Distinguishing pathogenic mutations from background genetic noise in cardiology: The use of large genome databases for genetic interpretation
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Distinguishing pathogenic mutations from background genetic noise in cardiology : The use of large genome databases for genetic interpretation. / Ghouse, J; Skov, M W; Bigseth, R S; Ahlberg, G; Kanters, J K; Olesen, M S.
In: Clinical Genetics, Vol. 93, No. 3, 03.2018, p. 459-466.Research output: Contribution to journal › Review › Research › peer-review
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TY - JOUR
T1 - Distinguishing pathogenic mutations from background genetic noise in cardiology
T2 - The use of large genome databases for genetic interpretation
AU - Ghouse, J
AU - Skov, M W
AU - Bigseth, R S
AU - Ahlberg, G
AU - Kanters, J K
AU - Olesen, M S
N1 - © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
PY - 2018/3
Y1 - 2018/3
N2 - 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.
AB - 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.
KW - ClinGen
KW - false-positive
KW - genetics
KW - inherited cardiac disease
KW - long QT syndrome
KW - online databases
UR - http://www.scopus.com/inward/record.url?scp=85030032967&partnerID=8YFLogxK
U2 - 10.1111/cge.13066
DO - 10.1111/cge.13066
M3 - Review
C2 - 28589536
VL - 93
SP - 459
EP - 466
JO - Clinical Genetics
JF - Clinical Genetics
SN - 0009-9163
IS - 3
ER -
ID: 191389861