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The Application of Clinical Genetics logoLink to The Application of Clinical Genetics
. 2014 Jan 16;7:15–32. doi: 10.2147/TACG.S35301

Pathogenesis of coronary artery disease: focus on genetic risk factors and identification of genetic variants

Sergi Sayols-Baixeras 1, Carla Lluís-Ganella 1, Gavin Lucas 1, Roberto Elosua 1,
PMCID: PMC3920464  PMID: 24520200

Abstract

Coronary artery disease (CAD) is the leading cause of death and disability worldwide, and its prevalence is expected to increase in the coming years. CAD events are caused by the interplay of genetic and environmental factors, the effects of which are mainly mediated through cardiovascular risk factors. The techniques used to study the genetic basis of these diseases have evolved from linkage studies to candidate gene studies and genome-wide association studies. Linkage studies have been able to identify genetic variants associated with monogenic diseases, whereas genome-wide association studies have been more successful in determining genetic variants associated with complex diseases. Currently, genome-wide association studies have identified approximately 40 loci that explain 6% of the heritability of CAD. The application of this knowledge to clinical practice is challenging, but can be achieved using various strategies, such as genetic variants to identify new therapeutic targets, personal genetic information to improve disease risk prediction, and pharmacogenomics. The main aim of this narrative review is to provide a general overview of our current understanding of the genetics of coronary artery disease and its potential clinical utility.

Keywords: coronary artery disease, pathogenesis, genetic risk factors, genetic variants

Introduction

Coronary artery disease (CAD) is the principal individual cause of mortality and morbidity worldwide. A recent report on the Global Burden of Disease, which proposes disability-adjusted life years (DALYs, calculated as the sum of years of life lost and years lived with disability) as a new metric to measure disease burden, indicates that CAD accounted for the largest proportion of DALYs due to a single cause worldwide in 2010, explaining 5% of the total number of DALYS (Figure 1).1

Figure 1.

Figure 1

The top eleven diseases explain 37.7% of the global burden of disease measured as DALYs, with coronary artery disease as the leading cause of DALYs in 2010.

Abbreviations: DALYs, disability-adjusted life years; AIDS, acquired immune deficiency syndrome; HIV, human immunodeficiency virus.

CAD is a complex chronic inflammatory disease, characterized by remodeling and narrowing of the coronary arteries supplying oxygen to the heart. It can have various clinical manifestations, including stable angina, acute coronary syndrome, and sudden cardiac death. It has a complex etiopathogenesis and a multifactorial origin related to environmental factors, such as diet, smoking, and physical activity, and genetic factors2 that modulate risk of the disease both individually and through interaction.

In this narrative review, we summarize the main etiopathogenic mechanisms that underlie CAD, with a focus on current knowledge concerning the genetic architecture of the disease and the clinical utility of this knowledge.

Atherosclerosis, the main etiopathogenic mechanism of CAD

Atherosclerosis is the main etiopathogenic process that causes CAD, and its progression is related to an interplay between environmental and genetic factors, with the latter exerting their effects either directly or via cardiovascular risk factors (Figure 2). Although clinical ischemic cardiovascular events usually appear after the fifth decade of life in men and the sixth decade of life in women, this process starts early in life, even during fetal development.3

Figure 2.

Figure 2

Genetic and environmental causes of development and progression of atherosclerosis act directly or through known intermediate traits.

Abbreviation: LDL, low-density lipoprotein.

Briefly, atherosclerosis is a silent progressive chronic process characterized by accumulation of lipids, fibrous elements, and inflammatory molecules in the walls of the large arteries.48 This process begins with the efflux of low-density lipoprotein (LDL) cholesterol to the subendothelial space, which can then be modified and oxidized by various agents. Oxidized/modified LDL particles are potent chemotactic molecules that induce expression of vascular cell adhesion molecule and intercellular adhesion molecule at the endothelial surface, and promote monocyte adhesion and migration to the subendothelial space. Monocytes differentiate to macrophages in the intima media. Recently, different subsets of monocytes have been identified, and their roles appear to be different according to the phase of atherosclerosis in which they are involved.9 Macrophages bind oxidized LDL via scavenger receptors to become foam cells,5 and also have proinflammatory functions, including the release of cytokines such as interleukins and tumor necrosis factor. The final result of this process is formation of the first typical atherosclerotic lesion, ie, the fatty streak, in which foam cells are present in the subendothelial space.

Other types of leukocytes, such as lymphocytes and mast cells, also accumulate in the subendothelial space.10 The cross-talk between monocytes, macrophages, foam cells, and T-cells results in cellular and humoral immune responses, and ultimately in a chronic inflammatory state with the production of several proinflammatory molecules.11,12 This process continues with the migration of smooth muscle cells from the medial layer of the artery into the intima, resulting in the transition from a fatty streak to a more complex lesion.5 Once smooth muscle cells are in the intima media, they produce extracellular matrix molecules, creating a fibrous cap that covers the original fatty streak. Foam cells inside the fibrous cap die and release lipids that accumulate in the extracellular space, forming a lipid-rich pool known as the necrotic core.13 The result of this process is formation of the second atherosclerotic lesion, the fibrous plaque.

The thickness of the fibrous cap is key for maintaining the integrity of the atherosclerotic plaque,8 and two types of plaque can be defined depending on the balance between formation and degradation of this fibrous cap, ie, stable and unstable or vulnerable. Stable plaques have an intact, thick fibrous cap composed of smooth muscle cells in a matrix rich in type I and III collagen.14 Protrusion of this type of plaque into the lumen of the artery produces flow-limiting stenosis, leading to tissue ischemia and usually stable angina. Vulnerable plaques have a thin fibrous cap made mostly of type I collagen and few or no smooth muscle cells, but abundant macrophages and proinflammatory and prothrombotic molecules.8,10 These plaques are prone to erosion or rupture, exposing the core of the plaque to circulating coagulation proteins, causing thrombosis, sudden occlusion of the artery lumen,8,10 and usually an acute coronary syndrome. Intraplaque hemorrhage is also a potential contributor to progression of atherosclerosis, and appears to occur when the vasa vasorum invades the intima from the adventitia.15

Study of the genetic architecture of disease

In order to study the genetic factors associated with a disease, several sequential steps must be followed. The first step involves quantification of the genetic component of the disease, which can be expressed as its heritability, ie, the proportion of the total population variance of the phenotype at a particular time or age that is attributable to genetic variation.16 The heritability of some phenotypes associated with arteriosclerosis has already been determined, and generally ranges from 40% to 55% (Table 1).17,18

Table 1.

Main results of different studies analyzing the heritability of several phenotypes associated with arteriosclerosis

Phenotype Heritability References
CAD
 Acute myocardial infarction 0.56 Nora et al89
 Mortality from 0.53–0.57 (men) Zdravkovic et al,90
 CAD 0.58 (women) Wienke et al91
 Coronary artery calcification 0.42 Peyser et al92
Atherosclerosis
 Carotid artery atherosclerosis 0.21–0.64 Xiang et al,93 Fox et al,94 Swan et al,95 North et al,96 Hunt et al97

Abbreviation: CAD, coronary artery disease.

