Occupational Asthma (OA) is the most common occupational lung disorder and diisocyanates the most common causes of OA. Despite many years of research, there are no reliable predictors of risk or susceptibility to diisocyanate asthm (DA). In our current candidate gene association study (NIOSH RO1), we replicated a Korean GWAS study by demonstrating that two SNP variants (rs10762058, rs7088181) of the CTNNA3 gene (coding for catenin) are associated with DA in Caucasian workers. T-catenin and catenin are cytoplasmic proteins that complex with E-cadherin to form the epithelial junctional complex (EJC), critical in cell-cell adhesion and for maintaining epithelial integrity. In this renewal, we hypothesize that fine mapping of CTNNA3 loci and genotyping other proteins of the EJC will reveal genetic functional variants that define susceptibility for DA among exposed workers. To test this hypothesis, we will use sequence capture and next generation DNA sequencing of 170 kB of the CTNNA3 locus containing rs10762058 and rs7088181 to identify new candidate SNPs with higher allele frequencies in DA+ workers compared to comparator asymptomatic workers (Aim 1). In Aim 2, we will genotype tagging SNPs (tSNP) within E-cadherin, and catenin genes with an r2=0.8 and minor allele frequency e 0.1. These and SNPs identified by DNA sequencing will be tested for associations with confirmed DA (n=150) versus two comparator groups of 150 asymptomatic workers (AWs) exposed to methylene diphenyl diisocyanate (MDI) or hexamethylene diisocyanate (HDI). All DA-associated SNPs will be re-genotyped and replicated in a distinct background population of 73 Korean workers with DA and 100 AWs. Finally (Aim 3), the possible functional role of replicable DA associated SNPs will be studied by measuring myc-tagged CTNNA3 protein expression in A549 cells transfected with constructs containing different coding region SNPs. The allele-specific changes in the expression of CTNNA3 will be detected by Western blot analysis using anti-myc antibody. This novel approach can identify functional DA-associated genotypes that will enable identification of exposed workers at highest risk and define safe exposure levels for genetically susceptible workers.