The polygenic risk score (PRS) represents the cumulative impact of numerous common genetic variants, which has demonstrated utility in predicting clinical phenotypes and outcomes for individuals. To date, a variety of distinct algorithmic approaches have been developed to forecast an individual's genetic susceptibility to diverse phenotypes. The risk alleles included within PRS have traditionally been identified through genome-wide association studies (GWAS) conducted primarily on European populations, which can lead to challenges when implementing PRS in underrepresented cohorts, such as the Vietnamese population. In this investigation, we leveraged available control data from the VN1K cohort and existing GWAS to enhance the utility for the Vietnamese population. Specifically, we focused the analysis on ten common disease conditions, including breast cancer (BC), colorectal cancer (CRC), gastric cancer (GC), chronic kidney disease (CKD), coronary artery disease (CAD), hyperlipidemia, osteoporosis, osteoarthritis, Parkinson's disease (PD), and Alzheimer's disease (AD). To optimize the accuracy of the polygenic risk scores (PRS), we evaluated the four most recently developed PRS algorithms using multiple GWAS datasets encompassing both East Asian and European ancestral backgrounds, then assessed the PRS contribution in explaining disease risk with genotyping data from case-control samples. For each disease, we experimented with various methods to identify the model that produced the best PRS score. The resulting r-squared values, which indicate the proportion of phenotypic variance explained by the PRS, ranged from 2% to 6%. Additionally, the Area Under the Curve (AUC) values for the PRS in distinguishing between case and control phenotypes ranged from 0.52 to 0.67. These findings provide valuable insights into diverse aspects of PRS implementation, particularly regarding the transfer of statistical inferences derived from GWAS conducted in other population cohorts.