The second step is to study the genetic architecture of the disease, ie, identify the loci, and within these loci, the genetic variants that modulate disease susceptibility. However, this task is one of the greatest challenges in current genetic research. Depending on the observed patterns of inheritance, it is possible to classify genetic diseases in two broad classes, ie, monogenic or Mendelian diseases, in which genetic variation in one gene accounts for most or all of the variation in disease risk;19 and complex diseases, which are characterized by complex patterns of inheritance caused by the combination of multiple genetic variants (often with a small effect) and environmental factors, and modulated by their mutual interaction.20 For example, in the case of CAD, the effects of known genetic variants range from an odds ratio of 1.04 to approximately 1.30 per copy of the risk allele.21 Studies of the genetic architecture of a disease generally have two approaches, ie, linkage and association studies.17

Linkage studies

In these studies, large families with several affected and unaffected relatives across one or more generations are identified and recruited.22,23 Classically, large numbers of genetic markers, uniformly distributed throughout the genome, are analyzed to see if their transmission from generation to generation is associated with the presence of the disease (segregation). The initial objective is to identify regions of the genome that contain genes predisposing to or causing the disease under study. Thereafter, the chromosomal region that segregates with the disease can be fine-mapped to identify the causal gene.17,22,23 This type of study has been successful in identifying many disease genes, particularly those that cause Mendelian traits, but less successful in identifying genes associated with complex diseases.24 In the case of CAD, notable successes include the identification of variants in ALOX5AP as being associated with coronary and cerebrovascular diseases,25 in MEF2A as being associated with CAD,26 and in PCSK9 as a gene for which variation is relevant in the metabolism of cholesterol.27

Association studies

Association studies are likely to be more effective tools than linkage studies for studying genetically complex traits because they can have greater statistical power to detect genetic variants with small effects.28 These types of studies evaluate the association between genetic variants (usually single nucleotide polymorphisms or copy number variations) and the presence/absence of a disease or a specific phenotype. The biggest challenges for this type of study are the accuracy of phenotype definition and replication of the findings. In order to identify genetic variants with small effects, large sample sizes are required, which are usually obtained by pooling different samples and populations, potentially with different phenotyping methods or criteria. In many cases, this heterogeneity results in dilution of the effects of causal genetic variants. In other cases, the phenotype itself may be difficult to define (eg, fibromyalgia) or show substantial intraindividual variability (eg, blood pressure), diluting the observable effect of the causal variant on the phenotype of interest. There are several types of association studies, as follows.

Candidate gene studies

Association studies in candidate genes, usually known to be related to intermediate traits, have been widely used for the study of complex diseases.4,29,30 This approach is based on an a priori hypothesis generated from knowledge of the disease pathogenesis or previous results.17,31 In the 1990s, this type of research became very popular and many studies analyzing the relationship between genetic variants and phenotypes were published, although their main findings were often difficult to replicate.32

Genome-wide association studies

The goal of genome-wide association studies (GWAS) is to identify genetic variants associated with complex phenotypes without the need for prior selection of candidate loci or genes.33 GWAS are based on two assumptions: first, a large proportion of common variation in the genome can be captured by a relatively small number of genetic variants, an hypothesis that is supported by evidence from the HapMap project;34 and, second, common complex diseases are mainly caused by common genetic variants.

This type of study became possible through technologic advances that allowed large numbers of single nucleotide polymorphisms to be genotyped throughout the genome and common patterns of linkage disequilibrium in different populations to be determined, thanks to studies like the Human Genome Project and the HapMap Project.34,35 This evidence allowed the possibility of searching the human genome for common variants associated with a huge variety of phenotypes and diseases.36 Moreover, powerful association analysis methods and software have also been developed.37,38 These studies are hypothesis-free, and due to the multiple comparisons and the need to reduce the burden of potential type I errors, they have to correct the P-value to be considered statistically significant according to the number of tests performed. Usually, this statistical significance threshold is located at a P-value, 10−8.

In parallel, international collaborations and consortia have provided new insights in medical research.39 While the number of studies that use this methodology has increased rapidly in recent years (Figure 3), this type of design is known to have some limitations (Table 2). These include the low proportion of heritability explained by the genetic variants identified, which has been found to be lower than 10% for most phenotypes,4,40 and the fact that the results are based on statistical association and do not provide functional insights. However, these studies have consistently identified hundreds of loci in dozens of clinically important phenotypes,4,41 providing further insights into the genetics of complex diseases.

Figure 3.

Figure 3

Number of articles published per year according to the genome-wide association studies catalog (accessed on September 27, 2013).

Table 2.

Comparison between candidate gene studies and GWAS

Feature Candidate gene studies Genome-wide association studies
Hypothesis Need a priori hypothesis Hypothesis-free
Number of genetic variants Limited (one to hundreds) Large (hundreds of thousands to millions, with imputation)
Sample size Limited (usually hundreds) Large (hundreds to hundreds of thousands)
Biases Selection bias
Confounding
Population stratification
Publication bias
Selection bias
Confounding
Population stratification (methods to control)
Limitations Sample size
Nonreplicability of results
Lack of thoroughness
Low genetic coverage
Control for multiple testing
Phenotype definition
Based on common variants
Statistical versus functional association
False positive rate Large Low
False negative rate Low Large

Note: Data summarized from many studies.28,98104

Abbreviation: GWAS, genome-wide association studies.

Whole-genome sequencing studies

The human genome contains approximately 3.1 billion nucleotides with approximately 56 million genetic variants. The exome, ie, the part of the genome formed by protein-coding exons, comprises approximately 30 million nucleotides and 23,500 genes.42 Rapidly improving whole-genome sequencing (WGS) technologies are creating new research avenues based on sequencing entire individuals,39,42 and the rapidly decreasing costs of WGS will soon allow this technology to be used for tackling the genetic architecture of disease.43 WGS are expected to contribute to better definition of the genetic basis of a range of phenotypes, responses to therapy, and clinical outcomes. Although WGS mainly focus on Mendelian disorders, the WGS approach is becoming important for identifying and analyzing rare variants, which might have larger effects on disease risk than the common variants identified by GWAS.42

One of the main disadvantages of WGS is the rate of false positive/false negative results in variant calling, and identification of the true causal genetic variant. Considering a false positive rate of 2%, an analysis of three billion genetic variants per genome would yield 60,000,000 miscalled variants. Therefore, false positives are expected to remain a major limitation of WGS, and alternative methods for validating variants identified by this approach will be necessary. Also, WGS will have a real challenge in identifying the true causal genetic variants among all alleles because all genes and proteins carry several nonpathogenic variants. For this reason, a classification of genetic variants according to the strength of the evidence for causality has been proposed as follows: disease-causing, likely disease-causing, disease-associated, functional but not associated with disease; and unknown biological function.42

