Genetics of Coronary Artery Disease

Abstract

HomeCirculationVol. 128, No. 10Genetics of Coronary Artery Disease Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessResearch ArticlePDF/EPUBGenetics of Coronary Artery Disease Wolfgang Lieb, MD, MSc and Ramachandran S. Vasan, MD Wolfgang LiebWolfgang Lieb From the Institute of Epidemiology, Christian Albrechts Universität Kiel, Kiel, Germany (W.L.); The Framingham Heart Study, Framingham, MA (R.S.V.); and Boston University School of Medicine, Section of Preventive Medicine (R.S.V.). Search for more papers by this author and Ramachandran S. VasanRamachandran S. Vasan From the Institute of Epidemiology, Christian Albrechts Universität Kiel, Kiel, Germany (W.L.); The Framingham Heart Study, Framingham, MA (R.S.V.); and Boston University School of Medicine, Section of Preventive Medicine (R.S.V.). Search for more papers by this author Originally published3 Sep 2013https://doi.org/10.1161/CIRCULATIONAHA.113.005350Circulation. 2013;128:1131–1138IntroductionCoronary heart disease affects ≈15.4 million individuals in the United States1 and is one of the main causes of mortality and morbidity.1 A familial component contributes to cardiovascular disease (CVD) susceptibility,2 but it was not until the emergence of genomewide association studies (GWAS) that genetic loci were identified that displayed consistent associations with coronary artery disease (CAD) across multiple cohorts. The present article describes recent advances in the understanding of the genetic basis of CAD.Main Results From GWAS and Recent Gene-Centric ApproachesFamily2 and twin3 studies provided convincing evidence that CAD clusters in families and has a heritable component2–5; however, the delineation of the specific genetic architecture that predisposes to CAD has been challenging, with initial candidate gene approaches yielding inconsistent results.6 In contrast to candidate gene approaches that focus on genetic variation in genes whose gene products are known to play an important role in cardiovascular physiology, GWAS simultaneously assess the association with CAD of hundreds of thousands of genetic variants distributed across the whole genome. Therefore, GWAS represent an essentially unbiased approach that is not limited by the current (patho)physiological understanding of CVD and bears the potential of discovering completely new molecular mechanisms that predispose to CAD.In 2007, the first GWAS for CAD were published.7–9 The main finding was a locus on chromosome 9p21, which is still the most consistently associated CAD locus to date.10 Subsequent studies revealed that this locus is related to a broad spectrum of vascular phenotypes, including CAD and myocardial infarction,7–9 coronary artery calcification,11 peripheral artery disease,12,13 and abdominal aortic aneurysm.14 To increase power for the detection of genetic variants with smaller effect sizes, large consortia have been built that combine genetic-epidemiological data from multiple cohorts and tens of thousands of participants. Examples are the CARDIoGRAM (Coronary Artery Disease Genome-wide Replication and Meta-Analysis)15 and the Coronary Artery Disease (C4D) Genetics consortium.16 Such consortia identified multiple additional loci associated with CAD and myocardial infarction.16–23 Complementary to the genomewide approaches, candidate gene–based and gene-centric approaches are being evaluated in a coordinated fashion, for example, within the CARe project (Candidate Gene Association Resource)24 and the IBC 50K CAD Consortium.25 Furthermore, the Metabochip,26 a custom array with ≈200 000 genetic variants associated with cardiometabolic traits in prior analyses, has been assessed in the context of CAD.20 To date, 50 genetic loci associated with CAD on a genomewide level (P<5×10−8) have been reported (Table). In aggregate, these loci explain ≈10% of the heritability of CAD,17,20 which leaves most of the CAD heritability unexplained. Interestingly, most genomewide significant hits were not related to traditional risk factors,16,20,33 which underscores the potential of GWAS to identify previously unknown genomic regions and biological pathways that contribute to disease susceptibility. The main traditional risk factors that show some evidence for association