Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis

Autoři: Tom G. Richardson aff001;  Eleanor Sanderson aff001;  Tom M. Palmer aff001;  Mika Ala-Korpela aff003;  Brian A. Ference aff007;  George Davey Smith aff001;  Michael V. Holmes aff001
Působiště autorů: Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom aff001;  Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, United Kingdom aff002;  Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Australia aff003;  Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland aff004;  NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland aff005;  Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Australia aff006;  Centre for Naturally Randomized Trials, University of Cambridge, Cambridge, United Kingdom aff007;  MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom aff008;  Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom aff009;  Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom aff010
Vyšlo v časopise: Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis. PLoS Med 17(3): e32767. doi:10.1371/journal.pmed.1003062
Kategorie: Research Article
doi: 10.1371/journal.pmed.1003062



Circulating lipoprotein lipids cause coronary heart disease (CHD). However, the precise way in which one or more lipoprotein lipid-related entities account for this relationship remains unclear. Using genetic instruments for lipoprotein lipid traits implemented through multivariable Mendelian randomisation (MR), we sought to compare their causal roles in the aetiology of CHD.

Methods and findings

We conducted a genome-wide association study (GWAS) of circulating non-fasted lipoprotein lipid traits in the UK Biobank (UKBB) for low-density lipoprotein (LDL) cholesterol, triglycerides, and apolipoprotein B to identify lipid-associated single nucleotide polymorphisms (SNPs). Using data from CARDIoGRAMplusC4D for CHD (consisting of 60,801 cases and 123,504 controls), we performed univariable and multivariable MR analyses. Similar GWAS and MR analyses were conducted for high-density lipoprotein (HDL) cholesterol and apolipoprotein A-I. The GWAS of lipids and apolipoproteins in the UKBB included between 393,193 and 441,016 individuals in whom the mean age was 56.9 y (range 39–73 y) and of whom 54.2% were women. The mean (standard deviation) lipid concentrations were LDL cholesterol 3.57 (0.87) mmol/L and HDL cholesterol 1.45 (0.38) mmol/L, and the median triglycerides was 1.50 (IQR = 1.11) mmol/L. The mean (standard deviation) values for apolipoproteins B and A-I were 1.03 (0.24) g/L and 1.54 (0.27) g/L, respectively. The GWAS identified multiple independent SNPs associated at P < 5 × 10−8 for LDL cholesterol (220), apolipoprotein B (n = 255), triglycerides (440), HDL cholesterol (534), and apolipoprotein A-I (440). Between 56%–93% of SNPs identified for each lipid trait had not been previously reported in large-scale GWASs. Almost half (46%) of these SNPs were associated at P < 5 × 10−8 with more than one lipid-related trait. Assessed individually using MR, LDL cholesterol (odds ratio [OR] 1.66 per 1-standard-deviation–higher trait; 95% CI: 1.49–1.86; P < 0.001), triglycerides (OR 1.34; 95% CI: 1.25–1.44; P < 0.001) and apolipoprotein B (OR 1.73; 95% CI: 1.56–1.91; P < 0.001) had effect estimates consistent with a higher risk of CHD. In multivariable MR, only apolipoprotein B (OR 1.92; 95% CI: 1.31–2.81; P < 0.001) retained a robust effect, with the estimate for LDL cholesterol (OR 0.85; 95% CI: 0.57–1.27; P = 0.44) reversing and that of triglycerides (OR 1.12; 95% CI: 1.02–1.23; P = 0.01) becoming weaker. Individual MR analyses showed a 1-standard-deviation–higher HDL cholesterol (OR 0.80; 95% CI: 0.75–0.86; P < 0.001) and apolipoprotein A-I (OR 0.83; 95% CI: 0.77–0.89; P < 0.001) to lower the risk of CHD, but these effect estimates attenuated substantially to the null on accounting for apolipoprotein B. A limitation is that, owing to the nature of lipoprotein metabolism, measures related to the composition of lipoprotein particles are highly correlated, creating a challenge in making exclusive interpretations on causation of individual components.


These findings suggest that apolipoprotein B is the predominant trait that accounts for the aetiological relationship of lipoprotein lipids with risk of CHD.

