Mendelian randomization (MR) is an approach that uses genetic variants associated with a modifiable exposure or biological intermediate to estimate the causal relationship between these variables and a medically relevant outcome. Although it was initially developed to examine the relationship between modifiable exposures/biomarkers and disease, its use has expanded to encompass applications in molecular epidemiology, systems biology, pharmacogenomics, and many other areas. The purpose of this review is to introduce MR, the principles behind the approach, and its limitations. We consider some of the new applications of the methodology, including informing drug development, and comment on some promising extensions, including two-step, two-sample, and bidirectional MR. We show how these new methods can be combined to efficiently examine causality in complex biological networks and provide a new framework to data mine high-dimensional studies as we transition into the age of hypothesis-free causality.


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Literature Cited

  1. 1. ACCORD Study Group 2010. Effects of combination lipid therapy in type 2 diabetes mellitus. N. Engl. J. Med. 362:1563–74 [Google Scholar]
  2. Allin KH, Nordestgaard BG, Zacho J, Tybjaerg-Hansen A, Bojesen SE. 2.  2010. C-reactive protein and the risk of cancer: a Mendelian randomization study. J. Natl. Cancer Inst. 102:202–6 [Google Scholar]
  3. Anderson CA, Boucher G, Lees CW, Franke A, D'Amato M. 3.  et al. 2011. Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47. Nat. Genet. 43:246–52 [Google Scholar]
  4. Angrist J, Krueger A. 4.  1992. Identification of causal effects using instrumental variables. J. Am. Stat. Assoc. 91:444–55 [Google Scholar]
  5. Angrist J, Pischke J. 5.  2009. Mostly Harmless Econometrics: An Empiricist's Companion Princeton, NJ: Princeton Univ. Press
  6. Arrowsmith J, Miller P. 6.  2013. Trial watch: phase II and phase III attrition rates 2011–2012. Nat. Rev. Drug Discov. 12:569 [Google Scholar]
  7. Ashburn TT, Thor KB. 7.  2004. Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3:673–83 [Google Scholar]
  8. Aten JE, Fuller TF, Lusis AJ, Horvath S. 8.  2008. Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Syst. Biol. 2:34 [Google Scholar]
  9. Baigent C, Keech A, Kearney PM, Blackwell L, Buck G. 9.  et al. 2005. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 366:1267–78 [Google Scholar]
  10. Barrett JC, Hansoul S, Nicolae DL, Cho JH, Duerr RH. 10.  et al. 2008. Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease. Nat. Genet. 40:955–62 [Google Scholar]
  11. Barter PJ, Caulfield M, Eriksson M, Grundy SM, Kastelein JJ. 11.  et al. 2007. Effects of torcetrapib in patients at high risk for coronary events. N. Engl. J. Med. 357:2109–22 [Google Scholar]
  12. Beer NL, Tribble ND, McCulloch LJ, Roos C, Johnson PR. 12.  et al. 2009. The P446L variant in GCKR associated with fasting plasma glucose and triglyceride levels exerts its effect through increased glucokinase activity in liver. Hum. Mol. Genet. 18:4081–88 [Google Scholar]
  13. Bell JT, Pai AA, Pickrell JK, Gaffney DJ, Pique-Regi R. 13.  et al. 2011. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 12:R10 [Google Scholar]
  14. Bernelot Moens SJ, van Capelleveen JC, Stroes ES. 14.  2014. Inhibition of ApoCIII: the next PCSK9?. Curr. Opin. Lipidol. 25:418–22 [Google Scholar]
  15. Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L. 15.  et al. 2009. Genome-wide DNA methylation profiling using Infinium® assay. Epigenomics 1:177–200 [Google Scholar]
