Portal de Periódicos da CAPES

Cardiovascular Genomics

2006; Lippincott Williams & Wilkins; Volume: 113; Issue: 11 Linguagem: Catalão

10.1161/circulationaha.105.560151

1524-4539

Marc S. Sabatine, Jonathan G. Seidman, Christine E. Seidman,

RNA modifications and cancer

HomeCirculationVol. 113, No. 11Cardiovascular Genomics Free AccessReview ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessReview ArticlePDF/EPUBCardiovascular Genomics Marc S. Sabatine, MD, MPH, Jonathan G. Seidman, PhD and Christine E. Seidman, MD Marc S. SabatineMarc S. Sabatine From the Cardiovascular Division, Brigham and Women's Hospital (M.S.S.), Department of Genetics, Harvard Medical School (J.G.S.), and Howard Hughes Medical Institute, Department of Genetics and Medicine, Brigham and Women's Hospital and Harvard Medical School (C.E.S.), Boston, Mass. , Jonathan G. SeidmanJonathan G. Seidman From the Cardiovascular Division, Brigham and Women's Hospital (M.S.S.), Department of Genetics, Harvard Medical School (J.G.S.), and Howard Hughes Medical Institute, Department of Genetics and Medicine, Brigham and Women's Hospital and Harvard Medical School (C.E.S.), Boston, Mass. and Christine E. SeidmanChristine E. Seidman From the Cardiovascular Division, Brigham and Women's Hospital (M.S.S.), Department of Genetics, Harvard Medical School (J.G.S.), and Howard Hughes Medical Institute, Department of Genetics and Medicine, Brigham and Women's Hospital and Harvard Medical School (C.E.S.), Boston, Mass. Originally published21 Mar 2006https://doi.org/10.1161/CIRCULATIONAHA.105.560151Circulation. 2006;113:e450–e455Case presentation: A 46-year-old man presents with an ST-elevation myocardial infarction (MI). He has neither hypertension nor diabetes, does not smoke, and has a total cholesterol level of 161 mg/dL with a high-density lipoprotein level of 43 mg/dL and a low-density lipoprotein level of 92 mg/dL. Emergency coronary angiography reveals an occluded left anterior descending artery as well as moderate atherosclerotic disease in both the right and left circumflex coronary arteries. The patient undergoes stenting of his left anterior descending artery with a good angiographic result. The next day, the patient asks, "Why did this happen to me? I don't smoke, I watch what I eat, and I exercise every day. This same thing happened to my father when he was my age and to my older brother just last year."BackgroundMost physicians are familiar with simple mendelian genetics wherein a rare mutation (by definition, one that occurs in 1% prevalence) and by convention are called polymorphisms (often single nucleotide polymorphisms or SNPs) rather than mutations. Moreover, the effect of each SNP on an individual's phenotype tends to be far more modest and may not be necessary or sufficient to cause the disease (Figure 1). TABLE 1. Simple (Mendelian) Versus Complex Human DiseasesSimpleComplexExamplesFamilial hypercholesterolemia; hypertrophic cardiomyopathy; Marfan syndrome; congenital long-QT syndromeHypertension; coronary heart disease; atrial fibrillationDisease prevalenceRareCommonNo. of genes involvedSingleMultipleFrequency of genetic variantRare ("mutation")Common ("polymorphism")Effect of variant on proteinSevereMildEffect of variant on phenotypeLarge (causative)Small (susceptibility)Relative importance of environmental factorsSmallLargeDownload figureDownload PowerPointFigure 1. Gaussian distribution of traits. Although disease states are treated as discrete outcomes, to understand complex genetic disorders, it may be easier to conceptualize the genetic effect as influencing an intermediate quantitative trait with a gaussian distribution. The presence of the variant allele causes a shift in the distribution of the trait toward the disease threshold. In the case of a simple mendelian disorder (eg, sickle cell anemia), a rare mutation (substitution of valine for glutamic acid in the β-globin chain) causes a very large shift in the trait (solubility of deoxygenated hemoglobin), which is sufficient to cause the disease. In contrast, for a complex disorder (eg, MI), a polymorphism (eg, in a gene encoding a platelet receptor) causes a more subtle shift in the trait (platelet aggregability) that increases the risk of the disease but alone is neither necessary nor sufficient, and the majority of individuals with this polymorphism will never develop the disease. Adapted with permission from Nature. 