Klíčová slova:

Apolipoproteins – Coronary heart disease – Genome-wide association studies – Cholesterol – Lipid analysis – Lipids – Lipoproteins – Molecular genetics


1. Cholesterol Treatment Trialist's Collaborators, Fulcher J, O'Connell R, Voysey M, Emberson J, Blackwell L, et al. Efficacy and safety of LDL-lowering therapy among men and women: meta-analysis of individual data from 174,000 participants in 27 randomised trials. Lancet. 2015;385(9976):1397–405. doi: 10.1016/S0140-6736(14)61368-4 25579834.

2. Cholesterol Treatment Trialists' Collaborators, Baigent C, Blackwell L, Emberson J, Holland LE, Reith C, 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(9753):1670–81. doi: 10.1016/S0140-6736(10)61350-5 21067804; PubMed Central PMCID: PMC2988224.

3. Cholesterol Treatment Trialists' Collaborators, Mihaylova B, Emberson J, Blackwell L, Keech A, Simes J, et al. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet. 2012;380(9841):581–90. doi: 10.1016/S0140-6736(12)60367-5 22607822; PubMed Central PMCID: PMC3437972.

4. Silverman MG, Ference BA, Im K, Wiviott SD, Giugliano RP, Grundy SM, et al. Association Between Lowering LDL-C and Cardiovascular Risk Reduction Among Different Therapeutic Interventions: A Systematic Review and Meta-analysis. Jama. 2016;316(12):1289–97. doi: 10.1001/jama.2016.13985 27673306

5. Collins R, Reith C, Emberson J, Armitage J, Baigent C, Blackwell L, et al. Interpretation of the evidence for the efficacy and safety of statin therapy. The Lancet. 2016;388(10059):2532–61. doi: 10.1016/S0140-6736(16)31357-5

6. Ference BA, Robinson J. G., Brook R. D., Catapano AL, Chapman MJ, Neff DR, Voros S, Giugliano RP, et al. Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes. New England Journal of Medicine. 2016;375(22):2144–53. doi: 10.1056/NEJMoa1604304 27959767

7. Ference BA, Majeed F., Penumetcha R., Flack JM, Brook RD. Effect of Naturally Random Allocation to Lower Low-Density Lipoprotein Cholesterol on the Risk of Coronary Heart Disease Mediated by Polymorphisms in NPC1L1, HMGCR, or Both. Journal of the American College of Cardiology. 2015;65(15):1552–61. doi: 10.1016/j.jacc.2015.02.020 WOS:000352956500009. 25770315

8. Ference BA, Yoo W., Alesh I., Mahajan N, Mirowska KK, Mewada A, Kahn J, Afonso L, et al. Effect of Long-Term Exposure to Lower Low-Density Lipoprotein Cholesterol Beginning Early in Life on the Risk of Coronary Heart Disease A Mendelian Randomization Analysis. Journal of the American College of Cardiology. 2012;60(25):2631–9. doi: 10.1016/j.jacc.2012.09.017 WOS:000312527000006. 23083789

9. 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. 2015;36(9):539–50. doi: 10.1093/eurheartj/eht571 24474739.

10. Holmes MV, Ala-Korpela M. What is 'LDL cholesterol'? Nat Rev Cardiol. 2019;16(4):197–8. doi: 10.1038/s41569-019-0157-6 30700860.

11. Sniderman AD, Thanassoulis G, Glavinovic T, Navar AM, Pencina M, Catapano A, et al. Apolipoprotein B Particles and Cardiovascular Disease: A Narrative Review. JAMA Cardiol. 2019;4(12): 1287–1295. doi: 10.1001/jamacardio.2019.3780 31642874.

12. Sniderman AD, Pencina M, Thanassoulis G. ApoB. Circ Res. 2019;124(10):1425–7. doi: 10.1161/CIRCRESAHA.119.315019 31070997.

13. White J, Swerdlow DI, Preiss D, Fairhurst-Hunter Z, Keating BJ, Asselbergs FW, et al. Association of Lipid Fractions With Risks for Coronary Artery Disease and Diabetes. JAMA Cardiol. 2016;1(6):692–9. doi: 10.1001/jamacardio.2016.1884 27487401.

14. 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(11):1345–52. doi: 10.1038/ng.2795 24097064; PubMed Central PMCID: PMC3904346.

