Figures
Abstract
Background
Coronary restenosis after percutaneous coronary intervention still remains a significant problem, despite all medical advances. Unraveling the mechanisms leading to restenosis development remains challenging. Many studies have identified genetic markers associated with restenosis, but consistent replication of the reported markers is scarce. The aim of the current study was to analyze the joined effect of previously in literature reported candidate genes for restenosis in the GENetic DEterminants of Restenosis (GENDER) databank.
Methodology/Principal Findings
Candidate genes were selected using a MEDLINE search including the terms ‘genetic polymorphism’ and ‘coronary restenosis’. The final set included 36 genes. Subsequently, all single nucleotide polymorphisms (SNPs) in the genomic region of these genes were analyzed in GENDER using set-based analysis in PLINK. The GENDER databank contains genotypic data of 2,571,586 SNPs of 295 cases with restenosis and 571 matched controls. The set, including all 36 literature reported genes, was, indeed, significantly associated with restenosis, p = 0.024 in the GENDER study. Subsequent analyses of the individual genes demonstrated that the observed association of the complete set was determined by 6 of the 36 genes.
Citation: Verschuren JJW, Trompet S, Postmus I, Sampietro ML, Heijmans BT, Houwing-Duistermaat JJ, et al. (2012) Systematic Testing of Literature Reported Genetic Variation Associated with Coronary Restenosis: Results of the GENDER Study. PLoS ONE 7(8): e42401. https://doi.org/10.1371/journal.pone.0042401
Editor: Yan Gong, College of Pharmacy, University of Florida, United States of America
Received: March 19, 2012; Accepted: July 5, 2012; Published: August 3, 2012
Copyright: © Verschuren et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by grants from the Interuniversity Cardiology Institute of the Netherlands (ICIN) http://www.icin.nl/, the European Community Framework FP7 Programme under grant agreement [n° HEALTH-F2-2009-223004; http://cordis.europa.eu/projects/90569_en.html], the Center for Medical Systems Biology (CMSB) [http://www.cmsb.nl], a center of excellence approved by the Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research (NWO), and the Netherlands Consortium for Healthy Ageing (NCHA) [http://www.healthy-ageing.nl]. JWJ is an established clinical investigator of the Netherlands Heart Foundation (2001D032) [http://www.hartstichting.nl/]. The funders had no role in study design, data collection and analysis, decision to publish or the preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Restenosis is a complex disease for which the causative mechanisms have not yet been fully identified. Despite medical advances, restenosis still remains a significant complication after percutaneous coronary intervention (PCI).[1] Identification of risk factors and underlying mechanisms could not only be useful in risk stratification of patients, they also contribute to our understanding of this condition. In addition, these factors could provide evidence on which to base individually tailored treatment and aid in the development of novel therapeutic modalities.[2] Unraveling the mechanisms leading to restenosis development remains challenging. Genetic susceptibility is known to play a role in the individuals risk of developing this complication.[1] Many studies have focused on identification of genetic markers associated with restenosis. Over the last decades genetic research has developed from candidate gene approaches [3]–[5] to multiplex arrays [6] and finally to genome wide association studies (GWAS).[7] Genetic variation in large array of plausible candidate genes have been associated with restenosis, however, consistent replication of the reported markers is scarce.[1] Possible explanations for this lack of consistency are the small sample size of many (especially relative more dated) studies, phenotype heterogeneity and lack of proper replication cohorts.
Currently more and more GWAS are being performed, investigating many diseases, including cardiovascular diseases.[8], [9] An advantage of GWAS is the hypothesis-free approach of this method, enabling identification of new genetic loci associated with the disease of interest. With respect to restenosis, a disadvantage of the GWAS approach is that due to the complexity of the disease the effect size of individual genetic markers is likely to be small and therefore hard to detect. Moreover, the availability of (large) replication cohorts is very limited. In 2011, the first GWAS on restenosis in the GENetic DEterminants of Restenosis (GENDER) study identified a new susceptibility locus on chromosome 12.[7] The fact that this GWAS only identified this previously unknown locus does not mean that genetic variation in the previously proposed candidate genes does not affect restenosis development. It merely indicates that the influence of other individual markers is probably too small to detect in the GWAS setting. Especially for the complex traits, a more appropriate approach to interpret GWAS data is to analyze the combined effect of a single nucleotide polymorphism (SNP) set, grouped per pathway or gene region.[10] To date, investigation into a possible joined effect of multiple genetic markers for restenosis has not been performed.