2000;405:847–856.Identifying Gene Variants That Contribute to DiseaseGenetic epidemiology studies typically rely on the coinheritance of known polymorphic DNA markers with nearby but unknown disease-causing variants. Traditionally, inheritance studies, which are performed in families and utilize linkage analysis, have been used to investigate monogenic, mendelian disorders.1 DNA markers close to the disease-causing gene are less likely to be separated by a recombination event than are markers far away from the disease-causing gene and therefore are more likely to be transmitted together from parent to offspring (Figure 2A). By gathering data on the inheritance patterns of markers and disease states within families, the distance between markers and the disease-causing gene can be estimated and thus the location of the disease-causing gene inferred. Although this approach has led to the discovery of many genes that are linked to human diseases, it has proven less useful in complex genetic disorders,2 in part because of reduced power to detect frequently occurring markers that convey only modest effects and in part because of phenocopies. Download figureDownload PowerPointFigure 2. Linkage vs association studies. Panel A, In linkage analysis, members of a family are genotyped at known polymorphic markers (in this case, 3 biallelic markers A/a, B/b, and D/d). The location of the disease-causing variant (shown here in red with an associated asterisk) is unknown to the investigator. Markers that are close to the disease-causing variant are unlikely to be separated by recombination during meiosis and therefore tend to be transmitted together from parents to offspring. In this case, in the first generation, alleles A, B, and D are associated with the disease state (filled symbol). In the second generation, among the affected individuals, because of recombination, 2 carry A and B (but not D) and 1 carries B and D (but not A). In the third generation, among the affected individuals 1 carries A and B, 1 carries B, and 1 carries D (because of recombination between B and the disease-causing variant). From these data, one can infer that the disease-causing variant is likely closest to marker B. Panel B, In an association study, one studies unrelated individuals, genotyping either known potentially direct causal variants or polymorphic markers that one hopes are in linkage disequilibrium across the population with the true disease-causing variant. In this case, a genetic polymorphism (inner red symbol) is found in 6 of 16 cases but in only 1 of 6 controls.In contrast, association studies investigate complex genetic disorders using unrelated individuals, testing for nonindependence between a genotype and the disease phenotype: if a genotype is truly related to a disease, it will be found more frequently in individuals with the disease than in those without the disease (Figure 2B). Within association studies, one can opt to genotype either putative causal variants (ie, direct association) or polymorphic markers that one hopes are close to the true disease-causing variant (indirect association).3 In the former case, the investigator typically selects functional SNPs in biologically relevant genes. Although intuitively appealing, such an approach is constrained by the current biological knowledge base and involves many assumptions. For that reason, others have advocated employing a comprehensive genome-wide scan using random DNA markers that blanket the entire genome. The cataloguing of ≈10 million SNPs4 and advances in high-throughput genotyping, including high-density DNA microarrays,5 make such genome-wide scans possible, albeit daunting. Akin