Mendelian randomization: the basic principles, methods and limitations_Chinese Journal of Evidence-Based Medicine

Authors：

YU Tianqi , XU Wentao , SU Yana ,  LI Jing

Research Center of Evidence-Based Medicine and Clinical Epidemiology, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;

Corresponding author：

LI Jing, Email: lijing68@Hotmail.com

Keywords：

Mendelian randomization; Instrumental variable; MR-Egger regression; Pleiotropy

DOI：

10.7507/1672-2531.202107008

Video：

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Abstract Full text Figures/Tables Video References Cited by

Mendelian randomization is a special type of instrumental variable analysis. Its application in the medical field increases in popularity because of its obvious advantages and the rapid development of genomics. This article aimed to introduce the basic concepts, principles, common methods, and limitations of Mendelian randomization. It is expected to provide guidance for researchers to conduct Mendelian randomization studies.

Citation： YU Tianqi, XU Wentao, SU Yana, LI Jing. Mendelian randomization: the basic principles, methods and limitations. Chinese Journal of Evidence-Based Medicine, 2021, 21(10): 1227-1234. doi: 10.7507/1672-2531.202107008 Copy

1.	Davey SG, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet, 2014, 23(R1): R89-98.
2.	Smith GD, Ebrahim S. Data dredging, bias, or confounding. BMJ, 2002, 325(7378): 1437-1438.
3.	Lawlor DA, Harbord RM, Sterne JA, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med, 2008, 27(8): 1133-1163.
4.	Burgess S, Timpson NJ, Ebrahim S, et al. Mendelian randomization: where are we now and where are we going? Int J Epidemiol, 2015, 44(2): 379-388.
5.	Willer CJ, Schmidt EM, Sengupta S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet, 2013, 45(11): 1274-1283.
6.	Zheng J, Baird D, Borges MC, et al. Recent developments in mendelian randomization studies. Curr Epidemiol Rep, 2017, 4(4): 330-345.
7.	Burgess S, Thompson SG. Mendelian randomization: methods for using genetic variants in causal estimation. Boca Raton: CRC Press, 2015.
8.	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): 512-525.
9.	Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol, 2016, 40(4): 304-314.
10.	Hartwig FP, Davies NM. Why internal weights should be avoided (not only) in MR-Egger regression. Int J Epidemiol, 2016, 45(5): 1676-1678.
11.	Haycock PC, Burgess S, Wade KH, et al. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am J Clin Nutr, 2016, 103(4): 965-978.
12.	Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res, 2017, 26(5): 2333-2355.
13.	Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference. Stat Med, 2014, 33(13): 2297-2340.
14.	Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Epidemiology, 2006, 17(4): 360-372.
15.	Shah BR, Laupacis A, Hux JE, et al. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol, 2005, 58(6): 550-559.
16.	Concato J, Peduzzi P, Holford TR, et al. Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol, 1995, 48(12): 1495-1501.
17.	Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70(1): 41-55.
18.	Ertefaie A, Small DS, Flory JH, et al. A tutorial on the use of instrumental variables in pharmacoepidemiology. Pharmacoepidemiol Drug Saf, 2017, 26(4): 357-367.
19.	Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol, 2004, 33(1): 30-42.
20.	Ziegler A, Konig IR, Pahlke F. A statistical approach to genetic epidemiology: concepts and applications, with an e-learning platform. Manhattan: John Wiley & Sons, 2010.
21.	Smith GD, Lawlor DA, Harbord R, et al. Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PLoS Med, 2007, 4(12): e352.
22.	Glymour MM, Tchetgen TEJ, Robins JM. Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions. Am J Epidemiol, 2012, 175(4): 332-339.
23.	Stearns FW. One hundred years of pleiotropy: a retrospective. Genetics, 2010, 186(3): 767-773.
24.	Wagner GP, Zhang J. The pleiotropic structure of the genotype-phenotype map: the evolvability of complex organisms. Nat Rev Genet, 2011, 12(3): 204-213.
25.	Cardon LR, Palmer LJ. Population stratification and spurious allelic association. Lancet, 2003, 361(9357): 598-604.
