PROJECT SUMMARY/ABSTRACT Asthma progression is associated with reduced growth and an increased decline in lung function (LF) that is thought to arise from a complex interplay of genes and environment. While several asthma genetic risk loci have been identified to-date, an in-depth utilization of how environmental factors interact with these loci remains limited. The methylome and the metabolome are both heavily influenced by the environment and recent studies confirm a link between both omes. Integrating DNA methylation with the metabolome could be a powerful approach to obtain converging evidence of specific pathways influencing asthmatic lung function trajectories at the genome-wide level. While previous studies have investigated the role of the metabolome or methylome with regard to lung function, none have investigated both “omes” simultaneously for lung function outcomes in asthmatics. The central hypothesis of this proposal is that DNA methylation (CpG sites) plays a critical role in modulating downstream metabolites and thus metabolic pathways, some of which may be driven by an underlying genetic effect on lung function in children with asthma. We want to specifically investigate multi-omic data from Wnt, Hippo and sphingolipid pathways, known to affect asthmatic lung function generally and in our preliminary data. We capitalize on the genetic, methylomic and metabolomic data generated from three large prospective childhood cohorts including two from the Trans-omics for Precision Medicine (TOPMed) consortium: Childhood Asthma Management Program (CAMP) and The Genetic Epidemiology of Asthma in Costa Rica (GECRA) study and an independent cohort: The Vitamin D Antenatal Asthma Reduction Trial (VDAART). This study seeks to integrate multiple omics using innovative and state-of-the-art methodologies: quantitative trait loci (QTL) mapping to identify genome-methylome associations with LF (Aim 1), QTL mapping and causal inference testing to evaluate methylome-metabolome associations with LF (Aim 2), correlation-based network methods to identify a highly correlated set of omic-driven biomarkers in dysregulated pathways (Aim 3). Priyadarshini Kachroo, PhD, MS is a bioinformatician whose long-term career goal is to transition towards becoming an independent data scientist with expertise in utilizing multi-omic approaches to complex disease phenotypes. As Dr. Kachroo completes these aims, her career development plan will support her training goals: 1) deepen clinical understanding of asthmatic LF phenotypes; 2) expand on the statistical skills including causal inference testing 3) develop skill-set of network methods for integrating multi-omic datasets 4) enhance skills in study-design, mentorship and the ethics of scientific conduct and communication of research. Dr. Kachroo’s strong quantitative and methodological background well position her to accomplish these goals, complete the aims of this proposal and prepare her for an independent research c...