Interpreting findings from Mendelian randomization using the MR-Egger method.
Open accessAbstract
Mendelian randomization-Egger (MR-Egger) is an analysis method for Mendelian randomization using summarized genetic data. MR-Egger consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. While conventional analysis methods for Mendelian randomization assume that all genetic variants satisfy the instrumental variable assumptions, the MR-Egger method is able to assess whether genetic variants have pleiotropic effects on the outcome that differ on average from zero (directional pleiotropy), as well as to provide a consistent estimate of the causal effect, under a weaker assumption-the InSIDE (INstrument Strength Independent of Direct Effect) assumption. In this paper, we provide a critical assessment of the MR-Egger method with regard to its implementation and interpretation. While the MR-Egger method is a worthwhile sensitivity analysis for detecting violations of the instrumental variable assumptions, there are several reasons why causal estimates from the MR-Egger method may be biased and have inflated Type 1 error rates in practice, including violations of the InSIDE assumption and the influence of outlying variants. The issues raised in this paper have potentially serious consequences for causal inferences from the MR-Egger approach. We give examples of scenarios in which the estimates from conventional Mendelian randomization methods and MR-Egger differ, and discuss how to interpret findings in such cases.
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Genetic variants in the IL6R gene region are associated with increased circulating levels of interleukin-6, but they decrease interleukin-6 signalling, leading to opposite directions of association with disease outcomes to those expected based on serum interleukin-6 measurements.
“As an example, although genetic variants in the IL6R gene region are associated with increased circulating levels of interleukin-6, they in fact decrease interleukin-6 signalling, leading to opposite directions of association with disease outcomes to those expected based on serum interleukin-6 measurements 65 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [65]
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Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009) · doi:10.1002/9780470743386 · Cited by 14,225 · see citing works
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · see citing works
Self-citationBorenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2021) · doi:10.1002/9781119558378 · Cited by 9,799 · see citing works
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922 · see citing works
Self-citationMendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050 · see citing works
Self-citationDavey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882 · see citing works
Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology
Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N., & Davey Smith, G. (2008) · Statistics in Medicine · doi:10.1002/sim.3034 · Cited by 4,618 · see citing works
Genetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534 · see citing works
Identification and estimation of local average treatment effects
Imbens, G. W., & Angrist, J. D. (1994) · Econometrica · doi:10.2307/2951620 · Cited by 4,101 · see citing works
Willer (2013) · doi:10.1038/ng.2797 · Cited by 2,983 · see citing works
Identification and Estimation of Local Average Treatment Effects
Angrist, J. D., & Imbens, G. W. (1995) · National Bureau of Economic Research · doi:10.3386/t0118 · Cited by 2,898 · see citing works
An Introduction to Generalized Linear Models.
Chang, P. C., & Dobson, A. J. (1991) · Journal of the American Statistical Association · doi:10.2307/2290548 · Cited by 2,543 · see citing works
A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease
the CARDIoGRAMplusC4D Consortium (2015) · Nature Genetics · doi:10.1038/ng.3396 · Cited by 2,447 · see citing works
Burgess, S., Bowden, J., Fall, T., Ingelsson, E., & Thompson, S. G. (2017) · Epidemiology · doi:10.1097/ede.0000000000000559 · Cited by 2,017 · see citing works
Self-citationA framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization
Bowden, J., Del Greco M, F., Minelli, C., Davey Smith, G., Sheehan, N., & Thompson, J. (2017) · Statistics in Medicine · doi:10.1002/sim.7221 · Cited by 1,887 · see citing works
Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease
Cardiogenics, Schunkert, H., the CARDIoGRAM Consortium, König, I. R., Kathiresan, S., Reilly, M. P., Assimes, T. L., Holm, H., Preuss, M., Stewart, A. F. R., et al. (2011) · Nature Genetics · doi:10.1038/ng.784 · Cited by 1,866 · see citing works
EPIC- InterAct Consortium, Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G., & Thompson, S. G. (2015) · European Journal of Epidemiology · doi:10.1007/s10654-015-0011-z · Cited by 1,803 · see citing works
Self-citationBowden, J., Fabiola Del Greco, M., Minelli, C., Smith, G. D., Sheehan, N. A., & Thompson, J. R. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw220 · Cited by 1,572 · see citing works
(1977) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,548 · see citing works
Mendelian randomization: prospects, potentials, and limitations
Smith, G. D. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh132 · Cited by 1,222 · see citing works
An introduction to instrumental variables for epidemiologists
Greenland, S. (2000) · International Journal of Epidemiology · doi:10.1093/ije/29.4.722 · Cited by 1,156 · see citing works
Burgess, S., Dudbridge, F., & Thompson, S. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6835 · Cited by 1,153 · see citing works
Self-citationDetection and interpretation of shared genetic influences on 42 human traits
Pickrell, J. K., Berisa, T., Liu, J. Z., Ségurel, L., Tung, J. Y., & Hinds, D. A. (2016) · Nature Genetics · doi:10.1038/ng.3570 · Cited by 1,151 · see citing works
Instruments for Causal Inference: An Epidemiologist's Dream?
