Cardio-renal-metabolic (CaReMe) conditions are common and the leading cause of mortality around the world. Genome-wide association studies have shown that these diseases are polygenic and share many genetic risk factors. Identifying individuals at high genetic risk will allow us to target prevention and treatment strategies. Polygenic risk scores (PRS) are aggregate weighted counts that can demonstrate an individual's genetic liability for disease. However, current PRS are often based on European ancestry individuals, limiting the implementation of precision medicine efforts in diverse populations. In this study, we develop PRS for six diseases and traits related to cardio-renal-metabolic disease in the Penn Medicine Biobank. We investigate their performance in both European and African ancestry individuals, and identify genetic and phenotypic overlap within these conditions. We find that genetic risk is associated with the primary phenotype in both ancestries, but this does not translate into a model of predictive value in African ancestry individuals. We conclude that future research should prioritize genetic studies in diverse ancestries in order to address this disparity.Recent studies show high prevalence of cardio-renal-metabolic conditions among adults in the USA 4 and together they are the leading cause of mortality around the world 5,6 . GWAS have identified more than 100 loci associated with common diseases such as coronary artery disease (CAD), body mass index (BMI), hypertension, renal failure and type 2 diabetes (T2D). This group of cardio, renal, and metabolic conditions are collectively referred to as CaReMe conditions. Among the individuals that are diagnosed with one disease (for example T2D), the prevalence of comorbidities such as hypertension, CAD, heart failure (HF), and chronic kidney disease (CKD) also increases. To evaluate disease risk in an individual, it is essential to also consider comorbid or secondary conditions related to the primary disease. There are several GWA-studies that have identified shared genetic associations between CaReMe conditions, demonstrating similarity in the underlying genetic architecture 7,8 . Pathophysiology of these conditions also show the cross-talk between organ systems and its effect on disease progression such as hemodynamic interaction between heart and kidney in heart failure 9 . With PRS, we can derive individuals' disease risk for each CaReMe condition using GWAS summary statistics. More importantly, PRS is derived from the effect of millions of genetic variants on a disease; so it accounts for an individual's genetic background. Therefore, PRS can evaluate the genetic overlap among coexisting or comorbid conditions. Phenome-wide Association Studies (PheWAS) can be used to identify links between disease risk and other conditions 10-12 . Using these strategies, we investigated whether cross-phenotype associations can provide insights into the contribution of risk for one disease risk on other conditions. Lastly, we also evaluated the effect of age, s...