Current knowledge of genetic architecture of CAD

Our understanding of the genetic architecture of CAD has improved considerably since 2007 when the first GWAS of this disease were published. The first two studies were published simultaneously and identified the 9p21 locus to be associated with myocardial infarction44 and CAD,45 and a third study replicated these findings.46 At the beginning of 2013, a meta-analysis of several GWAS identified a final set of about 40 genetic variants associated with CAD (Table 3) that explains approximately 6% of the heritability of CAD.21 Some of these variants are related to lipid metabolism, blood pressure, and inflammation, which confirms the importance of these pathways in the pathogenesis of CAD.21 In contrast, this study found no overlap between these CAD loci and those associated with type 2 diabetes or glucometabolic traits. Moreover, most of these CAD loci are located in intergenic regions, or in regions with unknown function or where the relationship to atherosclerosis or its intermediate traits is unknown.

Table 3.

Summary of main findings of most recent meta-analysis of genome-wide association studies in coronary artery disease, showing the lead single nucleotide polymorphism of each locus, the closest gene, chromosomal location, risk allele and frequency, P-value, and effect size of the reported associations

rsID Gene located at or near loci Chr Risk/nonrisk allele (risk allele frequency) Combined P-value Combined OR
rs602633 SORT1 1 C/A (0.77) 1.47 × 10−25 1.12
rs17114036 PPAP2B 1 A/G (0.91) 5.80 × 10−12 1.11
rs4845625 IL6R 1 T/C (0.47) 3.64 × 10−10 1.09
rs67258870 WDR12 2 C/T (0.11) 1.16 × 10−15 1.12
rs515135 APOB 2 G/A (0.83) 2.56 × 10−10 1.03
rs2252641 ZEB2-ACO74093 2 G/A (0.46) 5.30 × 10−8 1.06
rs1561198 VAMP5-VAMP8-GGCX 2 A/G (0.45) 1.22 × 10−10 1.07
rs6544713 ABCG5-ABCG8 2 T/C (0.30) 2.12 × 10−9 0.96
rs9818870 MRAS 3 T/C (0.14) 2.62 × 10−9 1.07
rs7692387 GUCY1A3 4 G/A (0.81) 2.65 × 10−11 1.13
rs1878406 EDNRA 4 T/C (0.15) 2.54 × 10−8 1.09
rs273909 SLC22A4-SLC22A5 5 C/T (0.14) 9.62 × 10−10 1.11
rs12190287 TCF21 6 C/G (0.59) 4.94 × 10−13 1.07
rs2048327 SLC22A3-LPAL2-LPA 6 G/A (0.35) 6.86 × 10−11 1.06
rs9369640 PHACTR1 6 A/C (0.65) 7.53 × 10−22 1.09
rs10947789 KCKN5 6 T/C (0.76) 9.81 × 10−9 1.01
rs4252120 PLG 6 T/C (0.73) 4.88 × 1010 1.07
rs11556924 ZC3HC1 7 C/T (0.65) 6.74 × 10−17 1.09
rs2023938 HDAC9 7 G/A (0.10) 4.94 × 10−8 1.13
rs264 LPL 8 G/A (0.86) 2.88 × 10−9 1.06
rs2954029 TRIB1 8 A/T (0.55) 4.75 × 10−9 1.05
rs1333049 CDKN2BAS1 9 C/G (0.47) 1.39 × 10−52 1.23
rs3217992 9 A/G (0.38) 7.75 × 10−57 1.16
rs579459 ABO 9 C/T (0.21) 2.66 × 10−8 1.07
rs12413409 CYP17A1-CNNM2-NT5C2 10 G/A (0.89) 6.26 × 10−8 1.10
rs2505083 KIAA1462 10 C/T (0.42) 1.35 × 10−11 1.06
rs501120 CXCL12 10 A/G (0.83) 1.79 × 10−9 1.07
rs2047009 10 C/A (0.48) 1.59 × 10−9 1.05
rs974819 PDGFD 11 A/G (0.29) 3.55 × 10−11 1.07
rs3184504 SH2B3 12 T/C (0.40) 5.44 × 10−11 1.07
rs4773144 COL4A1-COL4A2 13 G/A (0.42) 1.43 × 10−11 1.07
rs9515203 13 T/C (0.74) 5.85 × 10−12 1.08
rs9319428 FLT1 13 A/G (0.32) 7.32 × 10−11 1.10
rs2895811 HHIPL1 14 C/T (0.43) 4.08 × 10−10 1.06
rs7173743 ADAMTS7 15 T/C (0.58) 6.74 × 10−13 1.07
rs17514846 FURIN-FES 15 A/C (0.44) 9.33 × 10−11 1.04
rs12936587 RAI1-PEMT-RASD1 17 G/A (0.59) 1.24 × 10−9 1.06
rs2281727 SMG6 17 C/T (0.36) 7.83 × 10−9 1.05
rs1122608 LDLR 19 G/T (0.76) 6.33 × 10−14 1.10
rs9982601 Gene desert (KCNE2) 21 T/C (0.13) 7.67 × 10−17 1.13

Abbreviations: Chr, chromosome; OR, odds ratio.

Genetics of cardiovascular risk factors

Classical cardiovascular risk factors, such as hypertension, diabetes, dyslipidemia, and obesity, are also considered to be complex traits caused by the interplay between genetic and environmental factors, as in the case of CAD. The GWAS approach has had a similar degree of success in identifying the genetic architecture of these risk factors and that of CAD, in that only a small fraction of the heritability of these phenotypes has been explained (Table 4).4753 While some of these genetic variants are also associated with CAD risk, others are not, ie, they have such small effects that very sample sizes would be required to detect them.

Table 4.

