RT Journal
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
JF Genome Research 
JO Genome Research 
YR 2016 
FD May 01 
VO 26 
IS 5 
SP 670 
OP 680 
DO 10.1101/gr.192526.115 
UL http://genome.cshlp.org/content/26/5/670.abstract 
AB 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.