Detecting and correcting for bias in Mendelian randomization analyses using Gene-by-Environment interactions.
Open accessPublished version of a preprint
Abstract
Background: Mendelian randomization has developed into an established method for strengthening causal inference and estimating causal effects, largely due to the proliferation of genome-wide association studies. However, genetic instruments remain controversial as pleiotropic effects can introduce bias into causal estimates. Recent work has highlighted the potential of gene-environment interactions in detecting and correcting for pleiotropic bias in Mendelian randomization analyses. Methods: We introduce MR using Gene-by-Environment interactions (MRGxE) as a framework capable of identifying and correcting for pleiotropic bias, drawing upon developments in econometrics and epidemiology. If an instrument-covariate interaction induces variation in the association between a genetic instrument and exposure, it is possible to identify and correct for pleiotropic effects. The interpretation of MRGxE is similar to conventional summary Mendelian randomization approaches, with a particular advantage of MRGxE being the ability to assess the validity of an individual instrument. Results: We investigate the effect of BMI upon systolic blood pressure (SBP) using data from the UK Biobank and the GIANT consortium using a single instrument (a weighted allelic score). We find MRGxE produces findings in agreement with MR Egger regression in a two-sample summary MR setting, however, association estimates obtained across all methods differ considerably when excluding related participants or individuals of non-European ancestry. This could be a consequence of selection bias, though there is also potential for introducing bias by using a mixed ancestry population. Further, we assess the performance of MRGxE with respect to identifying and correcting for horizontal pleiotropy in a simulation setting, highlighting the utility of the approach even when the MRGxE assumptions are violated. Conclusions: By utilising instrument-covariate interactions within a linear regression framework, it is possible to identify and correct for pleiotropic bias, provided the average magnitude of pleiotropy is constant across interaction covariate subgroups.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The InSIDE assumption would be violated in the case of correlated pleiotropy, which occurs when genetic variants influence a confounder of the exposure and outcome.
“This assumption would be violated in the case of “correlated pleiotropy”, which occurs when genetic variants influence a confounder of the exposure and outcome (and hence there are correlated pleiotropic effects on the exposure and outcome) 88 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [88]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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-citationBowden, 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
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882 · see citing works
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
Mendelian randomization: genetic anchors for causal inference in epidemiological studies
Davey Smith, G., & Hemani, G. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu328 · Cited by 4,677 · see citing works
Self-citationGenetic 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
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption
Hartwig, F. P., Davey Smith, G., & Bowden, J. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx102 · Cited by 3,485 · see citing works
Self-citationInternational Journal of Epidemiology
Rothman, N., Garcia-closas, M., & Hein, D. W. (1973) · Journal of Medical Genetics · doi:10.1136/jmg.10.1.100-a · Cited by 2,902 · 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
A review of instrumental variable estimators for Mendelian randomization
Burgess, S., Small, D. S., & Thompson, S. G. (2015) · Statistical Methods in Medical Research · doi:10.1177/0962280215597579 · Cited by 1,749 · see citing works
Burgess, S., & Thompson, S. G. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu283 · Cited by 1,605 · see citing works
Evaluating the potential role of pleiotropy in Mendelian randomization studies
Hemani, G., Bowden, J., & Davey Smith, G. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy163 · Cited by 1,496 · see citing works
Self-citationCancer epidemiology biomarkers & prevention
Press, H. (1991) · Cited by 1,403 · see citing works
Influence of Weight Reduction on Blood Pressure: A Meta-Analysis of Randomized Controlled Trials
Neter, J. E., Stam, B. E., Kok, F. J., Grobbee, D. E., & Geleijnse, J. M. (2003) · Hypertension · doi:10.1161/01.hyp.0000094221.86888.ae · Cited by 1,391 · see citing works
Conley, T. G., Hansen, C., & Rossi, P. E. (2008) · The Review of Economics and Statistics · doi:10.1162/rest_a_00139 · Cited by 1,189 · see citing works
Triangulation in aetiological epidemiology
Lawlor, D. A., Tilling, K., & Davey Smith, G. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyw314 · Cited by 1,129 · see citing works
Using Geographic Variation in College Proximity to Estimate the Return to Schooling
Card, D. (1993) · National Bureau of Economic Research · doi:10.3386/w4483 · Cited by 1,075 · 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
The Fitting of Straight Lines if Both Variables are Subject to Error
Wald, A. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731868 · Cited by 930 · see citing works
National Bureau of Economic Research, Inc.
