Glucagon-like peptide 1 receptor (GLP1R) agonists are an important class of antidiabetic drugs with an attractive clinical profile – including improved glycemic control, weight loss, and decreased risk of major adverse cardiovascular events. While there is substantial variation in the magnitude of individual patients’ responses to these drugs, there are no validated approaches to identify patients most likely to have the largest responses and derive the most clinical benefit. This application proposes a genome-wide association study in the Old Order Amish population to identify genetic variants that predict individuals’ pharmacodynamic responses to GLP1R agonists. Based on preliminary data from the Principal Investigators’ research, the proposed project will measure pharmacodynamic endpoints related to beneficial effects of GLP1R agonists. Overweight/obese otherwise healthy volunteers will be recruited from the Old Order Amish population in Lancaster County, PA. In order to assess pharmacodynamic responses, research participants will undergo two frequently sampled intravenous glucose tolerance tests (FSIGT). The first FSIGT will be conducted at baseline prior to administration of drug. The second FSIGT will be conducted after six weeks of treatment with semaglutide (0.25 mg/wk X 4 wks; 0.5 mg/sk X 2 wks). The proposal proposes two specific aims: • Specific Aim #1. To identify genetic variants associated with effects of a GLP1R agonist to enhance glucose- stimulated first phase insulin secretion in the two FSIGTs (before and after administration of drug). • Specific Aim #2. To identify genetic variants associated with the effect of a GLP1R agonist to accelerate the rate of glucose disappearance as assessed in the two FSIGTs (before and after administration of drug). Genotyping will be conducted using a high-density array with comprehensive coverage of DNA sequence variants. The project will leverage a global imputation panel generated from whole genome sequence data on ~ 100K subjects including 1,025 Amish individuals obtained through the NHLBI-sponsored Trans-Omics for Precision Medicine (TOPMed) program. Previous genetic studies conducted in the Old Order Amish population have been highly predictive of observations in the general population and relevant patient populations. Based on these precedents, we anticipate that genetic variants in this study are very likely to be predictive of clinical responses of GLP1R agonist-treated type 2 diabetic patients. The proposed study is a step toward the long- term objective of identifying genetic biomarkers to predict an individual patient’s response to GLP1R agonists. Availability of predictive biomarkers would enable physicians to prescribe optimal therapies for each individual patient based on predictors of beneficial response. This type of Precision Medicine approach, based on predictive pharmacogenomic biomarkers, would be a transformational advance in the way diabetes drugs are prescribed.