Hernán, M. A., & Robins, J. M. (2006) · Epidemiology · doi:10.1097/01.ede.0000222409.00878.37 · Cited by 961 · see citing works
Burgess, S., Dudbridge, F., & Thompson, S. G. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv017 · Cited by 945 · see citing works
Self-citationMendelian randomization as an instrumental variable approach to causal inference
Didelez, V., & Sheehan, N. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206077743 · Cited by 923 · see citing works
Common variants associated with plasma triglycerides and risk for coronary artery disease
Do, R., Willer, C. J., Schmidt, E. M., Sengupta, S., Gao, C., Peloso, G. M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., et al. (2013) · Nature Genetics · doi:10.1038/ng.2795 · Cited by 862 · see citing works
Burgess, S. (2014) · International Journal of Epidemiology · doi:10.1093/ije/dyu005 · Cited by 640 · see citing works
Self-citationMethodological Challenges in Mendelian Randomization
VanderWeele, T. J., Tchetgen Tchetgen, E. J., Cornelis, M., & Kraft, P. (2014) · Epidemiology · doi:10.1097/ede.0000000000000081 · Cited by 555 · see citing works
An Introduction to Generalized Linear Models, Second Edition
Dobson, A. J. (2001) · Chapman & Hall/CRC texts in statistical science/Texts in statistical science · doi:10.1201/9781420057683 · Cited by 420 · see citing works
Jones, S. E., Tyrrell, J., Wood, A. R., Beaumont, R. N., Ruth, K. S., Tuke, M. A., Yaghootkar, H., Hu, Y., Teder-Laving, M., Hayward, C., et al. (2016) · PLOS Genetics · doi:10.1371/journal.pgen.1006125 · Cited by 388 · see citing works
Height, body mass index, and socioeconomic status: mendelian randomisation study in UK Biobank
Tyrrell, J., Jones, S. E., Beaumont, R. N., Astley, C. M., Lovell, R., Yaghootkar, H., Tuke, M. A., Ruth, K. S., Freathy, R. M., Hirschhorn, J. N., et al. (2016) · BMJ · doi:10.1136/bmj.i582 · Cited by 372 · see citing works
Association of Lipid Fractions With Risks for Coronary Artery Disease and Diabetes
White, J., Swerdlow, D. I., Preiss, D., Fairhurst-Hunter, Z., Keating, B. J., Asselbergs, F. W., Sattar, N., Humphries, S. E., Hingorani, A. D., & Holmes, M. V. (2016) · JAMA Cardiology · doi:10.1001/jamacardio.2016.1884 · Cited by 293 · see citing works
Use of Mendelian randomisation to assess potential benefit of clinical intervention.
Burgess, S., Butterworth, A., Malarstig, A., & Thompson, S. G. (2012) · BMJ · doi:10.1136/bmj.e7325 · Cited by 292 · see citing works
Self-citationGlymour, M. M., Tchetgen Tchetgen, E. J., & Robins, J. M. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kwr323 · Cited by 292 · see citing works
Burgess, S., & Thompson, S. G. (2015) · Chapman & Hall/CRC eBooks · doi:10.1201/b18084 · Cited by 266 · see citing works
Self-citationKang, H., Zhang, A., Cai, T. T., & Small, D. S. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.994705 · Cited by 265 · see citing works
Mendelian Randomization: Methods for Using Genetic Variants in Causal Estimation
Burgess, S., & Thompson, S. G. (2015) · Cited by 239 · see citing works
Self-citationCommentary: How to Report Instrumental Variable Analyses (Suggestions Welcome)
Swanson, S. A., & Hernán, M. A. (2013) · Epidemiology · doi:10.1097/ede.0b013e31828d0590 · Cited by 197 · see citing works
Corbin, L. J., Richmond, R. C., Wade, K. H., Burgess, S., Bowden, J., Smith, G. D., & Timpson, N. J. (2016) · Diabetes · doi:10.2337/db16-0418 · Cited by 184 · see citing works
Self-citationInstrumental Variable Estimators for Binary Outcomes
Clarke, P. S., & Windmeijer, F. A. G. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2012.734171 · Cited by 149 · see citing works
On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments
Windmeijer, F., Farbmacher, H., Davies, N., & Davey Smith, G. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1498346 · Cited by 144 · see citing works
Identification and Inference With Many Invalid Instruments
Kolesár, M., Chetty, R., Friedman, J. N., Glaeser, E. L., & Imbens, G. W. (2014) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2014.978175 · Cited by 140 · see citing works
Thomas, D. C., Lawlor, D. A., & Thompson, J. R. (2007) · Annals of Epidemiology · doi:10.1016/j.annepidem.2006.12.005 · Cited by 138 · see citing works
Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.