Summary of main findings of most recent meta-analyses of genome-wide association studies of cardiovascular risk factors, showing the lead single nucleotide polymorphism of each locus, the closest gene, chromosomal location, risk allele and frequency, and P-value of the reported associations

rsID Loci Chr Risk allele (Risk allele frequency) Combined P-value Known effect on CAD Reference
Obesity
 rs2815752 NEGR1 1 A (0.61) 1.61 × 10−22 No 50
 rs543874 SEC16B 1 G (0.19) 3.56 × 10−23 No 50
 rs1514175 TNNI3K 1 A (0.43) 8.16 × 10−14 No 50
 rs1555543 PTBP2 1 C (0.59) 3.68 × 10−10 No 50
 rs984222 TBX15-WARS2 1 G (0.64) 3.81 × 10−14 No 49
 rs2867125 TMEM18 2 C (0.83) 2.77 × 10−49 No 50
 rs713586 RBJ 2 C (0.47) 6.17 × 10−22 No 50
 rs887912 FANCL 2 T (0.29) 1.79 × 10−12 No 50
 rs10195252 GRB14 2 T (0.60) 2.09 × 10−24 No 49
 rs13078807 LRP1B 2 C (0.18) 1.35 × 10−10 No 50
 rs9816226 ETV5 3 T (0.82) 1.69 × 10−18 No 50
 rs13078807 CADM2 3 G (0.20) 3.94 × 10−11 No 50
 rs6795735 ADAMTS9 3 C (0.60) 9.79 × 10−14 No 49
 rs6784615 NISCH-STAB1 3 T (0.94) 3.84 × 10−10 No 49
 rs13107325 SLC39A8 4 T (0.07) 1.50 × 10−13 No 50
 rs10938397 GNPDA2 4 G (0.43) 3.78 × 10−31 No 50
 rs2112347 FLJ35779 5 T (0.63) 2.17 × 10−13 No 50
 rs4836133 ZNF608 5 A (0.48) 1.97 × 10−9 No 50
 rs681681 CPEB4 5 A (0.34) 1.91 × 10−9 No 49
 rs987237 TFAP2B 6 G (0.18) 2.90 × 10−20 No 50
 rs206936 NUDT3 6 G (0.21) 3.02 × 10−8 No 50
 rs9491696 RSPO3 6 G (0.48) 1.84 × 10−40 No 49
 rs6905288 VEGFA 6 A (0.56) 5.88 × 10−25 No 49
 rs1294421 LY86 6 G (0.61) 1.75 × 10−17 No 49
 rs1055144 NFE2L3 7 T (0.21) 9.97 × 10−18 No 49
 rs10968576 LRRN6C 9 G (0.31) 2.65 × 10−13 No 50
 rs10767664 BNF 11 A (0.78) 4.69 × 10−26 No 50
 rs3817334 MTCH2 11 T (0.40) 1.59 × 10−12 No 50
 rs4929949 RPL27A 11 C (0.52) 2.80 × 10−9 No 50
 rs7138803 FAIM2 12 A (0.38) 1.82 × 10−17 No 50
 rs718314 ITPR2-SSPN 12 G (0.26) 1.14 × 10−17 No 50
 rs1443512 HOXC13 12 A (0.24) 6.38 × 10−17 No 49
 rs4771122 MTIF3 13 G (0.24) 9.48 × 10−10 No 50
 rs10150332 NRXN3 14 C (0.21) 2.75 × 10−11 No 50
 rs11847697 PRKD1 14 T (0.04) 5.76 × 10−11 No 50
 rs2241423 MAP2K5 15 G (0.78) 1.19 × 10−18 No 50
 rs1558902 FTO 16 A (0.42) 4.8 × 10−120 No 50
 rs7359397 SH2B1 16 T (0.40) 1.88 × 10−20 No 50
 rs12444979 GPRC5B 16 C (0.87) 2.91 × 10−21 No 50
 rs571312 MC4R 18 A (0.24) 6.43 × 10−42 No 50
 rs29941 KCTD15 19 G (0.67) 3.01 × 10−9 No 50
 rs2287019 QPCTL 19 C (0.80) 1.88 × 10−16 No 50
 rs3810291 TMEM160 19 A (0.67) 1.64 × 10−12 No 50
 rs4823006 ZNRF3-KREMEN1 22 A (0.57) 1.10 × 10−11 No 49
Diabetes
 rs340874 PROX1 1 C (0.52) 6.6 × 10−12 No 47
 rs560887 G6PC2 2 C (0.70) 8.7 × 10−218 No 47
 rs780094 GCKR 2 C (0.62) 5.6 × 10−38 No 47
 rs243021 BCL11A 2 A (0.46) 2.9 × 10−15 No 52
 rs7578326 IRS1 2 A (0.64) 5.4 × 10−20 No 52
 rs11708067 ADCY5 3 A (0.78) 7.1 × 10−22 No 47
 rs11920090 SLC2A2 3 T (0.87) 8.1 × 10−13 No 47
 rs4457053 ZBED3 5 G (0.26) 2.8 × 10−12 No 52
 rs4607517 GCK 7 A (0.16) 6.5 × 10−92 No 47
 rs2191349 DGKB-TMEM195 7 T (0.52) 3.0 × 10−44 No 47
 rs972283 KLF14 7 G (0.55) 2.2 × 10−10 No 52
 rs11558471 SLC30A8 8 A (0.68) NA No 47
 rs896854 TP53INP1 8 T (0.48) 9.9 × 10−10 No 52
 rs7034200 GLIS3 9 A (0.49) 1.0 × 10−12 No 47
 rs13292136 CHCHD9 9 C (0.93) 2.8 × 10−8 No 52
 rs10885122 ADRA2A 10 G (0.87) 2.9 × 10−16 No 47
 rs4506565 TCF7L2 10 T (0.31) NA No 47
 rs10830963 MTNR1B 11 G (0.30) 5.8 × 10−175 No 47
 rs7944584 MADD 11 A (0.75) 2.0 × 10−18 No 47
 rs174550 FADS1 11 T (0.64) 1.7 × 10−15 No 47
 rs11605924 CRY2 11 A (0.49) 1.0 × 10−14 No 47
 rs231362 KCNQ1 11 G (0.52) 2.8 × 10−13 No 52
 rs1552224 CENTD2 11 A (0.88) 1.4 × 10−22 No 52
 rs1387153 MTNR1B 11 T (0.28) 7.8 × 10−15 No 52
 rs1531343 HMGA2 12 C (0.10) 3.6 × 10−9 No 52
 rs7957197 HNF1A 12 T (0.85) 2.4 × 10−8 No 52
 rs11634397 ZFAND6 15 G (0.60) 2.4 × 10−9 No 52
 rs11071657 C2CD4B 15 A (0.63) 3.6 × 10−8 No 47
 rs8042680 PRC1 15 A (0.22) 2.4 × 10−10 No 52
 rs5945326 DUSP9 X A (0.79) 3.0 × 10−10 No 52
Total cholesterol
 rs12027135 LDLRAP1 1 T (0.45) 4 × 10−11 Yes 51
 rs7515577 EVI5 1 A (0.21) 3 × 10−8 No 51
 rs2642442 MOSC1 1 T (0.32) 6 × 10−13 No 51
 rs514230 IRF2BP2 1 T (0.48) 5 × 10−14 No 51
 rs7570971 RAB3GAP1 2 C (0.34) 2 × 10−8 No 51
 rs2290159 RAF1 3 G (0.22) 4 × 10−9 No 51
 rs12916 HMGCR 5 T (0.39) 9 × 10−47 No 51
 rs6882076 TIMD4 5 C (0.35) 7 × 10−28 No 51
 rs3177928 HLA 6 G (0.16) 4 × 10−19 No 51
