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• W2317626178 abstract "HomeCirculation: Cardiovascular GeneticsVol. 7, No. 6Compiling the Complement of Genes Implicated in Coronary Artery Disease Free AccessEditorialPDF/EPUBAboutView PDFSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBCompiling the Complement of Genes Implicated in Coronary Artery Disease Charlotte Andersson, MD, PhD and Ramachandran S. Vasan, MD Charlotte AnderssonCharlotte Andersson From the The Framingham Heart Study, MA (C.A., R.S.V.); Department of Cardiology, Gentofte Hospital, University of Copenhagen, Denmark (C.A.); and Sections of Preventive Medicine and Cardiology, Departments of Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, MA (R.S.V.). Search for more papers by this author and Ramachandran S. VasanRamachandran S. Vasan From the The Framingham Heart Study, MA (C.A., R.S.V.); Department of Cardiology, Gentofte Hospital, University of Copenhagen, Denmark (C.A.); and Sections of Preventive Medicine and Cardiology, Departments of Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, MA (R.S.V.). Search for more papers by this author Originally published1 Dec 2014https://doi.org/10.1161/CIRCGENETICS.114.000909Circulation: Cardiovascular Genetics. 2014;7:738–740Coronary artery disease (CAD) and myocardial infarction cluster within families. First-degree relatives of affected individuals are at increased risk of developing the disease themselves, with the elevated risk being of a similar magnitude regardless of whether the affected proband is a sibling or a parent.1 Epidemiological data indicate that the estimated heritability of CAD and CAD-related death may be as high as 40% to 50%.2 Yet, common genetic variation (≈45 loci) identified in previous genome-wide association studies (GWAS) explains only a 10th of the heritability of CAD.3 The missing heritability may be attributable to rare genetic variants not captured by GWAS, tag single nucleotide polymorphisms (SNPs) that are not in complete linkage disequilibrium with the true causal variants, structural variation, alterations in regulatory units, epigenetic influences, gene–gene interactions, post-translational modification of proteins, or environmental and behavioral factors, including gene–environment interactions.4 Another hypothesis is that many causally related SNPs may be discarded from GWAS-based on their failure to achieve a genome-wide statistical significance level. As pointed out by Visscher,5 the agnostic approach epitomized by GWAS may inherently emphasize high specificity at the cost of low sensitivity (ie, it minimizes false-positive associations). A GWAS of 10 000 individuals has only 29% power to detect a SNP that explains 0.2% of the variance in a trait (at a type I error rate of 5×10–7).5 Thus, there may be a low probability (ie, 0.292=0.08) that 2 independent studies of 10 000 individuals each will discover the same genetic variant.5 The analysis of height supports the premise that GWAS may overlook some subthreshold loci (statistically speaking) that influence the trait. Height has an estimated heritability of 80%, with known SNPs (697) explaining only ≈16% of the phenotypic variation.6 However, when all common SNP variants were considered together (≈1 million); including those below the genome-wide significance threshold the estimated heritability increases to 50%.6 Thus, it is conceivable that common genetic variation may still explain a substantial proportion of the estimated heritability of common complex diseases, with an important contribution by variants that may not meet a stringent genome-wide significance threshold.Article see p 887In this issue, Xu et al7 have used another approach, which they refer to as a candidate pathway-based GWAS, in their search for variants in the complement system that may be associated with CAD. By integrating a candidate gene approach and quantitative gene expression measures, the investigators demonstrate statistically significant associations of 2 regulatory SNPs related to the complement pathway components 3 and 6 (C3 and C6), respectively. In a first step, they performed a focused analysis of 668 SNPs in 32 genes related to the complement system in a case–control study of individuals of Han Chinese ancestry with and without CAD. To avoid false-negative test