Erratum to: Interpreting findings from Mendelian randomization using the MR-Egger method (Eur J Epidemiol, 10.1007/s10654-017-0255-x);Interpreting findings from Mendelian randomization using the MR-Egger method

European Journal of Epidemiology

Volumn 32, Issue 5, 2017, Pages 377-389

  Burgess, Stephen ^a,b   Thompson, Simon G ^b  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Instrumental variable; Mendelian randomization; MR Egger; Robust methods; Summarized data

Indexed keywords

CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; GENETIC VARIABILITY; HUMAN; MENDELIAN RANDOMIZATION ANALYSIS; PLEIOTROPY; RANDOMIZED CONTROLLED TRIAL; BIOLOGICAL MODEL; GENETIC VARIATION; PROCEDURES; RANDOMIZATION; RISK FACTOR; STATISTICAL ANALYSIS;

DATA INTERPRETATION, STATISTICAL; GENETIC PLEIOTROPY; GENETIC VARIATION; HUMANS; MENDELIAN RANDOMIZATION ANALYSIS; MODELS, BIOLOGICAL; RANDOM ALLOCATION; RISK FACTORS;

EID: 85019688623     PISSN: 03932990     EISSN: 15737284     Source Type: Journal    
DOI: 10.1007/s10654-017-0276-5     Document Type: Erratum

References (52)

1. Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1–22. doi:10.1093/ije/dyg070.
2. Burgess S, Thompson SG. Mendelian randomization: methods for using genetic variants in causal estimation. London: Chapman & Hall; 2015.
3. Davey Smith G, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol. 2004;33(1):30–42. doi:10.1093/ije/dyh132.
4. Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res. 2007;16(4):309–30. doi:10.1177/0962280206077743.
5. Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29(4):722–9. doi:10.1093/ije/29.4.722.
6. Clarke PS, Windmeijer F. Instrumental variable estimators for binary outcomes. J Am Stat Assoc. 2012;107(500):1638–52. doi:10.1080/01621459.2012.734171.
7. Burgess S, Butterworth AS, Thompson JR. Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors. J Clin Epidemiol. 2015;. doi:10.1016/j.jclinepi.2015.08.001.
8. VanderWeele T, Tchetgen Tchetgen E, Cornelis M, Kraft P. Methodological challenges in Mendelian randomization. Epidemiology. 2014;25(3):427–35. doi:10.1097/ede.0000000000000081.
9. 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.
10. White J, Sofat R, Hemani G, et al. Plasma urate and coronary heart disease: Mendelian randomisation analysis. Lancet Diabetes Endocrinol. 2015;4:327–36. doi:10.1016/s2213-8587(15)00386-1.
11. Tyrrell J, Jones SE, Beaumont R, Astley CM, Lovell R, Yaghootkar H, Tuke M, Ruth KS, Freathy RM, Hirschhorn JN, et al. Height, body mass index, and socioeconomic status: Mendelian randomisation study in UK Biobank. Br Med J. 2016;352:i582. doi:10.1136/bmj.i582.
12. Jones SE, Tyrrell J, Wood AR, Beaumont RN, Ruth KS, Tuke MA, Yaghootkar H, Hu Y, Teder-Laving M, Hayward C, et al. Genome-wide association analyses in ≥ 119,000 individuals identifies thirteen morningness and two sleep duration loci. bioRxiv. 2016. doi:10.1101/031369.
13. Bonilla C, Lewis SJ, Martin RM, Donovan JL, Hamdy FC, Neal DE, Eeles R, Easton D, Kote-Jarai Z, Al Olama AA, et al. Pubertal development and prostate cancer risk: Mendelian randomization study in a population-based cohort. BMC Med. 2016;14:66. doi:10.1186/s12916-016-0602-x.
14. Burgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med. 2016;35(11):1880–906. doi:10.1002/sim.6835.
15. Kang H, Zhang A, Cai T, Small D. Instrumental variables estimation with some invalid instruments, and its application to Mendelian randomisation. J Am Stat Assoc. 2015;. doi:10.1080/01621459.2014.994705.
16. Lawlor D, Harbord R, Sterne J, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63. doi:10.1002/sim.3034.
17. Burgess S, Butterworth AS, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37(7):658–65. doi:10.1002/gepi.21758.
18. Thomas D, Lawlor D, Thompson J. Re: Estimation of bias in nongenetic observational studies using “Mendelian triangulation” by Bautista, et al. Ann Epidemiol. 2007;17(7):511–3. doi:10.1016/j.annepidem.2006.12.005.
19. Johnson T. Efficient calculation for multi-SNP genetic risk scores. Technical Report, The Comprehensive R Archive Network 2013. http://cran.r-project.org/web/packages/gtx/vignettes/ashg2012.pdf. Accessed 19 Nov 2014.
20. Burgess S, Dudbridge F, Thompson SG. Re: “Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects”. Am J Epidemiol. 2015;181(4):290–1.
21. Thompson S, Sharp S. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med. 1999;18(20):2693–708.
22. Kolesár M, Chetty R, Friedman J, Glaeser E, Imbens G. Identification and inference with many invalid instruments. J Bus Econ Stat. 2014;. doi:10.1080/07350015.2014.978175.
23. CARDIoGRAMplusC4D Consortium. A comprehensive1000 genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47:1121–30. doi:10.1038/ng.3396.
24. Burgess S. Plasma urate and coronary heart disease: fingerprint match, but no smoking gun. Lancet Diabetes Endocrinol. 2016;. doi:10.1016/S2213-8587(15)00425-8.
25. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14. doi:10.1002/gepi.21965.
26. Borenstein M, Hedges L, Higgins J, Rothstein H. Introduction to meta-analysis. Chapter 34: generality of the basic inverse-variance method. Chichester: Wiley; 2009.
27. Dobson A. An introduction to generalized linear models. London: Chapman & Hall; 2001. doi:10.1201/9781420057683.
28. Burgess S. Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int J Epidemiol. 2014;43(3):922–9. doi:10.1093/ije/dyu005.
29. 2 statistic. Int J Epidemiol. 2016;. doi:10.1093/ije/dyw220.
30. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206. doi:10.1038/nature14177.
31. Corbin LJ, Richmond RC, Wade KH, Burgess S, Bowden J, Smith GD, Timpson NJ. Body mass index as a modifiable risk factor for type 2 diabetes: refining and understanding causal estimates using Mendelian randomisation. Diabetes. 2016;. doi:10.2337/db16-0418.
32. Burgess S, Bowden J, Fall T, Ingelsson E, Thompson S. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology. 2017;28(1):30–42. doi:10.1097/EDE.0000000000000559.
33. Pickrell J, Berisa T, Segurel L, Tung JY, Hinds D. Detection and interpretation of shared genetic influences on 40 human traits. Nat Genet. 2016;. doi:10.1038/ng.3570.
34. White J, Swerdlow DI, Preiss D, Fairhurst-Hunter Z, Keating BJ, Asselbergs FW, Sattar N, Humphries SE, Hingorani AD, Holmes MV. 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.
35. Glymour M, Tchetgen Tchetgen E, Robins J. Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions. Am J Epidemiol. 2012;175(4):332–9. doi:10.1093/aje/kwr323.
36. Kang H, Kreuels B, Adjei O, Krumkamp R, May J, Small DS. The causal effect of malaria on stunting: a Mendelian randomization and matching approach. Int J Epidemiol. 2013;42(5):1390–8. doi:10.1093/ije/dyt116.
37. Windmeijer F, Farbmacher H, Davies N, Davey Smith G, White I. Selecting (in)valid instruments for instrumental variables estimation 2015. http://www.hec.unil.ch/documents/seminars/iems/1849.pdf.
38. Han C. Detecting invalid instruments using L1-GMM. Econ Lett. 2008;101:285–7.
39. Mosteller F, Tukey JW. Data analysis and regression: a second course in statistics. Boston, MA, USA: Addison–Wesley; 1977.
40. Huber PJ. Robust statistics. Berlin: Springer; 2011.
41. Burgess S, Bowden J, Dudbridge F, Thompson SG. Robust instrumental variable methods using multiple candidate instruments with application to Mendelian randomization. 2016. arXiv:1606.03729.
42. Imbens GW, Angrist JD. Identification and estimation of local average treatment effects. Econometrica. 1994;62(2):467–75. doi:10.2307/2951620.
43. Robins JM. The analysis of randomized and nonrandomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In: Health service research methodology: a focus on AIDS. Washington, DC, USA: National Center for Health Services Research; 1989. p. 113–159.
44. Hernán M, Robins J. Instruments for causal inference: an epidemiologist’s dream? Epidemiology. 2006;17(4):360–72. doi:10.1097/01.ede.0000222409.00878.37.
45. Swanson S, Hernán M. Commentary: how to report instrumental variable analyses (suggestions welcome). Epidemiology. 2013;24(3):370–4. doi:10.1097/ede.0b013e31828d0590.
46. Burgess S, CHD CRP Genetics Collaboration. Identifying the odds ratio estimated by a two-stage instrumental variable analysis with a logistic regression model. Stat Med. 2013;32(27):4726–47. doi:10.1002/sim.5871.
47. Burgess S, Butterworth A, Malarstig A, Thompson S. Use of Mendelian randomisation to assess potential benefit of clinical intervention. Br Med J. 2012;345:e7325. doi:10.1136/bmj.e7325.
48. Burgess S, Scott R, Timpson N, Davey Smith G, Thompson SG, EPIC-InterAct Consortium. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30(7):543–52. doi:10.1007/s10654-015-0011-z.
49. 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;. doi:10.1002/sim.7221.
50. The Global Lipids Genetics Consortium. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–83. doi:10.1038/ng.2797.
51. Schunkert H, König I, Kathiresan S, Reilly M, Assimes T, Holm H, Preuss M, Stewart A, Barbalic M, Gieger C, et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. 2011;43(4):333–8. doi:10.1038/ng.784.
52. Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet. 2013;45:1345–52. doi:10.1038/ng.2795.