15. Ference BA, Kastelein JJP, Ginsberg HN, Chapman MJ, Nicholls SJ, Ray KK, et al. Association of Genetic Variants Related to CETP Inhibitors and Statins With Lipoprotein Levels and Cardiovascular Risk. Jama. 2017;318(10):947–56. doi: 10.1001/jama.2017.11467 28846118; PubMed Central PMCID: PMC5710502.

16. Varbo A, Benn M, Tybjaerg-Hansen A, Jorgensen AB, Frikke-Schmidt R, Nordestgaard BG. Remnant cholesterol as a causal risk factor for ischemic heart disease. J Am Coll Cardiol. 2013;61(4):427–36. doi: 10.1016/j.jacc.2012.08.1026 23265341.

17. Phillips AN, Smith GD. How independent are "independent" effects? Relative risk estimation when correlated exposures are measured imprecisely. J Clin Epidemiol. 1991;44(11):1223–31. doi: 10.1016/0895-4356(91)90155-3 1941017.

18. Davey Smith G, Phillips AN. Correlation without a cause: an epidemiological odyssey. Int J Epidemiol. Forthcoming [2020].

19. Sniderman AD, Williams K, Contois JH, Monroe HM, McQueen MJ, de Graaf J, et al. A meta-analysis of low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, and apolipoprotein B as markers of cardiovascular risk. Circ Cardiovasc Qual Outcomes. 2011;4(3):337–45. doi: 10.1161/CIRCOUTCOMES.110.959247 21487090.

20. Emerging Risk Factors Collaboration, Di Angelantonio E, Gao P, Pennells L, Kaptoge S, Caslake M, et al. Lipid-related markers and cardiovascular disease prediction. Jama. 2012;307(23):2499–506. doi: 10.1001/jama.2012.6571 22797450; PubMed Central PMCID: PMC4211641.

21. Brunner FJ, Waldeyer C, Ojeda F, Salomaa V, Kee F, Sans S, et al. Application of non-HDL cholesterol for population-based cardiovascular risk stratification: results from the Multinational Cardiovascular Risk Consortium. Lancet. 2019;394(10215): 2173–2183. doi: 10.1016/S0140-6736(19)32519-X 31810609.

22. Welsh C, Celis-Morales CA, Brown R, Mackay DF, Lewsey J, Mark PB, et al. Comparison of Conventional Lipoprotein Tests and Apolipoproteins in the Prediction of Cardiovascular Disease. Circulation. 2019;140(7):542–52. doi: 10.1161/CIRCULATIONAHA.119.041149 31216866; PubMed Central PMCID: PMC6693929.

23. Mora S, Martin SS, Virani SS. Cholesterol Insights and Controversies From the UK Biobank Study. Circulation. 2019;140(7):553–5. doi: 10.1161/CIRCULATIONAHA.119.042134 31403842; PubMed Central PMCID: PMC6783127.

24. Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;139(25):e1082–e143. doi: 10.1161/CIR.0000000000000625 30586774.

25. Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41(1): 111–188. Epub 2019 Aug 31. doi: 10.1093/eurheartj/ehz455 31504418.

26. Ference BA, Kastelein JJP, Ray KK, Ginsberg HN, Chapman MJ, Packard CJ, et al. Association of Triglyceride-Lowering LPL Variants and LDL-C-Lowering LDLR Variants With Risk of Coronary Heart Disease. Jama. 2019;321(4):364–73. doi: 10.1001/jama.2018.20045 30694319; PubMed Central PMCID: PMC6439767.

27. Davey Smith G, Ebrahim S. 'Mendelian randomization': Can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiology. 2003;32(1):1–22. doi: 10.1093/ije/dyg070 12689998

28. Sanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings. Int J Epidemiol. 2019;48(3): 713–727. doi: 10.1093/ije/dyy262 30535378

29. Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47(10):1121–30. doi: 10.1038/ng.3396 26343387; PubMed Central PMCID: PMC4589895.

30. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. doi: 10.1371/journal.pmed.1001779 25826379; PubMed Central PMCID: PMC4380465.

31. Elliott P, Peakman TC, Biobank UK. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int J Epidemiol. 2008;37(2):234–44. doi: 10.1093/ije/dym276 18381398.

32. Fry D, Almond R, Moffat S, Gordon M, Singh P. Companion Document to Accompany Serum Biomarker Data [Internet]. UK Biobank Biomarker Project. 2019 [cited 2020 Feb 4]. Available from: https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf.