to linkage analysis, a disease-causing variant that arose many generations ago in close proximity to a DNA marker will be coinherited with that marker so strongly and persistently that it ultimately leads to an association at the population level (termed linkage disequilibrium) due to shared common ancestry.6Of note, haplotypes are linear arrangements of adjacent SNPs on the same chromosome and offer the potential to improve both genetic precision and genotyping efficiency. In association studies, recombination between the marker SNP and the disease-causing variant will erode the linkage disequilibrium in some patients and lead to confusing results. In contrast, a haplotype that is defined by the presence of 2 SNPs flanking the disease-causing variant would be more likely to remain linked with the disease-causing variant than would either SNP alone. Moreover, although many SNPs may fall within a haplotype, because of linkage disequilibrium it is necessary to genotype only a few SNPs (so-called haplotype tagging SNPs) to uniquely identify the haplotype. This approach permits a substantial reduction in genotyping effort7 and consequently reduces costs and the number of independent variables analyzed, thereby profoundly affecting the statistical analyses. To that end, the International HapMap project has reported the construction of a genome-wide map of linkage disequilibrium in multiple populations.8Potential Pitfalls in Genetic StudiesGenetic epidemiology studies have several special limitations (Table 2), including genetic heterogeneity (different genotypes leading to the same phenotype), phenotypic heterogeneity (variability in expressivity or clinical manifestations of a genetic disease), phenotypic imprecision (combining phenotypes with potentially different genetic contributions), phenocopy (a phenotype that mimics the genetically caused phenotype of interest), and population stratification. The latter is a special type of confounding that can occur whenever a non–ethnically homogeneous population is studied. If an ethnic group has a higher frequency of a disease for nongenetic reasons, any genetic variant that occurs more frequently in that ethnic group, even if it is not causally linked to the disease, will spuriously appear to be associated with the disease. TABLE 2. Limitations of Genetic Association StudiesGenotype Genetic heterogeneity: same phenotype caused by polymorphisms in different genes Allelic heterogeneity: same phenotype caused by different alleles of the same gene Genotyping error: misassignment of genotype Epistasis: the effect of a genotype depends on the presence of another genotype Gene-environment interactions: the effect of a genotype depends on the presence of an environmental factorPhenotype Phenotypic heterogeneity: variability in expressivity or clinical manifestations of a genetic disease Phenotypic misclassification: misassignment of phenotype Phenotypic imprecision: combining phenotypes with potentially different genetic contributions Phenocopy: a phenotype that mimics the genetically caused phenotype of interestStatistical issues Confounding: a type of bias in which an artefactual association between a predictor and an outcome is caused by a third factor independently associated with both the predictor and the outcome Population stratification: a non–ethnically homogeneous study population in which one ethnic group is overrepresented in cases and also happens to have a higher prevalence of certain non–disease-causing genetic variants Multiple testing: analyses that, without proper adjustment, predispose to false-positive studies (type I error) Small sample size: a design flaw that predisposes to false-negative studies (type II error) Publication bias: the observation that positive studies are more likely to be published than negative studies; this can lead to inflated effect estimates in meta-analysesType I statistical errors (false-positives) are a major problem due to multiple testing. With 30 000 genes and 10 million SNPs, the number of possible association