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

Yale University, New Haven CT

Investigators

Linked publications, trials & patents

Abstract

Complex relationships between genetics and non-genetic factors influence health outcomes. The All of Us (AoU) Program and the Million Veterans Program (MVP) include genetic, health and other information on all participants, and therefore provide an opportunity to identify factors contributing to health. 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 control for global ancestry, and there is no attempt to model the patchwork of local ancestry characteristic of most 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 other 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 some individuals removed from healthcare follow up due to common obstacles (e.g., access to healthcare services) makes it difficult to distinguish between the genetic and non-genetic factors that contribute to different 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 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 Outcomes, Phenotyping, and Statistics.