The goal of the current study is to investigate whether the last decade of research on genetics of restenosis has led to a set of genes that is associated with restenosis in a set-based analysis using the available genotypic data of the GENDER databank.
Methods
Gene Selection
Candidate genes previously associated with restenosis were selected after a search of literature of papers published up to November 2011. Genes were identified searching MEDLINE using keywords as ‘genetic polymorphism’, ‘candidate gene’, ‘restenosis’ and ‘percutaneous coronary intervention’. Selection criteria included a sample size of >250 patients and the observation of a significant association of a SNP with restenosis. The final set included 36 genes. All available SNPs from the GENDER GWAS databank within a 10-Kb window around these genes were analyzed.
Study Population
The design of GENDER and the genome-wide association study (GWAS), which has been performed in a subset of this study population, have both been described previously.[7], [11] In brief, GENDER included 3,104 consecutive unrelated symptomatic patients treated successfully by PCI for angina. The study protocol conforms to the Declaration of Helsinki and was approved by the ethics committees of each participating institution. Written informed consent was obtained from each participant before the PCI procedure. During a follow-up period of 9 months, the endpoint clinical restenosis, defined as renewed symptoms requiring target vessel revascularization (TVR) either by repeated PCI or CABG, by death from cardiac causes or myocardial infarction not attributable to another coronary event than the target vessel, was recorded. During follow-up, 346 patients developed clinical restenosis. Blood samples were collected at the index procedure for DNA isolation. The GWAS was performed in 325 cases of restenosis and 630 controls matched by gender, age, and some possible confounding clinical variables for restenosis in the GENDER study such as total occlusion, diabetes, current smoking and residual stenosis. Genotyping was performed using the Illumina Human 610-Quad Beadchips following the manufacturer’s instructions. After genotyping, samples and genetic markers were subjected to a stringent quality control protocol. The final dataset consisted of 866 individuals (295 cases, 571 controls) and 556,099 SNPs that passed all quality control criteria, together covering 89% of the common genetic variation in the European population.[7], [12] Imputation was performed with MACH software based on the HapMap II release 22 CEU build 36 using the default settings.[13] This program infers missing genotypes based on the known genotypic data of the samples together with haplotypes from a reference population provided by HapMap taken into account the degree of linkage disequilibrium (LD). After subsequent quality control, we excluded SNPs for further analyses with a call rate lower than 95% (n = 3335) or with a significant deviation from Hardy–Weinberg equilibrium (HWE) in controls (P<0.00001) (n = 79). The final GENDER Biobank dataset consisted of 866 (295 cases, 571 controls) individuals and 2,571,586 SNPs.
Statistical Analysis
The statistical analyses were performed using the set-based test of PLINK v1.07.[14] During this test, first a single SNP analysis of all SNPs within the set is performed. Subsequently a mean SNP statistic is calculated from the single SNP statistics of a maximum amount of independent SNPs below a certain p-value threshold. If SNPs are not independent and the LD (expressed in R2) is above a certain threshold, the SNP with the lowest p-value in the single SNP analysis is selected. This analysis is repeated in a certain amount of permutations of the phenotype. An empirical p-value for the SNP set is computed by calculating the number of times the test statistic of the simulated SNP sets exceeds that of the original SNP set. For the analysis of this study, the parameters were set to p-value threshold <0.05, R2 threshold <0.1, maximum number of SNPs = 5 and 10,000 permutations.
Initially, the set including all 36 genes is tested as a whole for the association with restenosis. Subsequent analysis of the individual genes will be justified only when the complete set is significantly associated with the endpoint.
Results
Patient characteristics are presented in Table 1. No significant differences were found between cases and controls regarding the known risk factors for restenosis (age, diabetes, smoking, stenting and previous restenosis). Hypertension and multivessel disease were more common in the cases compared to the controls.