  16. Boraska V, Franklin CS, Floyd JA, Thornton LM, Huckins LM. 16.  et al. 2014. A genome-wide association study of anorexia nervosa. Mol. Psychiatry 19:1085–94 [Google Scholar]
  17. Brion MJ, Shakhbazov K, Visscher PM. 17.  2013. Calculating statistical power in Mendelian randomization studies. Int. J. Epidemiol. 42:1497–501 [Google Scholar]
  18. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR. 18.  et al. 2015. An atlas of genetic correlations across human diseases and traits. BioRxiv. doi: 10.1101/014498
  19. Burgess S, Butterworth A, Thompson SG. 19.  2013. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37:658–65 [Google Scholar]
  20. Burgess S, Daniel RM, Butterworth AS, Thompson SG, EPIC-InterAct Consort. 20.  2015. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int. J. Epidemiol. 44484–95
  21. Burgess S, Davies NM, Thompson SG. 21.  (EPIC-InterAct Consort.) 2014. Instrumental variable analysis with a nonlinear exposure-outcome relationship. Epidemiology 25:877–85 [Google Scholar]
  22. Burgess S, Malarstig A. 22.  2013. Using Mendelian randomization to assess and develop clinical interventions: limitations and benefits. J. Comp. Eff. Res. 2:209–12 [Google Scholar]
  23. Burgess S, Thompson SG. 23.  2011. Bias in causal estimates from Mendelian randomization studies with weak instruments. Stat. Med. 30:1312–23 [Google Scholar]
  24. Burgess S, Thompson SG. 24.  2013. Use of allele scores as instrumental variables for Mendelian randomization. Int. J. Epidemiol. 42:1134–44 [Google Scholar]
  25. 25. C React. Protein Coron. Heart Dis. Genet. Collab 2011. Association between C reactive protein and coronary heart disease: Mendelian randomisation analysis based on individual participant data. BMJ 342:d548 [Google Scholar]
  26. Cannon CP, Shah S, Dansky HM, Davidson M, Brinton EA. 26.  et al. 2010. Safety of anacetrapib in patients with or at high risk for coronary heart disease. N. Engl. J. Med. 363:2406–15 [Google Scholar]
  27. Cohen JC, Boerwinkle E, Mosley TH Jr, Hobbs HH. 27.  2006. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354:1264–72 [Google Scholar]
  28. Cohen JC, Stender S, Hobbs HH. 28.  2014. APOC3, coronary disease, and complexities of Mendelian randomization. Cell Metab. 20:387–89 [Google Scholar]
  29. Colhoun HM, McKeigue PM, Davey Smith G. 29.  2003. Problems of reporting genetic associations with complex outcomes. Lancet 361:865–72 [Google Scholar]
  30. Collins R. 30.  2012. What makes UK Biobank special?. Lancet 379:1173–74 [Google Scholar]
  31. Czeizel AE, Dudas I. 31.  1992. Prevention of the first occurrence of neural-tube defects by periconceptional vitamin supplementation. N. Engl. J. Med. 327:1832–35 [Google Scholar]
  32. Davey Smith G. 32.  2007. Capitalizing on Mendelian randomization to assess the effects of treatments. J. R. Soc. Med. 100:432–35 [Google Scholar]
  33. Davey Smith G. 33.  2011. Random allocation in observational data: how small but robust effects could facilitate hypothesis-free causal inference. Epidemiology 22:460–63; discussion 467–68 [Google Scholar]
  34. Davey Smith G. 34.  2011. Use of genetic markers and gene-diet interactions for interrogating population-level causal influences of diet on health. Genes Nutr. 6:27–43 [Google Scholar]
  35. Davey Smith G, Ebrahim S. 35.  2003. “Mendelian randomization”: Can genetic epidemiology contribute to understanding environmental determinants of disease?. Int. J. Epidemiol. 32:1–22 [Google Scholar]
  36. Davey Smith G, Hemani G. 36.  2014. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23:R89–98 [Google Scholar]
