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Genetic Risk Factors and Mendelian Randomization in Cardiovascular Disease

  • Lipid Abnormalities and Cardiovascular Prevention (G De Backer, Section Editor)
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Abstract

Cardiovascular disease encompasses several diverse pathological states that place a heavy burden on individual and population health. The aetiological basis of many cardiovascular disorders is not fully understood. Growing knowledge of the genetic architecture underlying coronary heart disease, stroke, cardiac arrhythmias and peripheral vascular disease has confirmed some suspected causal pathways in these conditions but also uncovered many previously unknown mechanisms. Here, we consider the contribution of genetics to the understanding of cardiovascular disease risk. We evaluate the utility and relevance of findings from genome-wide association studies and explore the role that Mendelian randomisation has to play in exploiting these. Mendelian randomisation permits robust causal inference in an area of research where this has been hampered by bias and confounding in observational studies. In doing so, it provides evidence for causal processes in cardiovascular disease that could represent novel targets for much-needed new drugs for disease prevention and treatment.

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References

Papers of particular interest, published recently, have been highlighted as: •• Of major importance

  1. Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–47.

    Article  PubMed  CAS  Google Scholar 

  2. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008;336:1475–82.

    Article  PubMed Central  PubMed  Google Scholar 

  3. Fox KAA, Fitzgerald G, Puymirat E, Huang W, Carruthers K, Simon T, et al. Should patients with acute coronary disease be stratified for management according to their risk? Derivation, external validation and outcomes using the updated GRACE risk score. BMJ Open. 2014;4:e004425.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Brotman DJ, Walker E, Lauer MS, O’Brien RG. In search of fewer independent risk factors. Arch Intern Med. 2005;165:138–45.

    Article  PubMed  Google Scholar 

  5. Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, et al. Major lipids, apolipoproteins, and risk of vascular disease. JAMA. 2009;302:1993–2000.

    Article  PubMed  Google Scholar 

  6. Rapsomaniki E, Timmis A, George J, Pujades-Rodriguez M, Shah AD, Denaxas S, et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1·25 million people. Lancet. 2014;383:1899–911.

    Article  PubMed Central  PubMed  Google Scholar 

  7. Kaptoge S, Di Angelantonio E, Lowe G, Pepys MB, Thompson SG, Collins R, et al. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis. Lancet. 2010;375:132–40.

    Article  PubMed  CAS  Google Scholar 

  8. Danesh J, Kaptoge S, Mann AG, Sarwar N, Wood A, Angleman SB, et al. Long-term interleukin-6 levels and subsequent risk of coronary heart disease: two new prospective studies and a systematic review. PLoS Med. 2008;5:e78.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  9. Emerging Risk Factors Collaboration, Seshasai SRK, Kaptoge S, Thompson A, Di Angelantonio E, Gao P, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med. 2011;364:829–41.

    Article  Google Scholar 

  10. Fruchart J-C, Davignon J, Hermans MP, Al-Rubeaan K, Amarenco P, Assmann G, et al. Residual macrovascular risk in 2013: what have we learned? Cardiovasc Diabetol. 2014;13:26.

    Article  PubMed Central  PubMed  Google Scholar 

  11. Nordestgaard BG, Chapman MJ, Humphries SE, Ginsberg HN, Masana L, Descamps OS, et al. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. Eur Heart J. 2013;34:3478–90a. Recent European guidance on the clinical management of familial hypercholesterolaemia.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  12. Futema M, Plagnol V, Li K, Whittall RA, Neil HAW, Seed M, et al. Whole exome sequencing of familial hypercholesterolaemia patients negative for LDLR/APOB/PCSK9 mutations. J Med Genet. 2014;51:537–44.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  13. Fouchier SW, Dallinga-Thie GM, Meijers JCM, Zelcer N, Kastelein JJP, Defesche JC, et al. Mutations in STAP1 are associated with autosomal dominant hypercholesterolemia. Circ Res. 2014;115:552–5. Report of a novel locus responsible for causing familial hypercholesterolaemia.

    Article  PubMed  CAS  Google Scholar 

  14. Lange LA, Hu Y, Zhang H, Xue C, Schmidt EM, Tang Z-Z, et al. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. Am J Hum Genet. 2014;94:233–45. Demonstration of the use of whole-exome sequencing for investigating genetic determinants of atherosclerosis and coronary heart disease.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  15. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JPA, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9:356–69. A guide to the development, application and utility of genome-wide association studies.