In Figure 1 the QQ-plot of the GENDER GWAS after imputation is shown, demonstrating that no genomic inflation has occurred in this analysis (lambda = 1.027). The complete set of 36 genes, previously associated with restenosis in literature, contained 2,581 SNPs. A detailed description of the individual studies and candidate genes can be found in Table 2. The largest gene was chemokine (C-X3-C motif) receptor 1 (CX3CR1) of 316.54 kb, contributing 384 SNPs (14.8%), and glutathione peroxidase 1 (GPX1) was with 1.18 kb the smallest gene, only contributing 8 SNPs (0.3%). Analysis of the complete set using the set-based test demonstrated a significant association with clinical restenosis, with an empirical p-value of 0.024.
To determine which genes are mainly responsible for this association we subsequently investigated the association of the individual gene based sets. Six of the 36 genes were demonstrated to have an empirical p-value below 0.05 (Table 3). In order of descending p-values the associated genes are; angiotensin II receptor type 1 (AGTR, p = 0.028), glutathione peroxidase 1 (GPX1, p = 0.025), K(lysine) acetyltransferase 2B (KAT2B, also known as PCAF, p = 0.023), matrix metallopeptidase 12 (MMP12, p = 0.019), fibrinogen beta chain (FGB, p = 0.013) and vitamin D receptor (VDR, p = 0.012). Detailed information on the individual SNPs in these genes is depicted in Table 4. The SNP with the lowest individual p-value was rs11574027 in the VDR gene, p = 1.4E-04. In the complete GWAS analysis, which has been published in 2011 [7], this SNP ranked 116th. The strongest association in that analysis was found with a SNP in an intergenic region on chromosome 12, p = 1.0E-06.
Logistic regression models with and without the 11 SNPs described in Table 4 demonstrated that together these SNPs explained 9.0% (R Square improved from 0.008 to 0.098) of the occurrence of clinical restenosis in this cohort.
As a final analysis we removed the 6 significantly associated genes from the complete set. Subsequent analysis of the subset of the other 30 genes did not demonstrate a remaining joined effect, p = 0.65 after 10,000 permutations.
Discussion
With this study we aimed at clarifying the ambiguities regarding genetic predisposition for developing restenosis after PCI. We show that the joined effect of the complete spectrum of candidate genes, so far proposed to be involved in the restenotic process, results in a significant association with restenosis. This association is determined by six individual genes. Analyzing a subset containing the 30 genes not associated with the endpoint on an individual basis, did not show a remaining joined effect, making the involvement of genetic variation in these genes on restenosis development more unlikely.
The six associated genes span a wide range of different functions underlining the complexity of the disease. When examining the biological pathways with involvement of these genes, only the VDR and KAT2B genes share a common pathway. The genes are both involved in the Vitamin D receptor pathway described by BioCarta.[15] This pathway mainly involves the transcriptional regulating capacities of this receptor and is involved in controlling cellular growth, differentiation and apoptosis. Since these processes are all thought to be important contributors to the restenotic process, this indeed is a plausible pathway to be involved in restenosis development.[1].
The rationale of set-based analysis is to overcome the marginally weak effect of single SNPs by analyzing a set of SNPs, since these SNPs could jointly have strong genetic effects. Most studies utilizing the candidate gene approach analyzed only one or at most a few SNPs within the gene of interest. The likelihood that exactly those SNPs are the causal or associated SNPs is of course small. A broader approach, like this set-based analysis, is therefore more likely to detect an associated gene by combining multiple SNPs with a possible marginal individual effect.[16], [17] For the current study we used the PLINK software [14], although multiple statistical programs are available for this type of analysis. Gui et al. compared 7 tests analyzing the WTCCC Crohn’s Disease dataset.[18] One of their overall conclusions was that the set-based test in PLINK was the most powerful algorithm. Another study, applying PLINK set-based test, Global test, GRASS and SNP ratio test, for the analysis of three pathways regarding human longevity observed similar results with the different tests.[19].
