Abstract Large-scale genome wide association studies (GWAS) have identified a large number of genetic variants associated with complex diseases. The aggregation of all the variants that are known to contribute to the disease in the form of polygenic risk scores (PRS) improves the prediction of a range of complex diseases. Most PRS have been developed within European ancestry study samples and have shown to perform poorly in other race/ethnic groups, further exaggerating health disparities across ancestries. As genetic approaches for precision medicine become more popular, there is a critical need to responsively and pro-actively expand access to accurate PRS. Specifically, diabetes, and its associated complications are one of the biggest global health problems of the 21st century. In fact, type 1 and type 2 diabetes (T1D and T2D), gestational diabetes (GDM) and related complications are excellent disease models to study the utility of PRS for predicting heterogenous and complex health outcomes in a setting where dramatic racial/ethnic and socioeconomic disparities exist. Not only are PRS useful to predict T1D and T2D, but they can distinguish between T1D and T2D, and between T2D subtypes. The wealth of existing trans-ancestry GWAS data from diabetes subtypes, complications, and quantitative traits recently generated provides a unique opportunity for constructing highly transferable PRS across populations. To address the disparities in PRS across ancestries, we have assembled a multi-disciplinary team to aggregate and analyze the largest existing genetic data from more than 1.8 M individuals (35% non- European) with T1D, T2D, GDM and glycemia-related complications and quantitative traits to improve the PRS prediction of diabetes and progression across lifespan in diverse ancestries with these Aims: (1) Collection, harmonization and integration of large-scale, multi-ancestry cohorts with diabetes traits across the life-span and genomics for development, training and testing PRS for diverse ancestries; (2) Development of methods to improve PRS prediction in non-European populations by using Bayesian approaches that allow integration of linkage disequilibrium and summary statistics from several ancestries. (3) Development, testing, and comparing performance of PRS for each trait, development of risk prediction tools that integrate clinical and genetic risk factors, and assessment of scenarios where PRS improve the prediction. Accomplishing the aims of this proposal will demonstrate how genomic data can inform more efficient and targeted preventive strategies within healthcare systems and across ethnically diverse populations. Findings are expected to advance precision care of patients with diabetes and related conditions in people of diverse ancestral background and serve as a paradigm for many other complex diseases.

Narrative Type 1 and type 2 diabetes (T1D and T2D), gestational diabetes (GDM) and related complications are excellent disease models to study the utility of polygenic risk scores (PRS) for predicting heterogenous and complex health outcomes in a setting where dramatic racial/ethnic and socioeconomic disparities exist. To address the disparities in PRS across ancestries, we have assembled a multi-disciplinary team to aggregate and analyze the largest existing genetic data from more than 1.8 M individuals (35% non-European) with T1D, T2D, GDM and glycemia-related complications and quantitative traits to improve the PRS prediction of diabetes and progression across the lifespan in diverse ancestries. Accomplishing the aims of this proposal will demonstrate how genomic data can inform more efficient and targeted preventive strategies within healthcare systems and across ethnically diverse populations.