    Article  PubMed  CAS  Google Scholar 

  16. Hindorff LA, Junkins HA, Hall P, Mehta JP, Manolio TA. A catalog of published genome-wide association studies [Internet]. Cat Publ Genome-Wide Assoc Stud. [cited 2011 Jun 30]. Available from: www.genome.gov/gwastudies.

  17. 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. 2012;45:25–33. The largest meta-analysis of genome-wide association studies of coronary heart disease published to date.

    Article  PubMed  CAS  Google Scholar 

  18. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40:161–9.

    Article  PubMed  CAS  Google Scholar 

  19. Arsenault BJ, Boekholdt SM, Dubé M-P, Rhéaume E, Wareham NJ, Khaw K-T, et al. Lipoprotein(a) levels, genotype, and incident aortic valve stenosis: a prospective Mendelian randomization study and replication in a case-control cohort. Circ Cardiovasc Genet. 2014;7:304–10.

    Article  PubMed  CAS  Google Scholar 

  20. Kamstrup PR, Tybjaerg-Hansen A, Steffensen R, Nordestgaard BG. Genetically elevated lipoprotein(a) and increased risk of myocardial infarction. JAMA. 2009;301:2331–9.

    Article  PubMed  CAS  Google Scholar 

  21. Tarasov KV, Sanna S, Scuteri A, Strait JB, Orrù M, Parsa A, et al. COL4A1 is associated with arterial stiffness by genome-wide association scan. Circ Cardiovasc Genet. 2009;2:151–8.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  22. Swerdlow DI, Holmes MV, Kuchenbaecker KB, Engmann J, Shah T, Sofat R, et al. The interleukin-6 receptor as a potential target for coronary heart disease prevention: evaluation using Mendelian randomisation. Lancet. 2012;2012. A Mendelian randomisation study of the interleukin-6 receptor as a potential therapeutic target in coronary disease, providing evidence that this pathway has a causal role and may hold benefit for disease prevention. Findings were corroborated in a similar study by Sarwar et al., below.

  23. Sarwar N, Butterworth AS, Freitag DF, Gregson J, Willeit P, Gorman DN, et al. Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet. 2012;379:1205–13. A Mendelian randomisation study of the interleukin-6 receptor as a potential therapeutic target in coronary disease, providing evidence that this pathway has a causal role and may hold benefit for disease prevention. Findings were corroborated in a similar study by Swerdlow et al.

    Article  PubMed  CAS  Google Scholar 

  24. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–83.

    Article  PubMed  CAS  Google Scholar 

  25. Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. [Internet]. 2011 [cited 2011 Mar 13]; Available from: http://www.ncbi.nlm.nih.gov/pubmed/21378990.

  26. Peden JF, Hopewell JC, Saleheen D, Chambers JC, Hager J, Soranzo N, et al. A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat Genet. 2011;43:339–44.

    Article  CAS  Google Scholar 

  27. Johnson AD, Hwang S-J, Voorman A, Morrison A, Peloso GM, Hsu Y-H, et al. Resequencing and clinical associations of the 9p21.3 region: a comprehensive investigation in the Framingham heart study. Circulation. 2013;127:799–810.

    Article  PubMed Central  PubMed  Google Scholar 

  28. Roberts R, Stewart AFR. 9p21 and the genetic revolution for coronary artery disease. Clin Chem. 2012;58:104–12.

    Article  PubMed  CAS  Google Scholar 

  29. International Consortium for Blood Pressure Genome-Wide Association Studies, Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478:103–9. A large genome-wide association study reporting common genetic determinants of blood pressure.

