期刊
AMERICAN JOURNAL OF HUMAN GENETICS
卷 101, 期 3, 页码 315-325出版社
CELL PRESS
DOI: 10.1016/j.ajhg.2017.07.014
关键词
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资金
- NIH National Human Genome Research Institute (NHGRI) [1UM1HG009408]
- National Cancer Institute Division of Cancer Prevention [1R01CA197139]
- NHGRI Centers of Excellence in Genomic Sciences [P50HG004233]
- NHGRI [U01HG001715]
- One Brave Idea
- Canada Excellence Research Chairs Program
- NIH [5R01CA207029-02]
- National Science Foundation [CCF-1317653]
- NIH Director's Pioneer Award [DP1HG007811]
- Howard Hughes Medical Institute
- NIH National Institute of General Medical Sciences [R01GM109110]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1317694] Funding Source: National Science Foundation
Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of lookup tables of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.
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