26.	Thomas DC, Conti DV. Commentary: the concept of 'Mendelian Randomization'. Int J Epidemiol, 2004, 33(1): 21-25.
27.	王玉琢, 沈洪兵. 孟德尔随机化研究应用于因果推断的影响因素及其结果解读面临的挑战. 中华流行病学杂志, 2020, 41(8): 1231-1236.
28.	Debat V, David P. Mapping phenotypes: canalization, plasticity and developmental stability. Trends Ecol Evol, 2001, 16(10): 555-561.
29.	Davies NM, Holmes MV, Davey SG. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ, 2018, 362: k601.
30.	Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol, 2016, 40(7): 597-608.
31.	Bowden J, Del Greco MF, Minelli C, et al. 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.
32.	Sadreev II, Elsworth BL, Mitchell RE, et al. Navigating sample overlap, winner's curse and weak instrument bias in Mendelian randomization studies using the UK Biobank. medRxiv, 2021.
33.	Burgess S, Thompson SG. Use of allele scores as instrumental variables for Mendelian randomization. Int J Epidemiol, 2013, 42(4): 1134-1144.
34.	Davies NM, von Hinke Kessler Scholder S, Farbmacher H, et al. The many weak instruments problem and Mendelian randomization. Stat Med, 2015, 34(3): 454-468.
35.	Bowden J, Burgess S, Davey Smith G. Response to hartwig and davies. Int J Epidemiol, 2016, 45(5): 1679-1680.
36.	Stock JH, Wright JH, Yogo M. A survey of weak instruments and weak identification in generalized method of moments. J Bus Econ Stat, 2002, 20(4): 518-529.
37.	Burgess S, Thompson SG. Bias in causal estimates from Mendelian randomization studies with weak instruments. Stat Med, 2011, 30(11): 1312-1323.
38.	Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol, 2013, 178(7): 1177-1184.
39.	Hartwig FP, Davies NM, Hemani G, et al. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol, 2016, 45(6): 1717-1726.
40.	Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol, 2013, 37(7): 658-665.
41.	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): 1880-1906.
42.	Staiger DO, Stock JH. Instrumental variables regression with weak instruments. Econometric, 1997, 65: 557-586.
43.	Bowden J. Misconceptions on the use of MR-Egger regression and the evaluation of the InSIDE assumption. Int J Epidemiol, 2017, 46(6): 2097-2099.
44.	Borenstein M, Hedges LV, Higgins J, et al. Generality of the basic inverse-variance method. Introduct Met Analy, 2009: 311-319.
45.	Greco M FD, Minelli C, Sheehan NA, et al. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med, 2015, 34(21): 2926-2940.
46.	Guo Z, Kang H, Tony CT, et al. Confidence intervals for causal effects with invalid instruments by using two-stage hard thresholding with voting. J R Stat Soc Series B Stat Methodol, 2018, 80(4): 793-815.
47.	Jiang L, Oualkacha K, Didelez V, et al. Constrained instruments and their application to Mendelian randomization with pleiotropy. Genet Epidemiol, 2019, 43(4): 373-401.
48.	Kang H, Zhang A, Cai TT, et al. Instrumental variables estimation with some invalid instruments and its application to Mendelian randomization. J Am Stat Assoc, 2016, 111(513): 132-144.
49.	Slob EAW, Burgess S. A comparison of robust Mendelian randomization methods using summary data. Genet Epidemiol, 2020, 44(4): 313-329.
50.	Evans DM, Davey Smith G. Mendelian randomization: new applications in the coming age of hypothesis-free causality. Annu Rev Genomics Hum Genet, 2015, 16: 327-350.
51.	Relton CL, Davey Smith G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol, 2012, 41(1): 161-176.
52.	Ference BA, Majeed F, Penumetcha R, et al. 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: a 2×2 factorial Mendelian randomization study. J Am Coll Cardiol, 2015, 65(15): 1552-1561.
53.	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): 1985-1998.
54.	van Kippersluis H, Rietveld CA. Pleiotropy-robust Mendelian randomization. Int J Epidemiol, 2018, 47(4): 1279-1288.
55.	Schmidt AF, Dudbridge F. Mendelian randomization with Egger pleiotropy correction and weakly informative Bayesian priors. Int J Epidemiol, 2018, 47(4): 1217-1228.
56.	Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res, 2020, 4: 186.
57.	高雪, 薛付忠, 黄丽红, 等. 孟德尔随机化模型及其规范化应用的统计学共识. 中国卫生统计, 2021, 38(3): 471-475, 480.