  37. Davey Smith G, Lawlor DA, Harbord R, Timpson N, Day I, Ebrahim S. 37.  2007. Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PLOS Med. 4:e352 [Google Scholar]
  38. Davies NM, von Hinke Kessler Scholder S, Farbmacher H, Burgess S, Windmeijer F, Davey Smith G. 38.  2015. The many weak instruments problem and Mendelian randomization. Stat. Med. 34:454–68 [Google Scholar]
  39. Dehghan A, Dupuis J, Barbalic M, Bis JC, Eiriksdottir G. 39.  et al. 2011. Meta-analysis of genome-wide association studies in >80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation 123:731–38 [Google Scholar]
  40. Didelez V, Sheehan N. 40.  2007. Mendelian randomization as an instrumental variable approach to causal inference. Stat. Methods Med. Res. 16:309–30 [Google Scholar]
  41. Efron B, Tibshirani RJ. 41.  1993. An Introduction to the Bootstrap New York: Chapman & Hall
  42. Estrada K, Styrkarsdottir U, Evangelou E, Hsu YH, Duncan EL. 42.  et al. 2012. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet. 44:491–501 [Google Scholar]
  43. Evans DM, Brion MJ, Paternoster L, Kemp JP, McMahon G. 43.  et al. 2013. Mining the human phenome using allelic scores that index biological intermediates. PLOS Genet. 9:e1003919 [Google Scholar]
  44. Evans DM, Visscher PM, Wray NR. 44.  2009. Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Hum. Mol. Genet. 18:3525–31 [Google Scholar]
  45. Feldman HH, Doody RS, Kivipelto M, Sparks DL, Waters DD. 45.  et al. 2010. Randomized controlled trial of atorvastatin in mild to moderate Alzheimer disease: LEADe. Neurology 74:956–64 [Google Scholar]
  46. Ference BA, Yoo W, Alesh I, Mahajan N, Mirowska KK. 46.  et al. 2012. 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. J. Am. Coll. Cardiol. 60:2631–39 [Google Scholar]
  47. Fernandez-Suarez XM, Rigden DJ, Galperin MY. 47.  2014. The 2014 Nucleic Acids Research Database Issue and an updated NAR online Molecular Biology Database Collection. Nucleic Acids Res. 42:D1–6 [Google Scholar]
  48. Fieller EC. 48.  1954. Some problems in interval estimation. J. R. Stat. Soc. B 16:175–85 [Google Scholar]
  49. Fisher RA. 49.  1952. Statistical methods in genetics. Heredity 6:1–12 (Repr. 2010 Int. J. Epidemiol. 39:329–35 [Google Scholar]
  50. Fisk Green R, Byrne J, Crider KS, Gallagher M, Koontz D, Berry RJ. 50.  2013. Folate-related gene variants in Irish families affected by neural tube defects. Front. Genet. 4:223 [Google Scholar]
  51. Franke A, Balschun T, Sina C, Ellinghaus D, Hasler R. 51.  et al. 2010. Genome-wide association study for ulcerative colitis identifies risk loci at 7q22 and 22q13 (IL17REL). Nat. Genet. 42:292–94 [Google Scholar]
  52. Frost C, Thompson SG. 52.  2002. Correcting for regression dilution bias: comparison of methods for a single predictor variable. J. R. Stat. Soc. A 163:173–89 [Google Scholar]
  53. Gage SH, Davey Smith G, Zammit S, Hickman M, Munafo MR. 53.  2013. Using Mendelian randomisation to infer causality in depression and anxiety research. Depress. Anxiety 30:1185–93 [Google Scholar]
  54. Genovese MC, McKay JD, Nasonov EL, Mysler EF, da Silva NA. 54.  et al. 2008. Interleukin-6 receptor inhibition with tocilizumab reduces disease activity in rheumatoid arthritis with inadequate response to disease-modifying antirheumatic drugs: the tocilizumab in combination with traditional disease-modifying antirheumatic drug therapy study. Arthritis Rheum. 58:2968–80 [Google Scholar]
  55. Gibson WT. 55.  2015. Beneficial metabolic phenotypes caused by loss-of-function APOC3 mutations. Clin. Genet. 87:31–32 [Google Scholar]
