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Several studies have identified copy number variants (CNVs) as responsible for cardiac diseases associated with sudden cardiac death (SCD), but very few exhaustive analyses in large cohorts of patients have been performed, and they have been generally focused on a specific SCD-related disease. The aim of the present study was to screen for CNVs the most prevalent genes associated with SCD in a large cohort of patients who suffered sudden unexplained death or had an inherited cardiac disease (cardiomyopathy or channelopathy). A total of 1765 European patients were analyzed with a homemade algorithm for the assessment of CNVs using high-throughput sequencing data. Thirty-six CNVs were identified (2%), and most of them appeared to have a pathogenic role. The frequency of CNVs among cases of sudden unexplained death, patients with a cardiomyopathy or a channelopathy was 1.4% (8/587), 2.3% (20/874), and 2.6% (8/304), respectively. Detection rates were particularly high for arrhythmogenic cardiomyopathy (5.1%), long QT syndrome (4.7%), and dilated cardiomyopathy (4.4%). As such large genomic rearrangements underlie a non-neglectable portion of cases, we consider that their analysis should be performed as part of the routine genetic testing of sudden unexpected death cases and patients with SCD-related diseases.

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.