Gay, E. F., Adams, T. S., Frey, J. P., Sterrett, J. E., & Laidler, H. W. (1920) · Quarterly Publications of the American Statistical Association · doi:10.1080/15225445.1920.10503790 · Cited by 670 · see citing works
Robust research needs many lines of evidence
Munafò, M. R., & Davey Smith, G. (2018) · Nature · doi:10.1038/d41586-018-01023-3 · Cited by 666 · see citing works
Self-citationPerspectives on Psychological Science
Blase, A. E. K. A. S. L. (2011) · APS observer · Cited by 628 · see citing works
Methodological 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
Use of allele scores as instrumental variables for Mendelian randomization
Burgess, S., & Thompson, S. G. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt093 · Cited by 452 · see citing works
Emdin, C. A., Khera, A. V., Natarajan, P., Klarin, D., Zekavat, S. M., Hsiao, A. J., & Kathiresan, S. (2017) · JAMA · doi:10.1001/jama.2016.21042 · Cited by 378 · see citing works
Chen, L., Davey Smith, G., Harbord, R. M., & Lewis, S. J. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050052 · Cited by 314 · see citing works
Glymour, 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
Change in body mass index and its impact on blood pressure: a prospective population study
Drøyvold, W. B., Midthjell, K., Nilsen, T. I. L., & Holmen, J. (2005) · International Journal of Obesity · doi:10.1038/sj.ijo.0802944 · Cited by 248 · see citing works
Lewis, S. J., & Smith, G. D. (2005) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-05-0196 · Cited by 234 · see citing works
Holmes, M. V., Lange, L. A., Palmer, T., Lanktree, M. B., North, K. E., Almoguera, B., Buxbaum, S., Chandrupatla, H. R., Elbers, C. C., Guo, Y., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.12.014 · Cited by 216 · see citing works
Gene–obesogenic environment interactions in the UK Biobank study
Tyrrell, J., Wood, A. R., Ames, R. M., Yaghootkar, H., Beaumont, R. N., Jones, S. E., Tuke, M. A., Ruth, K. S., Freathy, R. M., Smith, G. D., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw337 · Cited by 208 · see citing works
Justice, A. E., Winkler, T. W., Feitosa, M. F., Graff, M., Fisher, V. A., Young, K. L., Barata, L., Deng, X., Czajkowski, J., Hadley, D., et al. (2017) · Nature Communications · doi:10.1038/ncomms14977 · Cited by 207 · see citing works
Mendelian Randomization for Strengthening Causal Inference in Observational Studies
Smith, G. D. (2010) · Perspectives on Psychological Science · doi:10.1177/1745691610383505 · Cited by 191 · see citing works
Does Greater Adiposity Increase Blood Pressure and Hypertension Risk?
Timpson, N. J., Harbord, R. M., Smith, G. D., Zacho, J., Tybjærg‐Hansen, A., & Nordestgaard, B. G. (2009) · Hypertension · doi:10.1161/hypertensionaha.109.130005 · Cited by 187 · see citing works
Rask‐Andersen, M., Karlsson, T., Ek, W. E., & Johansson, Å. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006977 · Cited by 166 · see citing works
Genotype–covariate interaction effects and the heritability of adult body mass index
Robinson, M. R., English, G., Moser, G., Lloyd‐Jones, L. R., Triplett, M. A., Zhu, Z., Nolte, I. M., van Vliet‐Ostaptchouk, J. V., Snieder, H., Study, T. L. C., et al. (2017) · Nature Genetics · doi:10.1038/ng.3912 · Cited by 149 · see citing works
Apfeltyp erhöht Diabetes- und KHK-Risiko
(2017) · DMW - Deutsche Medizinische Wochenschrift · doi:10.1055/s-0043-103789 · Cited by 142 · see citing works
Davey Smith, G. (2011) · Genes & Nutrition · doi:10.1007/s12263-010-0181-y · Cited by 134 · see citing works
Self-citationPleiotropy-robust Mendelian randomization.
