Cataloging multi-ancestry 'omic readouts of the environmental and genetic determinants of type 2 diabetes - ABSTRACT Our proposal aims to uncover the intricate relationships between environmental, genetic, and 'omic factors in Type 2 Diabetes (T2D) development. Given the limitations of environmental and genetic studies in fully elucidating T2D risk, we intend to identify their combined effects in diverse populations and their role in insulin resistance and beta-cell dysfunction. Central to our research is the exposome, representing environmental and behavioral factors, and the genome, representing polygenic risk, and its influence on insulin resistance, beta- cell function. Are there different genetic and exposomic pathways to insulin resistance and beta-cell dysfunction? Circulating blood 'omic assays, such as the metabolome and proteome, will be instrumental in deducing exposomic and genomic phenotype pathways related to insulin secretion and resistance that, in turn, ultimately lead to T2D. Aim 1. Perform and create software for exposome-wide by genome-wide polygenic predictors study (E by PRS) against insulin resistance, B-cell function and incident T2D. We hypothesize that it is possible to identify differences of the effect of the exposome and the genome across the diabetes physiome, including measures of insulin resistance (e.g, HOMA-IR), beta-cell function (HOMA-B, insulin levels), elevated glycemic traits (e.g., HA1C%) in CDC NHANES with linked Medicare, Framingham Heart Study (FHS), Multi- ethnic Study on Atherosclerosis (MESA), Coronary Artery Risk Development in Young Adults Study (CARDIA), and Mass General Brigham Biobank (MGBB). We will deliver software and data to identify the architecture of the longitudinal exposome and polygenic predictors in T2D and glycemic traits. Aim 2. Catalog circulating biomarkers associated with the exposome and the genome and relate these biomarkers to incident diabetes across biobanks. We hypothesize that circulating `omics will capture endogenous responses to the exposome and genome, called Exposure Biomarkers (ExBs), and Genome Biomarkers (GeBs), respectively. Biological responses will be associated with incident T2D, and output a database of predictions between the genome, exposome and circulating `omics (the “genome-exposome atlas”), deriving new biomarkers of exposure. We will use genetic approaches, including Mendelian randomization to infer causal effects between the ExB biomarkers and T2D. We expect this application to develop a new systems genetics/physiology approach for discovering elusive exposome, and exposome by PRS interactions, in T2D. Our open data and analytic products and pipelines will increase the value of existing federal resources, making exposome and genome research accessible to diabetes researchers.