    Article  CAS  Google Scholar 

  30. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42:937–48. A very large genome-wide association study reporting novel genetic determinants of obesity-related traits.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  31. Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet. 2010;42:436–40.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  32. Ellinor PT, Lunetta KL, Albert CM, Glazer NL, Ritchie MD, Smith AV, et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat Genet. 2012;44:670–5.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  33. Ellinor PT, Lunetta KL, Glazer NL, Pfeufer A, Alonso A, Chung MK, et al. Common variants in KCNN3 are associated with lone atrial fibrillation. Nat Genet. 2010;42:240–4.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  34. Butler AM, Yin X, Evans DS, Nalls MA, Smith EN, Tanaka T, et al. Novel loci associated with PR interval in a genome-wide association study of 10 African American cohorts. Circ Cardiovasc Genet. 2012;5:639–46.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  35. Sotoodehnia N, Isaacs A, de Bakker PIW, Dörr M, Newton-Cheh C, Nolte IM, et al. Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction. Nat Genet. 2010;42:1068–76.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  36. Ritchie MD, Denny JC, Zuvich RL, Crawford DC, Schildcrout JS, Bastarache L, et al. Genome- and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk. Circulation. 2013;127:1377–85.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  37. Pfeufer A, van Noord C, Marciante KD, Arking DE, Larson MG, Smith AV, et al. Genome-wide association study of PR interval. Nat Genet. 2010;42:153–9.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  38. Chambers JC, Zhao J, Terracciano CMN, Bezzina CR, Zhang W, Kaba R, et al. Genetic variation in SCN10A influences cardiac conduction. Nat Genet. 2010;42:149–52.

    Article  PubMed  CAS  Google Scholar 

  39. Holm H, Gudbjartsson DF, Arnar DO, Thorleifsson G, Thorgeirsson G, Stefansdottir H, et al. Several common variants modulate heart rate, PR interval and QRS duration. Nat Genet. 2010;42:117–22.

    Article  PubMed  CAS  Google Scholar 

  40. Smith JG, Avery CL, Evans DS, Nalls MA, Meng YA, Smith EN, et al. Impact of ancestry and common genetic variants on QT interval in African Americans. Circ Cardiovasc Genet. 2012;5:647–55.

    Article  PubMed Central  PubMed  Google Scholar 

  41. Chang K-C, Barth AS, Sasano T, Kizana E, Kashiwakura Y, Zhang Y, et al. CAPON modulates cardiac repolarization via neuronal nitric oxide synthase signaling in the heart. Proc Natl Acad Sci. 2008;105:4477–82.

    Article  PubMed Central  PubMed  Google Scholar 

  42. Smith NL, Felix JF, Morrison AC, Demissie S, Glazer NL, Loehr LR, et al. Association of genome-wide variation with the risk of incident heart failure in adults of European and African ancestry: a prospective meta-analysis from the cohorts for heart and aging research in genomic epidemiology (CHARGE) consortium. Circ Cardiovasc Genet. 2010;3:256–66.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  43. Villard E, Perret C, Gary F, Proust C, Dilanian G, Hengstenberg C, et al. A genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy. Eur Heart J. 2011;32:1065–76.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  44. Arnett DK, Li N, Tang W, Rao DC, Devereux RB, Claas SA, et al. Genome-wide association study identifies single-nucleotide polymorphism in KCNB1 associated with left ventricular mass in humans: the HyperGEN Study. BMC Med Genet. 2009;10:43.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  45. Del Greco MF, Pattaro C, Luchner A, Pichler I, Winkler T, Hicks AA, et al. Genome-wide association analysis and fine mapping of NT-proBNP level provide novel insight into the role of the MTHFR-CLCN6-NPPA-NPPB gene cluster. Hum Mol Genet. 2011;20:1660–71.

    Article  CAS  Google Scholar 

  46. Thanassoulis G, Campbell CY, Owens DS, Smith JG, Smith AV, Peloso GM, et al. Genetic associations with valvular calcification and aortic stenosis. N Engl J Med. 2013;368:503–12.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  47. Kamstrup PR, Tybjærg-Hansen A, Nordestgaard BG. Elevated lipoprotein(a) and risk of aortic valve stenosis in the general population. J Am Coll Cardiol. 2014;63:470–7.

    Article  PubMed  CAS  Google Scholar 

  48. Trégouët D-A, König IR, Erdmann J, Munteanu A, Braund PS, Hall AS, et al. Genome-wide haplotype association study identifies the SLC22A3-LPAL2-LPA gene cluster as a risk locus for coronary artery disease. Nat Genet. 2009;41:283–5.

    Article  PubMed  CAS  Google Scholar 

  49. Dichgans M, Malik R, König IR, Rosand J, Clarke R, Gretarsdottir S, et al. Shared genetic susceptibility to ischemic stroke and coronary artery disease: a genome-wide analysis of common variants. Stroke J Cereb Circ. 2014;45:24–36. A valuable comparison between the common genetic determinants of stroke and coronary heart disease highlighting shared aetiological pathways.

