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Cited by 19Genetic Associations and EpidemiologyAdvanced Causal Inference TechniquesStatistical Methods in Epidemiology

Using Genetic Instruments to Estimate Interactions in Mendelian Randomization Studies

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North, Teri-Louise, Davies, Neil M., Harrison, Sean, Carter, Alice R., Hemani, Gibran, Sanderson, Eleanor, Tilling, Kate, Howe, Laura D.·2019·Epidemiology
DOI 10.1097/ede.0000000000001096

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Abstract

ABSTRACT BACKGROUND The interactive effect of two exposures on an outcome can be confounded. We demonstrate the use of Mendelian Randomization (MR) to estimate unconfounded additive interactions. METHODS Using simulation, we test an extension to multivariable MR using two-stage least squares to estimate the additive interaction between two continuous exposures on a continuous outcome, including scenarios where one exposure has a causal effect on the other (mediation). The interaction parameters were set to be one third of the main effect’s parameters to impose a limit on the variance explained by interaction terms. We compare the performance of the two-stage least squares estimator to a Factorial MR design, in which genetic risk scores for each exposure are dichotomised to create four groups, akin to a factorial randomized controlled trial. As an illustrative example, we apply factorial MR and the 2SLS estimator to the interactive effect of education and BMI on systolic blood pressure in UK Biobank. RESULTS Our simulations demonstrate that factorial MR has very low statistical power; 5-7% at N=50,000 and 8-23% at N=500,000 across the range of parameters tested. The two-stage least squares estimator had higher power to detect interactions than factorial MR and a lower type I error. For N=500,000, the two-stage least squares estimator had a power ranging from 29.7-92.9% and a type I error ranging from 4-6%. 95% Monte Carlo confidence intervals suggested that the estimator was unbiased to a reasonable degree of accuracy at this sample size. In comparison, the power at N=50,000 was 7-55%. CONCLUSIONS A two-stage least squares estimator using genetic risk scores for each exposure is a more powerful alternative for detecting an unconfounded additive interaction of two exposures on an outcome than existing approaches that rely on dichotomising genetic risk scores, but it requires a large sample size and instruments of adequate strength.

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  • Non-linear Mendelian randomization has supported the J-shaped relationship between BMI and mortality risk, although it has also suggested the J-shape could be caused by the relationship differing for ever-smokers and never-smokers.

    “Non-linear MR has supported this, although it has also suggested that the J-shape could be caused by the relationship between BMI and mortality risk differing for ever-smokers [G] and never-smokers.109”

    From Mendelian randomization (2022) · cites this paper as [109]

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Research works printed but not linked: 4

  1. Wooldridge JM. Chapter 6: Additional Single-Equation Topics. Econometric Analysis of Cross Section and Panel Data. Second Edition ed. Cambridge, Massachusetts: The MIT Press; 2010. p. 133- 4.
  2. Fox J, Weisberg S. An {R} Companion to Applied Regression, Second Edition. Thousand Oaks, CA: Sage; 2011.
  3. Sanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings. bioRxiv. 2018.
  4. Brookes ST, Whitley E, Peters TJ, Mulheran PA, Egger M, Davey Smith G. Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives. Health technology assessment (Winchester, England). 2001;5(33):1-56.

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Authors
North, T.-L., Davies, N. M., Harrison, S., Carter, A. R., Hemani, G., Sanderson, E., Tilling, K., & Howe, L. D.
Affiliations
University of Bristol (GB)
ORCID
Venue
Epidemiology
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2019-11
Volume
30
Issue
6
Pages
e33-e35
ISSN
1044-3983 (ISSN-L)
DOI
10.1097/ede.0000000000001096
OpenAlex
W2970981400
Cited by
19
References
24
Citations by year
2025: 2 · 2024: 2 · 2023: 7 · 2022: 3 · 2021: 3 · 2020: 2
FWCI
1.21
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Epidemiology (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • Bristol Research (University of Bristol) (publishedVersion, other-oa)
  • Explore Bristol Research (submittedVersion, other-oa)
Funders
  • Wellcome Trust (208806/Z/17/Z)
  • University of Bristol (MC_UU_00011/1, MC_UU_00,011/3)
  • Medical Research Council (MR/M020894, MC_UU_00011/3, MC_UU_00011/1, MR/M020894/1, MC_UU_00011, MC_UU_00011/1, MC_UU_00011/3)
  • Economic and Social Research Council (ES/N000757/1)
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, multivariable Mendelian randomization, interaction estimation, polygenic risk score, two-stage least squares, factorial design, statistical power, type I error, mediation, additive interaction, genetic instruments, simulation study, causal inference, exposure-outcome association, sample size requirements
MeSH terms
Computer Simulation, Humans, Epidemiologic Research Design, Causality, Multivariate Analysis, Least-Squares Analysis, Genetic Predisposition to Disease, Mendelian Randomization Analysis

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