Genetics of Cardiovascular Disease: How Far Are We from Personalized CVD Risk Prediction and Management?

Abstract

Despite the rapid progress in diagnosis and treatment of cardiovascular disease (CVD), this disease remains a major cause of mortality and morbidity. Recent progress over the last two decades in the field of molecular genetics, especially with new tools such as genome-wide association studies, has helped to identify new genes and their variants, which can be used for calculations of risk, prediction of treatment efficacy, or detection of subjects prone to drug side effects. Although the use of genetic risk scores further improves CVD prediction, the significance is not unambiguous, and some subjects at risk remain undetected. Further research directions should focus on the "second level" of genetic information, namely, regulatory molecules (miRNAs) and epigenetic changes, predominantly DNA methylation and gene-environment interactions.

Keywords: cardiovascular disease; epigenetic; gene; gene score; interaction; polymorphism.

Figure 1

Genetic predisposition CVD. There are five nongenetic risk factors of CVD and all are under significant genetic control. Some selected genes with SNPs influencing mentioned risk factors and subsequently CVD are shown. Probably highest number of SNPs is associated with dyslipidemia, most powerful variants are associated with diabetes and obesity and the smallest effects have been observed in field of hypertension. Interestingly, smoking is by far the strongest predictor of CVD, but the list of genes associated with smoking behaviour is short and there is a lack of studies focused on these genes in CVD patients.

Figure 2

Theoretical example of gene score construction. Theoretical example of unweighted gene score (based on sum of the risky alleles) in group of patients and controls. With 10 genes (polymorphisms) the values could be between 0 and 20. In fact, the lowest and the highest values are almost never presented. There is significant shift in the distribution curve in patient groups to the higher numbers of risky alleles. Still, there is also significant overlap between the patients and controls in gene score values.