“TransEpiGen-omics” in cardiovascular disease research: Unraveling the genetic basis of complex diseases

Abstract

Summary. The genome, methylome, and transcriptome are functional components of a comprehensive network working in the background in support of our health. Thus, an integrative approach in combining and analyzing data from different sources and of different types is necessary in order to improve the understanding of biological processes and biological systems as a whole. In the past two decades, most candidate gene association studies in cardiovascular disease (CVD) have identified genes and variants that affect lipid levels, inflammation and the biology of the vascular wall. Further, a non-candidate-driven approach has yielded the definition of both biologically explainable and novel genes without a known biological background. In summary, the genetic susceptibility to CVD is mainly described by the influence of many common single nucleotide polymorphisms (SNPs), with the small effect size supporting the common disease/common variant hypothesis. Consequently, many integrative concepts were applied in order to distinguish functionally relevant genetic variants, especially noncoding ones. Expression quantitative trait loci (eQTL) analysis refers to the widespread regulation of gene expression mostly by cis-acting SNPs. MicroRNAs have also become interesting targets in both research and therapy in atherosclerosis and CVD. A number of miRNAs has been shown to have a role as risk factors for atherosclerosis progression, while some share their atheroprotective effect. The complex and multidimensional nature of cardiovascular risk factors and outcomes could additionally be resolved by research into epigenetic regulation. Distinct epigenomic patterns exist in key DNA elements (promoter CpG islands, intragenic CpG islands, gene bodies and H3K36me3-enriched regions) of the cardiac genome. Moreover, differential expression of each corresponding gene correlates with differential DNA methylation in heart failure. It is clear that the aim of cardiovascular -omics in the next decade is to find better algorithms to integrate as much as possible different types of data into the biological networks underlying the disease phenotype.