 rs2814982 C6orf106 6 C (0.11) 5 × 10−11 No 51
 rs9488822 FRK 6 A (0.35) 2 × 10−10 No 51
 rs12670798 DNAH11 7 T (0.23) 9 × 10−10 No 51
 rs2072183 NPC1L1 7 G (0.25) 3 × 10−11 No 51
 rs2081687 CYP7A1 8 C (0.35) 2 × 10−8 No 51
 rs2737229 TRPS1 8 A (0.30) 2 × 10−8 No 51
 rs2255141 GPAM 10 G (0.30) 2 × 10−10 No 51
 rs10128711 SPTY2D1 11 C (0.28) 3 × 10−8 No 51
 rs7941030 UBASH3B 11 C (0.38) 2 × 10−10 No 51
 rs11065987 BRAP 12 A (0.42) 7 × 10−12 No 51
 rs1169288 HNF1A 12 A (0.33) 1 × 10−14 No 51
 rs2000999 HPR 16 G (0.20) 3 × 10−24 No 51
 rs4420638 CILP2 19 T (0.07) 3 × 10−38 No 51
 rs492602 FLJ36070 19 A (0.49) 2 × 10−10 No 51
 rs2277862 ERGIC3 20 C (0.15) 4 × 10−10 No 51
 rs2902940 MAFB 20 A (0.29) 6 × 10−11 No 51
Triglycerides
 rs2131925 ANGPTL3 1 T (0.32) 9 × 10−43 No 51
 rs1042034 APOB 2 T (0.22) 1 × 10−45 Yes 51
 rs1260326 GCKR 2 C (0.41) 6 × 10−133 No 51
 rs10195252 COBLL1 2 T (0.40) 2 × 10−10 No 51
 rs645040 MSL2L1 3 T (0.22) 3 × 10−8 No 51
 rs442177 KLHL8 4 T (0.41) 9 × 10−12 No 51
 rs9686661 MAP3K1 5 C (0.20) 1 × 10−10 No 51
 rs2247056 HLA 6 C (0.25) 2 × 10−15 No 51
 rs13238203 TYW1B 7 C (0.04) 1 × 10−9 No 51
 rs17145738 MLXIPL 7 C (0.12) 6 × 10−58 No 51
 rs11776767 PINX1 8 G (0.37) 1 × 10−8 No 51
 rs1495741 NAT2 8 A (0.22) 5 × 10−14 No 51
 rs12678919 LPL 8 A (0.12) 2 × 10−115 Yes 51
 rs2954029 TRIB1 8 A (0.47) 3 × 10−55 Yes 51
 rs10761731 JMJD1C 10 A (0.43) 3 × 10−12 No 51
 rs2068888 CYP26A1 10 G (0.46) 2 × 10−8 No 51
 rs174546 FADS1–2–3 11 C (0.34) 5 × 10−24 No 51
 rs964184 APOA1 11 C (0.13) 7 × 10−240 Yes 51
 rs11613352 LRP1 12 C (0.23) 4 × 10−10 No 51
 rs2412710 CAPN3 15 G (0.02) 2 × 10−8 No 51
 rs2929282 FRMD5 15 A (0.05) 2 × 10−11 No 51
 rs11649653 CTF1 16 C (0.40) 3 × 10−8 No 51
 rs439401 APOE 19 C (0.36) 1 × 10−30 No 51
 rs5756931 PLA2G6 22 T (0.40) 4 × 10−8 No 51
Low-density lipoprotein
 rs2479409 PCSK9 1 A (0.23) 2 × 10−28 No 51
 rs629301 SORT1 1 T (0.22) 1 × 10−170 Yes 51
 rs1367117 APOB 2 G (0.30) 4 × 10−114 Yes 51
 rs499376 ABCG5/8 2 T (0.30) 2 × 10−47 Yes 51
 rs3757354 MYLIP 6 C (0.22) 1 × 10−11 No 51
 rs1800562 HFE 6 G (0.06) 6 × 10−10 No 51
 rs1564348 LPA 6 T (0.17) 2 × 10−17 Yes 51
 rs11136341 PLEC1 8 A (0.40) 4 × 10−13 No 51
 rs9411489 ABO 9 C (0.20) 6 × 10−13 Yes 51
 rs11220462 ST3GAL4 11 G (0.14) 1 × 10−15 No 51
 rs8017377 NYNRIN 14 G (0.47) 5 × 10−11 No 51
 rs7206971 OSBPL7 17 G (0.49) 2 × 10−8 No 51
 rs6511720 LDLR 19 G (0.11) 4 × 10−117 Yes 51
 rs4420638 APOE 19 A (0.17) 9 × 10−147 No 51
 rs6029526 TOP1 20 T (0.47) 4 × 10−19 No 51
High-density lipoprotein
 rs4660293 PABPC4 1 A (0.23) 4 × 10−10 No 51
 rs1689800 ZNF648 1 A (0.35) 3 × 10−10 No 51
 rs4846914 GALNT2 1 A (0.40) 4 × 10−21 No 51
 rs2972146 IRS1 2 T (0.37) 3 × 10−9 No 51
 rs12328675 COBLL1 2 T (0.13) 3 × 10−10 No 51
 rs13107325 SLC39A8 4 C (0.07) 7 × 10−11 No 51
 rs6450176 ARL15 5 G (0.26) 5 × 10−8 No 51
 rs2814944 C6orf106 6 G (0.16) 4 × 10−9 No 51
 rs605066 CITED2 6 T (0.42) 3 × 10−8 No 51
 rs1084651 LPA 6 G (0.16) 3 × 10−8 Yes 51
 rs2293889 TRPS1 8 G (0.41) 6 × 10−11 No 51
 rs4731702 KLF14 7 C (0.48) 1 × 10−15 No 51
 rs9987289 PPP1R3B 8 G (0.09) 6 × 10−25 No 51
 rs581080 TTC39B 9 C (0.18) 3 × 10−12 No 51
 rs1883025 ABCA1 9 C (0.25) 2 × 10−33 No 51
 rs2923084 AMPD3 11 A (0.17) 5 × 10−8 No 51
 rs3136441 LRP4 11 T (0.15) 3 × 10−18 No 51
 rs7134594 PDE3A 12 C (0.42) 4 × 10−8 No 51
 rs7134594 MVK 12 T (0.47) 7 × 10−15 No 51
 rs4759375 SBNO1 12 C (0.06) 7 × 10−9 No 51
 rs4765127 ZNF664 12 G (0.34) 3 × 10−10 No 51
 rs838880 SCARB1 12 T (0.31) 3 × 10−14 No 51
 rs1532085 LIPC 15 G (0.39) 3 × 10−96 No 51
 rs2652834 LACTB 15 G (0.20) 9 × 10−9 No 51
 rs3764261 CETP 16 C (0.32) 7 × 10−380 No 51
 rs16942887 LCAT 16 G (0.12) 8 × 10−33 No 51
 rs2925979 CMIP 16 C (0.30) 2 × 10−11 No 51
 rs11869286 STARD3 17 C (0.34) 1 × 10−13 No 51
 rs4148008 ABCA8 17 C (0.32) 2 × 10−10 No 51
 rs4129767 PGS1 17 A (0.49) 8 × 10−9 No 51
 rs7241918 LIPG 18 T (0.17) 3 × 10−49 No 51
 rs12967135 MC4R 18 G (0.23) 7 × 10−9 No 51
 rs7255436 ANGPTL4 19 A (0.47) 3 × 10−8 No 51
 rs737337 LOC55908 19 T (0.08) 3 × 10−9 No 51
 rs386000 LILRA3 19 G (0.20) 4 × 10−19 No 51
 rs1800961 HNF4A 20 C (0.03) 1 × 10−15 No 51
 rs6065906 PLTP 20 T (0.18) 2 × 10−22 No 51
 rs181362 UBE2L3 22 C (0.20) 1 × 10−8 No 51
rsID Loci Chr Risk allele (Risk allele frequency) P-value
Known effect On CAD Reference
SBP DBP