results, the authors used an uncorrected P<0.01 as the statistical significance threshold for proceeding to next step. Using this sequential approach, the investigators identified 2 SNPs (both P<8×10−3) in the C3a anaphylatoxin chemotactic receptor 1 (C3AR1) and C6 genes that were associated with CAD status; of note, the P values for these 2 SNPs were above the threshold for statistical significance after a standard Bonferroni correction, 0.05/668=7.5−10−5. Next, the authors investigated if the 2 identified SNPs were associated with expression levels of the C3AR1 and C6 genes, respectively, by searching publically available databases but did not find evidence for such an association for these specific SNPs. However, the investigators identified 2 cis-expression quantitative trait loci (eQTLs) variants that were associated with C3AR1 and C6 messenger RNA (mRNA) expression levels, respectively. In a third step, the authors corroborated an association between these expression quantitative trait loci genotypes (rs7842 and rs4400166) and actual gene expression levels measured in leukocytes of 266 randomly selected individuals. In a final step, the investigators confirmed the association of rs7842 and rs4400166 with CAD in 2 additional independent case–control samples. In a meta-analysis of the latter 2 samples, the unadjusted odds ratio for prevalent CAD was 1.41, P=1.89×10−10 for rs7842, and 1.23, P=1.69×10−4 for rs4400166, strongly suggesting that at least genetic variation in C3AR1 is associated with CAD.Several immune-related diseases have been associated with atherosclerotic CAD in epidemiological studies. Numerous reports also underscore the central role of systemic inflammation in the pathogenesis of CAD.8,9 Treatments that reduce the risk of myocardial infarction may also reduce inflammation and vice versa.10,11 Accordingly, the observations by Xu et al7 are interesting and intriguing, and also biologically plausible. Experimental evidence suggests that the complement cascade may be involved in the development of atherosclerosis,12,13 in part by promoting inflammation and phagocytosis of cholesterol crystals within the plaque. The activation of the complement system involves different pathways depending on the trigger (known as the classic, alternative, and lectin pathways), but all of them activate C3, which after cleavage to C3a and C3b, results in downstream activation of distal components of the cascade, including C6, C7, and C8. Of note, cholesterol crystals in the arterial wall can activate the complement system directly.14 Circulating C3 levels have been correlated with previous myocardial infarction, stroke, and CAD, and with standard cardiovascular risk factors in unselected samples.15,16 C3a enhances inflammation by stimulating mast cells and the numbers of mast cells may be ≤200-fold higher at sites of plaque erosion in coronary arteries.17 Mast cells are proatherosclerotic because they release proinflammatory cytokines within the plaques.18 Interestingly, C6-deficient rabbits fed a cholesterol-rich diet are protected from the development of atherosclerosis.19 These aforementioned data implicate the complement system in the pathogenesis of CAD.The study by Xu et al7 is an initial step toward further extending our understanding of the involvement of the complement system genes and their products in the pathogenesis of CAD using large-scale genetic data in humans. The study was based on a case–control sample of individuals of Han Chinese ancestry. In this context, it is also noteworthy that the SNPs that are associated with incident CAD may differ from those associated with prevalent CAD. Additional studies are warranted to replicate the reported findings in other ethnicities, with the evaluation of incident CAD and the incidence of other forms of cardiovascular disease, including stroke. In addition, CAD is a continuum that extends from the development of atherosclerotic plaques through the development of plaque vulnerability to the acute thrombotic events that eventually culminate in an acute myocardial infarction. The genetic determinants implicated in CAD (including the role of the complement system) may vary across this disease continuum, a premise that warrants further genetic studies investigating the underlying pathophenotypes of stable versus