1. Davey SG, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet, 2014, 23(R1): R89-98.
2. Smith GD, Ebrahim S. Data dredging, bias, or confounding. BMJ, 2002, 325(7378): 1437-1438.
3. Lawlor DA, Harbord RM, Sterne JA, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med, 2008, 27(8): 1133-1163.
4. Burgess S, Timpson NJ, Ebrahim S, et al. Mendelian randomization: where are we now and where are we going? Int J Epidemiol, 2015, 44(2): 379-388.
5. Willer CJ, Schmidt EM, Sengupta S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet, 2013, 45(11): 1274-1283.
6. Zheng J, Baird D, Borges MC, et al. Recent developments in mendelian randomization studies. Curr Epidemiol Rep, 2017, 4(4): 330-345.
7. Burgess S, Thompson SG. Mendelian randomization: methods for using genetic variants in causal estimation. Boca Raton: CRC Press, 2015.
8. 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): 512-525.
9. Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol, 2016, 40(4): 304-314.
10. Hartwig FP, Davies NM. Why internal weights should be avoided (not only) in MR-Egger regression. Int J Epidemiol, 2016, 45(5): 1676-1678.
11. Haycock PC, Burgess S, Wade KH, et al. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am J Clin Nutr, 2016, 103(4): 965-978.
12. Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res, 2017, 26(5): 2333-2355.
13. Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference. Stat Med, 2014, 33(13): 2297-2340.
14. Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Epidemiology, 2006, 17(4): 360-372.
15. Shah BR, Laupacis A, Hux JE, et al. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol, 2005, 58(6): 550-559.
16. Concato J, Peduzzi P, Holford TR, et al. Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol, 1995, 48(12): 1495-1501.
17. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70(1): 41-55.
18. Ertefaie A, Small DS, Flory JH, et al. A tutorial on the use of instrumental variables in pharmacoepidemiology. Pharmacoepidemiol Drug Saf, 2017, 26(4): 357-367.
19. Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol, 2004, 33(1): 30-42.
20. Ziegler A, Konig IR, Pahlke F. A statistical approach to genetic epidemiology: concepts and applications, with an e-learning platform. Manhattan: John Wiley & Sons, 2010.
21. Smith GD, Lawlor DA, Harbord R, et al. Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PLoS Med, 2007, 4(12): e352.
22. Glymour MM, Tchetgen TEJ, Robins JM. Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions. Am J Epidemiol, 2012, 175(4): 332-339.
23. Stearns FW. One hundred years of pleiotropy: a retrospective. Genetics, 2010, 186(3): 767-773.
24. Wagner GP, Zhang J. The pleiotropic structure of the genotype-phenotype map: the evolvability of complex organisms. Nat Rev Genet, 2011, 12(3): 204-213.
25. Cardon LR, Palmer LJ. Population stratification and spurious allelic association. Lancet, 2003, 361(9357): 598-604.
26. Thomas DC, Conti DV. Commentary: the concept of 'Mendelian Randomization'. Int J Epidemiol, 2004, 33(1): 21-25.
27. 王玉琢, 沈洪兵. 孟德尔随机化研究应用于因果推断的影响因素及其结果解读面临的挑战. 中华流行病学杂志, 2020, 41(8): 1231-1236.
28. Debat V, David P. Mapping phenotypes: canalization, plasticity and developmental stability. Trends Ecol Evol, 2001, 16(10): 555-561.
29. Davies NM, Holmes MV, Davey SG. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ, 2018, 362: k601.
30. Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol, 2016, 40(7): 597-608.
31. Bowden J, Del Greco MF, Minelli C, et al. 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.
