Evaluating polygenic risk scores for predicting cardiometabolic traits and disease risks in the Taiwan Biobank

Abstract

Type 2 diabetes (T2D) and hypertension are common comorbidities and, along with hyperlipidemia, serve as risk factors for cardiovascular diseases. This study aimed to evaluate the predictive value of polygenic risk scores (PRSs) on cardiometabolic traits related to T2D, hypertension, and hyperlipidemia and the incidence of these three diseases in Taiwan Biobank samples. Using publicly available, large-scale Genome-Wide Association Studies (GWAS) summary statistics, we constructed cross-ethnic PRSs for T2D, hypertension, body mass index (BMI), and 9 quantitative traits typically used to define the three diseases. A composite PRS (cPRS) for each of the 9 traits was constructed by aggregating the significant PRSs of its genetically correlated traits. The associations of each 9 trait at baseline as well as the change of trait values during a 3 to 6-year follow-up period with its cPRS were evaluated. The predictive performances of cPRSs in predicting future incidences of T2D, hypertension, and hyperlipidemia were assessed. The cPRSs had significant associations with baseline and changes of trait values in 3-6 years and explained a higher proportion of variance for all traits than individual PRSs. Furthermore, models incorporating disease-related cPRSs, along with clinical features and relevant trait measurements achieved area under the curves (AUC) values of 87.8%, 83.7%, and 75.9% for predicting future T2D, hypertension, and hyperlipidemia in 4 to 6 years, respectively. This study revealed the complex genetic correlation structures of quantitative traits associated with the three diseases and underscores the potential of PRSs to improve future prediction models for T2D, hypertension, and hyperlipidemia.