Extending the MR‐Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy

Abstract

Methods have been developed for Mendelian randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariable Mendelian randomization, in which a genetic variant may be associated with multiple risk factors so long as any association with the outcome is via the measured risk factors (measured pleiotropy), and the MR‐Egger (Mendelian randomization‐Egger) method, in which a genetic variant may be directly associated with the outcome not via the risk factor of interest, so long as the direct effects of the variants on the outcome are uncorrelated with their associations with the risk factor (unmeasured pleiotropy). In this paper, we extend the MR‐Egger method to a multivariable setting to correct for both measured and unmeasured pleiotropy. We show, through theoretical arguments and a simulation study, that the multivariable MR‐Egger method has advantages over its univariable counterpart in terms of plausibility of the assumption needed for consistent causal estimation and power to detect a causal effect when this assumption is satisfied. The methods are compared in an applied analysis to investigate the causal effect of high‐density lipoprotein cholesterol on coronary heart disease risk. The multivariable MR‐Egger method will be useful to analyse high‐dimensional data in situations where the risk factors are highly related and it is difficult to find genetic variants specifically associated with the risk factor of interest (multivariable by design), and as a sensitivity analysis when the genetic variants are known to have pleiotropic effects on measured risk factors.

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References 34

1. 1.

   Smith GD, Ebrahim S. Mendelian randomization: Can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1‐22.

   Not in our library10.1093/ije/dyg070

2. 2.

   Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133‐1163.

   Not in our library10.1002/sim.3034

3. 3.

   Sleiman PM, Grant SF. Mendelian randomization in the era of genomewide association studies. Clin Chem. 2010;56(5):723‐728.

   Not in our library10.1373/clinchem.2009.141564

4. 4.

   Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013;42(5):1497‐1501.

   Not in our library10.1093/ije/dyt179

5. 5.

   Freeman G, Cowling BJ, Schooling CM. Power and sample size calculations for Mendelian randomization studies using one genetic instrument. Int J Epidemiol. 2013;42(4):1157‐1163.

   Not in our library10.1093/ije/dyt110

6. 6.
   Use of allele scores as instrumental variables for Mendelian randomization · 2013

   No notice
7. 7.
   Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects · 2015

   No notice
8. 8.
   Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator · 2016

   No notice
9. 9.
   Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression · 2015

   No notice
10. 10.

    Helgadottir A, Gretarsdottir S, Thorleifsson G, et al. Variants with large effects on blood lipids and the role of cholesterol and triglycerides in coronary disease. Nat Genet. 2016;48(6):634‐639.

    Not in our library10.1038/ng.3561

11. 11.
    Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors · 2015

    No notice
12. 12.
    Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods · 2015

    No notice
13. 13.

    Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29(4):722‐729.

    Not in our library10.1093/ije/29.4.722

14. 14.

    Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res. 2007;16(4):309‐330.

    Not in our library10.1177/0962280206077743

15. 15.

    Johnson T. Efficient calculation for multi‐SNP genetic risk scores, The Comprehensive R Archive Network; 2013 http://cran.r‐project.org/web/packages/gtx/vignettes/ashg2012.pdf. Accessed June 14, 2016.

    Not in our library

16. 16.

    Angrist JD, Imbens GW. Two‐stage least squares estimation of average causal effects in models with variable treatment intensity. J Am Stat Assoc. 1995;90(430):431‐442.

    Not in our library

17. 17.
    Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data · 2013

    No notice
18. 18.

    Thompson SG, Sharp SJ. Explaining heterogeneity in meta‐analysis: A comparison of methods. Stat Med. 1999;18(20):2693‐2708.

    Not in our library10.1002/(sici)1097-0258(19991030)18:20<2693::aid-sim235>3.0.co;2-v

19. 19.

    Kolesár M, Chetty R, Friedman J, Glaeser E, Imbens G. Identification and inference with many invalid instruments. J Bus Econ Stat. 2015;33(4):474‐484. http://doi.org.10.1080/07350015.2014.978175

    Not in our library

20. 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):290‐291.

    Not in our library10.1093/aje/kwv017

21. 21.

    Burgess S, Harshfield E. Mendelian randomization to assess causal effects of blood lipids on coronary heart disease: Lessons from the past and applications to the future. Curr Opin Endocrinol Diabetes. 2016;23(2):124‐130.

    Not in our library10.1097/med.0000000000000230

22. 22.

    Burgess S, Freitag DF, Khan H, Gorman DN, Thompson SG. Using multivariable Mendelian randomization to disentangle the causal effects of lipid fractions. PLoS One. 2014;9(10):e108891.

    Not in our library10.1371/journal.pone.0108891

23. 23.

    Willer CJ, Schmidt EM, Sengupta S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274‐1283.

    Not in our library10.1038/ng.2797

24. 24.

    Do R, Willer CJ, Schmidt EM, et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet. 2013;45(11):1345‐1352.

    Not in our library10.1038/ng.2795

25. 25.

    Nikpay M, Goel A, Won HH, et al. A comprehensive 1,000 Genomes‐based genome‐wide association meta‐analysis of coronary artery disease. Nat Genet. 2015;47(10):1121‐1130.

    Not in our library10.1038/ng.3396

26. 26.

    Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low‐density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499‐502.

    Not in our library

27. 27.

    Di Angelantonio E, Sarwar N, Perry P, et al. Major lipids, apolipoproteins, and risk of vascular disease. JAMA. 2009;302(18):1993‐2000.

    Not in our library10.1001/jama.2009.1619

28. 28.

    Fritz MS, Mackinnon DP. Required sample size to detect the mediated effect. Psychol Sci. 2007;18(3):233‐239.

    Not in our library10.1111/j.1467-9280.2007.01882.x

29. 29.

    White J, Sofat R, Hemani G, et al. Plasma urate concentration and risk of coronary heart disease: A Mendelian randomisation analysis. Lancet Diabetes Endocrinol. 2016;4(4):327‐336.

    Not in our library10.1016/s2213-8587(15)00386-1

30. 30.

    Burgess S, Butterworth AS, Thompson JR. Beyond Mendelian randomization: How to interpret evidence of shared genetic predictors. J Clin Epidemiol. 2016;69:208‐216.

    Not in our library10.1016/j.jclinepi.2015.08.001

31. 31.

    Relton CL, Davey Smith G. Is epidemiology ready for epigenetics? Int J Epidemiol. 2012;41(1):5‐9.

    Not in our library10.1093/ije/dys006

32. 32.

    Suhre K, Raffler J, Kastenmuller G. Biochemical insights from population studies with genetics and metabolomics. Arch Biochem Biophys. 2016;589:168‐176.

    Not in our library10.1016/j.abb.2015.09.023

33. 33.

    Kastenmuller G, Raffler J, Gieger C, Suhre K. Genetics of human metabolism: An update. Hum Mol Genet. 2015;24(R1):R93‐R101.

    Not in our library10.1093/hmg/ddv263

34. 34.
    Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic · 2016

    No notice

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