Genetic Risk Score Constructed from Polymorphisms in the VEGFA, TBX5, and SMAD7 Genes Provides Novel Insights into the Molecular Mechanisms of the Tetralogy of Fallot and Ventricular Septal Defect (Case-Control Study from the Pakistani Population) - Ashiq, Sana; Sabar, Muhammad Farooq; Hyder, Syed Najam (The American journal of cardiology, 2023)
Congenital heart defects are common and complex birth-defect malformations in developed and developing countries. It is a multifactorial disease that involves the interaction of either gene-gene or gene-environment. This comparative study was the first report on the genotypic-phenotypic correlation in the Pakistani population. The single nucleotide polymorphisms (SNPs) were further tested for association with maternal diabetes mellitus or hypertension. In addition, the cumulative genetic risk score (GRS) for low to moderately-associated SNPs was calculated for each study subject, which can ultimately guide us for better therapeutic options and prevention strategies. According to the predefined selection criteria, 376 subjects were recruited. The multiplex mini-sequencing genotyping technique opted for the cost-effective genotyping of selected loci. The association of variants with the disease was examined using logistic regression analysis. The statistical and graphical analysis was conducted using SPSS, Haploview, SNPStats, and GraphPad Prism. The results for all SNPs analysis suggested a nonsignificant association with overall congenital heart defect risk except rs3809923. However, interestingly on stratified analysis variants, rs3809923 and rs3809922 showed an association only with tetralogy of Fallot. The remaining risk factor analysis for maternal hypertension and diabetes mellitus association with SNPs were nonsignificant. The GRS was the first time constructed for this low to moderately-associated variants. Interestingly, the cumulative GRS was significantly different from the control group revealing the cumulative effect of these polymorphisms panel in patients. In conclusion, the use of GRS in the clinical setting can predict better risk association and patient outcomes.