Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators.
Open accessAbstract
Mendelian randomization (MR) is a method for estimating the causal relationship between an exposure and an outcome using a genetic factor as an instrumental variable (IV) for the exposure. In the traditional MR setting, data on the IV, exposure, and outcome are available for all participants. However, obtaining complete exposure data may be difficult in some settings, due to high measurement costs or lack of appropriate biospecimens. We used simulated data sets to assess statistical power and bias for MR when exposure data are available for a subset (or an independent set) of participants. We show that obtaining exposure data for a subset of participants is a cost-efficient strategy, often having negligible effects on power in comparison with a traditional complete-data analysis. The size of the subset needed to achieve maximum power depends on IV strength, and maximum power is approximately equal to the power of traditional IV estimators. Weak IVs are shown to lead to bias towards the null when the subsample is small and towards the confounded association when the subset is relatively large. Various approaches for confidence interval calculation are considered. These results have important implications for reducing the costs and increasing the feasibility of MR studies.
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Several large consortia have made summarized genetic association estimates publicly available for millions of variants.
“Several large consortia have made such estimates publicly available for millions of variants 30, 35, 36 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [36]
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- Authors
- Pierce, B. L., & Burgess, S.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Pierce, B. L.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-07-17
- Volume
- 178
- Issue
- 7
- Pages
- 1177-1184
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- PMC
- PMC3783091
- OpenAlex
- W2151872224
- Cited by
- 1,389
- References
- 18
- Citations by year
- 2026: 90 · 2025: 221 · 2024: 378 · 2023: 204 · 2022: 123 · 2021: 121 · 2020: 64 · 2019: 44 · 2018: 57 · 2017: 22 · 2016: 38 · 2015: 15 · 2014: 10 · 2013: 1
- FWCI
- 14.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- American Journal of Epidemiology (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Defense (W81XWH)
- Wellcome Trust (100114)
- National Institute for Health and Care Research (NF-SI-0512-10165)
- British Heart Foundation (SP/08/007/23628, RG/08/014/24067)
- National Institutes of Health (W81XWH, CA014599)
- Directorate for Biological Sciences
- Medical Research Council (MR/L003120/1)
- National Cancer Institute (P30CA014599)
- National Institute of Environmental Health Sciences (R01 ES02050)
- 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, instrumental variable estimation, subsampling, statistical power, estimation bias, weak instruments, confounding, confidence interval estimation
- MeSH terms
- Computer Simulation, Humans, Sample Size, Mendelian Randomization Analysis
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