For the current study we analyzed the data using a threshold of linkage disequilibrium defined by R2≥0.1. The standard setting in PLINK is a R2 of 0.5. In our opinion this threshold is too high for the intended analysis for this study. A higher threshold will include more SNPs in higher LD, which would be unfavorable, since we were interested in independent loci contributing to the risk of restenosis. By decreasing this threshold, only SNPs were selected that had a R2 below 0.1, and thus independent of each other.
Although hypertension and multivessel disease were more frequent in cases compared to controls we decided not to correct for these variables. In the complete GENDER population these variables were not independent predictors for restenosis development [11], so the differences in the current subpopulation likely resulted by chance during the selection process. Also, other studies provide no convincing data that hypertension is related to restenosis [1]. It is therefore unlikely that previous associations of some of the current candidates genes (VDR, FGB, AGTR1 and GPX1) with hypertension[20]–[23], have influenced our results, although this cannot be completely excluded.
A limitation of the current study could be that we analyzed imputed genotypic data, which introduces some amount of uncertainty. However, since we were interested in the combined effect of SNPs, an extensive genomic coverage was paramount for this analysis. Only analyzing the genotyped GWAS data would have resulted in the coverage of some of the smaller genes by only 1 or 2 SNPs. Therefore we decided that the more extensive genomic coverage of the imputed dataset outweighed the small introduction of possible error. A second limitation is that the analyses were only performed in the GENDER population. Availability of other populations with thorough genetic data on restenosis is however very limited. To our knowledge, the GWAS on restenosis in the GENDER population is the first, and only, examining this endpoint on a genome wide scale. Finally, the conclusions of this study are only based on genetic analyses. Functional studies should be performed to elucidate the biological consequences of these findings.
In conclusion, with these results we demonstrate that the efforts in unraveling the genetic factors influencing the risk of restenosis of the last years has resulted in a set of genes that joint together is indeed likely to be associated with restenosis, despite the overt inconsistencies of the individual studies. Confirmation of the association of these genes with the occurrence of restenosis after PCI helps our understanding of the genetic etiology of the disease. Future additional research strategies, like biological pathway analysis of GWAS data or even (exome) sequencing, might help us find the missing heritability of restenosis after PCI and increase our knowledge of the biological mechanistic background of restenosis development. This knowledge could subsequently result in identification of new treatment targets or development of novel preventive measure or risk stratification models.
Author Contributions
Conceived and designed the experiments: JJWV ST IP MLS BTH JJH-D EPS JWJ. Performed the experiments: JJWV ST MLS. Analyzed the data: JJWV ST. Contributed reagents/materials/analysis tools: ST IP MLS BTH JWJ. Wrote the paper: JJWV ST IP BTH JWJ.
References
- 1. Jukema JW, Verschuren JJ, Ahmed TA, Quax PH (2012) Restenosis after PCI. Part 1: pathophysiology and risk factors. Nat Rev Cardiol 9: 53–62.
- 2. Jukema JW, Ahmed TA, Verschuren JJ, Quax PH (2012) Restenosis after PCI. Part 2: prevention and therapy. Nat Rev Cardiol 9: 79–90.
- 3. Kastrati A, Koch W, Berger PB, Mehilli J, Stephenson K, et al. (2000) Protective role against restenosis from an interleukin-1 receptor antagonist gene polymorphism in patients treated with coronary stenting. J Am Coll Cardiol 36: 2168–2173.
- 4. Koch W, Bottiger C, Mehilli J, von BN, Neumann FJ, et al. (2001) Association of a CD18 gene polymorphism with a reduced risk of restenosis after coronary stenting. Am J Cardiol 88: 1120–1124.
- 5. Monraats PS, Pires NM, Schepers A, Agema WR, Boesten LS, et al. (2005) Tumor necrosis factor-alpha plays an important role in restenosis development. FASEB J 19: 1998–2004.
- 6. Monraats PS, Pires NM, Agema WR, Zwinderman AH, Schepers A, et al. (2005) Genetic inflammatory factors predict restenosis after percutaneous coronary interventions. Circulation 112: 2417–2425.