33. Anderson CA, Pettersson FH, Clarke GM, Cardon LR, Morris AP, Zondervan KT. Data quality control in genetic case-control association studies. Nat Protoc. 2010;5(9):1564–73. doi: 10.1038/nprot.2010.116 21085122; PubMed Central PMCID: PMC3025522.

34. Loh PR, Tucker G, Bulik-Sullivan BK, Vilhjalmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet. 2015;47(3):284–90. doi: 10.1038/ng.3190 25642633; PubMed Central PMCID: PMC4342297.

35. Loh PR, Kichaev G, Gazal S, Schoech AP, Price AL. Mixed-model association for biobank-scale datasets. Nat Genet. 2018;50(7):906–8. doi: 10.1038/s41588-018-0144-6 29892013; PubMed Central PMCID: PMC6309610.

36. Mitchell R, Elsworth BL, Raistrick CA, Paternoster L, Hemani G, Gaunt TR. MRC IEU UK Biobank GWAS pipeline version 2. 2019 [Internet]. University of Bristol [cited 2019 Aug 13]. Available from: https://data.bris.ac.uk/data/dataset/pnoat8cxo0u52p6ynfaekeigi.

37. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. doi: 10.1038/s41586-018-0579-z 30305743.

38. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7. doi: 10.1186/s13742-015-0047-8 25722852; PubMed Central PMCID: PMC4342193.

39. 1000 Genomes Project Consortium, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491(7422):56–65. doi: 10.1038/nature11632 23128226; PubMed Central PMCID: PMC3498066.

40. Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83. doi: 10.1038/ng.2797 24097068; PubMed Central PMCID: PMC3838666.

41. Kettunen J, Demirkan A, Wurtz P, Draisma HH, Haller T, Rawal R, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016;7:11122. doi: 10.1038/ncomms11122 27005778.

42. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. doi: 10.1136/bmj.k601 30002074; PubMed Central PMCID: PMC6041728 interests and declare that we have no competing interests.

43. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89–98. doi: 10.1093/hmg/ddu328 25064373; PubMed Central PMCID: PMC4170722.

44. Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol. 2016;45(6): 1961–1974. doi: 10.1093/ije/dyw220 27616674.

45. Sanderson E, Spiller W, Bowden J. Testing and Correcting for Weak Instruments in Two-sample Summary Data Multivariable Mendelian Randomisation. bioRxiv. Forthcoming 2020.

46. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7: e34408. doi: 10.7554/eLife.34408 29846171; PubMed Central PMCID: PMC5976434.

47. Wickham H. ggplot2 –Elegant Graphics for Data Analysis (2nd Edition). London and Stuttgart, Germany: Springer Nature; 2016.

48. Viechtbauer W. Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software. 2010;36(3):48. doi: 10.18637/jss.v036.i03

49. Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature. 2019;567(7748):305–7. doi: 10.1038/d41586-019-00857-9 30894741.

50. Sterne JA, Davey Smith G. Sifting the evidence-what's wrong with significance tests? BMJ. 2001;322(7280):226–31. doi: 10.1136/bmj.322.7280.226 11159626; PubMed Central PMCID: PMC1119478.

51. Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Maria Glymour M, et al. Guidelines for performing Mendelian randomization investigations [version 1; peer review: awaiting peer review]. Wellcome Open Res 2019;4:186.

52. Holmes MV, Ala-Korpela M, Smith GD. Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat Rev Cardiol. 2017;14(10):577–90. doi: 10.1038/nrcardio.2017.78 28569269; PubMed Central PMCID: PMC5600813.

53. Bowden J, Davey Smith G., Haycock P. C., Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genetic Epidemiology. 2016;40(4):304–14. doi: 10.1002/gepi.21965 27061298

54. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98. doi: 10.1093/ije/dyx102 29040600; PubMed Central PMCID: PMC5837715.

55. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. doi: 10.1093/ije/dyv080 26050253.

56. Rees JMB, Wood AM, Burgess S. Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy. Stat Med. 2017;36(29):4705–18. doi: 10.1002/sim.7492 28960498; PubMed Central PMCID: PMC5725762.

57. Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27(R2):R195–R208. doi: 10.1093/hmg/ddy163 29771313; PubMed Central PMCID: PMC6061876.