    Article  CAS  Google Scholar 

  50. Traylor M, Farrall M, Holliday EG, Sudlow C, Hopewell JC, Cheng Y-C, et al. Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol. 2012;11:951–62.

    Article  PubMed Central  PubMed  Google Scholar 

  51. Ashton HA, Buxton MJ, Day NE, Kim LG, Marteau TM, Scott RAP, et al. The Multicentre Aneurysm Screening Study (MASS) into the effect of abdominal aortic aneurysm screening on mortality in men: a randomised controlled trial. Lancet. 2002;360:1531–9.

    Article  PubMed  CAS  Google Scholar 

  52. Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. 2007;316:1491–3.

    Article  PubMed  CAS  Google Scholar 

  53. Helgadottir A, Thorleifsson G, Magnusson KP, Grétarsdottir S, Steinthorsdottir V, Manolescu A, et al. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet. 2008;40:217–24.

    Article  PubMed  CAS  Google Scholar 

  54. Harrison SC, Kalea AZ, Holmes MV, Agu O, Humphries SE. Genomic research to identify novel pathways in the development of abdominal aortic aneurysm. Cardiol Res Pract. 2012;2012:852829.

    PubMed Central  PubMed  Google Scholar 

  55. Gretarsdottir S, Baas AF, Thorleifsson G, Holm H, den Heijer M, de Vries J-PPM, et al. Genome-wide association study identifies a sequence variant within the DAB2IP gene conferring susceptibility to abdominal aortic aneurysm. Nat Genet. 2010;42:692–7.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  56. Bown MJ, Jones GT, Harrison SC, Wright BJ, Bumpstead S, Baas AF, et al. Abdominal aortic aneurysm is associated with a variant in low-density lipoprotein receptor-related protein 1. Am J Hum Genet. 2011;89:619–27.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  57. Bradley DT, Hughes AE, Badger SA, Jones GT, Harrison SC, Wright BJ, et al. A variant in LDLR is associated with abdominal aortic aneurysm. Circ Cardiovasc Genet. 2013;6:498–504.

    Article  PubMed  CAS  Google Scholar 

  58. Harrison SC, Smith AJP, Jones GT, Swerdlow DI, Rampuri R, Bown MJ, et al. Interleukin-6 receptor pathways in abdominal aortic aneurysm. Eur Heart J. 2012;34:3707–16.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  59. Hayden EC. Technology: the $1,000 genome. Nature. 2014;507:294–5.

    Article  PubMed  CAS  Google Scholar 

  60. Holmes MV, Harrison S, Talmud PJ, Hingorani AD, Humphries SE. Utility of genetic determinants of lipids and cardiovascular events in assessing risk. Nat Rev Cardiol. 2011;8:207–21. A comprehensive review of the underlying research and clinical utility of using genetics for predicting cardiovascular disease risk.

    Article  PubMed  CAS  Google Scholar 

  61. Ashley EA, Butte AJ, Wheeler MT, Chen R, Klein TE, Dewey FE, et al. Clinical assessment incorporating a personal genome. Lancet. 2010;375:1525–35.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  62. Ganna A, Magnusson PKE, Pedersen NL, de Faire U, Reilly M, Arnlöv J, et al. Multilocus genetic risk scores for coronary heart disease prediction. Arterioscler Thromb Vasc Biol. 2013;33:2267–72.

    Article  PubMed  CAS  Google Scholar 

  63. Tikkanen E, Havulinna AS, Palotie A, Salomaa V, Ripatti S. Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease. Arterioscler Thromb Vasc Biol. 2013;33:2261–6.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  64. Hughes MF, Saarela O, Stritzke J, Kee F, Silander K, Klopp N, et al. Genetic markers enhance coronary risk prediction in men: the MORGAM prospective cohorts. PLoS One. 2012;7:e40922.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  65. Brautbar A, Pompeii LA, Dehghan A, Ngwa JS, Nambi V, Virani SS, et al. A genetic risk score based on direct associations with coronary heart disease improves coronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC), but not in the Rotterdam and Framingham Offspring, Studies. Atherosclerosis. 2012;223:421–6.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  66. Hernesniemi JA, Seppälä I, Lyytikäinen L-P, Mononen N, Oksala N, Hutri-Kähönen N, et al. Genetic profiling using genome-wide significant coronary artery disease risk variants does not improve the prediction of subclinical atherosclerosis: the Cardiovascular Risk in Young Finns Study, the Bogalusa Heart Study and the Health 2000 Survey—a meta-analysis of three independent studies. PLoS One. 2012;7:e28931.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  67. Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet. 2010;376:1393–400. A clinically focused report of a study investigating the incorporation of genetic information into coronary heart disease risk prediction.

