Asthma affects over 17 million people in the United States. Nearly one-half of patients do not respond to treatment with the most effective classes of currently available asthma therapeutics. In order to develop novel therapies, a comprehensive catalog of modifiable genetic targets and their metabolic pathways that contribute to the development and progression of asthma is needed. Genomic technologies including expression microarrays and high-throughput genotyping platforms offer an unprecedented opportunity to advance this process. The overarching premise of this project is that combining gene expression data with population genetics will lead to the identification of critical molecules that contribute to the development and progression of asthma. We will generate genome-wide gene expression profiles from total RNA derived from peripheral blood CD4+ lymphocytes from 370 young adults with asthma of varying severity participating in the Childhood Asthma Management Program Continuation Study (CAMP CS2) to identify differentially expressed gene transcripts that are associated with asthma severity phenotypes. Using DNA samples from these subjects and their parents, we will then genotype single nucleotide polymorphisms (SNPs) mapping to 200 of the most differentially expressed genes and perform family-based genotype-gene-expression association analysis to identify regulatory SNPs (rSNP) that strongly regulate gene expression. Those genes with the strongest evidence of rSNP regulation will be evaluated as asthma candidate genes evaluating whether SNPs in these genes are associated with asthma-related clinical phenotypes. Using this approach we anticipate that we will identify at least 20 genes with significant evidence of cis-acting regulatory genetic variation and that many of these genes will also harbor genetic variation that directly influence asthma susceptibility and severity. Genes with the strongest evidence of association will be evaluated in other family- based asthma cohorts for evidence of replicated association and will be resequenced as part of a SNP- discovery effort to identify functional polymorphisms. We anticipate that by combining gene expression data with population genetics, this project will identify novel genes that harbor genetic variation that influence the natural history of asthma, thereby identifying ideal asthma-candidate genes for possible therapeutic targeting. These findings could ultimately lead to the development of novel therapies and a clinical prognostic test for this common disease.