Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique.
Open accessAbstract
Mendelian randomization studies are often performed in an instrumental variables framework, using germline genetic variants as instruments for modifiable disease risk factors or exposures.1–3 Mendelian randomization analysis depends on assuming that the genetic variants: (i) are associated with the exposure (the relevance assumption); (ii) have no common cause with the outcome (the independence assumption); and (iii) have effects on the outcome that are solely mediated by the exposure (the exclusion restriction assumption).1–3 Summary data Mendelian randomization refers to methods which use summary-level instrument-exposure and instrument-outcome association results (typically, per-allele regression coefficients and standard errors) to obtain causal effect estimates. Two-sample Mendelian randomization refers to the application of Mendelian randomization methods to summary association results estimated in non-overlapping sets of individuals. These data can be obtained from the published literature, typically from summary results provided by consortia of genome-wide association studies (GWAS), or estimated directly from individual-level participant data.4 Recent examples include studies evaluating the causal effects of adiposity-related traits on risk of breast, ovarian, prostate, lung and colorectal cancers,5 of body mass index on type 2 diabetes6 and of telomere length on several health outcomes.7 As with Mendelian randomization in general, two-sample Mendelian randomization is analogous to methods originally developed in econometrics.8,9 Recent developments in two-sample Mendelian randomization are based on methods originally developed for meta-analysis.10,11 Scatter plots of all empirical Mendelian randomization studies in PubMed from 1 January 2011 to 24 October 2016. Left panel: absolute number of one-sample (dotted line) and subsample and/or two-sample Mendelian randomization studies (solid line). Right panel: proportion of subsample and/or two-sample Mendelian randomization studies (among all one-sample and subsample and/or two-sample studies). The dotted line indicates the 50% value. Even though the most natural application of summary data Mendelian randomization methods is in the two-sample setting (i.e. when instrument-exposure and instrument-outcome associations were estimated in non-overlapping sets of individuals), it is possible in principle to use summary data methods in the one-sample context – i.e. when instrument-exposure and instrument-outcome associations are estimated in the same sample. However, summary data Mendelian randomization analyses using instrument-exposure and instrument-outcome associations in the same sample or in partially overlapping samples may be prone to weak instrument bias towards the exposure-outcome estimate that would obtained using conventional methods (typically a regression of the outcome on the exposure). Therefore, using instrument-exposure and instrument-outcome associations estimated in non-overlapping samples is preferable.14 However, given that many two-sample Mendelian randomization applications use summary data from large GWAS consortia, it is possible that in many cases the instrument-exposure and instrument-outcome datasets partially overlap due to studies participating in both consortia; and detecting if this occurs and to what degree may depend on careful assessment of the description of the studies included in each consortium. The aims of this paper are to highlight the importance of harmonizing the genetic instrumental variables used for estimating the instrument-exposure and instrument-outcome associations, and to propose steps for doing this and checking the quality of the harmonization process in two-sample Mendelian randomization. In a paper recently published in the IJE, the general issue of data harmonization is discussed.15 Appropriate data harmonization is clearly essential when combining two or more independently generated datasets. This is particularly true for two-sample Mendelian randomization, because GWAS results rarely have harmonized effect (or coded) alleles. In genetic association studies, it is often assumed that genetic variants have additive (or per-allele) effects, which corresponds to coding the genotypes numerically according to the number of copies of one of the alleles. So, if a given variant was coded as AA = 0, AC = 1 and CC = 2 (i.e. according to the number of copies of the C allele), then C is termed the effect allele, and A the other (or non-coded, or baseline) allele. Table 1 provides an overview of the steps typically required to harmonize datasets of summary results of genetic associations for two-sample Mendelian randomization, based on the guidelines provided by Fortier and colleagues.15. Below, we will focus on two-sample Mendelian randomization using summary results from GWAS consortia. Overview of the data harmonization process for two-sample Mendelian randomization applications, based on the guidelines provid
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Empirical investigations in European populations have shown that associations between genetic variants and many traits are no stronger than would be expected due to chance alone.
“Empirical investigations in European populations have shown that associations between genetic variants and many traits are no stronger than would be expected due to chance alone 46, 47 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [46]
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“minor allele frequency close to 50%) were removed from the analysis to avoid possible issues (Bowden et al., 2015; Hartwig et al., 2016).”
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- Authors
- Hartwig, F. P., Davies, N. M., Hemani, G., & Davey Smith, G.
- Affiliations
- Universidade Federal de Pelotas (BR)
- University of Bristol (GB)
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol
- Corresponding author
- Hartwig, Fernando Pires
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-12-01
- Volume
- 45
- Issue
- 6
- Pages
- 1717-1726
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC5722032
- OpenAlex
- W2597850678
- Cited by
- 860
- References
- 41
- Citations by year
- 2026: 69 · 2025: 153 · 2024: 232 · 2023: 120 · 2022: 82 · 2021: 67 · 2020: 62 · 2019: 39 · 2018: 26 · 2017: 9 · 2016: 1
- FWCI
- 20.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International Journal of Epidemiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Explore Bristol Research (publishedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- The Medical Research Council (MRC) and the University of Bristol fund the MRC Integrative Epidemiology Unit (MC_UU_12013/1, MC_UU_12013/9)
- Economics and Social Research Council (ESRC) via a Future Research Leaders Fellowship (ES/N000757/1)
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol (MC_UU_12013/1)
- 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
- weak instrument bias, causal inference, instrumental variables, exclusion restriction, independence assumption, causal effect estimation, genetic instruments, body mass index, adiposity, cancer risk, type 2 diabetes, telomere length, methodological critique
- MeSH terms
- Humans, Research Design, Guidelines as Topic, Mendelian Randomization Analysis
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