A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization
Open accessAbstract
Mendelian randomization (MR) uses genetic data to probe questions of causality in epidemiological research, by invoking the Instrumental Variable (IV) assumptions. In recent years, it has become commonplace to attempt MR analyses by synthesising summary data estimates of genetic association gleaned from large and independent study populations. This is referred to as two‐sample summary data MR. Unfortunately, due to the sheer number of variants that can be easily included into summary data MR analyses, it is increasingly likely that some do not meet the IV assumptions due to pleiotropy. There is a pressing need to develop methods that can both detect and correct for pleiotropy, in order to preserve the validity of the MR approach in this context. In this paper, we aim to clarify how established methods of meta‐regression and random effects modelling from mainstream meta‐analysis are being adapted to perform this task. Specifically, we focus on two contrastin g approaches: the Inverse Variance Weighted (IVW) method which assumes in its simplest form that all genetic variants are valid IVs, and the method of MR‐Egger regression that allows all variants to violate the IV assumptions, albeit in a specific way. We investigate the ability of two popular random effects models to provide robustness to pleiotropy under the IVW approach, and propose statistics to quantify the relative goodness‐of‐fit of the IVW approach over MR‐Egger regression. © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd
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Cochran's Q and I2 statistics can be used to assess evidence of heterogeneity of causal effects estimated by each of the genetic variants.
“Cochran’s Q and I2 statistics can be used to assess evidence of heterogeneity of causal effects estimated by each of the genetic variants.100”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [100]
Differences between the two samples in population characteristics such as age, sex, and socio-economic background can affect not only the interpretation of causal estimates, but also the validity of causal inferences.
“Alternatively, the two samples could differ substantially according to population characteristics such as age, sex, socio-economic background, and so on 32 . Such differences can affect not only the interpretation of causal estimates, but also the validity of causal inferences 33 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [32]
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- Authors
- Bowden, J., Del Greco M, F., Minelli, C., Davey Smith, G., Sheehan, N., & Thompson, J.
- Affiliations
- University of Bristol (GB)
- Center for Biomedicine EURAC research Bolzano Italy
- Imperial College London (GB)
- MRC Integrative Epidemiology Unit University of Bristol U.K.
- University of Leicester (GB)
- Corresponding author
- Bowden, Jack
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2017-01-23
- Volume
- 36
- Issue
- 11
- Pages
- 1783-1802
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- PMC
- PMC5434863
- OpenAlex
- W2581245397
- Cited by
- 1,887
- References
- 34
- Citations by year
- 2026: 144 · 2025: 363 · 2024: 537 · 2023: 338 · 2022: 171 · 2021: 131 · 2020: 93 · 2019: 47 · 2018: 38 · 2017: 23
- FWCI
- 36.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Statistics in Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Leicester Research Archive (University of Leicester) (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Open Research (University of Surrey) (submittedVersion)
- Funders
- MRC Methodology Research Fellowship (MR/N501906/1)
- MRC Integrative Epidemiology Unit at the University of Bristol (MCUU12013/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- pleiotropy, random effects models, meta-regression, goodness-of-fit statistics, causal inference
- MeSH terms
- Data Interpretation, Statistical, Humans, Meta-Analysis as Topic, Models, Statistical, Mendelian Randomization Analysis, Genetic Pleiotropy
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