ABSTRACT Asthma is a major public health problem in the United States, affecting 11 million children. Despite advances in asthma care, African Americans (AAs) are 4 times more likely to be hospitalized and 5 times more likely to die from asthma than European Americans (EAs). Several factors could be responsible for the observed asthma racial disparities including genetic and non-genetic factors. While epigenetics appear to serve as a critical biological switch between genetic vulnerability and socio-environmental exposures, limited studies are available that directly map the socio- environmental exposures with asthmatic epigenome/genome information. In addition, current approach do not leverage existing geospatial data such as environmental exposure and neighborhood socioeconomic conditions to improve asthma risk prediction. In this proposal, we will utilize comprehensive geocoding algorithms, novel statistical methods to integrate social, clinical, environmental, genetic, and epigenetic data into a composite score for asthma risk stratification and prediction. The overall objective of this research is to conduct genome-wide Methyl-Seq analysis and leverage existing well-phenotyped AA pediatric asthma cohort with extensive socio-environmental exposures and ancestry-tailored multi-ethnic genotyping array (MEGA) data from Cincinnati Pediatrics Repository to accurately determine and develop ancestry- specific asthma risk stratification and prediction models. The objective of this application is to undertake an epigenome-wide association study (EWAS), incorporating geocoded neighborhood- and individual-level socio-environmental predictors, and novel analytical strategies to create a composite risk score incorporating methylation risk score (MRS), ancestry, environmental exposures and social characteristics to predict asthma. We will accomplish these objectives through the following Specific Aims: 1) Develop an ancestry-specific methylation risk score (MRS) for asthma and test its association with socio-environmental exposures contributing to asthma risk. 2) Determine the mediation effects of MRS between genetic ancestry and asthma risk. 3) Develop a multivariable risk predictive model for asthma incorporating MRS, genetic ancestry, clinical, and socio-environmental risk factors. The proposed research is innovative because this will be the first time a MRS approach will be used to develop a population-based risk profile in asthmatics. The study will provide insights in the use of risk stratification for screening and targeted interventions. This work is significant because it can serve as a model to study the composite effect of MRS, ancestry, socio-environmental, and clinical risk factors on racial disparities in other well-documented common complex diseases beyond asthma.