Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.
Open accessAbstract
A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This "multivariable Mendelian randomization" approach is similar to the simultaneous assessment of several treatments in a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a valid multivariable Mendelian randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol.
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Multivariable instrumental variable analysis and a series of other relevant MR sensitivity analyses showed no major deviations and did not change conclusions from the main instrumental variable findings.
“These analyses showed no major deviations and did not change conclusions from the main instrumental variable findings.”
From Plasma levels of apolipoprotein E, <i>APOE</i> genotype, and all-cause and cause-specific mortality in 105 949 individuals from a white general population cohort (2019) · cites this paper as [66]
The Simulation Study reports that some estimates did not change substantially across simulated parameters, but it does not assess the citing study’s instrumental-variable findings. I searched for sensitivity analyses showing no major deviations or unchanged conclusions; no passage in our copy reports that result. (LLM, conf=0.76)
Experimental — accuracy not yet measuredMendelian randomization analyses for investigating drug targets often use variants in a single gene region, typically the gene that encodes the protein target under investigation.
“Mendelian randomization analyses for investigating drug targets often use variants in a single gene region, typically the gene that encodes the protein target under investigation 57, 58 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [57]
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- Authors
- Burgess, S., & Thompson, S. G.
- Corresponding author
- Burgess, S.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-01-27
- Volume
- 181
- Issue
- 4
- Pages
- 251-260
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- PMC
- PMC4325677
- OpenAlex
- W4210703904
- Cited by
- 1,605
- References
- 32
- Citations by year
- 2026: 131 · 2025: 257 · 2024: 417 · 2023: 252 · 2022: 181 · 2021: 118 · 2020: 104 · 2019: 62 · 2018: 30 · 2017: 22 · 2016: 20 · 2015: 7 · 2014: 1
- FWCI
- 17.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- American Journal of Epidemiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (100114)
- National Institute for Health and Care Research (NF-SI-0512-10165)
- British Heart Foundation (RG/08/014/24067)
- Medical Research Council (MR/L003120/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
- Keywords
- multivariable Mendelian randomization, causal effect estimation, triglycerides, coronary artery disease, LDL cholesterol, HDL cholesterol, lipid fractions, instrumental variables, cardiovascular risk
- MeSH terms
- Coronary Disease, United Kingdom, Humans, Cholesterol, HDL, Cholesterol, LDL, Mathematical Computing, Models, Genetic, Predictive Value of Tests, Risk Factors, Sensitivity and Specificity, Triglycerides, Genetic Variation, Biomarkers, Causality, Mendelian Randomization Analysis, Genetic Pleiotropy
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