with CAD-associated single-nucleotide polymorphisms (SNPs) are lipid and blood pressure traits.17,20Table. Genetic Loci Associated With Coronary Artery Disease or Myocardial InfarctionBandrs NumberGene(s)Risk AlleleRisk AlleleFrequencyOR (95% CI)P ValueReference (Examples)1p13.3rs599839SORT1A0.771.29 (1.18–1.40)74.05×10−97, 17, 221p32.2rs17114036PPAP2BA0.911.17 (1.13–1.22)173.81×10–19171p32.3rs11206510PCSK9T0.811.15 (1.10–1.21)229.6×10–9221q21.3rs4845625IL6RT0.471.04 (1.02–1.07)203.55×10−8201q41rs17465637MIA3C0.721.14 (1.10–1.19)221.4×10–97, 17, 222p11.2rs1561198VAMP5-VAMP8-GGCXA0.451.05 (1.03–1.07)204.48×10−9202p21rs6544713ABCG5-ABCG8T0.301.06 (1.04–1.09)208.72×10−1020, 25, 272p24.1rs2123536TTC32-WDR35T0.391.12 (1.08–1.16)196.83×10–11192p24.1rs515135APOBG0.831.08 (1.05–1.11)204.80×10−10202q22.3rs2252641ZEB2-AC074093.1G0.461.04 (1.02–1.06)203.66×10−8202q33.2rs6725887WDR12C0.141.17 (1.11–1.23)221.3×10–817, 223q22.3rs2306374MRASC0.181.12 (1.07–1.16)173.34×10–817, 214q31.22rs1878406EDNRAT0.151.06 (1.02–1.11)202.54×10−8204q32.1rs7692387GUCY1A3G0.811.06 (1.03–1.09)204.57×10−919, 205q31.1rs273909SLC22A4-SLC22A5C0.141.09 (1.05–1.12)201.43×10−8206p21.2rs10947789KCNK5T0.761.06 (1.03–1.08)201.63×10−8206p21.31rs17609940ANKS1AG0.751.07 (1.05–1.10) 171.36×10–8176p21.32rs9268402C6orf10-BTNL2G0.591.16 (1.12–1.20)192.77×10–15196p21.33rs3869109HLA-C, HLA-B, HCG27G0.551.1428*1.12×10–9286p24.1rs12526453PHACTR1C0.651.12 (1.08–1.17)221.3×10–917, 226p24.1rs6903956C6orf105A0.071.65 (1.44–1.90)292.55×10–13296q23.2rs12190287TCF21C0.621.08 (1.06–1.10)171.07×10–12176q25.3rs3798220LPAC0.021.51 (1.33–1.70)173.00×10−1117, 236q26rs4252120PLGT0.731.06 (1.03–1.09)205.00×10−9207p21.1rs2023938HDAC9G0.101.07 (1.04–1.11)204.94×10−8207q22.3rs10953541BCAP29C0.81.08 (1.05–1.11)163.12×10–8167q32.2rs11556924ZC3HC1C0.621.09 (1.07–1.12)179.18×10–18178p21.3rs264LPLG0.861.05 (1.02–1.08)205.06×10−9208q24.13rs2954029TRIB1A0.551.04 (1.02–1.06)204.53×10−820, 259p21.3rs1333049CDKN2A, CDKN2BC0.471.37 (1.26–1.48)71.80×10−147–99q34.2rs579459ABOC0.211.10 (1.07–1.13)174.08×10–1417, 27, 3010p11.23rs2505083KIAA1462C0.381.07 (1.04–1.09)163.87×10–816, 1810q11.21rs1746048CXCL12C0.841.17 (1.11–1.24)227.4×10−97, 17, 2210q23.31rs1412444LIPAT0.421.09 (1.07–1.12)162.76×10–1316, 25, 3110q24.32rs12413409CYP17A1, CNNM2,NT5C2G0.891.12 (1.08–1.16)171.03×10–91711q22.3rs974819PDGFDT0.321.07 (1.04–1.09)162.41×10–91611q23.3rs964184ZNF259, APOA5-A4-C3-A1G0.131.13 (1.10–1.16)171.02×10–171712q21.33rs7136259ATP2B1T0.391.11 (1.08–1.15)195.68×10-101912q24.12rs3184504SH2B3T0.381.13 (1.08–1.18)328.6×10–820, 3213q12.3rs9319428FLT1A0.321.05 (1.03–1.08)201.01×10−82013q34rs4773144COL4A1,COL4A2G0.441.07 (1.05–1.09)173.84×10–91714q32.2rs2895811HHIPL1C0.431.07 (1.05–1.10)171.14×10-101715q25.1rs3825807ADAMTS7A0.571.08 (1.06–1.10)171.07×10–1216, 17, 3015q26.1rs17514846FURIN-FESA0.441.05 (1.03–1.08)204.49×10−102017p11.2rs12936587RASD1,SMCR3,PEMTG0.561.07 (1.05–1.09)174.45×10-101717p13.3rs216172SMG6, SRRC0.371.07 (1.05–1.09)171.15×10–91717q21.32rs46522UBE2Z, GIP,ATP5G1, SNF8T0.531.06 (1.04–1.08)171.81×10–81719p13.2rs1122608LDLRG0.751.15 (1.10–1.20)221.9×10–917, 2219q13.32rs2075650APOE/TOMM40G0.141.14 (1.09–1.19)253.2×10–820, 2521q22.11rs9982601SLC5A3-MRPS6-KCNE2T0.131.20 (1.14–1.27)226.4×10–1117, 22In each row, data on rs number, gene(s) at the locus, risk allele, risk allele frequency, and P value were obtained from the same reference as the OR. CI indicates confidence interval; OR, odds ratio; and rs Number, reference single-nucleotide polymorphism number.*The 95% CI of the OR was not numerically provided.Genetic Predictors in Patients With Established CADGenetic analyses were not restricted to prevalent or incident CAD. SNPs are increasingly evaluated as prognostic factors in patients with established CAD. Genetic variations at 1p13.3 and 1q41 (both loci have been associated with prevalent CAD in prior GWAS)7,22 were associated with cardiovascular outcomes in patients with established CAD (including the outcomes of readmission for CVD and survival),34 and genetic variation in the thrombomodulin gene was associated with long-term survival in patients undergoing bypass surgery.35 These findings underscore the concept that a common or overlapping genetic architecture might influence several stages along the atherosclerotic