Despite the availability of three major classes of medications for asthma, the response to therapy is highly variable, with as many one-half of all patients being non-responders.  Pharmacogenetics provides the promise of "personalized medicine", whereby an individual's response to therapy will be guided a priori by his or her genetic make-up.  While we have made substantial progress in identifying candidate genes influencing the response to asthma medications, none of these associations accounts for more than a small proportion of the variability in the response.  It is clear that to accomplish the goal of "personalized medicine" in asthma, we need to identify multiple additional pharmacogenetics loci.  Recent advances in technology offer the unprecedented ability to rapidly identify the genetic variants that influence drug treatment response on a genome-wide scale.  The major goal of this project is to enhance our prior work in the field of asthma pharmacogenetics via the efficient, but detailed, identification of novel genes associated with the asthmatic response to medications.  To accomplish this, we have structured our specific aims as follows: 1.  We will analyze GWAS data from 3493 subjects in asthma clinical trials of B2-adrenergic and glucocorticoid treatment response, and perform association testing to determine which genetic variants are associated with an altered therapeutic response.  Machine learning approaches will be utilized to model the genetic association data.  2.  Robust and replicated pharmacogenetic associations will be explored at the molecular, cellular, and integrative genomic levels to establish functional variants and pharmacogenetic mechanisms.  Functional methods will involve bioinformatics tools, mRNA profiling, systems genetics, and cellular and animal models of asthma therapeutic responsiveness.  3.  We will develop and validate a set of predictive tests of asthma therapeutic response to short acting B2-adrenergic and inhaled glucocorticoid drugs using clinical and functional knowledge and high-resolution analysis of genetic associations, including epistasis and gene-drug interaction.  Additional aims relate to our collaboration with PharmGKB and the PGRN and helping investigators interested in pharmacogenetics.  In addition to the GWAS samples, nearly 7000 DNA samples and matching asthma phenotypic data are available for replication testing to validate the initial GWAS results and predictive tests.  We believe that these findings will uncover sufficient new information that working models of prognostic tests combining genetic and clinical traits in the prediction of drug response in asthma will result.  RELEVANCE: Asthma affects an estimated 300 million individuals worldwide and accounts for approximately $20 billion in direct health care costs in the United States annually, with the greatest proportion of those costs allocated to medication and hospitalization costs.  The identification of genetic variants that can be used as the basis of a prognostic test to predict which individuals will or will not respond to therapy, thereby minimizing both need for trial and error medication dispensing and risk of hospitalizations due to inadequate therapy, has the potential to substantially decrease both morbidity and financial burden related to asthma.