardiac death. We sought to add new data to the existing knowledge of genetic mutations contributing to LQTS to both expand our understanding of its genetic basis and assess the value of genetic testing in clinical decision-making. Direct sequencing of the five major contributing genes, KCNQ1, KCNH2, SCN5A, KCNE1, and KCNE2, was performed in a cohort of 115 non-related LQTS patients. Pathogenicity of the variants was analyzed using family segregation, allele frequency from public databases, conservation analysis, and Condel and Provean in silico predictors. Phenotype-genotype correlations were analyzed statistically. Sequencing identified 36 previously described and 18 novel mutations. In 51.3% of the index cases, mutations were found, mostly in KCNQ1, KCNH2, and SCN5A; 5.2% of cases had multiple mutations. Pathogenicity analysis revealed 39 mutations as likely pathogenic, 12 as VUS, and 3 as non-pathogenic. Clinical analysis revealed that 75.6% of patients with QTc≥500 ms were genetically confirmed. Our results support the use of genetic testing of KCNQ1, KCNH2, and SCN5A as part of the diagnosis of LQTS and to help identify relatives at risk of SCD. Further, the genetic tools appear more valuable as disease severity increases. However, the identification of genetic variations in the clinical investigation of single patients using bioinformatic tools can produce erroneous conclusions regarding pathogenicity. Therefore segregation studies are key to determining causality.