disease spectrum. This premise is further supported by the observation that certain genetic loci are related to several vascular traits, as has been reported, for example, for the chromosome 9p21 locus (see above). On a parallel note, genetic variants also contribute to the susceptibility for sudden cardiac death in patients with established CAD, including SNPs in the genes CASQ2, GPD1L, and NOS1AP.36Thus, genetic predisposition is not only relevant for the development of CAD but has also emerged as a potential prognostic factor in patients with established disease. It needs to be assessed, however, whether genetic information is useful to refine clinical decisions when treating patients with CAD, a premise that requires further investigations. So far, data on the efficacy of genotype-based treatment strategies versus conventional care from randomized, controlled trials are often lacking. Newer initiatives are addressing this gap in our scientific knowledge for some clinical settings.37Impact of CAD Phenotype Definition on Effect Estimates in Genetic Association StudiesSince large consortia have been built to detect genetic variants with smaller effect sizes, it has emerged as an important question whether and how different phenotype definitions for CAD in such combined or meta-analyses might affect the results. Some prior studies reported slightly stronger genetic association signals in patients with angiographically confirmed CAD than in CAD patients without angiographic data.17 Furthermore, family-based analyses revealed different heritability estimates for distinct subphenotypes of CAD.38 On the other hand, there is a remarkable consistency in genetic association findings across cohorts with varying phenotype definitions, which underscores the notion that different manifestations of CAD might have a common genetic architecture. Kitsios and colleagues39 reanalyzed data from 965 individual studies that assessed the association of 32 genetic variants (in 22 genes) with CAD. The authors observed substantial variability in the phenotype definitions used across studies (for both cases and controls), but these differences contributed relatively little to the overall between-study heterogeneity. Furthermore, more stringent phenotype definitions (eg, acute coronary syndrome or angiographically documented CAD) did not lead to systematically different association measures in genetic analyses compared with broader definitions of the disease.39 The authors conclude that all available “evidence for CAD phenotypes should be considered” in genetic meta-analyses.39Replicated and consistent association signals from genetic-epidemiological studies stimulate further research. Conceptually, this workup can be categorized in at least 3 main areas of research: First, the identification of the causal genetic variant at a given locus and the elucidation of the precise biological mechanism by which a genetic locus leads to increased CAD susceptibility; second, the application of genetic variants in risk prediction models to forecast more precisely the onset or the natural course of disease or to predict more accurately the response to a given treatment; and third, the identification of newer therapeutic targets and conduction of randomized trials that focus on these targets.Functional Workup of GWAS ResultsTo clarify the biological mechanisms responsible for a genetic association signal, genetic-epidemiological information is increasingly combined with data from other high-throughput -omics technologies and with cellular or animal experiments. Several studies assessed whether significantly associated CAD SNPs affect the expression of nearby genes in different tissues.16,20,40 Additional workup strategies include the identification of the causal variant at a given locus through fine mapping or through knockdown or overexpression experiments of the putative causal genetic variant in cellular and animal models, for example, to assess potential consequences of the genetic variant on physiological traits (eg, lipid levels).In a landmark article, Musunuru and colleagues41 elucidated much of the biological mechanism of how a newly discovered locus on chromosome 1p13 contributes to CAD susceptibility. In prior analyses, noncoding SNPs at 1p13 were associated with