Interaction-based Mendelian randomization with measured and unmeasured gene-by-covariate interactions

Studies leveraging gene-environment (GxE) interactions within Mendelian randomization (MR) analyses have prompted the emergence of two similar methodologies: MR-GxE and MR-GENIUS. Such methods are attractive in allowing for pleiotropic bias to be corrected when using individual instruments. Specific...

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Veröffentlicht in:PloS one Jg. 17; H. 8; S. e0271933
Hauptverfasser: Spiller, Wes, Hartwig, Fernando Pires, Sanderson, Eleanor, Davey Smith, George, Bowden, Jack
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 10.08.2022
Public Library of Science (PLoS)
Schlagworte:
Alcohol use
Analysis
Bias
Biology and Life Sciences
Blood pressure
Blood Pressure - genetics
Body mass
Body Mass Index
Body size
Causality
Estimates
Evaluation
Exercise
Genes
Genome-Wide Association Study
Medicine and Health Sciences
Mendel's law
Mendelian Randomization Analysis - methods
Physical Sciences
Randomization
Research and Analysis Methods
Sensitivity analysis
United Kingdom
ISSN:1932-6203, 1932-6203
Online-Zugang:Volltext
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Zusammenfassung:Studies leveraging gene-environment (GxE) interactions within Mendelian randomization (MR) analyses have prompted the emergence of two similar methodologies: MR-GxE and MR-GENIUS. Such methods are attractive in allowing for pleiotropic bias to be corrected when using individual instruments. Specifically, MR-GxE requires an interaction to be explicitly identified, while MR-GENIUS does not. We critically examine the assumptions of MR-GxE and MR-GENIUS in the absence of a pre-defined covariate, and propose sensitivity analyses to evaluate their performance. Finally, we explore the effect of body mass index (BMI) upon systolic blood pressure (SBP) using data from the UK Biobank, finding evidence of a positive effect of BMI on SBP. We find both approaches share similar assumptions, though differences between the approaches lend themselves to differing research settings. Where a suitable gene-by-covariate interaction is observed MR-GxE can produce unbiased causal effect estimates. MR-GENIUS can circumvent the need to identify interactions, but as a consequence relies on either the MR-GxE assumptions holding globally, or additional information with respect to the distribution of pleiotropic effects in the absence of an explicitly defined interaction covariate.
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Competing Interests: The authors have declared that no competing interests exist.
Current address: MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0271933