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The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective way. Methods commonly used to understand and predict drug resistance rely on limited clinical studies from patients who are refractory to drugs or on laborious evolution experiments with poor coverage of the gene variants. Here, we report an integrative functional variomics methodology combining deep sequencing and a Bayesian statistical model to provide a comprehensive list of drug resistance alleles from complex variant populations. Dihydrofolate reductase, the target of methotrexate chemotherapy drug, was used as a model to identify functional mutant alleles correlated with methotrexate resistance. This systematic approach identified previously reported resistance mutations, as well as novel point mutations that were validated in vivo. Use of this systematic strategy as a routine diagnostics tool widens the scope of successful drug research and development.
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CN and GG were supported by grants from UBC Pharmaceutical Sciences and NASA. PF was supported by PhRMA Foundation Informatics Grant 2013080079. The funders had no role in study
Wong, Lai H.; Sinha, Sunita; Bergeron, Julian R.; Mellor, Joseph C.; Giaever, Guri; Flaherty, Patrick; and Nislow, Corey, "Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions" (2016). PLoS Genetics. 1283.