Obesity, Type 2 diabetes (T2DM), and dyslipidemia are metabolic disorders that promote the development of coronary artery (CAD) and peripheral arterial disease (PAD). Collectively, these cardio metabolic conditions are leading causes of illness and death among Veterans. A substantial proportion of the variation in risk of clinical complications related to these conditios remains unexplained despite an understanding of the root factors involved. The VA Million Veteran Program (MVP) links information from Veterans' electronic heath record (EHR) to biomarker data measured from blood and provides an unparalleled opportunity to further explore the genetic basis of cardio metabolic diseases. We propose to use the genome wide genotyping data from the first 200,000 participants in MVP linked to the EHR to uncover novel associations between genetic variation and risk of cardio metabolic disease. To perform this research, we have assembled a team of investigators with extensive experience in VA based clinical research and population genetics. Many members of our team have not only participated in, but also have led, the most productive international collaborations over the last 10 years that have studied the genetic basis of cardio metabolic diseases. Our consortium includes investigators from 5 VISNs based at Palo Alto, Philadelphia, Phoenix, Bedford, and Albany as well as from Stanford University and the University of Pennsylvania. In Aim 1, we will establish optimal definitions of five cardio metabolic traits: body mass index, blood levels of cholesterol, as well as diagnoses of Type 2 diabetes (T2DM), CAD, PAD, using EHR derived information on medical diagnoses and treatments, physical exam and lab measures, and medication usage. Preliminary results of our queries of VA EHR data using the most liberal definitions of the traits have identified approximately 160,000 participants with lipid measurements, 195,000 participants with measurements of body-mass index, 100,000 participants with T2DM or prediabetes, 46,000 participants with CAD, and 9,000 participants with PAD. For quantitative traits, we will derive and study not only single time point measures but also long term averages for each individual. For outcomes, we will optimize our definitions by assessing the relationship between established risk factors including phenotype specific genetic risk scores and case-control status. In Aim 2, we will perform a series of genome wide association studies to confirm known loci and to identify novel genetic variation associated with our traits of interest. We will also use the comprehensive VA EHR to examine for the presence of gene-environment interactions. Finally, in Aim 3, we will apply novel statistical algorithms that will improve our understanding of the genetic variation that contributes to the risk of cardio metabolic diseases in both the African American and the Hispanic American populations by leveraging similarities in the genetic architecture among different race/ethnic groups. Successful completion of this project will help us to more thoroughly comprehend the underlying causes of cardio metabolic disease and to develop novel therapies that are safe, effective, and personalized. These discoveries will also result in the more reliable identification of individuals at risk for these disorders, allowing for the more optimal delivery of primary prevention strategies within the VA population.