Blood pressure
 rs2932538 MOV10 1 G (0.75) 1.2 × 10−9 9.9 × 10−10 No 48
 rs17367504 MTHFR-NPPB 1 G (0.15) 8.7 × 10−22 3.5 × 10−19 No 48
 rs13002573 FIGN 2 G (0.20) 3.25 × 10−7 4.02 × 10−2 No 53
 rs1446468 FIGN 2 T (0.53) 1.82 × 10−12 6.88 × 10−9 No 53
 rs319690 MAP4 3 T (0.51) 4.74 × 10−8 1.84 × 10−8 No 53
 rs13082711 SLC4A7 3 T (0.78) 1.5 × 10−6 3.8 × 10−9 No 48
 rs419076 MECOM 3 T (0.47) 1.80 × 10−13 2.1 × 10−12 No 48
 rs3774372 ULK4 3 T (0.83) 0.39 9.0 × 10−14 No 48
 rs871606 CHIC2 4 T (0.86) 3.04 × 10−4 8.85 × 10−1 No 53
 rs13107325 SLC39A8 4 T (0.05) 3.3 × 10−14 2.3 × 10−17 No 48
 rs13139571 GUCY1A3-GUCY1B3 4 C (0.76) 1.2 × 10−6 2.2 × 10−10 Yes 48
 rs1458038 FGF5 4 T (0.29) 1.5 × 10−23 8.5 × 10−25 No 48
 rs1173771 NPR3-C5orf23 5 G (0.60) 1.8 × 10−16 9.1 × 10−12 No 48
 rs11953630 EBF1 5 T (0.37) 3.0 × 10−11 3.8 × 10−13 No 48
 rs1799945 HFE 6 G (0.14) 7.7 × 10−12 1.5 × 10−15 No 48
 rs805303 BAT2-BAT5 6 G (0.61) 1.5 × 10−11 4.4 × 10−10 No 48
 rs17477177 PIK3CG 7 T (0.72) 5.67 × 10−11 1.40 × 10−1 No 53
 rs2071518 NOV 8 T (0.17) 2.08 × 10−2 3.89 × 10−3 No 53
 rs2782980 ADRB1 10 T (0.20) 7.66 × 10−7 9.60 × 10−8 No 53
 rs4373814 CACNB2 10 G (0.55) 4.8 × 10−11 4.4 × 10−10 No 48
 rs932764 PLCE1 10 G (0.44) 7.1 × 10−16 8.1 × 10−7 No 48
 rs1813353 CACNB2 10 T (0.68) 2.6 × 10−12 2.3 × 10−15 No 48
 rs4590817 C10orf107 10 G (0.84) 4.0 × 10−12 1.3 × 10−12 No 48
 rs11191548 CYP17A1-NT5C2 10 T (0.91) 6.9 × 10−26 9.4 × 10−13 Yes 48
 rs11222084 ADAMTS8 11 T (0.38) 4.00 × 10−4 3.44 × 10−2 No 53
 rs7129220 ADM 11 G (0.89) 3.0 × 10−12 6.4 × 10−8 No 48
 rs633185 FLJ32810-TMEM133 11 G (0.28) 1.2 × 10−17 2.0 × 10−15 No 48
 rs381815 PLEKHA7 11 T (0.26) 5.3 × 10−11 5.3 × 10−10 No 48
 rs17249754 ATP2B1 12 G (0.84) 1.8 × 10−18 1.2 × 10−14 No 48
 rs3184504 SH2B3 12 T (0.47) 3.8 × 10−18 3.6 × 10−25 Yes 48
 rs10850411 TBX5-TBX3 12 T (0.70) 5.4 × 10−8 5.4 × 10−10 No 48
 rs2521501 FURIN-FES 15 T (0.31) 5.2 × 10−19 1.9 × 10−15 Yes 48
 rs1378942 CYP1A1-ULK3 15 C (0.35) 5.7 × 10−23 2.7 × 10−26 No 48
 rs17608766 GOSR2 17 T (0.86) 1.1 × 10−10 0.017 No 48
 rs12940887 ZNF652 17 T (0.38) 1.8 × 10−10 2.3 × 10−14 No 48
 rs1327235 JAG1 20 G (0.46) 1.9 × 10−8 1.4 × 10−15 No 48
 rs6015450 GNAS-EDN3 20 G (0.12) 3.9 × 10−23 5.6 × 10−23 No 48

Abbreviations: Chr, chromosome; CAD, coronary artery disease; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Clinical utility of genetic knowledge

The identification of genetic variants associated with disease has allowed us to improve our understanding of its pathogenesis, and ultimately to reduce the burden of disease at both the individual and population levels. Information derived from genetic studies could potentially help to reduce the burden of disease in three main ways, ie, the identification of new pharmacologic targets, improvements in identification of high-risk individuals, and pharmacogenomics.

Identification of new pharmacologic targets

Genetic studies can shed light on new metabolic pathways associated with the development and progression of atherosclerosis, and provide clues for identifying new pharmacologic targets. The following two examples illustrate the promise as well as the potential difficulties of this field.

PCSK9

A clear example of the success of genetic studies in identifying molecules that may become new therapeutic targets is the PCSK9 gene. This gene was initially discovered by linkage studies to be associated with autosomal dominant hypercholesterolemia,27 for which new causal mutations were identified in 2003.54 The PCSK9 protein is crucial for metabolism of LDL cholesterol through its role in degradation of the LDL receptor, such that inhibition of this protein could become a viable treatment for hypercholesterolemia.55 Recent clinical trials in patients with primary hypercholesterolemia have shown that combination treatment with REGN727/SAR236553, a human monoclonal antibody to PCSK9, and either 10 mg or 80 mg of atorvastatin resulted in significantly greater reduction of LDL cholesterol than that obtained by 80 mg of atorvastatin alone.56

9p21 region

The genetic variants associated with CAD at the 9p21 locus, which has been the top hit in all CAD GWAS since 2007, lie in an intergenic region close to a cluster of cell-cycle regulating tumor suppressor genes (CDKN2A and CDKN2B) that overlap with a nonprotein coding RNA (CDKN2BAS or ANRIL). While various hypotheses have been proposed to explain the functional basis of this association, the mechanism remains unclear,39,57 and this has prevented the identification of a therapeutic target.