tests is enormous. Techniques have been developed to control experiment-wise error rates that include setting the false discovery rate and performing permutation testing. Replication of results in an independent population before proclaiming a confirmed association remains the best approach9 and one that is now required by leading journals. Type II statistical errors (false-negatives) are also a problem due to small sample size. The excess risk or benefit associated with a polymorphism may be modest in magnitude and therefore only detectable with large studies on the order of at least 1000 case-control pairs, although this conventional wisdom has recently been challenged.10Finally, it is important to remember that statistical association is not proof of causality. A polymorphism associated with a disease is unlikely to be the causal variant but rather to be in strong linkage disequilibrium with the true causal variant within the same gene or even potentially in nearby genes. This can lead to inconsistencies between studies if the causal variants arose independently in different populations.Examples of Genetic Approaches to Coronary Artery DiseaseLinkage AnalysisIn an attempt to apply traditional linkage analysis to the complex genetic disorder of coronary artery disease, researchers performed a genome-wide scan on a single family with an apparent autosomal dominant pattern of coronary artery disease. They found significant linkage with a marker in a region that contains the myocyte enhancer factor-2 family (MEF2A) gene.11 However, a different research group was not able to replicate this finding in individuals with sporadic MI.12 Other groups have used linkage analysis to large numbers of multiplex families with a variety of coronary heart disease phenotypes.13–16 Each group has found significant linkage but to different chromosomal regions. Whether the different loci identified in each study reflect phenotypic imprecision or false-positive results remains unclear.Multilocus Candidate Gene StudiesIn 2002, one of the largest genetic association studies for MI was published, involving 112 polymorphisms in 71 candidate genes in 5061 unrelated individuals.17 The investigators conducted a staged approach, setting a more modest threshold for identifying candidate SNPs in a small cohort and then a more stringent threshold for validating the associations in a second, larger cohort. In analyses stratified by gender, their approach yielded 1 SNP in men (in the connexin 37 gene) and, surprisingly, 2 other SNPs in women (in the PAI-1 and stromelysin genes). Several other, smaller multilocus studies have been published, but none of these studies has identified the same genetic variants as associated with MI. Of note, one of the groups that found linkage between the gene encoding 5-lipoxygenase activating protein and coronary heart disease16 has subsequently reported an association between a haplotype spanning the gene encoding leukotriene A4 hydrolase and MI.18 Although it is intriguing that these genes encode proteins within the same biochemical pathway, it is notable that the associations were found in different populations, one white and one black.Genome-Wide Association StudiesA group in Japan examined 92 788 SNPs in a large case-control study involving 1133 cases with MI and 1006 controls.19 They identified 3 SNPs in the lymphotoxin-α gene that were strongly associated with MI. Subsequent molecular biology studies demonstrated that the variants were associated with transcriptional and functional changes in lymphotoxin-α. Another group of researchers performed a genome-wide association study examining 11 053 SNPs in 6891 genes, using 3 sequential studies to validate their findings.20 They found variants in 4 genes, none previously implicated in MI, that were consistently associated with MI in all 3 stages. As with the multilocus studies, no 2 groups have identified the same variant.PharmacogenomicsInvestigators have explored the interaction between genetic variants and response to cardiovascular