Harnessing gene expression to identify the genetic basis of drug resistance Export

@article{citeulike:5938665, abstract = {The advent of cost-effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants interact with the environment. We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug-free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene–drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug.}, author = {Chen, Bo-Juen and Causton, Helen C. and Mancenido, Denesy and Goddard, Noel L. and Perlstein, Ethan O. and Pe'er, Dana}, citeulike-article-id = {5938665}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/msb.2009.69}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/msb200969}, day = {13}, doi = {10.1038/msb.2009.69}, journal = {Molecular Systems Biology}, keywords = {causal, drug, expression, gene, genotype, phenotype, resistance, yeast}, month = {October}, posted-at = {2009-10-14 21:06:11}, priority = {2}, publisher = {Nature Publishing Group}, title = {Harnessing gene expression to identify the genetic basis of drug resistance}, url = {http://dx.doi.org/10.1038/msb.2009.69}, volume = {5}, year = {2009} }

TY - JOUR ID - citeulike:5938665 L3 - citeulike-article-id:5938665 N2 - The advent of cost-effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants interact with the environment. We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug-free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene–drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug. JF - Molecular Systems Biology TI - Harnessing gene expression to identify the genetic basis of drug resistance PB - Nature Publishing Group VL - 5 KW - causal KW - drug KW - expression KW - gene KW - genotype KW - phenotype KW - resistance KW - yeast AU - Chen, Bo-Juen AU - Causton, Helen AU - Mancenido, Denesy AU - Goddard, Noel AU - Perlstein, Ethan AU - Pe'er, Dana PY - 2009/10/13/ UR - http://dx.doi.org/10.1038/msb.2009.69 ER -