58. Mora S, Rifai N, Buring JE, Ridker PM. Fasting compared with nonfasting lipids and apolipoproteins for predicting incident cardiovascular events. Circulation. 2008;118(10):993–1001. doi: 10.1161/CIRCULATIONAHA.108.777334 18711012; PubMed Central PMCID: PMC2574817.

59. Emerging Risk Factors Collaboration, Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, et al. Major lipids, apolipoproteins, and risk of vascular disease. Jama. 2009;302(18):1993–2000. doi: 10.1001/jama.2009.1619 19903920; PubMed Central PMCID: PMC3284229.

60. Boren J, Williams KJ. The central role of arterial retention of cholesterol-rich apolipoprotein-B-containing lipoproteins in the pathogenesis of atherosclerosis: a triumph of simplicity. Curr Opin Lipidol. 2016;27(5):473–83. doi: 10.1097/MOL.0000000000000330 27472409.

61. Ala-Korpela M. The culprit is the carrier, not the loads: cholesterol, triglycerides and apolipoprotein B in atherosclerosis and coronary heart disease. Int J Epidemiol. 2019;48(5): 1389–1392. doi: 10.1093/ije/dyz068 30968109.

62. Williams KJ, Tabas I. The response-to-retention hypothesis of early atherogenesis. Arterioscler Thromb Vasc Biol. 1995;15(5):551–61. doi: 10.1161/01.atv.15.5.551 7749869; PubMed Central PMCID: PMC2924812.

63. Dewey FE, Gusarova V, O'Dushlaine C, Gottesman O, Trejos J, Hunt C, et al. Inactivating Variants in ANGPTL4 and Risk of Coronary Artery Disease. N Engl J Med. 2016;374(12):1123–33. doi: 10.1056/NEJMoa1510926 26933753; PubMed Central PMCID: PMC4900689.

64. Graham MJ, Lee RG, Brandt TA, Tai LJ, Fu W, Peralta R, et al. Cardiovascular and Metabolic Effects of ANGPTL3 Antisense Oligonucleotides. N Engl J Med. 2017;377(3):222–32. doi: 10.1056/NEJMoa1701329 28538111.

65. Musunuru K, Pirruccello JP, Do R, Peloso GM, Guiducci C, Sougnez C, et al. Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. N Engl J Med. 2010;363(23):2220–7. doi: 10.1056/NEJMoa1002926 20942659; PubMed Central PMCID: PMC3008575.

66. HPS3/TIMI55-REVEAL Collaborative Group, Bowman L, Hopewell JC, Chen F, Wallendszus K, Stevens W, et al. Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease. N Engl J Med. 2017;377(13):1217–27. doi: 10.1056/NEJMoa1706444 28847206.

67. Holmes MV. Human genetics and drug development. N Engl J Med. 2019;380:1076–9. doi: 10.1056/NEJMe1901565 30865805

68. Rader DJ. Apolipoprotein A-I Infusion Therapies for Coronary Disease: Two Outs in the Ninth Inning and Swinging for the Fences. JAMA Cardiol. 2018;3(9):799–801. doi: 10.1001/jamacardio.2018.2168 30046821.

69. Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36(11): 1783–1802. doi: 10.1002/sim.7221 28114746.

70. Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 2018;9(1):224. doi: 10.1038/s41467-017-02317-2 29335400; PubMed Central PMCID: PMC5768719.

71. Goldstein JL, Brown MS. A century of cholesterol and coronaries: from plaques to genes to statins. Cell. 2015;161(1):161–72. doi: 10.1016/j.cell.2015.01.036 25815993; PubMed Central PMCID: PMC4525717.

72. Libby P, Buring JE, Badimon L, Hansson GK, Deanfield J, Bittencourt MS, et al. Atherosclerosis. Nat Rev Dis Primers. 2019;5(1):56. doi: 10.1038/s41572-019-0106-z 31420554.

73. Hegele RA. Plasma lipoproteins: genetic influences and clinical implications. Nat Rev Genet. 2009;10(2):109–21. doi: 10.1038/nrg2481 19139765.

74. Wurtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T, et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation. 2015;131(9):774–85. doi: 10.1161/CIRCULATIONAHA.114.013116 25573147; PubMed Central PMCID: PMC4351161.

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