drugs with the hope of more precisely defining efficacy and safety profiles. To that end, polymorphisms in the genes encoding HMG-CoA reductase,21 apolipoprotein E,22 and the ADAMTS-1 metalloproteinase23 appear to predict the magnitude of change in lipid levels and/or the reduction in adverse clinical outcomes in response to statin therapy. Polymorphisms in the genes that encode β-adrenergic receptors have been associated not only with the risk of developing heart failure24 but also with improvement in ejection fraction in response to β-blocker therapy.25 Finally, the anticoagulant effect of a dose of warfarin is affected by polymorphisms in the genes that encode CYP2C9 and vitamin K epoxide reductase complex 1.26,27ConclusionsLearning from early forays, researchers are now designing better genetic epidemiological studies to detect the subtle but likely important contribution of genetic variation to common cardiovascular diseases. The hallmarks of a good genetic association study include accurate and comprehensive genotyping, evaluation of a large number of carefully phenotyped patients, and replication of significant associations. The requisite scope of such studies underscores the need for a multidisciplinary and multicenter collaborative approach. With the pace of advances into the molecular basis for atherothrombosis, management of patients such as the one described in our case study will, in the near future, likely include genetic analysis to improve risk stratification and tailor therapy appropriately.FootnotesCorrespondence to Marc S. Sabatine, MD, MPH, Cardiovascular Division, Brigham and Women's Hospital, Boston, MA 02115. E-mail [email protected] References 1 Botstein D, White RL, Skolnick M, Davis RW. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet. 1980; 32: 314–331.MedlineGoogle Scholar2 Altmuller J, Palmer LJ, Fischer G, Scherb H, Wjst M. Genomewide scans of complex human diseases: true linkage is hard to find. Am J Hum Genet. 2001; 69: 936–950.CrossrefMedlineGoogle Scholar3 Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005; 6: 95–108.CrossrefMedlineGoogle Scholar4 Sachidanandam R, Weissman D, Schmidt SC, Kakol JM, Stein LD, Marth G, Sherry S, Mullikin JC, Mortimore BJ, Willey DL, Hunt SE, Cole CG, Coggill PC, Rice CM, Ning Z, Rogers J, Bentley DR, Kwok PY, Mardis ER, Yeh RT, Schultz B, Cook L, Davenport R, Dante M, Fulton L, Hillier L, Waterston RH, McPherson JD, Gilman B, Schaffner S, Van Etten WJ, Reich D, Higgins J, Daly MJ, Blumenstiel B, Baldwin J, Stange-Thomann N, Zody MC, Linton L, Lander ES, Altshuler D. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature. 2001; 409: 928–933.CrossrefMedlineGoogle Scholar5 Matsuzaki H, Loi H, Dong S, Tsai YY, Fang J, Law J, Di X, Liu WM, Yang G, Liu G, Huang J, Kennedy GC, Ryder TB, Marcus GA, Walsh PS, Shriver MD, Puck JM, Jones KW, Mei R. Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array. Genome Res. 2004; 14: 414–425.CrossrefMedlineGoogle Scholar6 Cardon LR, Bell JI. Association study designs for complex diseases. Nat Rev Genet. 2001; 2: 91–99.CrossrefMedlineGoogle Scholar7 Johnson GC, Esposito L, Barratt BJ, Smith AN, Heward J, Di Genova G, Ueda H, Cordell HJ, Eaves IA, Dudbridge F, Twells RC, Payne F, Hughes W, Nutland S, Stevens H, Carr P, Tuomilehto-Wolf E, Tuomilehto J, Gough SC, Clayton DG, Todd JA. Haplotype tagging for the identification of common disease genes. Nat Genet. 2001; 29: 233–237.CrossrefMedlineGoogle Scholar8 Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P. A haplotype map of the human genome. Nature. 2005; 437: 1299–1320.CrossrefMedlineGoogle Scholar9 Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG. Replication validity of genetic association studies. Nat Genet. 2001; 29: 306–309.CrossrefMedlineGoogle Scholar10 Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J. Complement factor H polymorphism in age-related macular degeneration. Science. 2005; 308: 385–389.CrossrefMedlineGoogle Scholar11 Wang L, Fan C, Topol SE, Topol EJ, Wang Q. Mutation of MEF2A in an inherited disorder with features of coronary artery disease. Science. 