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Variant interpretation: functional assays to the rescue

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Starita, Lea M., Ahituv, Nadav, Dunham, Maitreya J., Kitzman, Jacob O., Roth, Frederick P., Seelig, Georg, Shendure, Jay, Fowler, Douglas M.·2017·The American Journal of Human Genetics
DOI 10.1016/j.ajhg.2017.07.014PMC PMC5590843
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Abstract

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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  • There is a pressing need to characterise the functional consequences of as many VUS as possible experimentally, to guide both research and clinical decision-making.

    “It follows from these observations that there is a pressing need to characterise the functional consequences of as many VUS as possible experimentally, to guide both research and clinical decision-making (33).”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [33]

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Cited by

Sorted by citations

Authors
Starita, L. M., Ahituv, N., Dunham, M. J., Kitzman, J. O., Roth, F. P., Seelig, G., Shendure, J., & Fowler, D. M.
Affiliations
  • University of Washington (US)
  • University of California, San Francisco (US)
  • University of Michigan (US)
  • Canadian Institute for Advanced Research (CA)
  • Howard Hughes Medical Institute (US)
Corresponding author
  • Starita, Lea M.
  • Fowler, Douglas M.
ORCID
Venue
The American Journal of Human Genetics
Publisher
Elsevier BV
Published
2017-09
Volume
101
Issue
3
Pages
315-325
ISSN
0002-9297 (ISSN-L)
DOI
10.1016/j.ajhg.2017.07.014
PMC
PMC5590843
OpenAlex
W2752403657
Cited by
381
References
13
Citations by year
2026: 52 · 2025: 40 · 2024: 56 · 2023: 28 · 2022: 35 · 2021: 50 · 2020: 44 · 2019: 39 · 2018: 29 · 2017: 7
FWCI
21.10
Type
journal article
Language
en
Open access
Licence
Locations
  • The American Journal of Human Genetics (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • NIH National Human Genome Research Institute (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
  • Canadian Institute for Advanced Research
  • NIH National Institute of General Medical Sciences (R01GM109110)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
variant interpretation, pathogenicity prediction, functional assays, rare variants, variant effect prediction, look-up table, machine learning, medical genetics
MeSH terms
Disease, Humans, Genetic Variation, Genome, Human, Computational Biology

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