Mendelian randomisation approaches to the study of prenatal exposures: A systematic review
Open accessAbstract
Background: Mendelian randomisation (MR) designs apply instrumental variable techniques using genetic variants to study causal effects. MR is increasingly used to evaluate the role of maternal exposures during pregnancy on offspring health. Objectives: We review the application of MR to prenatal exposures and describe reporting of methodologic challenges in this area. Data sources: We searched PubMed, EMBASE, Medline Ovid, Cochrane Central, Web of Science, and Google Scholar. Study selection and data extraction: Eligible studies met the following criteria: (a) a maternal pregnancy exposure; (b) an outcome assessed in offspring of the pregnancy; and (c) a genetic variant or score proposed as an instrument or proxy for an exposure. Synthesis: We quantified the frequency of reporting of MR conditions stated, techniques used to examine assumption plausibility, and reported limitations. Results: Forty‐three eligible studies were identified. When discussing challenges or limitations, the most common issues described were known potential biases in the broader MR literature, including population stratification (n = 29), weak instrument bias (n = 18), and certain types of pleiotropy (n = 30). Of 22 studies presenting point estimates for the effect of exposure, four defined their causal estimand. Twenty‐four studies discussed issues unique to prenatal MR, including selection on pregnancy (n = 1) and pleiotropy via postnatal exposure (n = 10) or offspring genotype (n = 20). Conclusions: Prenatal MR studies frequently discuss issues that affect all MR studies, but rarely discuss problems specific to the prenatal context, including selection on pregnancy and effects of postnatal exposure. Future prenatal MR studies should report and attempt to falsify their assumptions, with particular attention to issues specific to prenatal MR. Further research is needed to evaluate the impacts of biases unique to prenatal MR in practice.
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Published evidence from Mendelian randomisation studies is often hampered by inadequate reporting.
“but the published evidence is often hampered by inadequate reporting”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [24]
The quality of reporting of Mendelian randomisation studies has been inconsistent.
“As a result, the quality of reporting of these studies has been inconsistent.”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [24]
Little consensus exists around the reporting of Mendelian randomisation studies.
“Despite the growth in MR applications and methods and the increasing relevance of MR findings, little consensus exists around the reporting of MR studies.”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [24]
Many reports of Mendelian randomisation studies do not clearly state or examine the various assumptions of Mendelian randomisation methods.
“Empirical evidence22 23 24 indicates that many reports of MR studies do not clearly state or examine the various assumptions of MR methods”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [24]
Many reports of Mendelian randomisation studies report insufficient details on the data sources.
“and report insufficient details on the data sources”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [24]
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Skrivankova, V. W., Richmond, R. C., Woolf, B. A. R., Yarmolinsky, J., Davies, N. M., Swanson, S. A., VanderWeele, T. J., Higgins, J. P. T., Timpson, N. J., Dimou, N., et al. (2021) · JAMA · doi:10.1001/jama.2021.18236 · Cited by 3,994 · see citing works
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Self-citationCausal inference with observational data: the need for triangulation of evidence
Hammerton, G., & Munafò, M. R. (2021) · Psychological Medicine · doi:10.1017/s0033291720005127 · Cited by 262 · see citing works
“Diemer and colleagues (Diemer, Labrecque, Neumann, Tiemeier, & Swanson, 2020) describe the reporting of methodological limitations of MR studies in the context of prenatal exposure research and find that weak instrument bias is reported less often as a potential limitation than pleiotropy or…”
BackgroundYang, Q., Sanderson, E., Tilling, K., Borges, M. C., & Lawlor, D. A. (2019) · doi:10.1101/19009605 · Cited by 85 · see citing works
- Authors
- Diemer, E. W., Labrecque, J. A., Neumann, A., Tiemeier, H., & Swanson, S. A.
- Affiliations
- Erasmus MC (NL)
- Jewish General Hospital (CA)
- Harvard University (US)
- Corresponding author
- Diemer, Elizabeth W.
- Venue
- Paediatric and Perinatal Epidemiology
- Publisher
- Wiley
- Published
- 2020-08-11
- Volume
- 35
- Issue
- 1
- Pages
- 130-142
- ISSN
- 0269-5022 (ISSN-L)
- 1365-3016
- PMC
- PMC7891574
- OpenAlex
- W3048876172
- Cited by
- 21
- References
- 106
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 4 · 2023: 2 · 2022: 5 · 2021: 4
- FWCI
- 0.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Paediatric and Perinatal Epidemiology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, cc-by-nc-nd)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, cc-by-nc-nd)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (024.001.003)
- H2020 Marie Skłodowska-Curie Actions (721567)
- 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, prenatal exposure, offspring health, causal inference, population stratification, weak instrument bias, pleiotropy, postnatal exposure, reporting quality, genetic instruments
- MeSH terms
- Female, Humans, Pregnancy, Genetic Variation, Bias, Causality, Mendelian Randomization Analysis
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