An extended set of yeast-based functional assays accurately identifies human disease mutations
- Song Sun1,2,3,4,5,
- Fan Yang1,2,3,4,
- Guihong Tan1,2,
- Michael Costanzo1,2,
- Rose Oughtred6,
- Jodi Hirschman6,
- Chandra L. Theesfeld6,
- Pritpal Bansal1,2,3,4,
- Nidhi Sahni7,8,10,
- Song Yi7,8,
- Analyn Yu1,2,3,4,
- Tanya Tyagi1,2,3,4,
- Cathy Tie4,
- David E. Hill7,8,
- Marc Vidal7,8,
- Brenda J. Andrews1,2,
- Charles Boone1,2,
- Kara Dolinski6 and
- Frederick P. Roth1,2,3,4,7,9
- 1Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada;
- 2Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada;
- 3Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada;
- 4Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, Ontario M5G 1X5, Canada;
- 5Department of Medical Biochemistry and Microbiology, Uppsala University, SE-75123 Uppsala, Sweden;
- 6Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA;
- 7Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA;
- 8Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA;
- 9Canadian Institute for Advanced Research, Toronto, Ontario, M5G 1Z8, Canada
- Corresponding author: fritz.roth{at}utoronto.ca
Abstract
We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.192526.115.
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Freely available online through the Genome Research Open Access option.
- Received March 26, 2015.
- Accepted March 8, 2016.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