  56. Graham MJ, Lee RG, Bell TA III, Fu W, Mullick AE. 56.  et al. 2013. Antisense oligonucleotide inhibition of apolipoprotein C-III reduces plasma triglycerides in rodents, nonhuman primates, and humans. Circ. Res. 112:1479–90 [Google Scholar]
  57. Hahn J, Hausman J, Kuersteiner G. 57.  2004. Estimation with weak instruments: accuracy of higher-order bias and MSE approximations. Econ. J. 7:272–306 [Google Scholar]
  58. Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L. 58.  et al. 2010. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42:949–60 [Google Scholar]
  59. Hingorani A, Finan C, Sofat R, Overington JP, Casas JP. 59.  2015. Mendelian randomization and genomics assisted drug development. Mendelian Randomizing: How Genes Can Reveal the Biological and Environmental Causes of Disease G Davey Smith Oxford, UK: Oxford Univ. Press. In press [Google Scholar]
  60. Holmes MV, Dale CE, Zuccolo L, Silverwood RJ, Guo Y. 60.  et al. 2014. Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individual participant data. BMJ 349:g4164 [Google Scholar]
  61. Holmes MV, Lange LA, Palmer T, Lanktree MB, North KE. 61.  et al. 2014. Causal effects of body mass index on cardiometabolic traits and events: a Mendelian randomization analysis. Am. J. Hum. Genet. 94:198–208 [Google Scholar]
  62. 62. HPS2-THRIVE Collab. Group 2014. Effects of extended-release niacin with laropiprant in high-risk patients. N. Engl. J. Med. 371:203–12 [Google Scholar]
  63. Inoue A, Solon G. 63.  2010. Two-sample instrumental variables estimators. Rev. Econ. Stat. 92:557–61 [Google Scholar]
  64. 64. Interleukin-6 Recep. Mendel. Randomisation Anal. Consort 2012. The interleukin-6 receptor as a target for prevention of coronary heart disease: a Mendelian randomisation analysis. Lancet 379:1214–24 [Google Scholar]
  65. Jansen H, Samani NJ, Schunkert H. 65.  2014. Mendelian randomization studies in coronary artery disease. Eur. Heart J. 35:1917–24 [Google Scholar]
  66. Jorgensen AB, Frikke-Schmidt R, Nordestgaard BG, Tybjaerg-Hansen A. 66.  2014. Loss-of-function mutations in APOC3 and risk of ischemic vascular disease. N. Engl. J. Med. 371:32–41 [Google Scholar]
  67. Katan MB. 67.  1986. Apolipoprotein E isoforms, serum cholesterol, and cancer. Lancet 327:507–8 (Repr. 2004 Int. J. Epidemiol. 33:9 [Google Scholar]
  68. Katan MB. 68.  2004. Commentary: Mendelian randomization, 18 years on. Int. J. Epidemiol. 33:10–11 [Google Scholar]
  69. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS. 69.  et al. 2005. Complement factor H polymorphism in age-related macular degeneration. Science 308:385–89 [Google Scholar]
  70. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R. 70.  et al. 2013. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat. Genet. 45:1452–58 [Google Scholar]
  71. Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN. 71.  et al. 2010. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467:832–38 [Google Scholar]
  72. Li R, Tsaih SW, Shockley K, Stylianou IM, Wergedal J. 72.  et al. 2006. Structural model analysis of multiple quantitative traits. PLOS Genet. 2:e114 [Google Scholar]
  73. Lyngdoh T, Vuistiner P, Marques-Vidal P, Rousson V, Waeber G. 73.  et al. 2012. Serum uric acid and adiposity: deciphering causality using a bidirectional Mendelian randomization approach. PLOS ONE 7:e39321 [Google Scholar]
  74. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K. 74.  et al. 2007. The NCBI dbGaP database of genotypes and phenotypes. Nat. Genet. 39:1181–86 [Google Scholar]
  75. 75. Major Depress. Disord. Work. Group Psychiatr. GWAS Consort 2013. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18:497–511 [Google Scholar]