    Article  PubMed Central  PubMed  Google Scholar 

  68. Humphries SE, Drenos F, Ken-Dror G, Talmud PJ. Coronary heart disease risk prediction in the era of genome-wide association studies: current status and what the future holds. Circulation. 2010;121:2235–48.

    Article  PubMed  Google Scholar 

  69. Annas GJ, Elias S. 23andMe and the FDA. N Engl J Med. 2014;370:985–8.

    Article  PubMed  CAS  Google Scholar 

  70. Davey Smith G, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22. A guide to the theoretical and practical basis of Mendelian randomisation.

    Article  Google Scholar 

  71. Ebrahim S, Davey Smith G. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum Genet. 2008;123:15–33. A review of the role of Mendelian randomisation in overcoming some of the limitations of traditional epidemiology.

    Article  PubMed  Google Scholar 

  72. Baigent C, Blackwell L, Emberson J, Holland LE, Reith C, Bhala N, et al. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet. 2010;376:1670–81.

    Article  PubMed  CAS  Google Scholar 

  73. Blood Pressure Lowering Treatment Trialists’ Collaboration, Ninomiya T, Perkovic V, Turnbull F, Neal B, Barzi F, et al. Blood pressure lowering and major cardiovascular events in people with and without chronic kidney disease: meta-analysis of randomised controlled trials. BMJ. 2013;347:f5680.

    Article  PubMed Central  Google Scholar 

  74. Myung S-K, Ju W, Cho B, Oh S-W, Park SM, Koo B-K, et al. Efficacy of vitamin and antioxidant supplements in prevention of cardiovascular disease: systematic review and meta-analysis of randomised controlled trials. BMJ. 2013;346:f10.

    Article  PubMed Central  PubMed  Google Scholar 

  75. Hingorani A, Humphries S. Nature’s randomised trials. Lancet. 2005;366:1906–8. A concise introduction to the similarities between Mendelian randomisation and the traditional randomised controlled trial.

    Article  PubMed  Google Scholar 

  76. Mendel G. Experiments in plant-hybridisation. Brunn Nat Hist Soc. 1865. Gregor Mendel’s seminal work on the inheritance of pea plant characteristics. The seminal description of the laws of what has become known as ‘Mendelian’ inheritance. These underly the Mendelian randomisation principle.

  77. Linsel-Nitschke P, Götz A, Erdmann J, Braenne I, Braund P, Hengstenberg C, et al. Lifelong reduction of LDL-cholesterol related to a common variant in the LDL-receptor gene decreases the risk of coronary artery disease—a Mendelian Randomisation study. PLoS One. 2008;3:e2986.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  78. Ference BA, Julius S, Mahajan N, Levy PD, Williams KA, Flack JM. Clinical effect of naturally random allocation to lower systolic blood pressure beginning before the development of hypertension. Hypertension. 2014;63:1182–8.

    Article  PubMed  CAS  Google Scholar 

  79. Luke MM, Kane JP, Liu DM, Rowland CM, Shiffman D, Cassano J, et al. A polymorphism in the protease-like domain of apolipoprotein(a) is associated with severe coronary artery disease. Arterioscler Thromb Vasc Biol. 2007;27:2030–6.

    Article  PubMed  CAS  Google Scholar 

  80. Sarwar N, Sandhu MS, Ricketts SL, Butterworth AS, Di Angelantonio E, Boekholdt SM, et al. Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies. Lancet. 2010;375:1634–9. A large-scale Mendelian randomisation study implicating triglyceride-mediated aetiological pathways in the development of coronary heart disease.

    Article  PubMed  CAS  Google Scholar 

  81. Holmes MV, Asselbergs FW, Palmer TM, Drenos F, Lanktree MB, Nelson CP, et al. Mendelian randomization of blood lipids for coronary heart disease. Eur Heart J. 2014. Report of a study using Mendelian randomisation techniques to investigate the causal contributions of major blood lipid fractions in coronary heart disease.

