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  5. MRSamePopTest: introducing a simple falsification test for the two-sample mendelian randomisation 'same population' assumption
 
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MRSamePopTest: introducing a simple falsification test for the two-sample mendelian randomisation 'same population' assumption
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MRSamePopTest introducing a simple falsification test for the two-sample mendelian randomisation same population assumption.pdf (833.09 KB)
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Author(s)
Woolf, Benjamin
Mason, Amy
Zagkos, Loukas
Sallis, Hannah
Munafò, Marcus R
more
Type
Journal Article
Abstract
Two-sample MR is an increasingly popular method for strengthening causal inference in epidemiological studies. For the effect estimates to be meaningful, variant-exposure and variant-outcome associations must come from comparable populations. A recent systematic review of two-sample MR studies found that, if assessed at all, MR studies evaluated this assumption by checking that the genetic association studies had similar demographics. However, it is unclear if this is sufficient because less easily accessible factors may also be important. Here we propose an easy-to-implement falsification test. Since recent theoretical developments in causal inference suggest that a causal effect estimate can generalise from one study to another if there is exchangeability of effect modifiers, we suggest testing the homogeneity of variant-phenotype associations for a phenotype which has been measured in both genetic association studies as a method of exploring the 'same-population' test. This test could be used to facilitate designing MR studies with diverse populations. We developed a simple R package to facilitate the implementation of our proposed test. We hope that this research note will result in increased attention to the same-population assumption, and the development of better sensitivity analyses.
Date Issued
2024-01-17
Date Acceptance
2024-01-03
Citation
BMC Research Notes, 2024, 17 (1)
URI
http://hdl.handle.net/10044/1/109105
DOI
https://www.dx.doi.org/10.1186/s13104-024-06684-0
ISSN
1756-0500
Publisher
BMC
Journal / Book Title
BMC Research Notes
Volume
17
Issue
1
Copyright Statement
© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
License URL
http://creativecommons.org/licenses/by/4.0/
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/38233927
PII: 10.1186/s13104-024-06684-0
Subjects
Causality
Genetic Association Studies
Genome-Wide Association Study
Mendelian Randomization Analysis
Phenotype
Population homogeneity
Sensitivity analysis
Two-sample Mendelian randomisation
Publication Status
Published
Coverage Spatial
England
Article Number
ARTN 27
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