32. Sadreev II, Elsworth BL, Mitchell RE, et al. Navigating sample overlap, winner's curse and weak instrument bias in Mendelian randomization studies using the UK Biobank. medRxiv, 2021.
33. Burgess S, Thompson SG. Use of allele scores as instrumental variables for Mendelian randomization. Int J Epidemiol, 2013, 42(4): 1134-1144.
34. Davies NM, von Hinke Kessler Scholder S, Farbmacher H, et al. The many weak instruments problem and Mendelian randomization. Stat Med, 2015, 34(3): 454-468.
35. Bowden J, Burgess S, Davey Smith G. Response to hartwig and davies. Int J Epidemiol, 2016, 45(5): 1679-1680.
36. Stock JH, Wright JH, Yogo M. A survey of weak instruments and weak identification in generalized method of moments. J Bus Econ Stat, 2002, 20(4): 518-529.
37. Burgess S, Thompson SG. Bias in causal estimates from Mendelian randomization studies with weak instruments. Stat Med, 2011, 30(11): 1312-1323.
38. Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol, 2013, 178(7): 1177-1184.
39. Hartwig FP, Davies NM, Hemani G, et al. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol, 2016, 45(6): 1717-1726.
40. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol, 2013, 37(7): 658-665.
41. 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): 1880-1906.
42. Staiger DO, Stock JH. Instrumental variables regression with weak instruments. Econometric, 1997, 65: 557-586.
43. Bowden J. Misconceptions on the use of MR-Egger regression and the evaluation of the InSIDE assumption. Int J Epidemiol, 2017, 46(6): 2097-2099.
44. Borenstein M, Hedges LV, Higgins J, et al. Generality of the basic inverse-variance method. Introduct Met Analy, 2009: 311-319.
45. Greco M FD, Minelli C, Sheehan NA, et al. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med, 2015, 34(21): 2926-2940.
46. Guo Z, Kang H, Tony CT, et al. Confidence intervals for causal effects with invalid instruments by using two-stage hard thresholding with voting. J R Stat Soc Series B Stat Methodol, 2018, 80(4): 793-815.
47. Jiang L, Oualkacha K, Didelez V, et al. Constrained instruments and their application to Mendelian randomization with pleiotropy. Genet Epidemiol, 2019, 43(4): 373-401.
48. Kang H, Zhang A, Cai TT, et al. Instrumental variables estimation with some invalid instruments and its application to Mendelian randomization. J Am Stat Assoc, 2016, 111(513): 132-144.
49. Slob EAW, Burgess S. A comparison of robust Mendelian randomization methods using summary data. Genet Epidemiol, 2020, 44(4): 313-329.
50. Evans DM, Davey Smith G. Mendelian randomization: new applications in the coming age of hypothesis-free causality. Annu Rev Genomics Hum Genet, 2015, 16: 327-350.
51. Relton CL, Davey Smith G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol, 2012, 41(1): 161-176.
52. Ference BA, Majeed F, Penumetcha R, et al. 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: a 2×2 factorial Mendelian randomization study. J Am Coll Cardiol, 2015, 65(15): 1552-1561.
53. 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): 1985-1998.
54. van Kippersluis H, Rietveld CA. Pleiotropy-robust Mendelian randomization. Int J Epidemiol, 2018, 47(4): 1279-1288.
55. Schmidt AF, Dudbridge F. Mendelian randomization with Egger pleiotropy correction and weakly informative Bayesian priors. Int J Epidemiol, 2018, 47(4): 1217-1228.
56. Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res, 2020, 4: 186.
57. 高雪, 薛付忠, 黄丽红, 等. 孟德尔随机化模型及其规范化应用的统计学共识. 中国卫生统计, 2021, 38(3): 471-475, 480.

Chinese Journal of Evidence-Based Medicine

Mendelian randomization: the basic principles, methods and limitations

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