  76. McGuinness B, O'Hare J, Craig D, Bullock R, Malouf R, Passmore P. 76.  2010. Statins for the treatment of dementia. Cochrane Database Syst. Rev. 2010:CD007514 [Google Scholar]
  77. Millstein J, Zhang B, Zhu J, Schadt EE. 77.  2009. Disentangling molecular relationships with a causal inference test. BMC Genet. 10:23 [Google Scholar]
  78. Minelli C, Thompson JR, Tobin MD, Abrams KR. 78.  2004. An integrated approach to the meta-analysis of genetic association studies using Mendelian randomization. Am. J. Epidemiol. 160:445–52 [Google Scholar]
  79. Morgan TH. 79.  1919. Physical Basis of Heredity Philadelphia: JB Lipincott
  80. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV. 80.  et al. 2012. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44:981–90 [Google Scholar]
  81. Munafo MR, Timofeeva MN, Morris RW, Prieto-Merino D, Sattar N. 81.  et al. 2012. Association between genetic variants on chromosome 15q25 locus and objective measures of tobacco exposure. J. Natl. Cancer Inst. 104:740–48 [Google Scholar]
  82. 82. Myocard. Infarct. Genet. Consort. Investig 2014. Inactivating mutations in NPC1L1 and protection from coronary heart disease. N. Engl. J. Med. 371:2072–82 [Google Scholar]
  83. 83. Nat. Genet. Eds 2012. Asking for more. Nat. Genet. 44:733 [Google Scholar]
  84. Nimptsch K, Aleksandrova K, Boeing H, Janke J, Lee YA. 84.  et al. 2015. Association of CRP genetic variants with blood concentrations of C-reactive protein and colorectal cancer risk. Int. J. Cancer 136:1181–92 [Google Scholar]
  85. Noveck R, Stroes ES, Flaim JD, Baker BF, Hughes S. 85.  et al. 2014. Effects of an antisense oligonucleotide inhibitor of C-reactive protein synthesis on the endotoxin challenge response in healthy human male volunteers. J. Am. Heart Assoc. 3:e001084 [Google Scholar]
  86. Omenn GS, Goodman GE, Thornquist MD, Balmes J, Cullen MR. 86.  et al. 1996. Effects of a combination of beta carotene and vitamin A on lung cancer and cardiovascular disease. N. Engl. J. Med. 334:1150–55 [Google Scholar]
  87. Osganian SK, Stampfer MJ, Rimm E, Spiegelman D, Hu FB. 87.  et al. 2003. Vitamin C and risk of coronary heart disease in women. J. Am. Coll. Cardiol. 42:246–52 [Google Scholar]
  88. Palmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH. 88.  et al. 2012. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat. Methods Med. Res. 21:223–42 [Google Scholar]
  89. Palmer TM, Sterne JA, Harbord RM, Lawlor DA, Sheehan NA. 89.  et al. 2011. Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses. Am. J. Epidemiol. 173:1392–403 [Google Scholar]
  90. Phillips AN, Davey Smith G. 90.  1991. How independent are “independent” effects? Relative risk estimation when correlated exposures are measured imprecisely. J. Clin. Epidemiol. 44:1223–31 [Google Scholar]
  91. Pichler I, Del Greco MF, Gogele M, Lill CM, Bertram L. 91.  et al. 2013. Serum iron levels and the risk of Parkinson disease: a Mendelian randomization study. PLOS Med. 10:e1001462 [Google Scholar]
  92. Pierce BL, Ahsan H, Vanderweele TJ. 92.  2011. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int. J. Epidemiol. 40:740–52 [Google Scholar]
  93. Pierce BL, Burgess S. 93.  2013. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am. J. Epidemiol. 178:1177–84 [Google Scholar]
  94. Pierce BL, VanderWeele TJ. 94.  2012. The effect of non-differential measurement error on bias, precision and power in Mendelian randomization studies. Int. J. Epidemiol. 41:1383–93 [Google Scholar]
