TY  - JOUR
A1  - Sun, Song
A1  - Yang, Fan
A1  - Tan, Guihong
A1  - Costanzo, Michael
A1  - Oughtred, Rose
A1  - Hirschman, Jodi
A1  - Theesfeld, Chandra L.
A1  - Bansal, Pritpal
A1  - Sahni, Nidhi
A1  - Yi, Song
A1  - Yu, Analyn
A1  - Tyagi, Tanya
A1  - Tie, Cathy
A1  - Hill, David E.
A1  - Vidal, Marc
A1  - Andrews, Brenda J.
A1  - Boone, Charles
A1  - Dolinski, Kara
A1  - Roth, Frederick P.
T1  - An extended set of yeast-based functional assays accurately identifies human disease mutations
Y1  - 2016/05/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 670 
EP  - 680 
DO  - 10.1101/gr.192526.115 
VL  - 26 
IS  - 5 
UR  - http://genome.cshlp.org/content/26/5/670.abstract 
N2  - 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. 
ER  -