White, J., Sofat, R., Hemani, G., Shah, T., Engmann, J., Dale, C., Shah, S., Kruger, F. A., Giambartolomei, C., Swerdlow, D. I., et al. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00386-1 · Cited by 132 · see citing works
Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors
Burgess, S., Butterworth, A. S., & Thompson, J. R. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.08.001 · Cited by 92 · see citing works
Self-citationThe causal effect of malaria on stunting: a Mendelian randomization and matching approach
Kang, H., Kreuels, B., Adjei, O., Krumkamp, R., May, J., & Small, D. S. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt116 · Cited by 77 · see citing works
Burgess, S. (2013) · Statistics in Medicine · doi:10.1002/sim.5871 · Cited by 72 · see citing works
Self-citationBurgess, S., Bowden, J., Dudbridge, F., & Thompson, S. G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03729 · Cited by 63 · see citing works
Self-citationDetecting invalid instruments using L1-GMM
Han, C. (2008) · Economics Letters · doi:10.1016/j.econlet.2008.09.004 · Cited by 51 · see citing works
Bonilla, C., Lewis, S. J., Martin, R. M., Donovan, J., Hamdy, F. C., Neal, D. E., Eeles, R. A., Easton, D. F., Kote‐Jarai, Z., Olama, A. A. A., et al. (2016) · BMC Medicine · doi:10.1186/s12916-016-0602-x · Cited by 46 · see citing works
Detection and interpretation of shared genetic influences on 40 human traitsPreprint
Pickrell, J. K., Berisa, T., Ségurel, L., Tung, J. Y., & Hinds, D. A. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/019885 · Cited by 23 · see citing works
On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments
Windmeijer, F. A. G., Farbmacher, H., Davies, N. M., & Smith, G. D. (2018) · Figshare · doi:10.6084/m9.figshare.6843794 · Cited by 21 · see citing works
Plasma urate and coronary heart disease: fingerprint match, but no smoking gun
Burgess, S. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00425-8 · Cited by 2 · see citing works
Self-citationExplaining heterogeneity in meta-analysis: a comparison of methods
Thompson, S. G., & Sharp, S. J. (1999) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19991030)18:20
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [40]Huber PJ Robust statistics 2011 Berlin Springer
Cited by
Sorted by citations
Guidelines for performing Mendelian randomization investigations
Burgess, S., Davey Smith, G., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., et al. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.2 · Cited by 1,273 · see citing works
Self-citationChoi, K. W., Chen, C.-Y., Stein, M. B., Klimentidis, Y. C., Wang, M.-J., Koenen, K. C., Smoller, J. W., & Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.4175 · Cited by 744 · see citing works
Distinguishing genetic correlation from causation across 52 diseases and complex traits
O’Connor, L. J., & Price, A. L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0255-0 · Cited by 394 · see citing works
Modifiable pathways in Alzheimer’s disease: Mendelian randomisation analysis
Larsson, S. C., Traylor, M., Malik, R., Dichgans, M., Burgess, S., & Markus, H. S. (2017) · BMJ · doi:10.1136/bmj.j5375 · Cited by 382 · see citing works
Self-citationLarge-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk
Walker, K. A., Chen, J., Zhang, J., Fornage, M., Yang, Y., Zhou, L., Grams, M. E., Tin, A., Daya, N., Hoogeveen, R. C., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00064-0 · Cited by 212 · see citing works
Burgess, S., Thompson, D. J., Rees, J. M. B., Day, F. R., Perry, J. R., & Ong, K. K. (2017) · Genetics · doi:10.1534/genetics.117.300191 · Cited by 197 · see citing works
Self-citationRosoff, D. B., Smith, G. D., & Lohoff, F. W. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.3554 · Cited by 186 · see citing works
Björnson, E., Adiels, M., Taskinen, M.-R., Burgess, S., Rawshani, A., Borén, J., & Packard, C. J. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad337 · Cited by 183 · see citing works
Self-citationWalker, K. A., Chen, J., Shi, L., Yang, Y., Fornage, M., Zhou, L., Schlosser, P., Surapaneni, A., Grams, M. E., Duggan, M. R., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.adf5681 · Cited by 148 · see citing works