- 7. Sampietro ML, Trompet S, Verschuren JJ, Talens RP, Deelen J, et al. (2011) A genome-wide association study identifies a region at chromosome 12 as a potential susceptibility locus for restenosis after percutaneous coronary intervention. Hum Mol Genet 20: 4748–4757.
- 8. O’Donnell CJ, Nabel EG (2011) Genomics of cardiovascular disease. N Engl J Med 365: 2098–2109.
- 9. Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, et al. (2008) Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PLoS One 3: e3583.
- 10. Ma L, Han S, Yang J, Da Y (2010) Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies. PLoS One 5: e15006.
- 11. Agema WR, Monraats PS, Zwinderman AH, de Winter RJ, Tio RA, et al. (2004) Current PTCA practice and clinical outcomes in The Netherlands: the real world in the pre-drug-eluting stent era. Eur Heart J 25: 1163–1170.
- 12. Sampietro ML, Pons D, de Knijff P, Slagboom PE, Zwinderman A, et al. (2009) A genome wide association analysis in the GENDER study. Neth Heart J 17: 262–264.
- 13. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34: 816–834.
- 14. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575.
- 15.
Biocarta website. Biocarta pathway. Available: http://www.biocarta.com/pathfiles/h_vdrpathway.asp. Accessed 2012 Mar 1..
- 16. Fridley BL, Biernacka JM (2011) Gene set analysis of SNP data: benefits, challenges, and future directions. Eur J Hum Genet 19: 837–843.
- 17. Torkamani A, Topol EJ, Schork NJ (2008) Pathway analysis of seven common diseases assessed by genome-wide association. Genomics 92: 265–272.
- 18. Gui H, Li M, Sham PC, Cherny SS (2011) Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn’s Disease dataset. BMC Res Notes 4: 386.
- 19. Deelen J, Uh HW, Monajemi R, van HD, Thijssen PE, et al. (2011) Gene set analysis of GWAS data for human longevity highlights the relevance of the insulin/IGF-1 signaling and telomere maintenance pathways. Age (Dordr ) In press. 10.1007/s11357–011–9340–3 [doi].
- 20. Swapna N, Vamsi UM, Usha G, Padma T (2011) Risk conferred by FokI polymorphism of vitamin D receptor (VDR) gene for essential hypertension. Indian J Hum Genet 17: 201–206.
- 21. Kolz M, Baumert J, Gohlke H, Grallert H, Doring A, et al. (2009) Association study between variants in the fibrinogen gene cluster, fibrinogen levels and hypertension: results from the MONICA/KORA study. Thromb Haemost 101: 317–324.
- 22. Niu W, Qi Y (2010) Association of the angiotensin II type I receptor gene +1166 A>C polymorphism with hypertension risk: evidence from a meta-analysis of 16474 subjects. Hypertens Res 33: 1137–1143.
- 23. Mansego ML, Solar GM, Alonso MP, Martinez F, Saez GT, et al. (2011) Polymorphisms of antioxidant enzymes, blood pressure and risk of hypertension. J Hypertens 29: 492–500.
- 24. Falcone C, Emanuele E, Buzzi MP, Ballerini L, Repetto A, et al. (2007) The-374T/A variant of the rage gene promoter is associated with clinical restenosis after coronary stent placement. Int J Immunopathol Pharmacol 20: 771–777.
- 25. Shim CY, Park S, Yoon SJ, Park HY, Kim HT, et al. (2007) Association of RAGE gene polymorphisms with in-stent restenosis in non-diabetic Korean population. Cardiology 107: 261–268.
- 26. Gross CM, Perrot A, Geier C, Posch MG, Hassfeld S, et al. (2007) Recurrent in-stent restenosis is not associated with the angiotensin-converting enzyme D/I, angiotensinogen Thr174Met and Met235Thr, and the angiotensin-II receptor 1 A1166C polymorphism. J Invasive Cardiol 19: 261–264.
