We aim to analyze the genetic architecture of a wide variety of cardiometabolic diseases and their risk factors. We are interested in utilizing numerous data sources to identify DNA variants associated with our disease and risk factors of interest to better understand how genetics confer disease risk. We will conduct genetic association studies to link genetic sequence variants with risk of cardiometabolic disease and risk factors. We will also integrate multiple datatypes mapping to identify novel variant associations with gene expression, protein expression, and other molecular datatypes through methods known as expression quantitative trait locus (eQTL). Similar methods will be applied to link genetic variants to other intermediate traits including epigenetic markers, protein levels, and metabolite levels which may mediate cardiometabolic disease and its risk factors. These integrative approaches will enable us to elaborate powerful datasets for downstream causal inference experiments to evaluate the casual links between risk factors, intermediate phenotypes/traits, and cardiometabolic diseases.
