Distinguishing pathogenic mutations from background genetic noise in cardiology: The use of large genome databases for genetic interpretation.

Advances in clinical genetic testing have led to increased insight into the human genome, including how challenging it is to interpret rare genetic variation. In some cases, the ability to detect genetic mutations exceeds the ability to understand their clinical impact, limiting the advantage of these technologies. Obstacles in genomic medicine are many and include: understanding the level of certainty/uncertainty behind pathogenicity determination, the numerous different variant interpretation-guidelines used by clinical laboratories, delivering the certain or uncertain result to the patient, helping patients evaluate medical decisions in light of uncertainty regarding the consequence of the findings. Through publication of large publicly available exome/genome databases, researchers and physicians are now able to highlight dubious variants previously associated with different cardiac traits. Also, continuous efforts through data sharing, international collaborative efforts to develop disease-gene-specific guidelines, and computational analyses using large data, will indubitably assist in better variant interpretation and classification. This article discusses the current, and quickly changing, state of variant interpretation resources within cardiovascular genetic research, e.g., publicly available databases and ways of how cardiovascular genetic counselors and geneticists can aid in improving variant interpretation in cardiology.

Integration of 60,000 exomes and ACMG guidelines question the role of Catecholaminergic Polymorphic Ventricular Tachycardia-associated variants.

Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT) is a highly lethal cardiac arrhythmia disease occurring during exercise or psychological stress. CPVT has an estimated prevalence of 1:10,000 and has mainly been associated with variants in calcium-regulating genes. Identification of potential false-positive pathogenic variants was conducted by searching the Exome Aggregation Consortium (ExAC) database (n = 60,706) for variants reported to be associated with CPVT. The pathogenicity of the interrogated variants was assessed using guidelines from the American College of Medical Genetics and Genomics (ACMG) and in silico prediction tools. Of 246 variants 38 (15%) variants previously associated with CPVT were identified in the ExAC database. We predicted the CPVT prevalence to be 1:132. The ACMG standards classified 29% of ExAC variants as pathogenic or likely pathogenic. The in silico predictions showed a reduced probability of disease-causing effect for the variants identified in the exome database (p < 0.001). We have observed a large overrepresentation of previously CPVT-associated variants in a large exome database. Based on the frequency of CPVT in the general population, it is less likely that the previously proposed variants are associated with a highly penetrant monogenic form of the disease.