- 27. Wijpkema JS, van Haelst PL, Monraats PS, Bruinenberg M, Zwinderman AH, et al. (2006) Restenosis after percutaneous coronary intervention is associated with the angiotensin-II type-1 receptor 1166A/C polymorphism but not with polymorphisms of angiotensin-converting enzyme, angiotensin-II receptor, angiotensinogen or heme oxygenase-1. Pharmacogenet Genomics 16: 331–337.
- 28. Oguri M, Kato K, Hibino T, Yokoi K, Segawa T, et al. (2007) Genetic risk for restenosis after coronary stenting. Atherosclerosis 194: e172–e178.
- 29. Shimada K, Miyauchi K, Mokuno H, Watanabe Y, Iwama Y, et al. (2004) Promoter polymorphism in the CD14 gene and concentration of soluble CD14 in patients with in-stent restenosis after elective coronary stenting. Int J Cardiol 94: 87–92.
- 30. Tiroch K, Koch W, Mehilli J, Bottiger C, Schomig A, et al. (2009) P27 and P53 gene polymorphisms and restenosis following coronary implantation of drug-eluting stents. Cardiology 112: 263–269.
- 31. van Tiel CM, Bonta PI, Rittersma SZ, Beijk MA, Bradley EJ, et al. (2009) p27kip1-838C>A single nucleotide polymorphism is associated with restenosis risk after coronary stenting and modulates p27kip1 promoter activity. Circulation 120: 669–676.
- 32. Oguri M, Kato K, Hibino T, Yokoi K, Segawa T, et al. (2007) Identification of a polymorphism of UCP3 associated with recurrent in-stent restenosis of coronary arteries. Int J Mol Med 20: 533–538.
- 33. Niessner A, Marculescu R, Kvakan H, Haschemi A, Endler G, et al. (2005) Fractalkine receptor polymorphisms V2491 and T280M as genetic risk factors for restenosis. Thromb Haemost 94: 1251–1256.
- 34. Horibe H, Yamada Y, Ichihara S, Watarai M, Yanase M, et al. (2004) Genetic risk for restenosis after coronary balloon angioplasty. Atherosclerosis 174: 181–187.
- 35. Tiroch KA, Sibbing D, Koch W, Roosen-Runge T, Mehilli J, et al. (2010) Protective effect of the CYP2C19 *17 polymorphism with increased activation of clopidogrel on cardiovascular events. Am Heart J 160: 506–512.
- 36. Monraats PS, Rana JS, Zwinderman AH, de Maat MP, Kastelein JP, et al. (2005) -455G/A polymorphism and preprocedural plasma levels of fibrinogen show no association with the risk of clinical restenosis in patients with coronary stent placement. Thromb Haemost 93: 564–569.
- 37. Pons D, Monraats PS, de Maat MP, Pires NM, Quax PH, et al. (2007) The influence of established genetic variation in the haemostatic system on clinical restenosis after percutaneous coronary interventions. Thromb Haemost 98: 1323–1328.
- 38. Martinez-Rios MA, Pena-Duque MA, Fragoso JM, Delgadillo-Rodriguez H, Rodriguez-Perez JM, et al. (2009) Tumor necrosis factor alpha and interleukin 10 promoter polymorphisms in Mexican patients with restenosis after coronary stenting. Biochem Genet 47: 707–716.
- 39. Koch W, Tiroch K, von BN, Schomig A, Kastrati A (2003) Tumor necrosis factor-alpha, lymphotoxin-alpha, and interleukin-10 gene polymorphisms and restenosis after coronary artery stenting. Cytokine 24: 161–171.
- 40. Monraats PS, Kurreeman FA, Pons D, Sewgobind VD, de Vries FR, et al. (2007) Interleukin 10: a new risk marker for the development of restenosis after percutaneous coronary intervention. Genes Immun 8: 44–50.
- 41. Marculescu R, Mlekusch W, Exner M, Sabeti S, Michor S, et al. (2003) Interleukin-1 cluster combined genotype and restenosis after balloon angioplasty. Thromb Haemost 90: 491–500.
- 42. Zee RY, Fernandez-Ortiz A, Macaya C, Pintor E, Fernandez-Cruz A, et al. (2003) IL-1 cluster genes and occurrence of post-percutaneous transluminal coronary angioplasty restenosis: a prospective, angiography-based evaluation. Atherosclerosis 171: 259–264.