Improved identification of high-risk individuals

In the case of CAD, primary prevention strategies in healthy asymptomatic individuals are very important because the first clinical manifestation of the disease is often catastrophic (MI or sudden death). Two main prevention strategies can be defined: the population approach, based on public health policies that affect the whole population, such as smoking bans;58 and the approach that targets high-risk individuals, based on implementing intensive preventive treatment in individuals at high risk of having the disease, based on their cardiovascular risk factor profile.59 Two main screening strategies are usually undertaken to identify high-risk individuals, ie, opportunistic screening and high-risk screening. In opportunistic screening, evaluation of cardiovascular risk factors and estimation of CAD risk is carried out in all individuals who come into contact with the health care system for any reason. Risk functions are the most commonly used method for estimating individual risk of having a CAD event, usually for a 10-year period.5961 Risk functions are mathematical equations that estimate the probability of developing CAD/cardiovascular disease using information about cardiovascular risk factors that are strongly and independently related to CAD and can be evaluated by simple procedures in the laboratory or doctor’s office.

Depending on their estimated risk, it is possible to categorize individuals into different risk categories (low, intermediate, high, and very high), and these categories are used to determine the intensity of preventive cardiovascular measures to be applied, which may range from lifestyle recommendations to prescription of drugs with various clinical objectives. Although risk functions can accurately predict the numbers of events that will occur in each risk category, many CAD events occur in individuals whose risk is too low to justify intensive treatment.62 For this reason, considerable effort has been invested in improving the classification of these intermediate-risk individuals into more appropriate risk categories.

Several biomarkers, including genetic variants, have been analyzed as candidates for improving the predictive capacity of risks functions.63 The main advantage of genetic variants is that they remain invariable throughout life, so it is possible to determine a person’s genetic risk profile before the development of an adverse cardiovascular risk factor profile, which would allow primary prevention measures to be undertaken earlier in life.2,63 Another advantage is the lower cost and higher replicability of genotyping compared with other cardiovascular risk factors. Among the limitations, the small effect sizes of known variants are most notable, despite the highly statistically significant associations between these variants and CAD risk.

Several studies have evaluated the effects on the predictive capacity of classical risk functions when genetic factors are taken into account. While most of the studies have found that these genetic variants (usually expressed as a single variable corresponding to the number of risk alleles carried, known as a genetic risk score) are associated with risk of future CAD events, they have not been found to improve the ability to discriminate between those individuals at particular risk who will develop the disease, although they do improve the reclassification of individuals into more appropriate risk categories, especially those at intermediate risk (Table 5).

Table 5.

Main characteristics and results of studies assessing improvement of predictive capacity of classical cardiovascular risk functions after inclusion of genetic information

Author Population Clinical outcome Genetic variants Other covariates
Results
Risk factors Family history Association Discrimination NRI Clinical NRI
Case-control
 Davies et al105 OHGS3,323Ca/2,319 Co
WTCC: 1,926 Ca/2,938 Co
CAD One SNP(9p21)
12 SNPs (related and unrelated CVRF)
Yes No NR ΔAUC 0.003
ΔAUC 0.008
 Anderson et al106 Patients undergoing coronary angiography: 1,086 Ca/482 Co CAD 5 SNPs (related and unrelated CVRF) Yes Yes OR 1.24 ΔAUC 0.008 16.0% 28.3%
 Qi et al107 Hispanic: 1,989 Ca/2,096 Co Ml 3 SNPs (related and unrelated CVFR) Yes No OR 1.18 ΔAUC 0.010
 Qi et al108 Type II DM patients: 1,076
Ca/1,430 Co
CAD 5 SNPs (related and unrelated CVRF) Yes No OR 1.19
 Lv et al105 Chinese Han population: 1,007 Ca/889 Co CAD 8 SNPs (related and unrelated CVRF) Yes No OR 1.28 ΔAUC 0.022
 Patel et al110 US population: 1,338 Ca/1,649 Co (>70 years) Ml <70 years 11 SNPs Yes Yes OR 1.12 ΔAUC 0.012
Case-cohort
 Hughes et al111 Middle-aged men, European general population
632 Ca/1,361 subcohort
CAD 11 SNPs + two haplotypes
11 SNPs + 4 SNPs (unrelated CVRF)
Yes
Yes
No
No
NR
NR
ΔAUC 0.009
ΔAUC 0.011
7.5%
6.5%
6.3%
5.1%
 Vaarhorst et al112 European general population 742 Ca/2,22l subcohort 12.1 years follow-up, CAD 29 SNPs (unrelated CVRF) Yes No HR 1.12 2.8% NR
Cohort
 Morrison et al113 ARIC, US general population 13,907 13 years follow-up, 1,452 CAD 10 SNPs – White population
11 SNPs – Black population
Yes No HR 1.10
HR 1.20
ΔAUC 0.002
ΔAUC 0.011
NR NR
 Kathiresan et al114 Malmö, European general population, 4,232 10.6 years follow-up, 238 CVD 9 SNPs, lipid-related Yes No HR 1.15 ΔAUC 0.003
 Talmud et al115 NPHS-II, UK middle-aged men, 2,742 15 years follow-up, 270 CAD 1 SNP(9p21) Yes Yes AA versus AG 1.38
AA versus GG 1.57
ΔAUC 0.02 13.8% NR
 Brautbar et al116 ARIC, US general population (Whites), 9,998 14.6 years follow-up, 1,349 CAD 1 SNP(9p21) Yes No HR 1.20 ΔAUC 0.004 0.8% 6.2
 Paynter et al117 WGHS, US middle-aged
White women, 22,192
10.2 years follow-up, 1,349 CVD 1 SNP(9p21) Yes Yes AA versus AG 1.25
AA versus GG 1.32
ΔAUC 0.002 Framingham, 2.7%
Reynolds, 0.2%
NR
 Paynter et al118 WGHS, US White women, 19,313 12.3 years follow-up, 777 CVD 101 SNPs
12 SNPs (related and unrelated CVRF)
Yes Yes HR 1.00
HR 1.04
ΔAUC 0.000
ΔAUC 0.001
0.5%
0.5%
NR
 Ripatti et al119 General European population, 30,725 10.7 years follow-up, 1,264 CHD 13 SNPs (related and unrelated CVRF) Yes Yes HR 1.66(Q5 versus Q1) ΔAUC 0.001 2.2% 9.7%
 Shiffman et al120 CHS, US old population (>65 years), 4,284 12.6 years follow-up, Ml 1 SNP(9p2l)
1 SNP(KIF6719 Arg) carriers
Yes No HR 1.22 (White men)
HR 1.16 (White women)
HR NR (Black men)HR 1.42 (White men)
HR 1.05 (White women)
ΔAUC 0.005
ΔAUC 0.002
ΔAUC 0.034
ΔAUC 0.015
ΔAUC −0.001
2.1%
−1.8%
18.2%
2.7%
0.7%
 Thanassoulis et al121 Framingham, US general population, 3,014 11 years follow-up, 182 hard CHD 13 SNPs (related and unrelated CVRF) Yes Yes HR 1.07 ΔAUC 0.002 19.0% NR
 Lluis-Ganella et al122 General population,
Framingham 3,537 + REGICOR 2,351
11.9 years follow-up, 536 CHD 8 SNPs (unrelated CVRF) Yes Yes HR 1.13 ΔAUC No 6.4% 17.4%
 Gransbo et al123 Malmö, 24,777 11.7 years follow-up, 2,668 CVD 9p21 variant Yes No HR 1.17 ΔAUC 0.001 1.2%
 Isaacs et al124 Erasmus Family study 2,269 + Rotterdam Study 8,130 9.5 years follow-up, 924 CHD Lipid-related GRS Yes No GRSTC HR 1.09
GRSLDL HR 1.08
GRSHDL HR 0.99
GRSTG HR 1.04
ΔAUC 0.000
 Ganna et al125 6 Swedish cohorts, 10,612 781 CHD GRSglobal, 395
GRSCHD, 46
Yes No HR 1.54
HR 1.52
ΔAUC 0.002
ΔAUC 0.004
4.2%
4.9%
Tikkanen et al126 4 Finnish cohorts, 24,124 12 years follow-up, 1,093 CHD 28 SNPs (related and unrelated CVRF) Yes Yes HR 1.27 ΔAUC 0.003 5.0% 27.0%

