An extended set of yeast-based functional assays accurately identifies human disease mutations
- Song Sun1,
- Fan Yang2,
- Guihong Tan2,
- Michael Costanzo2,
- Rose Oughtred3,
- Jodi Hirschman3,
- Chandra Theesfeld3,
- Pritpal Bansal2,
- Nidhi Sahni4,
- Song Yi4,
- Analyn Yu2,
- Tanya Tyagi2,
- Cathy Tie2,
- David E. Hill4,
- Marc Vidal4,
- Brenda J. Andrews2,
- Charles Boone2,
- Kara Dolinski3 and
- Frederick P. Roth2,5
- 1 University of Toronto, Uppsala University;
- 2 University of Toronto;
- 3 Princeton University;
- 4 Dana-Farber Cancer Institute
- ↵* Corresponding author; email: 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 one-billion-year-diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.
- Received March 26, 2015.
- Accepted March 8, 2016.
- Published by Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International license), as described at http://creativecommons.org/licenses/by/4.0/.
