Interpretation of mendelian randomization using one measure of an exposure that varies over time
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Background: Mendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g., weight measured once between ages 40 and 60). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time. Methods: We propose an approach that emphasises the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example. Results: We show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the liability as induced by a specific genotype that gives rise to the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the liability of exposure is constant over time), as we demonstrate by estimating the effect of BMI measured at different ages on systolic blood pressure. Conclusions: Practitioners should not interpret MR results as timepoint-specific direct or total causal effects, but as the effect of changing the liability that causes the entire exposure trajectory. Estimates of how the effects of a genetic variant on an exposure vary over time are needed to interpret timepoint-specific causal effects.
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RCTs estimate the effect of a particular intervention or treatment over the timeframe of the study, whereas MR estimates the lifetime effects.
“The above example highlights an important difference between RCTs and MR: RCTs estimate the effect of a particular intervention or treatment over the timeframe of the study, whereas MR estimates the lifetime effects.21”
From Mendelian randomization (2022) · cites this paper as [21]
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- Authors
- Morris, T. T., Heron, J., Sanderson, E., Smith, G. D., & Tilling, K.
- Affiliations
- University of Bristol (GB)
- Corresponding author
- Morris, Tim T
- Venue
- International Journal of Epidemiology
- Publisher
- openRxiv
- Published
- 2021-11-21
- Volume
- 51
- Issue
- 6
- Pages
- 1899-1909
- ISSN
- 0300-5771 (ISSN-L)
- PMC
- PMC9749705
- OpenAlex
- W4285733940
- Cited by
- 58
- References
- 42
- Citations by year
- 2026: 11 · 2025: 8 · 2024: 14 · 2023: 15 · 2022: 10
- FWCI
- 7.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Epidemiology (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Bristol Research (University of Bristol) (publishedVersion, cc-by)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- Explore Bristol Research (submittedVersion, cc-by)
- Funders
- University of Bristol (MC_UU_00011/1, MC_UU_00,011/3)
- Simons Institute for the Theory of Computing, University of California Berkeley
- Medical Research Council (MC_UU_00011/3, MC_UU_00011/1, MC_UU_00011)
- 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, exposure trajectories, time-varying exposure, liability model, causal effect estimation, body mass index, systolic blood pressure, genetic variants
- MeSH terms
- Adult, Blood Pressure, Humans, Middle Aged, Causality, Body Mass Index, Mendelian Randomization Analysis
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