Unknown

Dataset Information

0

Interpreting secondary cardiac disease variants in an exome cohort.


ABSTRACT: BACKGROUND:Massively parallel sequencing to identify rare variants is widely practiced in medical research and in the clinic. Genome and exome sequencing can identify the genetic cause of a disease (primary results), but it can also identify pathogenic variants underlying diseases that are not being sought (secondary or incidental results). A major controversy has developed surrounding the return of secondary results to research participants. We have piloted a method to analyze exomes to identify participants at risk for cardiac arrhythmias, cardiomyopathies, or sudden death. METHODS AND RESULTS:Exome sequencing was performed on 870 participants not selected for arrhythmia, cardiomyopathy, or a family history of sudden death. Exome data from 22 cardiac arrhythmia- and 41 cardiomyopathy-associated genes were analyzed using an algorithm that filtered results on genotype quality, frequency, and database information. We identified 1367 variants in the cardiomyopathy genes and 360 variants in the arrhythmia genes. Six participants had pathogenic variants associated with dilated cardiomyopathy (n=1), hypertrophic cardiomyopathy (n=2), left ventricular noncompaction (n=1), or long-QT syndrome (n=2). Two of these participants had evidence of cardiomyopathy and 1 had left ventricular noncompaction on echocardiogram. Three participants with likely pathogenic variants had prolonged QTc. Family history included unexplained sudden death among relatives. CONCLUSIONS:Approximately 0.5% of participants in this study had pathogenic variants in known cardiomyopathy or arrhythmia genes. This high frequency may be due to self-selection, false positives, or underestimation of the prevalence of these conditions. We conclude that clinically important cardiomyopathy and dysrhythmia secondary variants can be identified in unselected exomes.

SUBMITTER: Ng D 

PROVIDER: S-EPMC3887521 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

altmetric image

Publications


<h4>Background</h4>Massively parallel sequencing to identify rare variants is widely practiced in medical research and in the clinic. Genome and exome sequencing can identify the genetic cause of a disease (primary results), but it can also identify pathogenic variants underlying diseases that are not being sought (secondary or incidental results). A major controversy has developed surrounding the return of secondary results to research participants. We have piloted a method to analyze exomes to  ...[more]

Similar Datasets

| S-EPMC5961741 | biostudies-literature
| S-EPMC6318774 | biostudies-literature
| S-EPMC5611418 | biostudies-literature
| S-EPMC4791490 | biostudies-literature
| S-EPMC4100812 | biostudies-other
| S-EPMC5501078 | biostudies-literature
| S-EPMC5848494 | biostudies-literature
| S-EPMC6137878 | biostudies-other