Genetic & Social Determinants of Health: Center for Admixture Science and Technology

PROJECT SUMMARY It is imperative to understand the underlying sources of the large health disparities among individuals from different racial and ethnic groups living in the United States (US). Complex relationships between genetics and social factors influence health outcomes. Approximately 33% of people in the US belong to an ethnic minority

group and ~12.5% live below the federal poverty line. Historical and recent mixing of Europeans, Native Americans, Africans and Asians resulted in the US population having a relatively large number of admixed individuals who carry ancestry from outside their self-identified race. The All of Us (AoU) Program and the Million

Veterans Program (MVP) include genetic, health and socioeconomic information on all participants, and therefore provide an opportunity to identify factors contributing to health disparities. However, the AoU program and MVP require their data to stay within local hosting sites, therefore conducting joint analyses on these cohorts

requires the development of algorithms that enable privacy-protecting distributed computing (i.e., without revealing individual-level data). There are three important gaps in understanding genetic determinants of health: 1) most studies have been dominated by European individuals, and while they control for global ancestry, there

is no attempt to model the patchwork of local ancestry characteristic of admixed individuals; 2) GWAS are primarily conducted using SNPs, while important sources of ancestry-specific genetic variation (tandem repeats (TRs) and the major histocompatibility complex (MHC) interval) are not assayed; and 3) most GWAS do not

adjust for socioeconomic factors. The American College of Medical Genetics and Genomics (ACMG) has published a list of medically actionable cancer and cardiovascular genes recommended for return of incidental findings of pathogenic variants to reduce morbidity and mortality, but having minorities excluded from healthcare

follow up due to common barriers (e.g., language and access) makes it difficult to distinguish between the genetic and socioeconomic factors that contribute to disparate health outcomes. The goal of the CAST (Center for Admixture Science and Technology) program is to improve the clinical utility of genetic information for all

populations living in the US. In Aim 1, we will develop and apply multivariate models of disease risk prediction that incorporate local ancestry, complex variants (TRs and HLA types). In Aim 2, we will conduct scalable distributed computing using data from millions of individuals across the AoU and MVP compute enclaves. In Aim

3, we will develop new approaches to characterize phenotypes using electronic health records and surveys from AoU and MVP, assess the impact of including social determinants of health in our models, and prospectively evaluate them with new AoU and MVP participants. To achieve these goals, we assembled a highly

interdisciplinary group of researchers with expertise in Genetics, Genome Biology, Data Sharing Policy and Technology, Health Disparities, Phenotyping, and Statistics.