Coronary Artery Disease Genetics Enlightened by Genome-Wide Association Studies

Abstract

Many cardiovascular diseases are facilitated by strong inheritance. For example, large-scale genetic studies identified hundreds of genomic loci that affect the risk of coronary artery disease. At each of these loci, common variants are associated with disease risk with robust statistical evidence but individually small effect sizes. Only a minority of candidate genes found at these loci are involved in the pathophysiology of traditional risk factors, but experimental research is making progress in identifying novel, and, in part, unexpected mechanisms. Targets identified by genome-wide association studies have already led to the development of novel treatments, specifically in lipid metabolism. This review summarizes recent genetic and experimental findings in this field. In addition, the development and possible clinical usefulness of polygenic risk scores in risk prediction and individualization of treatment, particularly in lipid metabolism, are discussed.

Keywords: CAD, coronary artery disease; CXCL1, chemokine (C-X-C motif) ligand 1; GWAS, genome-wide association study; LDLR, low-density lipoprotein receptor; LPL, lipoprotein lipase; MI, myocardial infarction; PCSK9, proprotein convertase subtilisin/kexin type 9; cardiovascular diseases; coronary artery disease; genome-wide association studies; lncRNA, long non-coding RNA; polygenic risk scores; precision medicine.

Graphical abstract

Figure 1

Currently Known CAD Genes and Supposed Mechanisms Mechanisms were grouped to initiation, plaque progression, and platelet function but are not limited to this rough classification. Previously unknown genes were grouped to the proposed pathways if experimental data (using the queries “gene name” and “atherosclerosis” in PubMed) was published in the meantime: LMOD1 (30); NBEAL1 (31); IRS1 (32); PLCG1 (33); NCK1 (34,35); PRDM16 (36); HHIPL1 (37); MFGE8 (38); HNRNPUL1 (39); RAC1 (40,41); and DDX5 (42). CAD = coronary artery diseased. Modified after and updated from Erdmann et al. (12). Modified image material available at Servier Medical Art under a Creative Commons Attribution 3.0 Unsupported License.

Figure 2

Generation and Use of Polygenic Risk Scores Identification: genome-wide association studies are required to identify variants that are associated with CAD and early-onset myocardial infarction (EOMI). Bioinformatic tools enable the imputation of not directly genotyped variants to increase the numbers of variants that can then be compared between healthy control subjects and cases. Statistical analysis leads to the identification of variants that are associated with CAD at the genome-wide level of significance (p < 10⁻⁸). Modeling: in a second step, polygenic risk scores are based on modeling the sum of particular risk alleles, integrating their effect sizes. Polygenic risk scores follow a Gaussian distribution with most subjects carrying an intermediate number of risk alleles and a small portion either carrying a small or a large number of risk alleles. Application: genetic information is the only tool in risk prediction and management that is basically available at birth and could be used to predict risk and tailor prevention strategies. During life, the influence of risk factors and their management becomes increasingly important. In older adults, imaging to detect atherosclerosis and its complications remains the main diagnostic tool. Over time, the strategy shifts from prevention in young and middle-aged subjects to treatment and secondary prevention. However, genetic information could, be used at all stages to predict risk in young and middle-aged individuals (1), evaluate the beneficial effects of lifestyle but also pharmacological interventions (2), and predict the response to a given treatment strategy (e.g., statins or PCSK9 inhibitors) (3). For details, see text. Modified image material available at Servier Medical Art under a Creative Commons Attribution 3.0 Unsupported License. Abbreviations as in Figure 1.

Central Illustration

The Identification of Genetic Variants Influencing Coronary Artery Disease Risk Affects Several Fields From a deeper knowledge of the: 1) pathophysiological processes, 2) novel treatment targets were identified; 3) the interplay between genetic factors and traditional risk factors such as obesity, hypertension, smoking, or hyperlipidemia but also noise and air pollution is the subject of extensive research. Finally, genetic risk scores may in the future 4) improve risk prediction and lead to the development of 5) individualized treatment strategies, the cornerstone of precision medicine. PCSK9 = proprotein convertase subtilisin/kexin type 9. Modified image material available at Servier Medical Art under a Creative Commons Attribution 3.0 Unsupported License.