Ruderfer, Douglas M.Roberts, David C.Schreiber, StuartPerlstein, Ethan O.Kruglyak, Leonid2010-10-062009Ruderfer, Douglas M., David C. Roberts, Stuart L. Schreiber, Ethan O. Perlstein, and Leonid Kruglyak. 2009. Using Expression and Genotype to Predict Drug Response in Yeast. PLoS ONE 4(9): e6907.1932-6203http://nrs.harvard.edu/urn-3:HUL.InstRepos:4459930Personalized, or genomic, medicine entails tailoring pharmacological therapies according to individual genetic variation at genomic loci encoding proteins in drug-response pathways. It has been previously shown that steady-state mRNA expression can be used to predict the drug response (i.e., sensitivity or resistance) of non-genotyped mammalian cancer cell lines to chemotherapeutic agents. In a real-world setting, clinicians would have access to both steady-state expression levels of patient tissue(s) and a patient's genotypic profile, and yet the predictive power of transcripts versus markers is not well understood. We have previously shown that a collection of genotyped and expression-profiled yeast strains can provide a model for personalized medicine. Here we compare the predictive power of 6,229 steady-state mRNA transcript levels and 2,894 genotyped markers using a pattern recognition algorithm. We were able to predict with over 70% accuracy the drug sensitivity of 104 individual genotyped yeast strains derived from a cross between a laboratory strain and a wild isolate. We observe that, independently of drug mechanism of action, both transcripts and markers can accurately predict drug response. Marker-based prediction is usually more accurate than transcript-based prediction, likely reflecting the genetic determination of gene expression in this cross.en-USgenetics and genomicscomparative genomicscomplex traitsdisease modelsfunctional genomicsgene expressionpharmacogenomicsJournal Article2010-10-0610.1371/journal.pone.0006907