Key result
Public genome databases and data sharing help distinguish pathogenic mutations from background genetic noise.
This review highlights the importance of large genome databases and collaborative efforts in improving the interpretation of rare genetic variants in cardiovascular medicine.
Supports improved variant interpretation in clinical cardiology; leaves open standardization of gene-specific guidelines.
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.
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Ghouse et al. (2017) conducted a review in Cardiovascular genetic variation. Large genome databases and variant interpretation guidelines was evaluated. The use of large publicly available genome databases, data sharing, and disease-gene-specific guidelines assists in distinguishing pathogenic mutations from background genetic noise in cardiology.
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