van Kippersluis, H., & Rietveld, C. A. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyx002 · Cited by 125 · see citing works
Genetic variation at CHRNA5-CHRNA3-CHRNB4 interacts with smoking status to influence body mass index
Freathy, R. M., Kazeem, G., Morris, R. W., Johnson, P., Paternoster, L., Ebrahim, S. B. J., Hattersley, A. T., Hill, A., Hingorani, A. D., Holst, J. J., et al. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr077 · Cited by 122 · see citing works
Spiller, W., Davies, N. M., & Palmer, T. M. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyy195 · Cited by 116 · see citing works
Self-citationAlcohol intake and cardiovascular risk factors: A Mendelian randomisation study
Cho, Y., Shin, S.-Y., Won, S., Relton, C. L., Smith, G. D., & Shin, M. (2015) · Scientific Reports · doi:10.1038/srep18422 · Cited by 107 · see citing works
Trends in group inequalities and interindividual inequalities in BMI in the United States, 1993–2012
Krishna, A., Razak, F. A., Lebel, A., Smith, G. D., & Subramanian, S. V. (2015) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.114.100073 · Cited by 59 · see citing works
Taylor, A. E., Lu, F., Carslake, D., Hu, Z., Qian, Y., Liu, S., Chen, J., Shen, H., & Smith, G. D. (2015) · Scientific Reports · doi:10.1038/srep14005 · Cited by 45 · see citing works
Why internal weights should be avoided (not only) in MR-Egger regression
Hartwig, F. P., & Davies, N. M. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw240 · Cited by 38 · see citing works
Small, D. S. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1109.1070 · Cited by 28 · see citing works
Pleiotropy-robust Mendelian RandomizationPreprint
van Kippersluis, H., & Rietveld, C. A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/072603 · Cited by 27 · see citing works
Small, D. S. (2012) · Journal of Statistical Research · Cited by 24 · see citing works
TESTING INSTRUMENT VALIDITY AND IDENTIFICATION WITH INVALID INSTRUMENTS
Kédagni, D. (2018) · Cited by 10 · see citing works
Testing Instrument Validity and Identication with Invalid Instruments
Slichter, D. (2014) · Cited by 9 · see citing works
Self-citationLi, L., Liao, J., Cheung, C. Y.-L., Ikram, M. K., Tai, E. S., Wong, T. Y., & Cheng, C. (2016) · Scientific Reports · doi:10.1038/srep22031 · Cited by 7 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [34]Mackenbach JP. Health and deprivation—inequality and the North In: Townsend P, Phillimore P, Beattie A (eds). Health Policy, Vol. 10. London: Routledge, 1988, p. 207.
Cited by
Sorted by citations
Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229 · see citing works
Evaluating the potential role of pleiotropy in Mendelian randomization studies
Hemani, G., Bowden, J., & Davey Smith, G. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy163 · Cited by 1,496 · see citing works
Self-citationGuidelines 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
Mendelian Randomization: Concepts and Scope
Richmond, R. C., & Davey Smith, G. (2022) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a040501 · Cited by 841 · see citing works
Self-citationBias in Mendelian randomization due to assortative mating
Hartwig, F. P., Davies, N. M., & Davey Smith, G. (2018) · Genetic Epidemiology · doi:10.1002/gepi.22138 · Cited by 99 · see citing works
Mendelian Randomization analysis of the causal effect of adiposity on hospital costs
Dixon, P., Hollingworth, W., Harrison, S., Davies, N. M., & Davey Smith, G. (2020) · Journal of Health Economics · doi:10.1016/j.jhealeco.2020.102300 · Cited by 48 · see citing works
Self-citationHowe, L. J., Tudball, M., Davey Smith, G., & Davies, N. M. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyab208 · Cited by 48 · see citing works
Self-citationHow humans can contribute to Mendelian randomization analyses.
Burgess, S., & Davey Smith, G. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz152 · Cited by 20 · see citing works
Self-citationSpiller, W., Hartwig, F. P., Sanderson, E., Smith, G. D., & Bowden, J. (2020) · doi:10.1101/2020.07.27.20162909 · Cited by 15 · see citing works
Self-citation
- Authors
- Spiller, W., Slichter, D., Bowden, J., & Davey Smith, G.
- Affiliations
- University of Bristol (GB)
- Binghamton University (US)
- Population Health Sciences, University of Bristol, Bristol, UK
- Corresponding author
- Spiller, Wes
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-11-20
- Volume
- 48
- Issue
- 3
- Pages
- 702-712
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC6659360
- OpenAlex
- W2952562325
- Cited by
- 90
- References
- 54
- Citations by year
- 2026: 2 · 2025: 14 · 2024: 19 · 2023: 5 · 2022: 10 · 2021: 9 · 2020: 17 · 2019: 10 · 2018: 3 · 2017: 1
- FWCI
- 5.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Epidemiology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Explore Bristol Research (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- Medical Research Council
- University of Bristol
- MRC Integrative Epidemiology Unit (MC_UU_00011/1, MC_UU_00011/2)
- Wellcome Trust (108902/B/15/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Mendelian randomization, gene-environment interaction, pleiotropy, assessment validity, body mass index, adiposity, systolic blood pressure, causal inference, genetic instruments
- MeSH terms
- Blood Pressure, Computer Simulation, Humans, Obesity, Bias, Body Mass Index, Biological Specimen Banks, Adiposity, Mendelian Randomization Analysis, Genetic Pleiotropy, Gene-Environment Interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
