P55Next-generation sequencing used to discover novel genetic variants predisposing to heart disease

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Abstract

Purpose

Cardiovascular disease is the leading cause of death worldwide. Most cardiomyopathies and channelopathies are genetic in origin and are characterized by their risk of premature death and chronic morbidity, affecting both the patient and his/her family. The detection of mutations in patients allows for therapeutic or preventive measures to be established as part of the genetic counseling. However, the large number of genes involved, makes it difficult to analyze using conventional techniques. The purpose of this study is the genetic characterization of heart disease patients in a fast, comprehensive, and cost-effective manner using an NGS approach, which includes 72 genes associated with different pathologies, coupled with a robust bioinformatics pipeline.

Methods

We developed a methodology for resequencing 72 genes (49 genes associated with cardiomyopathy, arrhythmogenic right ventricular dysplasia, Marfan syndrome, aortic aneurysm, and 23 genes associated with Brugada syndrome, long QT and short QT syndromes, familial atrial fibrillation and catecholaminergic polymorphic ventricular tachycardia). The design was done in our Bioinformatics Unit and includes 750Kb of exons, splicing regions and 5' and 3' unstranslated regions of the 72 selected genes. Exonic regions were thus captured (SureSelect, Agilent), and sequenced in the SOLiD v4 platform. The results were confirmed by Sanger sequencing. A set of 12 cases with known mutations was used for validation studies.

Results

To apply this methodology, we selected 26 patients (11 cases with aortic aneurism/Marfan syndrome, 2 cases with arrhythmogenic right ventricular dysplasia, 4 cases with Hypertrophic cardiomyopathy, 4 cases with long QT syndrome, 1 case with familial Arrhythmia, 1 case with Brugada syndrome and 3 cases with family history of sudden death). We detected 47 relevant nucleotide changes: 5 disease causing mutations and 42 unclassified variations, of which 36 are novel changes. Using in silico predictions for UVs, we classified 6 of this variants as likely pathogenic and 4 as unlikely pathogenic.

Conclusions

This NGS platform, which includes 72 genes associated with different cardiac pathologies, permits a quick and high throughput analysis of those genes. This study is important to explain the phenotypic variability of heart disease and to help establish new mutation-disease associations.

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