  95. Plenge RM, Scolnick EM, Altshuler D. 95.  2013. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12:581–94 [Google Scholar]
  96. Preiss D, Seshasai SR, Welsh P, Murphy SA, Ho JE. 96.  et al. 2011. Risk of incident diabetes with intensive-dose compared with moderate-dose statin therapy: a meta-analysis. JAMA 305:2556–64 [Google Scholar]
  97. Prizment AE, Folsom AR, Dreyfus J, Anderson KE, Visvanathan K. 97.  et al. 2013. Plasma C-reactive protein, genetic risk score, and risk of common cancers in the Atherosclerosis Risk in Communities study. Cancer Causes Control 24:2077–87 [Google Scholar]
  98. Proitsi P, Lupton MK, Velayudhan L, Newhouse S, Fogh I. 98.  et al. 2014. Genetic predisposition to increased blood cholesterol and triglyceride lipid levels and risk of Alzheimer disease: a Mendelian randomization analysis. PLOS Med. 11:e1001713 [Google Scholar]
  99. 99. Psychiatr. GWAS Consort. Bipolar Disord. Work. Group 2011. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet. 43:977–83 [Google Scholar]
  100. Relton CL, Davey Smith G. 100.  2010. Epigenetic epidemiology of common complex disease: prospects for prediction, prevention, and treatment. PLOS Med. 7:e1000356 [Google Scholar]
  101. Relton CL, Davey Smith G. 101.  2012. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int. J. Epidemiol. 41:161–76 [Google Scholar]
  102. Richmond RC, Davey Smith G, Ness AR, den Hoed M, McMahon G, Timpson NJ. 102.  2014. Assessing causality in the association between child adiposity and physical activity levels: a Mendelian randomization analysis. PLOS Med. 11:e1001618 [Google Scholar]
  103. Rietveld CA, Medland SE, Derringer J, Yang J, Esko T. 103.  et al. 2013. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340:1467–71 [Google Scholar]
  104. Rimm EB, Stampfer MJ, Ascherio A, Giovannucci E, Colditz GA, Willett WC. 104.  1993. Vitamin E consumption and the risk of coronary heart disease in men. N. Engl. J. Med. 328:1450–56 [Google Scholar]
  105. Rosa GJ, Valente BD, de los Campos G, Wu XL, Gianola D, Silva MA. 105.  2011. Inferring causal phenotype networks using structural equation models. Genet. Sel. Evol. 43:6 [Google Scholar]
  106. Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C. 106.  et al. 2002. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women's Health Initiative randomized controlled trial. JAMA 288:321–33 [Google Scholar]
  107. Sanseau P, Agarwal P, Barnes MR, Pastinen T, Richards JB. 107.  et al. 2012. Use of genome-wide association studies for drug repositioning. Nat. Biotechnol. 30:317–20 [Google Scholar]
  108. Sattar N, Preiss D, Murray HM, Welsh P, Buckley BM. 108.  et al. 2010. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet 375:735–42 [Google Scholar]
  109. Schadt EE, Lamb J, Yang X, Zhu J, Edwards S. 109.  et al. 2005. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37:710–17 [Google Scholar]
  110. 110. Schizophr. Work. Group Psychiatr. Genomics Consort 2014. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–27 [Google Scholar]
  111. Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL. 111.  et al. 2011. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43:333–38 [Google Scholar]
  112. Schwartz GG, Olsson AG, Abt M, Ballantyne CM, Barter PJ. 112.  et al. 2012. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N. Engl. J. Med. 367:2089–99 [Google Scholar]
  113. Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R. 113.  et al. 2014. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46:543–50 [Google Scholar]
  114. Shin SY, Petersen AK, Wahl S, Zhai G, Romisch-Margl W. 114.  et al. 2014. Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids. Genome Med. 6:25 [Google Scholar]