2003; 302: 1578–1581.CrossrefMedlineGoogle Scholar12 Weng L, Kavaslar N, Ustaszewska A, Doelle H, Schackwitz W, Hebert S, Cohen JC, McPherson R, Pennacchio LA. Lack of MEF2A mutations in coronary artery disease. J Clin Invest. 2005; 115: 1016–1020.CrossrefMedlineGoogle Scholar13 Broeckel U, Hengstenberg C, Mayer B, Holmer S, Martin LJ, Comuzzie AG, Blangero J, Nurnberg P, Reis A, Riegger GA, Jacob HJ, Schunkert H. A comprehensive linkage analysis for myocardial infarction and its related risk factors. Nat Genet. 2002; 30: 210–214.CrossrefMedlineGoogle Scholar14 Wang Q, Rao S, Shen GQ, Li L, Moliterno DJ, Newby LK, Rogers WJ, Cannata R, Zirzow E, Elston RC, Topol EJ. Premature myocardial infarction novel susceptibility locus on chromosome 1P34–36 identified by genomewide linkage analysis. Am J Hum Genet. 2004; 74: 262–271.CrossrefMedlineGoogle Scholar15 Hauser ER, Crossman DC, Granger CB, Haines JL, Jones CJ, Mooser V, McAdam B, Winkelmann BR, Wiseman AH, Muhlestein JB, Bartel AG, Dennis CA, Dowdy E, Estabrooks S, Eggleston K, Francis S, Roche K, Clevenger PW, Huang L, Pedersen B, Shah S, Schmidt S, Haynes C, West S, Asper D, Booze M, Sharma S, Sundseth S, Middleton L, Roses AD, Hauser MA, Vance JM, Pericak-Vance MA, Kraus WE. A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD Study. Am J Hum Genet. 2004; 75: 436–447.CrossrefMedlineGoogle Scholar16 Helgadottir A, Manolescu A, Thorleifsson G, Gretarsdottir S, Jonsdottir H, Thorsteinsdottir U, Samani NJ, Gudmundsson G, Grant SF, Thorgeirsson G, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Johannsson H, Gudmundsdottir O, Gurney ME, Sainz J, Thorhallsdottir M, Andresdottir M, Frigge ML, Topol EJ, Kong A, Gudnason V, Hakonarson H, Gulcher JR, Stefansson K. The gene encoding 5-lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nat Genet. 2004; 36: 233–239.CrossrefMedlineGoogle Scholar17 Yamada Y, Izawa H, Ichihara S, Takatsu F, Ishihara H, Hirayama H, Sone T, Tanaka M, Yokota M. Prediction of the risk of myocardial infarction from polymorphisms in candidate genes. N Engl J Med. 2002; 347: 1916–1923.CrossrefMedlineGoogle Scholar18 Helgadottir A, Manolescu A, Helgason A, Thorleifsson G, Thorsteinsdottir U, Gudbjartsson DF, Gretarsdottir S, Magnusson KP, Gudmundsson G, Hicks A, Jonsson T, Grant SF, Sainz J, O'Brien SJ, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Levey AI, Abramson JL, Reilly MP, Vaccarino V, Wolfe ML, Gudnason V, Quyyumi AA, Topol EJ, Rader DJ, Thorgeirsson G, Gulcher JR, Hakonarson H, Kong A, Stefansson K. A variant of the gene encoding leukotriene A4 hydrolase confers ethnicity-specific risk of myocardial infarction. Nat Genet. 2006; 38: 68–74.CrossrefMedlineGoogle Scholar19 Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T, Sato H, Hori M, Nakamura Y, Tanaka T. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet. 2002; 32: 650–654.CrossrefMedlineGoogle Scholar20 Shiffman D, Ellis SG, Rowland CM, Malloy MJ, Luke MM, Iakoubova OA, Pullinger CR, Cassano J, Aouizerat BE, Fenwick RG, Reitz RE, Catanese JJ, Leong DU, Zellner C, Sninsky JJ, Topol EJ, Devlin JJ, Kane JP. Identification of four gene variants associated with myocardial infarction. Am J Hum Genet. 2005; 77: 596–605.CrossrefMedlineGoogle Scholar21 Chasman DI, Posada D, Subrahmanyan L, Cook NR, Stanton VP Jr, Ridker PM. Pharmacogenetic study of statin therapy and cholesterol reduction. JAMA. 2004; 291: 2821–2827.CrossrefMedlineGoogle Scholar22 Gerdes LU, Gerdes C, Kervinen K, Savolainen M, Klausen IC, Hansen PS, Kesaniemi YA, Faergeman O. The apolipoprotein epsilon4 allele determines prognosis and the effect on prognosis of simvastatin in survivors of myocardial infarction: a substudy of the Scandinavian Simvastatin Survival Study. Circulation. 2000; 101: 1366–1371.CrossrefMedlineGoogle Scholar23 Sabatine MS, Ray KK, Iakoubova OA, Kirchgessner T, Tsuchihashi Z, Packard CJ, Pfeffer MA, Ploughman LM, Zerba KE, Devlin JJ, Shaw PM, Shepherd J, Braunwald E. A polymorphism in a metalloproteinase gene predicts ability of pravastatin to reduce death or MI. Circulation. 