- 43. Monraats PS, Rana JS, Nierman MC, Pires NM, Zwinderman AH, et al. (2005) Lipoprotein lipase gene polymorphisms and the risk of target vessel revascularization after percutaneous coronary intervention. J Am Coll Cardiol 46: 1093–1100.
- 44. Chung SL, Chiou KR, Charng MJ (2006) 677TT polymorphism of methylenetetrahydrofolate reductase in combination with low serum vitamin B12 is associated with coronary in-stent restenosis. Catheter Cardiovasc Interv 67: 349–355.
- 45. Koch W, Ndrepepa G, Mehilli J, Braun S, Burghartz M, et al. (2003) Homocysteine status and polymorphisms of methylenetetrahydrofolate reductase are not associated with restenosis after stenting in coronary arteries. Arterioscler Thromb Vasc Biol 23: 2229–2234.
- 46. Gomma AH, Elrayess MA, Knight CJ, Hawe E, Fox KM, et al. (2002) The endothelial nitric oxide synthase (Glu298Asp and-786T>C) gene polymorphisms are associated with coronary in-stent restenosis. Eur Heart J 23: 1955–1962.
- 47. Pons D, Monraats PS, Zwinderman AH, de Maat MP, Doevendans PA, et al. (2009) Metabolic background determines the importance of NOS3 polymorphisms in restenosis after percutaneous coronary intervention: A study in patients with and without the metabolic syndrome. Dis Markers 26: 75–83.
- 48. Gorchakova O, Koch W, von BN, Mehilli J, Schomig A, et al. (2003) Association of a genetic variant of endothelial nitric oxide synthase with the 1 year clinical outcome after coronary stent placement. Eur Heart J 24: 820–827.
- 49. Rudez G, Pons D, Leebeek F, Monraats P, Schrevel M, et al. (2008) Platelet receptor P2RY12 haplotypes predict restenosis after percutaneous coronary interventions. Hum Mutat 29: 375–380.
- 50. Bottiger C, Koch W, Lahn C, Mehilli J, von BN, et al. (2003) 4G/5G polymorphism of the plasminogen activator inhibitor-1 gene and risk of restenosis after coronary artery stenting. Am Heart J 146: 855–861.
- 51. Pons D, Trompet S, de Craen AJ, Thijssen PE, Quax PH, et al. (2011) Genetic variation in PCAF, a key mediator in epigenetics, is associated with reduced vascular morbidity and mortality: evidence for a new concept from three independent prospective studies. Heart 97: 143–150.
- 52. Neugebauer P, Goldbergova-Pavkova M, Kala P, Bocek O, Jerabek P, et al. (2009) Nuclear receptors gene polymorphisms and risk of restenosis and clinical events following coronary stenting. Vnitr Lek 55: 1135–1140.
- 53. Koch W, Jung V, von BN, Schomig A, Kastrati A (2004) Peroxisome proliferator-activated receptor gamma gene polymorphisms and restenosis in diabetic patients after stenting in coronary arteries. Diabetologia 47: 1126–1127.
- 54. Rittersma SZ, Boekholdt SM, Koch KT, Geuzebroek R, Bax M, et al. (2004) Thrombospondin gene polymorphisms and the risk of angiographic coronary in-stent restenosis. Am J Med 116: 499–500.
- 55. Kojima S, Iwai N, Tago N, Ono K, Ohmi K, et al. (2004) p53Arg72Pro polymorphism of tumour suppressor protein is associated with luminal narrowing after coronary stent placement. Heart 90: 1069–1070.
- 56. Zee RY, Cook NR, Kim CA, Fernandez-Cruz A, Lindpaintner K (2004) TP53 haplotype-based analysis and incidence of post-angioplasty restenosis. Hum Genet 114: 386–390.
- 57. Monraats PS, Fang Y, Pons D, Pires NM, Pols HA, et al. (2010) Vitamin D receptor: a new risk marker for clinical restenosis after percutaneous coronary intervention. Expert Opin Ther Targets 14: 243–251.