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

• Published in: Clinical genetics 2018-03, Vol.93 (3), p.459-466
• Main Authors: Ghouse, J., Skov, M.W., Bigseth, R.S., Ahlberg, G., Kanters, J.K., Olesen, M.S.
• Format: Article
• Language: English
• Subjects: Cardiology; ClinGen; Computer applications; Data processing; Databases, Genetic; Ethnic Groups - genetics; Exome; false‐positive; Genetic Association Studies; Genetic Background; Genetic counseling; Genetic diversity; Genetic Predisposition to Disease; Genetic screening; Genetic Testing; genetics; Genome, Human; Genomes; Genomics - methods; Heart diseases; Heart Diseases - diagnosis; Heart Diseases - genetics; Humans; inherited cardiac disease; long QT syndrome; Mutation; online databases; Pathogenicity; Web Browser
• ISSN: 0009-9163; 1399-0004
• DOI: 10.1111/cge.13066

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. The frequencies of the non‐reference allele for the Exome Aggregation Consortium populations African/African American (AFR) and Non‐Finnish Europeans (NFE) for the gene SCN5A. Each dot represents a distinct variant. Purple dots represents private sites (for which the non‐reference allele frequency is non‐zero in one population but zero in the other population) for AFR. Orange dots represent private sites for NFE and green dots represents non‐private sites (non‐reference allele frequency >0% in both populations) for both populations. AF, allele frequency; MAF, minor allele frequency.