2004; 110 (suppl III): III-426. Abstract.LinkGoogle Scholar24 Small KM, Wagoner LE, Levin AM, Kardia SL, Liggett SB. Synergistic polymorphisms of beta1- and alpha2C-adrenergic receptors and the risk of congestive heart failure. N Engl J Med. 2002; 347: 1135–1142.CrossrefMedlineGoogle Scholar25 Mialet Perez J, Rathz DA, Petrashevskaya NN, Hahn HS, Wagoner LE, Schwartz A, Dorn GW, Liggett SB. Beta 1-adrenergic receptor polymorphisms confer differential function and predisposition to heart failure. Nat Med. 2003; 9: 1300–1305.CrossrefMedlineGoogle Scholar26 Higashi MK, Veenstra DL, Kondo LM, Wittkowsky AK, Srinouanprachanh SL, Farin FM, Rettie AE. Association between CYP2C9 genetic variants and anticoagulation-related outcomes during warfarin therapy. JAMA. 2002; 287: 1690–1698.CrossrefMedlineGoogle Scholar27 Rieder MJ, Reiner AP, Gage BF, Nickerson DA, Eby CS, McLeod HL, Blough DK, Thummel KE, Veenstra DL, Rettie AE. Effect of VKORC1 haplotypes on transcriptional regulation and warfarin dose. N Engl J Med. 2005; 352: 2285–2293.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Meireles-Brandão J, Meireles-Brandão L, Coelho R and Rocha-Gonçalves F (2022) Long-term outcomes of primary cardiovascular prevention: A retrospective study at a referral center in Portugal, Revista Portuguesa de Cardiologia, 10.1016/j.repc.2021.05.017, Online publication date: 1-May-2022. Samimi-Fard S, Abreu-Gonzalez P, Dominguez-Rodriguez A and Jimenez-Sosa A (2011) A case-control study of melatonin receptor type 1A polymorphism and acute myocardial infarction in a Spanish population, Journal of Pineal Research, 10.1111/j.1600-079X.2011.00903.x, 51:4, (400-404), Online publication date: 1-Nov-2011. Murphy M and O'Brien T (2009) DYSLIPIDEMIAS Pharmacology and Therapeutics, 10.1016/B978-1-4160-3291-5.50027-5, (303-320), . Koch W, Hoppmann P, Biele J, Mueller J, Schömig A and Kastrati A (2008) Fibrinogen Genes and Myocardial Infarction, Arteriosclerosis, Thrombosis, and Vascular Biology, 28:4, (758-763), Online publication date: 1-Apr-2008. Hoffman J (2008) Epidemiology of congenital heart disease: etiology, pathogenesis, and incidence Fetal Cardiology, 10.3109/9781439807989.008, (101-110), Online publication date: 1-Dec-2008. Tang N, Wang L, Yang L, Zhou B, Gu H, Sun Q, Cong R, Zhu H and Wang B (2008) A polymorphism in the resistin gene promoter and the risk of coronary artery disease in a Chinese population, Clinical Endocrinology, 10.1111/j.1365-2265.2007.03003.x, 68:1, (82-87), Online publication date: 1-Jan-2008. Nelson P, O'Malley K, Feezor R, Moldawer L and Seeger J (2007) Genomic and proteomic determinants of lower extremity revascularization failure: Rationale and study design, Journal of Vascular Surgery, 10.1016/j.jvs.2007.03.011, 45:6, (A82-A91), Online publication date: 1-Jun-2007. Tziakas D and Chalikias G (2007) Role of apolipoprotein E genotype in coronary artery disease, Future Cardiology, 10.2217/14796678.3.5.537, 3:5, (537-551), Online publication date: 1-Sep-2007. Kayser S (2007) Pharmacogenomics and the Potential for Personalized Therapeutics in Cardiovascular Disease, Progress in Cardiovascular Nursing, 10.1111/j.0889-7204.2007.06129.x, 22:2, (104-107), Online publication date: 1-Feb-2007. Karan R, Cvjeticanin S, Kovacevic-Kostic N, Nikolic D, Velinovic M, Milicevic V and Obrenovic-Kircanski B (2018) Genetic and Environmental Dispositions for Cardiovascular Variability: A Pilot Study, Journal of Clinical Medicine, 10.3390/jcm7090232, 7:9, (232) Deniz E, Jonas S, Khokha M and Choma M (2019) Quantitative Phenotyping of Xenopus Embryonic Heart Pathophysiology Using Hemoglobin Contrast Subtraction Angiography to Screen Human Cardiomyopathies, Frontiers in Physiology, 10.3389/fphys.2019.01197, 10 March 21, 2006Vol 113, Issue 11 Advertisement Article InformationMetrics https://doi.org/10.1161/CIRCULATIONAHA.105.560151PMID: 16549641 Originally publishedMarch 21, 2006 PDF download Advertisement SubjectsAcute Coronary SyndromesGeneticsOmics