Credible Mendelian Randomization Studies: Approaches for Evaluating the Instrumental Variable Assumptions
Open accessAbstract
As with other instrumental variable (IV) analyses, Mendelian randomization (MR) studies rest on strong assumptions. These assumptions are not routinely systematically evaluated in MR applications, although such evaluation could add to the credibility of MR analyses. In this article, the authors present several methods that are useful for evaluating the validity of an MR study. They apply these methods to a recent MR study that used fat mass and obesity-associated (FTO) genotype as an IV to estimate the effect of obesity on mental disorder. These approaches to evaluating assumptions for valid IV analyses are not fail-safe, in that there are situations where the approaches might either fail to identify a biased IV or inappropriately suggest that a valid IV is biased. Therefore, the authors describe the assumptions upon which the IV assessments rely. The methods they describe are relevant to any IV analysis, regardless of whether it is based on a genetic IV or other possible sources of exogenous variation. Methods that assess the IV assumptions are generally not conclusive, but routinely applying such methods is nonetheless likely to improve the scientific contributions of MR studies.
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Overidentification tests, such as the Sargan test, leverage the expectation that if all proposed IVs are valid, they should deliver identical IV effect estimates.
“These tests, such as the Sargan test,66 leverage the expectation that if all proposed IVs are valid, they should deliver identical IV effect estimates.”
From Mendelian randomization (2022) · cites this paper as [66]
Another relatively straightforward joint test of all assumptions comes from comparing the effect estimate with that obtained by use of a more traditional confounding-adjustment approach.
“Another relatively straightforward joint test of all assumptions comes from comparing the effect estimate with that obtained by use of a more traditional confounding-adjustment approach.73”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [73]
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- Authors
- Glymour, M. M., Tchetgen Tchetgen, E. J., & Robins, J. M.
- Affiliations
- Harvard University (US)
- Corresponding author
- Glymour, M. M.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-01-12
- Volume
- 175
- Issue
- 4
- Pages
- 332-339
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- PMC
- PMC3366596
- OpenAlex
- W2100190891
- Cited by
- 292
- References
- 32
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 34 · 2023: 19 · 2022: 18 · 2021: 16 · 2020: 15 · 2019: 14 · 2018: 25 · 2017: 20 · 2016: 25 · 2015: 22 · 2014: 22 · 2013: 24 · 2012: 7
- FWCI
- 22.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Epidemiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r37ai032475-23)
- National Institute of Mental Health (RC4MH092707, MH092707-01)
- National Institute of Allergy and Infectious Diseases (R37-AI032475)
- National Institute of Environmental Health Sciences (R21ES019712, ES019712-01)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Mendelian randomization, assessment validity, FTO, obesity, mental illness, genetic instruments, causal inference
- MeSH terms
- Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Mental Disorders, Data Interpretation, Statistical, Evaluation Studies as Topic, Genetic Markers, Humans, Obesity, Proteins, Reproducibility of Results, Epidemiologic Research Design, Bias, Causality, Confounding Factors, Epidemiologic, Body Mass Index, Mendelian Randomization Analysis
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