A SOCIAL EPIGENOMIC APPROACH TO HEALTH DISPARITIES IN CARDIOVASCULAR RISK FACTORS Individual-level socioeconomic factors such as education, income/wealth, and occupation have long been known to profoundly affect risk for cardiovascular diseases, and these effects accumulate across the life course to create systematic disadvantage that manifests in a wide range of health disparities. In addition, there is now increasing evidence that neighborhood-level disadvantage also negatively impacts cardiovascular health, both cross-sectionally and longitudinally, even after accounting for individual-level socioeconomic factors. One mechanism by which individual-level and neighborhood-level disadvantage may influence cardiovascular health is through epigenomic modifications of genes regulating adaptive cellular pathways (e.g. inflammation and immune response). To better understand the biological mechanisms underlying health disparities in cardiovascular disease, we propose to investigate the impact of individual and neighborhood disadvantage on the epigenome. We will conduct the discovery work in two epidemiologic studies ? the Multi- Ethnic Study of Atherosclerosis (MESA, N=1,264) and the Genetic Epidemiology Network of Arteriopathy (GENOA, N=1,728) ? and replicate our results in other cohorts with similar measures, including the Atherosclerosis Risk in Communities study (ARIC, N=3,911) and the Health and Retirement Study (HRS, N=2,000). This multi-cohort strategy increases scientific rigor by reducing false positives while improving our higher-dimensional understanding of the social epigenomic architecture underlying cardiovascular disease. To facilitate this multi-cohort approach, we will begin by harmonizing the measures of individual and neighborhood disadvantage (Aim 1) to enable us to identify and replicate DNA methylation (DNAm) sites that are associated with these measures of disadvantage (Aim 2) and then evaluate whether the DNAm sites are mediators of the well-established relationships between disadvantage and cardiovascular risk factors (Aim 3). Finally, we will perform pathway analysis to characterize the key biological pathways implicated by the DNAm sites identified in the previous Aims, and investigate their association with gene expression in MESA and GENOA (Aim 4).