Constrained instruments and their application to Mendelian randomization with pleiotropy
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Abstract
In Mendelian randomization (MR), inference about causal relationship between a phenotype of interest and a response or disease outcome can be obtained by constructing instrumental variables from genetic variants. However, MR inference requires three assumptions, one of which is that the genetic variants only influence the outcome through phenotype of interest. Pleiotropy, that is, the situation in which some genetic variants affect more than one phenotype, can invalidate these genetic variants for use as instrumental variables; thus a naive analysis will give biased estimates of the causal relation. Here, we present new methods (constrained instrumental variable [CIV] methods) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists and when the pleiotropic phenotypes are available. We demonstrate that a smoothed version of CIV performs approximate selection of genetic variants that are valid instruments, and provides unbiased estimates of the causal effects. We provide details on a number of existing methods, together with a comparison of their performance in a large series of simulations. CIV performs robustly across different pleiotropic violations of the MR assumptions. We also analyzed the data from the Alzheimer’s disease (AD) neuroimaging initiative (ADNI; Mueller et al., 2005. Alzheimer's Dementia, 11(1), 55–66) to disentangle causal relationships of several biomarkers with AD progression.
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Recent methods such as sisVIVE and adaptive LASSO provide MR estimates that are robust to pleiotropy under certain assumptions.
“Recent methods such as sisVIVE71 and adaptive LASSO72 provide MR estimates that are robust to pleiotropy under certain assumptions.”
From Mendelian randomization (2022) · cites this paper as [72]
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Self-citationA flexible framework for robust and efficient Mendelian randomization with debiasing
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- Authors
- Jiang, L., Oualkacha, K., Didelez, V., Ciampi, A., Rosa‐Neto, P., Benedet, A. L., Mathotaarachchi, S., Richards, J. B., Greenwood, C. M. T., & Initiative, A. F. T. A. D. N.
- Affiliations
- Jewish General Hospital (CA)
- Université du Québec à Montréal (CA)
- University of Bremen (DE)
- Douglas Mental Health University Institute (CA)
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging Douglas Hospital, McGill University Montreal Quebec Canada
- McGill University (CA)
- Corresponding author
- Greenwood, Celia M. T.
- ORCID
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2019-01-12
- Volume
- 43
- Issue
- 4
- Pages
- 373-401
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC6537099
- OpenAlex
- W2949803117
- Cited by
- 23
- References
- 91
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 4 · 2019: 3 · 2018: 2
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Genetic Epidemiology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Funders
- Alzheimer's Disease Neuroimaging Initiative (W81XWH‐12‐2‐0012)
- 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
- Mendelian randomization, pleiotropy, instrumental variable selection, causal effect estimation, genetic variants, Alzheimer's disease, biomarkers, ADNI, causal inference
- MeSH terms
- Algorithms, Humans, Models, Genetic, Phenotype, Genetic Variation, Confounding Factors, Epidemiologic, Genetic Association Studies, Mendelian Randomization Analysis, Genetic Pleiotropy
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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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