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Characterizing genetic variants for clinical action

Authors: Erin M. Ramos; Corina Din‐Lovinescu; Jonathan S. Berg; Lisa D. Brooks; Audrey Duncanson; Michael Dunn; Peter Good; +28 Authors

Characterizing genetic variants for clinical action

Abstract

Genome‐wide association studies, DNA sequencing studies, and other genomic studies are finding an increasing number of genetic variants associated with clinical phenotypes that may be useful in developing diagnostic, preventive, and treatment strategies for individual patients. However, few variants have been integrated into routine clinical practice. The reasons for this are several, but two of the most significant are limited evidence about the clinical implications of the variants and a lack of a comprehensive knowledge base that captures genetic variants, their phenotypic associations, and other pertinent phenotypic information that is openly accessible to clinical groups attempting to interpret sequencing data. As the field of medicine begins to incorporate genome‐scale analysis into clinical care, approaches need to be developed for collecting and characterizing data on the clinical implications of variants, developing consensus on their actionability, and making this information available for clinical use. The National Human Genome Research Institute (NHGRI) and the Wellcome Trust thus convened a workshop to consider the processes and resources needed to: (1) identify clinically valid genetic variants; (2) decide whether they are actionable and what the action should be; and (3) provide this information for clinical use. This commentary outlines the key discussion points and recommendations from the workshop. © 2014 Wiley Periodicals, Inc.

Keywords

RETURN, 610, clinical actionability, Education, electronic health records (EHR), FUTURE, 616, Humans, DNA sequencing, GENOME-WIDE ASSOCIATION, Precision Medicine, database, BEDSIDE, pharmacogenomics, UTILITY, EMERGE NETWORK, Information Dissemination, ELECTRONIC MEDICAL-RECORDS, Genetic Variation, BASE-PAIRS, United States, National Human Genome Research Institute (U.S.), genomic medicine, Phenotype, DISEASES, HEALTH, Medical Informatics

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    54
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
54
Top 10%
Top 10%
Top 10%
bronze