Notes:

P-value<0.05.

Abbreviations: ARIC, Atherosclerosis Risk in Communities study; CAD, coronary artery disease; Ml, myocardial infarction; CVD, cardiovascular disease; OHGS, Ottawa Heart Genomics Study; WTCCC, Wellcome Trust Case Control Consortium; Ca, Cases; Co, Controls; DM, diabetes mellitus; NPHS-11: Northwick Park Heart Study 11; WGHS, Women Genome Health Study; CHS, Cardiac Health Study; REGICOR, Registre Gironi del Cor (Girona Heart Registry); OR, odds ratio; HR, hazard ratio; NR, not reported; AUC, area under the receiver operating curve; NRI, Net Reclassification Index; CVRF, cardiovascular risk factors; GRS, genetic risk score; SNP, single nucleotide polymorphism.

Pharmacogenomics

Pharmacogenomics is the study of the relationship between genetic variability and a patient’s response to drug treatment, ie, the efficacy of the drug and/or its adverse effects.6468 Candidate gene and GWAS approaches have been used to identify genetic variants associated with variability in drug response, including several examples in the cardiovascular field,69,70 the majority of which have focused on statins, antiplatelet drugs, oral anticoagulants, or beta-blockers. The case of statins and the antiplatelet agent clopidogrel provide two interesting examples in this area.

Statins are widely prescribed to reduce plasma cholesterol levels and cardiovascular risk, and although the majority of patients show a 30%–50% reduction in LDL cholesterol, high interindividual variability is observed.71 Several genetic variants in the HMGCR, APOE, CETP, and CLMN genes have been reported to be associated with this interindividual variability, but the results have been discordant.69,70 Similarly, a variant in the KIF6 gene has been reported to modulate the effect of statins on clinical outcome,72,73 but recent studies have not corroborated this finding.74,75 Finally, more than one variant in the SLCO1B1 gene is consistently associated with the risk of simvastatin-induced myopathy, with an odds ratio >4.69,76

Our second example concerns the prodrug clopidogrel, which is converted into an active metabolite that selectively and irreversibly binds to the P2Y12 receptor on the platelet membrane. Conversion is achieved by the hepatic cytochrome P450 system in a two-step oxidative process, and cytochrome P450 2C19 is involved in both of these steps. The response to treatment with clopidogrel varies markedly between individuals, and the causes of a poor response are not clearly understood, but have been suggested to be related to clinical, cellular, or genetic factors.66,77,78 In March 2010, the US Food and Drug Administration added a “boxed warning” to the labeling of clopidogrel, including a reference to patients who do not effectively metabolize the drug and therefore may not receive the full benefits on the basis of their genetic characteristics.79 Recently, the American College of Cardiology Foundation and the American Heart Association have published a consensus document addressing this US Food and Drug Administration warning,80 stating that the role of genetic tests and the clinical implications and consequences of this testing remain to be determined. Moreover, three recent meta-analyses question the validity of this warning based on the fact that the reported associations are mainly driven by studies with small sample sizes;78,81,82 thus, they concluded that current evidence does not support the use of individualized clopidogrel regimens guided by the CYP2C19 genotype.

Conclusion

In the past 7 years, GWAS have contributed substantially to our understanding of the genetic architecture of complex diseases, including CAD. To date, approximately 40 unique loci have been found to be robustly associated with disease risk in large samples from several populations, a much higher number than those identified by linkage and candidate gene association studies. However, these variants explain only a small proportion of the heritability of CAD.40 Additional efforts to improve the analysis strategies, including new imputation and meta-analytic methods, analysis of gene-gene and gene-environment interactions, the integration of different omics, and use of sequencing technologies, are being performed.8385

Although it is not yet clear if or how all of this information on the genetic architecture of CAD can be translated into clinical practice,86 we already have some exciting examples of its potential utility. To identify new therapeutic targets, we must first make the difficult transition from the statistical associations reported in GWAS to the functional mechanisms behind these associations. Research on the use of genetic information to improve cardiovascular risk estimation in individuals at intermediate risk can be carried out as a second step or in parallel, and further studies to develop new ways to include this information in risk functions, to evaluate its cost-effectiveness, and to explore the ethical issues are also warranted.8789 Finally, although medicine is always a “personalized science and art”, use of genetic information to identify the most effective and least harmful drug for each patient is also a goal of so-called genetic personalized medicine.

Acknowledgments

This work was supported by the Spanish Ministry of Science and Innovation through the Carlos III Health Institute (Red de Inves-tigación Cardiovascular; Programa HERACLES RD12/0042), Health Research Fund (FIS-PI09/90506), and the Government of Catalunya through the Agency for Management of University and Research Grants (2009 SGR 1195). We acknowledge the contribution of ThePaperMill (http://www.thepapermill.cat) to the critical reading and English language review of this paper.

Footnotes

Disclosure

The authors report no other conficts of interest in this work.

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