A robust and efficient method for Mendelian randomization with hundreds of genetic variants
Open accessAbstract
Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation.
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The constrained maximum likelihood (MR-cML) method performs selection of invalid instruments and estimation allowing any of the three instrument variable assumptions to be violated via either uncorrelated or correlated pleiotropy.
“The constrained maximum likelihood (MR-cML) 25 is such a method that performs selection of invalid instruments and estimation allowing any of the three instrument variable assumptions to be violated via either uncorrelated or correlated pleiotropy.”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [25]
The MR-cML method offers a data perturbation/resampling scheme to account for uncertainty in model selection and so achieve better inferences in finite samples.
“In addition to asymptotically valid inference, it also offers a data perturbation/resampling scheme to account for uncertainty in model selection and so achieve better inferences in finite samples.”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [25]
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- Authors
- Burgess, S., Foley, C. N., Allara, E., Staley, J. R., & Howson, J. M. M.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Burgess, Stephen
- Venue
- Nature Communications
- Published
- 2020-01-17
- Volume
- 11
- Issue
- 1
- Pages
- 376-376
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6969055
- OpenAlex
- W2996249643
- Cited by
- 681
- References
- 58
- Citations by year
- 2026: 76 · 2025: 156 · 2024: 201 · 2023: 82 · 2022: 78 · 2021: 57 · 2020: 29 · 2019: 2
- FWCI
- 36.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Explore Bristol Research (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo
- Apollo (cc-by)
- Funders
- RCUK | Medical Research Council (MR/L003120/1, MC_UU_00002/7)
- British Heart Foundation (RG/13/13/30194)
- DH | National Institute for Health Research
- Wellcome Trust (204623/Z/16/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
- Keywords
- Mendelian randomization, robust estimation, causal inference, HDL cholesterol, triglycerides, coronary artery disease, platelet aggregation, blood cell traits, lipid-lowering mechanism
- MeSH terms
- Coronary Disease, Diabetes Mellitus, Type 2, Humans, Cholesterol, HDL, Cholesterol, LDL, Models, Genetic, Phenotype, Research Design, Risk Factors, Triglycerides, Genetic Variation, Reproducibility of Results, Body Mass Index, Molecular Epidemiology, Genetic Predisposition to Disease, Genetic Association Studies, Mendelian Randomization Analysis, Genetic Pleiotropy
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