  82. Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM, et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet. 2013;45:1345–52. An innovative Mendelian randomisation study using novel techniques to investigate further the causal contributions of plasma triglycerides in coronary disease.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  83. Hafiane A, Genest J. HDL, atherosclerosis, and emerging therapies. Cholesterol. 2013;2013:891403. A review discussing the controversial role of HDL cholesterol in coronary heart disease and the potential utility of pharmacological modulation of HDL-C.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  84. Frikke-Schmidt R, Nordestgaard BG, Stene MCA, Sethi AA, Remaley AT, Schnohr P, et al. Association of loss-of-function mutations in the ABCA1 gene with high-density lipoprotein cholesterol levels and risk of ischemic heart disease. JAMA. 2008;299:2524–32.

    Article  PubMed  CAS  Google Scholar 

  85. Haase CL, Tybjærg-Hansen A, Qayyum AA, Schou J, Nordestgaard BG, Frikke-Schmidt R. LCAT, HDL cholesterol and ischemic cardiovascular disease: a Mendelian randomization study of HDL cholesterol in 54,500 individuals. J Clin Endocrinol Metab. 2012;97:E248–56.

    Article  PubMed  CAS  Google Scholar 

  86. Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, et al. Plasma HDL cholesterol and risk of myocardial infarction: a Mendelian randomisation study. Lancet [Internet]. 2012 [cited 2012 Jun 2]; Available from: http://www.ncbi.nlm.nih.gov/pubmed/22607825. An important Mendelian randomisation study providing evidence that one aspect of HDL cholesterol metabolism appears not to have a causal role in the development of coronary heart disease.

  87. Hansson GK. Atherosclerosis—an immune disease: the Anitschkov Lecture 2007. Atherosclerosis. 2009;202:2–10.

    Article  PubMed  CAS  Google Scholar 

  88. Wensley F, Gao P, Burgess S, Kaptoge S, Di Angelantonio E, Shah T, et al. Association between C reactive protein and coronary heart disease: Mendelian randomisation analysis based on individual participant data. BMJ. 2011;342:d548. A large collaborative Mendelian randomisation study providing strong evidence against a causal role for C-reactive protein in coronary heart disease.

    Article  PubMed  Google Scholar 

  89. Singh JA, Beg S, Lopez-Olivo MA. Tocilizumab for rheumatoid arthritis. Cochrane Database Syst. Rev. Online. 2010;CD008331

  90. Holmes MV, Lange LA, Palmer T, Lanktree MB, North KE, Almoguera B, et al. Causal effects of body mass index on cardiometabolic traits and events: a Mendelian randomization analysis. Am J Hum Genet. 2014;94:198–208. An allele score-based Mendelian randomisation study investigating the causal contribution of body mass index—a complex phenotype—in other areas of cardiometabolic disease.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  91. Nordestgaard BG, Palmer TM, Benn M, Zacho J, Tybjærg-Hansen A, Davey Smith G, et al. The effect of elevated body mass index on ischemic heart disease risk: causal estimates from a Mendelian randomisation approach. PLoS Med. 2012;9:e1001212.

    Article  PubMed Central  PubMed  Google Scholar 

  92. Ronksley PE, Brien SE, Turner BJ, Mukamal KJ, Ghali WA. Association of alcohol consumption with selected cardiovascular disease outcomes: a systematic review and meta-analysis. BMJ. 2011;342:d671.

    Article  PubMed Central  PubMed  Google Scholar 

  93. Holmes MV, Dale CE, Zuccolo L, Silverwood RJ, Guo Y, Ye Z, et al. Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individual participant data. BMJ. 2014;349:g4164. A landmark genetic investigation of the much-debated causal role of alcohol consumption in a range of cardiometabolic disease phenotypes.

    Article  PubMed Central  PubMed  Google Scholar 

  94. Landry Y, Gies J-P. Drugs and their molecular targets: an updated overview. Fundam Clin Pharmacol. 2008;22:1–18.

    Article  PubMed  CAS  Google Scholar 

  95. Nicholls SJ, Cavender MA, Kastelein JJP, Schwartz G, Waters DD, Rosenson RS, et al. Inhibition of secretory phospholipase A(2) in patients with acute coronary syndromes: rationale and design of the vascular inflammation suppression to treat acute coronary syndrome for 16 weeks (VISTA-16) trial. Cardiovasc Drugs Ther. 2012;26:71–5.