an adverse lipid profile and the risk of CAD.7,22,42 Through fine mapping, a close survey of genetic variation around the top SNPs in a given locus, and a series of experiments, the authors identified rs12740374 as the putative causal variant of the locus. This variant is located within a transcription factor binding site. The CAD risk allele alters this binding site and thereby affects the expression of the SORT1 gene in the liver.41 The SORT1 gene product sortilin 1 modulates cellular low-density lipoprotein (LDL) uptake in vitro43 and secretion of very low-density lipoprotein particles in experimental settings,41 thereby influencing circulating lipid concentrations.44 Thus, the current evidence indicates that the genetic risk variant at the 1p13 locus leads to impaired SORT1 expression in the liver, higher hepatic secretion of very low-density lipoprotein, and reduced cellular LDL uptake and is, as a consequence, associated with higher circulating LDL levels, thereby increasing CAD risk.44 This work was one of the first examples of how GWAS and subsequent functional analyses identified an entirely new molecular mechanism that affected circulating lipid levels and predisposition to CAD. The sortilin pathway could be a potential therapeutic target to prevent dyslipidemia and reduce CAD risk.Use of Genetic Information for Risk Prediction and Assessment of Clinical UtilityThe use of genetic information to more accurately predict (1) the development of diseases in asymptomatic individuals, (2) the natural course of disease, or (3) the response to therapy in patients with established disease is one of the key motivations for the Human Genome Project and efforts to unravel the genetic architecture of diseases. In this context, it is of central interest whether genetic variants add information to such prediction models beyond that rendered by established risk factors. Different performance measures have been established to assess the incremental contribution of biomarkers, including genetic variation.45 Important indices include calibration (how well the predicted and observed absolute disease risks agree), discrimination, and reclassification.45 Discrimination refers to the ability to distinguish individuals who will develop the disease from those who will not,45 and reclassification quantifies how many individuals (with or without the outcome of interest) will be categorized in a more accurate risk category if genetic information is added to models with established risk factors.Given the complexity of the genetic architecture of CAD, with several variants conferring modest increases in relative disease risk (Table), genetic information is often aggregated by use of genetic risk scores that sum up and weight the number of risk alleles carried by each individual. In general, genetic risk scores for CAD are independently associated with CAD, even after adjustment for classic CVD risk factors.46–49 This is not surprising given that many of the CAD-associated risk variants are not related to traditional risk factors.33 However, genetic risk scores only modestly improved discrimination (if at all) and reclassification.46–48,50,51Analyses in the Atherosclerosis Risk in Communities (ARIC) study revealed that after the addition of genetic information from the 9p21 locus to a prediction model based on established risk factors, 12% to 13% of participants in the intermediate-risk category (10-year risk, 5%–20%) were reclassified.52 The authors emphasized that this reclassification might affect treatment decisions and treatment goals, for example, for LDL cholesterol, in a relevant proportion of individuals.52 However, future studies need to establish whether such reclassification and alternative treatment strategies based on genetic variation ultimately improve patient outcomes.45,53 One strategy to assess the clinical utility of genetic biomarkers is to conduct a randomized trial in which a treatment strategy based on genetic information is compared to usual care (in which no genetic information is considered).45,53Conceptually, knowledge of genetic variation may improve patient outcome in several ways, not just via better medical decisions by the physician.53 Knowledge of an increased genetic susceptibility to CAD (as reflected by a high genetic