Brower, M. A., Hai, Y., Jones, M. R., Guo, X., Chen, Y. D. I., Rotter, J. I., Krauss, R. M., Legro, R. S., Azziz, R., & Goodarzi, M. O. (2019) · Human Reproduction · doi:10.1093/humrep/dey343 · Cited by 115 · see citing works
Korologou-Linden, R., Bhatta, L., Brumpton, B. M., Howe, L. D., Millard, L. A. C., Kolaric, K., Ben-Shlomo, Y., Williams, D. M., Smith, G. D., Anderson, E. L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32183-6 · Cited by 71 · see citing works
Wu, B.-S., Zhang, Y.-R., Li, H.-Q., Kuo, K., Chen, S.-D., Dong, Q., Liu, Y., & Yu, J.-T. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01599-x · Cited by 69 · see citing works
Impact of Cortisol on Reduction in Muscle Strength and Mass: A Mendelian Randomization Study
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O’Loughlin, J., Casanova, F., Jones, S. E., Hagenaars, S. P., Beaumont, R. N., Freathy, R. M., Watkins, E. R., Vetter, C., Rutter, M. K., Cain, S. W., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01157-3 · Cited by 44 · see citing works
Depression and Osteoporosis: A Mendelian Randomization Study
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Schooling, C., Zhao, J., Au Yeung, S., & Leung, G. (2020) · eLife · doi:10.7554/elife.58567 · Cited by 39 · see citing works
Zhang, L., Zhuang, Z., Zhang, G., Huang, T., & Fan, D. (2022) · BMC Neurology · doi:10.1186/s12883-021-02522-z · Cited by 31 · see citing works
Haworth, S., Kho, P. F., Holgerson, P. L., Hwang, L.-D., Timpson, N. J., Rentería, M. E., Johansson, I., & Cuellar-Partida, G. (2021) · European Journal of Human Genetics · doi:10.1038/s41431-020-00734-4 · Cited by 29 · see citing works
Zhao, J., Pan, X., Hao, D., Zhao, Y., Chen, Y., Zhou, S., Peng, H., & Zhuang, Y. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1190230 · Cited by 20 · see citing works
He, Y., Chen, C.-L., He, J., & Liu, S.-D. (2023) · World Journal of Gastroenterology · doi:10.3748/wjg.v29.i44.5872 · Cited by 10 · see citing works
Wang, F., Liu, Y.-L., Jiang, C.-H., Wu, H.-Y., Jin, J., Sun, Y.-W., He, Z.-X., Kang, L., & Fang, X. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.09.030 · Cited by 8 · see citing works
Xiao, J., Zhou, X., Xia, T., Zhang, W., Xing, X., Zhang, Y., & Xiong, L. (2025) · European Spine Journal · doi:10.1007/s00586-025-08794-0 · Cited by 6 · see citing works
Yang, L., Xu, M., Gao, X., Liu, J., Zhang, D., Zhang, Z., Ye, Z., Wen, J., & Liu, P. (2024) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2508292 · Cited by 6 · see citing works
Wang, Z., Zhu, H., Chen, L., Gan, C., Min, W., Xiao, J., Zou, Z., & He, Y. (2024) · Journal of Attention Disorders · doi:10.1177/10870547241264660 · Cited by 4 · see citing works
The hidden genetic and microbial networks connecting neuropsychiatric and digestive disorders
Xiong, J., Xu, T., Wang, Z., Huang, Y., Zhang, S., Yang, G., Yang, J., Gao, S., Wang, T., Jian, X., et al. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.07.029 · Cited by 3 · see citing works
“…effects and assess robustness to potential assumption violations, particularly horizontal pleiotropy (violation of assumption 3), five complementary MR methods were applied, including inverse-variance weighted (IVW), weighted median, weighted mode, simple mode, and MR-Egger regression[30 – 32].”
- Authors
- Burgess, S., & Thompson, S. G.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Burgess, Stephen
- Venue
- European Journal of Epidemiology
- Published
- 2017-05
- Volume
- 32
- Issue
- 5
- Pages
- 377-389
- ISSN
- 0393-2990 (ISSN-L)
- 1573-7284
- PMC
- PMC5506233
- OpenAlex
- W2614566443
- Cited by
- 4,571
- References
- 55
- Citations by year
- 2026: 430 · 2025: 1101 · 2024: 1621 · 2023: 693 · 2022: 312 · 2021: 196 · 2020: 119 · 2019: 55 · 2018: 33 · 2017: 5
- FWCI
- 39.11
- Type
- journal article
- Language
- en
- Update notices
- correction (2017-06-29)
- Open access
- hybrid
- doi.org
- Locations
- European Journal of Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Wellcome Trust/Royal Society (204623/Z/16/Z)
- British Heart Foundation (CH/12/2/29428)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- MR-Egger, Mendelian randomization, causal effect estimation, type I error, sensitivity analysis
- MeSH terms
- Data Interpretation, Statistical, Humans, Models, Biological, Random Allocation, Risk Factors, Genetic Variation, Mendelian Randomization Analysis, Genetic Pleiotropy
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