  115. Silverwood RJ, Holmes MV, Dale CE, Lawlor DA, Whittaker JC. 115.  et al. 2014. Testing for non-linear causal effects using a binary genotype in a Mendelian randomization study: application to alcohol and cardiovascular traits. Int. J. Epidemiol. 43:1781–90 [Google Scholar]
  116. Smith JG, Luk K, Schulz CA, Engert JC, Do R. 116.  et al. 2014. Association of low-density lipoprotein cholesterol-related genetic variants with aortic valve calcium and incident aortic stenosis. JAMA 312:1764–71 [Google Scholar]
  117. Sofat R, Hingorani AD, Smeeth L, Humphries SE, Talmud PJ. 117.  et al. 2010. Separating the mechanism-based and off-target actions of cholesteryl ester transfer protein inhibitors with CETP gene polymorphisms. Circulation 121:52–62 [Google Scholar]
  118. Spearman C. 118.  1904. The proof and measurement of association between two things. Am. J. Psychol. 15:72–101 [Google Scholar]
  119. Spearman C. 119.  2010. The proof and measurement of association between two things. Int. J. Epidemiol. 39:1137–50 [Google Scholar]
  120. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G. 120.  et al. 2010. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42:937–48 [Google Scholar]
  121. Stahl EA, Raychaudhuri S, Remmers EF, Xie G, Eyre S. 121.  et al. 2010. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat. Genet. 42:508–14 [Google Scholar]
  122. Staiger D, Stock J. 122.  1997. Instrumental variables regression with weak instruments. Econometrica 65:557–86 [Google Scholar]
  123. Stampfer MJ, Hennekens CH, Manson JE, Colditz GA, Rosner B, Willett WC. 123.  1993. Vitamin E consumption and the risk of coronary disease in women. N. Engl. J. Med. 328:1444–49 [Google Scholar]
  124. Stein EA, Mellis S, Yancopoulos GD, Stahl N, Logan D. 124.  et al. 2012. Effect of a monoclonal antibody to PCSK9 on LDL cholesterol. N. Engl. J. Med. 366:1108–18 [Google Scholar]
  125. Swerdlow DI, Preiss D, Kuchenbaecker KB, Holmes MV, Engmann JE. 125.  et al. 2015. HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials. Lancet 385:351–61 [Google Scholar]
  126. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM. 126.  et al. 2010. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466:707–13 [Google Scholar]
  127. 127. TG HDL Work. Group Exome Seq. Proj., Natl. Heart Lung Blood Inst 2014. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N. Engl. J. Med. 371:22–31 [Google Scholar]
  128. Thakkinstian A, Chailurkit L, Warodomwichit D, Ratanachaiwong W, Yamwong S. 128.  et al. 2014. Causal relationship between body mass index and fetuin-A level in the Asian population: a bidirectional Mendelian randomization study. Clin. Endocrinol. 81:197–203 [Google Scholar]
  129. Thomas DC, Lawlor DA, Thompson JR. 129.  2007. Re: Estimation of bias in nongenetic observational studies using “Mendelian triangulation” by Bautista et al. Ann. Epidemiol. 17:511–13 [Google Scholar]
  130. Thrift AP, Shaheen NJ, Gammon MD, Bernstein L, Reid BJ. 130.  et al. 2014. Obesity and risk of esophageal adenocarcinoma and Barrett's esophagus: a Mendelian randomization study. J. Natl. Cancer Inst. 106:dju252 [Google Scholar]
  131. Timpson NJ, Lawlor DA, Harbord RM, Gaunt TR, Day IN. 131.  et al. 2005. C-reactive protein and its role in metabolic syndrome: Mendelian randomisation study. Lancet 366:1954–59 [Google Scholar]
  132. Timpson NJ, Nordestgaard BG, Harbord RM, Zacho J, Frayling TM. 132.  et al. 2011. C-reactive protein levels and body mass index: elucidating direction of causation through reciprocal Mendelian randomization. Int. J. Obes. 35:300–8 [Google Scholar]
