Format

Send to

Choose Destination
Am J Hum Genet. 2017 Sep 7;101(3):315-325. doi: 10.1016/j.ajhg.2017.07.014.

Variant Interpretation: Functional Assays to the Rescue.

Author information

1
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. Electronic address: lstarita@uw.edu.
2
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA.
3
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
4
Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Bioinformatics & Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
5
Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON M5G 1X5, Canada; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Canadian Institute for Advanced Research, Toronto, ON M5G 1Z8, Canada.
6
Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA; Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA.
7
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Howard Hughes Medical Institute, Seattle, WA 98195, USA.
8
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Department of Bioengineering, University of Washington, Seattle, WA 98195, USA. Electronic address: dfowler@uw.edu.

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.

PMID:
28886340
PMCID:
PMC5590843
DOI:
10.1016/j.ajhg.2017.07.014
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center