risk score) might also lead to greater compliance and better adherence to risk factor therapy by the patient,53,54 a premise that is currently being investigated in an ongoing clinical trial.54 In patients referred to a preventive cardiology clinic with an estimated 10-year CVD risk ≥6% or an estimated ≥20% CVD risk over 30 years, the investigators will assess whether knowledge of the genetic risk score by the patient further improves risk factor levels (eg, LDL levels, blood pressure) during short-term follow-up, even though patient management and treatment decisions will not be influenced by the results of the genetic risk score (the results of the genetic risk score will be disclosed after the treatment decisions have been made).54Currently, the use of genetic information to modify therapeutic strategies is not broadly recommended because of the lack of efficacy data from clinical trials in most clinical settings. Emerging clinical trials in this field will provide important insights into the potential clinical utility of genetic markers.37 Furthermore, because of their modest effect sizes,55 genetic markers may not be suitable as screening tools in asymptomatic individuals at present.ConclusionsGWAS and collaborative gene-centric approaches, in conjunction with other large-scale -omics technologies, have substantially improved our understanding of the molecular basis of CAD by identifying consistently associated genetic variants and by elucidating some of the underlying pathomechanisms. However, the effect sizes per risk allele have been modest (Table), which leaves much of the CAD heritability unexplained. GWAS and candidate gene approaches typically have focused on relatively common SNPs with a minor allele frequency >5%. More recently, new sequencing technologies have enabled whole-exome (whereby all protein-coding segments of the DNA are analyzed) or whole-genome sequencing at declining costs and facilitate more comprehensive analyses of rare genetic variants (minor allele frequency <1%) to assess their significance for CAD.56 Furthermore, it is increasingly recognized that CVD processes are governed by complex biological networks.57 Important technical advances form the basis for measurement of several components of such biological networks. Therefore, genetic information will increasingly be analyzed in the context of complementary gene expression, proteomic data, and metabolomic data. As an example, data on protein-protein interactions can be considered in the analyses of GWAS, and by combining information on protein-protein interactions with GWAS data, new groups of genes can be identified that might be relevant for the pathogenesis of CAD,58 even though the SNPs in isolation do not reach genomewide statistical significance.58 Additional research efforts include analyses of epigenetic modifications59 and of interactions between genes or between genes and environmental factors. Furthermore, modifiers of gene expression and protein processing increasingly are analyzed. Ideally, these different components will be evaluated in combined analytic approaches to elucidate entire biological systems governing physiological function of the cardiovascular system.57 In that context, repeated (at different points in time) and comprehensive molecular phenotyping of large samples under steady state conditions and in the context of challenges that cause perturbations of physiological traits are important requisites to improve our understanding of the biological networks underlying cardiovascular function and disease development.60Beyond the identification of new disease-associated biomarker profiles, systematic analyses are needed to elucidate the underlying biological mechanisms, and intensified research is required to assess the potential clinical utility of genetic variants in patients with established CAD and their potential use as screening tools in asymptomatic individuals.Sources of FundingThis work was supported by the National Heart, Lung, and Blood Institute’s Framingham Heart Study (contract No. N01-HC-25195).DisclosuresNone.FootnotesCorrespondence to Wolfgang Lieb, MD, MSc, Institute of Epidemiology, Christian Albrechts Universität zu Kiel, Campus UK-SH, Haus 1, Niemannsweg 11, 24105 Kiel, Germany. E-mail [email protected]