  133. 133. Tob. Genet. Consort 2010. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42:441–47 [Google Scholar]
  134. Varbo A, Benn M, Davey Smith G, Timpson NJ, Tybjaerg-Hansen A, Nordestgaard BG. 134.  2015. Remnant cholesterol, low-density lipoprotein cholesterol, and blood pressure as mediators from obesity to ischemic heart disease. Circ. Res. 116665–73
  135. Vimaleswaran KS, Berry DJ, Lu C, Tikkanen E, Pilz S. 135.  et al. 2013. Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis of multiple cohorts. PLOS Med. 10e1001383
  136. Visscher PM, Brown MA, McCarthy MI, Yang J. 136.  2012. Five years of GWAS discovery. Am. J. Hum. Genet. 90:7–24 [Google Scholar]
  137. Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M. 137.  et al. 2012. Plasma HDL cholesterol and risk of myocardial infarction: a Mendelian randomisation study. Lancet 380:572–80 [Google Scholar]
  138. Waddington CH. 138.  1942. The epigenotype. Endeavor 1: 18–20 (Repr. 2012 Int. J. Epidemiol. 41:10–13 [Google Scholar]
  139. Wagner GP, Zhang J. 139.  2011. The pleiotropic structure of the genotype-phenotype map: the evolvability of complex organisms. Nat. Rev. Genet. 12:204–13 [Google Scholar]
  140. Wald A. 140.  1940. The fitting of straight lines if both variables are subject to error. Ann. Math. Stat. 11:284–300 [Google Scholar]
  141. Wang ZY, Zhang HY. 141.  2013. Rational drug repositioning by medical genetics. Nat. Biotechnol. 31:1080–82 [Google Scholar]
  142. 142. Wellcome Trust Case Control Consort 2007. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–78 [Google Scholar]
  143. Welsh P, Polisecki E, Robertson M, Jahn S, Buckley BM. 143.  et al. 2010. Unraveling the directional link between adiposity and inflammation: a bidirectional Mendelian randomization approach. J. Clin. Endocrinol. Metab. 95:93–99 [Google Scholar]
  144. Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C. 144.  et al. 2013. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45:1238–43 [Google Scholar]
  145. Wium-Andersen MK, Ørsted DD, Nordestgaard BG. 145.  2014. Elevated C-reactive protein, depression, somatic diseases, and all-cause mortality: a Mendelian randomization study. Biol. Psychiatry 76:249–57 [Google Scholar]
  146. Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME, Visscher PM. 146.  2013. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14:507–15 [Google Scholar]
  147. Wurtz P, Kangas AJ, Soininen P, Lehtimaki T, Kahonen M. 147.  et al. 2013. Lipoprotein subclass profiling reveals pleiotropy in the genetic variants of lipid risk factors for coronary heart disease: a note on Mendelian randomization studies. J. Am. Coll. Cardiol. 62:1906–8 [Google Scholar]
  148. Yadav U, Kumar P, Yadav SK, Mishra OP, Rai V. 148.  2014. Polymorphisms in folate metabolism genes as maternal risk factor for neural tube defects: an updated meta-analysis. Metab. Brain Dis. 30:7–24 [Google Scholar]
  149. Yehoshua Z, de Amorim Garcia Filho CA, Nunes RP, Gregori G, Penha FM. 149.  et al. 2014. Systemic complement inhibition with eculizumab for geographic atrophy in age-related macular degeneration: the COMPLETE study. Ophthalmology 121:693–701 [Google Scholar]
  150. Yin P, Voight BF. 150.  2015. MeRP: a high-throughput pipeline for Mendelian randomization analysis. Bioinformatics 31957–59
  151. Zeilinger S, Kuhnel B, Klopp N, Baurecht H, Kleinschmidt A. 151.  et al. 2013. Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLOS ONE 8:e63812 [Google Scholar]

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