Genome-wide association studies (GWASs) have been used to discover genetic polymorphisms that affect cardiovascular diseases (CVDs). Structural equation modelling (SEM) has been identified as a robust multivariate analysis tool. However, there is a paucity of research that has conducted SEM in African populations. The purpose of this study was to create a model that may be used to examine the relationships between genetic polymorphisms and their respective cardiovascular risk (CVR) factors. The procedure involved three steps. Firstly, the creation of latent variables and the hypothesis model. Next, confirmatory factor analysis (CFA) to examine the relationships between the latent variables, SNPs, dyslipidemia and metabolic syndrome, with their respective indicators. Then finally, model fitting using JASP statistical software v.0.16.4.0. The indicators for the SNPs and dyslipidemia all indicated significant factor loadings, −0.96 to 0.91 (p= <0.001) and 0.92 to 0.96 (p≤ 0.001), respectively. The indicators for metabolic syndrome also had significant coefficients of 0.20 (p= 0.673), 0.36 (p= 0.645) and 0.15 (p= 0.576), but they were not statistically significant. There were no significant relationships observed between the SNPs, dyslipidemia and metabolic syndrome. The SEM produced an acceptable model according to the fit indices.