    Article  PubMed  CAS  Google Scholar 

  96. Holmes MV, Simon T, Exeter HJ, Folkersen L, Asselbergs FW, Guardiola M, et al. Secretory phospholipase A2-IIA and cardiovascular disease: a Mendelian randomization study. J Am Coll Cardiol. 2013;62(21):1966–76.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  97. Nicholls SJ, Kastelein JJP, Schwartz GG, Bash D, Rosenson RS, Cavender MA, et al. Varespladib and cardiovascular events in patients with an acute coronary syndrome: the VISTA-16 randomized clinical trial. JAMA. 2014;311:252–62.

    Article  PubMed  CAS  Google Scholar 

  98. Brousseau ME, Schaefer EJ, Wolfe ML, Bloedon LT, Digenio AG, Clark RW, et al. Effects of an inhibitor of cholesteryl ester transfer protein on HDL cholesterol. N Engl J Med. 2004;350:1505–15.

    Article  PubMed  CAS  Google Scholar 

  99. Barter PJ, Caulfield M, Eriksson M, Grundy SM, Kastelein JJP, Komajda M, et al. Effects of torcetrapib in patients at high risk for coronary events. N Engl J Med. 2007;357:2109–22.

    Article  PubMed  CAS  Google Scholar 

  100. Sofat R, Hingorani AD, Smeeth L, Humphries SE, Talmud PJ, Cooper J, et al. Separating the mechanism-based and off-target actions of cholesteryl ester transfer protein inhibitors with CETP gene polymorphisms. Circulation. 2010;121:52–62. A novel application of Mendelian randomisation for investigating adverse drug effects, providing evidence that the hypertensive effect of torcetrapib therapy was an off-target action of the drug.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  101. Sattar N, Preiss D, Murray HM, Welsh P, Buckley BM, de Craen AJ, et al. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet. 2010;375:735–42. A large meta-analysis of randomised trials demonstrating the increased risk of new-onset type 2 diabetes with statin treatment.

    Article  PubMed  CAS  Google Scholar 

  102. Preiss D, Seshasai SRK, Welsh P, Murphy SA, Ho JE, Waters DD, et al. Risk of incident diabetes with intensive-dose compared with moderate-dose statin therapy: a meta-analysis. JAMA. 2011;305:2556–64. Corroboration of the increased diabetes risk with statin therapy, showing that high-intensity statin therapy led to the greatest risk of diabetes.

    Article  PubMed  CAS  Google Scholar 

  103. Swerdlow DI, Preiss D, Kuchenbaecker KB, Holmes MV, Engmann JEL, Shah T, et al. HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials. The Lancet [Internet]. 2014 [cited 2014 Sep 24]; Available from: http://linkinghub.elsevier.com/retrieve/pii/S0140673614611831. A study using data from randomised trials and Mendelian randomisation to investigate the aetiology of the increased risk of diabetes with statin therapy.

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Acknowledgments

Daniel I. Swerdlow has been supported by a Medical Research Council doctoral training award and by the Rosetrees Trust.

Aroon D. Hingorani is supported by National Institute for Health Research University College London Hospitals Biomedical Research Centre.

Steve E. Humphries is a British Heart Foundation Professor, and he is supported by the British Heart Foundation (RG008/08) and by National Institute for Health Research University College London Hospitals Biomedical Research Centre.

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Conflict of Interest

Daniel I. Swerdlow acts as a paid consultant for Pfizer Inc.

Aroon D. Hingorani and Steve E. Humphries declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

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Authors and Affiliations

  1. Institute of Cardiovascular Science, University College London, 222 Euston Road, London, NW1 2DA, UK

    Daniel I. Swerdlow & Aroon D. Hingorani

  2. Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, UK

    Steve E. Humphries

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  1. Daniel I. Swerdlow
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  2. Aroon D. Hingorani
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  3. Steve E. Humphries
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Correspondence to Daniel I. Swerdlow.

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This article is part of the Topical Collection on Lipid Abnormalities and Cardiovascular Prevention

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Swerdlow, D.I., Hingorani, A.D. & Humphries, S.E. Genetic Risk Factors and Mendelian Randomization in Cardiovascular Disease. Curr Cardiol Rep 17, 33 (2015). https://doi.org/10.1007/s11886-015-0584-x

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