unstable CAD.PerspectiveThe candidate pathway-based approach used by Xu et al7 for mining extant GWAS data may help illuminate the genetic basis of CAD and bridge the gap in unexplained heritability of the condition. By focusing systematically on known biological pathways previously implicated in atherosclerosis it is conceivable that other novel genetic variants associated with CAD may be identified. This relatively inexpensive method that combines the agnostic and the candidate gene approaches can be readily applied to publically available databases. This hybrid approach may probably reveal more common variants associated with CAD than GWAS with their stringent statistical significance thresholds, but likely comes with the potential trade-off of a higher rate of false-positive findings. Replication studies are, therefore, critical when such an approach is used. Furthermore, the journey from gene/variant discovery to unraveling of variant function and then to medical inferences and clinical applications of genetic variation is a multistep process that take time.20 Statistical associations are not necessarily causal and the downstream functional consequences of genetic variation are often unknown. Therefore, integrating multiple forms of functional genomic data beyond the exploration of expression quantitative trait loci data already done by Xu et al,7 including but not limited to annotation of GWAS-discovered SNP data using Encyclopedia of DNA Elements (ENCODE) project data21 (eg, targeting noncoding regulatory variation) and other -omics data (ie, metabolomics and proteomics) may help us elucidate the genetic architecture of CAD. The statistical ways of handling pathway-based analyses, including the opportunities and the pitfalls, are beyond the scope of this editorial, but are outlined elsewhere.22 Additional studies in model systems are also warranted to investigate the functional significance of genetic variants (including those in the complement pathway). In summary, the work by Xu et al7 highlights the importance of the many complementary approaches for identifying the complement of genes and nongenic regions implicated in the pathogenesis of CAD.DisclosuresThis work was supported in part by N01-HC-25195. Dr Andersson was supported by an independent research grant from the Danish Agency For Science, Technology, and Innovation (the Danish Medical Research Council, grant no. FSS—11–120873).FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Correspondence to Charlotte Andersson, MD, PhD, Framingham Heart Study, 73 Mt Wayte Ave, Suite #2, Framingham, MA 01702. E-mail [email protected]References1. Nielsen M, Andersson C, Gerds TA, Andersen PK, Jensen TB, Køber L, et al.. Familial clustering of myocardial infarction in first-degree relatives: a nationwide study.Eur Heart J. 2013; 34:1198–1203. doi: 10.1093/eurheartj/ehs475.CrossrefMedlineGoogle Scholar2. Ganesh SK, Arnett DK, Assimes TL, Basson CT, Chakravarti A, Ellinor PT, et al.. Genetics and genomics for the prevention and treatment of cardiovascular disease: update: a scientific statement from the american heart association.Circulation. 2013; 128:2813–2851.LinkGoogle Scholar3. Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, et al.. Large-scale association analysis identifies new risk loci for coronary artery disease.Nat Genet. 2013; 45:25–33.CrossrefMedlineGoogle Scholar4. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al.. Finding the missing heritability of complex diseases.Nature. 2009; 461:747–753. doi: 10.1038/nature08494.CrossrefMedlineGoogle Scholar5. Visscher PM. Sizing up human height variation.Nat Genet. 2008; 40:489–490. doi: 10.1038/ng0508-489.CrossrefMedlineGoogle Scholar6. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al.; Electronic Medical Records and Genomics (eMERGE) Consortium; MIGen Consortium; PAGE Consortium; LifeLines Cohort Study. Defining the role of common variation in the genomic and biological architecture of adult human height.Nat Genet. 2014; 46:1173–1186. doi: 10.1038/ng.3097.CrossrefMedlineGoogle Scholar7. Xu C, Yang Q, Xiong H, Wang L, Cai J, Wang F, et al.. Candidate pathway-based GWAS identifies novel associations of genomic variants in the complement system associated with coronary artery disease.Circ Cardiovasc Genet. 2014; 7:2883–2888.LinkGoogle Scholar8. Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C, et al.. Multiple biomarkers for the prediction of first major cardiovascular events and death.N Engl J Med. 2006; 355:2631–2639. doi: 10.1056/NEJMoa055373.CrossrefMedlineGoogle Scholar9. Melander O, Newton-Cheh C, Almgren P, Hedblad B, Berglund G, Engström G, et al.. Novel and conventional biomarkers for prediction of incident cardiovascular events in the community.JAMA. 2009; 302:49–57. doi: 10.1001/jama.2009.943.CrossrefMedlineGoogle Scholar10. Ridker PM, Danielson E, Fonseca FA, Genest J, Gotto AM, Kastelein JJ, et al.; JUPITER Study Group. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein.N Engl J Med. 2008; 359:2195–2207. doi: 10.1056/NEJMoa0807646.CrossrefMedlineGoogle Scholar11. Ahlehoff O, Skov L, Gislason G, Lindhardsen J, Kristensen SL, Iversen L, et al.. Cardiovascular disease event rates in patients with severe psoriasis treated with systemic anti-inflammatory drugs: a Danish real-world cohort study.J Intern Med. 2013; 273:197–204. doi: 10.1111/j.1365-2796.2012.02593.x.CrossrefMedlineGoogle Scholar12. Lappegård KT, Garred P, Jonasson L, Espevik T, Aukrust P, Yndestad A, et al.. A vital role for complement in heart disease.Mol Immunol. 2014; 61:126–134. doi: 10.1016/j.molimm.2014.06.036.CrossrefMedlineGoogle Scholar13. Speidl WS, Kastl SP, Huber K, Wojta J. Complement in atherosclerosis: friend or foe?J Thromb Haemost. 2011; 9:428–440. doi: 10.1111/j.1538-7836.2010.04172.x.CrossrefMedlineGoogle Scholar14. Samstad EO, Niyonzima N, Nymo S, Aune MH, Ryan L, Bakke SS, et al.. Cholesterol crystals induce complement-dependent inflammasome activation and cytokine release.J Immunol. 2014; 192:2837–2845. doi: 10.4049/jimmunol.1302484.CrossrefMedlineGoogle Scholar15. Muscari A, Massarelli G, Bastagli L, Poggiopollini G, Tomassetti V, Drago G, et al.. Relationship of serum C3 to fasting insulin, risk factors and previous ischaemic events in middle-aged men.Eur Heart J. 2000; 21:1081–1090. doi: 10.1053/euhj.1999.2013.CrossrefMedlineGoogle Scholar16. Onat A, Uzunlar B, Hergenç G, Yazici M, Sari I, Uyarel H, et al.. Cross-sectional study of complement C3 as a coronary risk factor among men and women.Clin Sci (Lond). 2005; 108:129–135. doi: 10.1042/CS20040198.CrossrefMedlineGoogle Scholar17. Kovanen PT, Kaartinen M, Paavonen T. Infiltrates of activated mast cells at the site of coronary atheromatous erosion or rupture in myocardial infarction.Circulation. 1995; 92:1084–1088.LinkGoogle Scholar18. Sun J, Sukhova GK, Wolters PJ, Yang M, Kitamoto S, Libby P, et al.. Mast cells promote atherosclerosis by releasing proinflammatory cytokines.Nat Med. 2007; 13:719–724. doi: 10.1038/nm1601.CrossrefMedlineGoogle Scholar19. Schmiedt W, Kinscherf R, Deigner HP, Kamencic H, Nauen O, Kilo J, et al.. Complement C6 deficiency protects against diet-induced atherosclerosis in rabbits.Arterioscler Thromb Vasc Biol. 1998; 18:1790–1795.LinkGoogle Scholar20. Green ED, Guyer MS; National Human Genome Research Institute. Charting a course for genomic medicine from base pairs to bedside.Nature. 2011; 470:204–213. doi: 10.1038/nature09764.CrossrefMedlineGoogle Scholar21. Kellis M, Wold B, Snyder MP, Bernstein BE, Kundaje A, Marinov GK, et al.. Defining functional DNA elements in the human genome.Proc Natl Acad Sci U S A. 2014; 111:6131–6138. doi: 10.1073/pnas.1318948111.CrossrefMedlineGoogle Scholar22. Ramanan VK, Shen L, Moore JH, Saykin AJ. Pathway analysis of genomic data: concepts, methods, and prospects for future development.Trends Genet. 2012; 28:323–332. doi: 10.1016/j.tig.2012.03.004.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Wang X, Cui N, Zhang S, Liu Z, Ma J and Ming L (2019) Leukocyte telomere length, mitochondrial DNA copy number, and coronary artery disease risk and severity: A two-stage case-control study of 3064 Chinese subjects, Atherosclerosis, 10.1016/j.atherosclerosis.2019.03.010, 284, (165-172), Online publication date: 1-May-2019. Thornton S, Raghu H, Cruz C, Frederick M, Palumbo J, Mullins E, Almholt K, Usher P and Flick M (2017) Urokinase plasminogen activator and receptor promote collagen-induced arthritis through expression in hematopoietic cells, Blood Advances, 10.1182/bloodadvances.2016004002, 1:9, (545-556), Online publication date: 28-Mar-2017. 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