Bias due to participant overlap in two‐sample Mendelian randomization
Open accessAbstract
Mendelian randomization analyses are often performed using summarized data. The causal estimate from a one‐sample analysis (in which data are taken from a single data source) with weak instrumental variables is biased in the direction of the observational association between the risk factor and outcome, whereas the estimate from a two‐sample analysis (in which data on the risk factor and outcome are taken from non‐overlapping datasets) is less biased and any bias is in the direction of the null. When using genetic consortia that have partially overlapping sets of participants, the direction and extent of bias are uncertain. In this paper, we perform simulation studies to investigate the magnitude of bias and Type 1 error rate inflation arising from sample overlap. We consider both a continuous outcome and a case‐control setting with a binary outcome. For a continuous outcome, bias due to sample overlap is a linear function of the proportion of overlap between the samples. So, in the case of a null causal effect, if the relative bias of the one‐sample instrumental variable estimate is 10% (corresponding to an F parameter of 10), then the relative bias with 50% sample overlap is 5%, and with 30% sample overlap is 3%. In a case‐control setting, if risk factor measurements are only included for the control participants, unbiased estimates are obtained even in a one‐sample setting. However, if risk factor data on both control and case participants are used, then bias is similar with a binary outcome as with a continuous outcome. Consortia releasing publicly available data on the associations of genetic variants with continuous risk factors should provide estimates that exclude case participants from case‐control samples.
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When many SNPs are used as IVs, each with a very small effect, this can create a non-trivial bias towards the conventional effect estimate, known as weak instrument bias.
“When many SNPs are used as IVs, each with a very small effect, this can create a non-trivial bias towards the conventional effect estimate, known as weak instrument bias43.”
From Mendelian randomization (2022) · cites this paper as [43]
The winner's curse bias is a linear function of the number of individuals included in both samples, so the consequences of a small amount of overlap may not be severe.
“The bias is a linear function of the number of individuals included in both samples, so the consequences of a small amount of overlap may not be severe.94”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [94]
If the authors used the same or similar individuals to estimate the SNP-exposure and SNP-outcome associations, MR estimates could be biased by a form of the winner's curse.
“If the authors used the same or similar individuals to estimate the SNP-exposure and the SNP-outcome associations, MR estimates could be biased by a form of the winner’s curse,94 95 which occurs when the statistically strongest associations—usually using a P value threshold—are selected from the discovery sample.”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [94]
If published summarized association estimates have already been adjusted for a variable causally downstream of the exposure or outcome, collider bias can occur.
“If published summarized association estimates have already been adjusted for a variable causally downstream of the exposure or outcome, collider bias (see Section 7) can occur 38 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [38]
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- Authors
- Burgess, S., Davies, N. M., & Thompson, S. G.
- Affiliations
- University of Cambridge (GB)
- University of Bristol (GB)
- Corresponding author
- Burgess, Stephen
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2016-09-14
- Volume
- 40
- Issue
- 7
- Pages
- 597-608
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC5082560
- OpenAlex
- W2520048741
- Cited by
- 1,624
- References
- 63
- Citations by year
- 2026: 134 · 2025: 234 · 2024: 414 · 2023: 297 · 2022: 181 · 2021: 175 · 2020: 84 · 2019: 65 · 2018: 26 · 2017: 11 · 2016: 3
- FWCI
- 28.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Genetic Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Explore Bristol Research (publishedVersion, cc-by)
- Apollo (University of Cambridge) (acceptedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Apollo
- Funders
- Medical Research Council (G0800270)
- British Heart Foundation (SP/09/002)
- European Research Council (268834)
- European Commission (HEALTH‐F2‐2012‐279233)
- Wellcome Trust (100114)
- University of Bristol (MC_UU_12013/1, MC_UU_12013/9)
- 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
- two-sample Mendelian randomization, sample overlap, bias, case-control data, continuous outcomes, binary outcomes
- MeSH terms
- Humans, Male, Models, Genetic, Risk Factors, Genetic Variation, Bias, Case-Control Studies, Mendelian Randomization Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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