Journal
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
Volume 48, Issue 3, Pages 702-712Publisher
OXFORD UNIV PRESS
DOI: 10.1093/ije/dyy204
Keywords
Mendelian randomization; invalid instruments; pleiotropy; MRGxE; gene-environment interaction
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Funding
- Medical Research Council (MRC)
- University of Bristol [MC_UU_00011/1, MC_UU_00011/2]
- Wellcome Trust [108902/B/15/Z]
- MRC [MC_UU_00011/1, MC_UU_00011/2] Funding Source: UKRI
- Wellcome Trust [108902/B/15/Z] Funding Source: Wellcome Trust
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Background: Mendelian randomization (MR) has developed into an established method for strengthening causal inference and estimating causal effects, largely due to the proliferation of genome-wide association studies. However, genetic instruments remain controversial, as horizontal pleiotropic effects can introduce bias into causal estimates. Recent work has highlighted the potential of gene-environment interactions in detecting and correcting for pleiotropic bias in MR analyses. Methods: We introduce MR using Gene-by-Environment interactions (MRGxE) as a framework capable of identifying and correcting for pleiotropic bias. If an instrument-covariate interaction induces variation in the association between a genetic instrument and exposure, it is possible to identify and correct for pleiotropic effects. The interpretation of MRGxE is similar to conventional summary MR approaches, with a particular advantage of MRGxE being the ability to assess the validity of an individual instrument. Results: We investigate the effect of adiposity, measured using body mass index (BMI), upon systolic blood pressure (SBP) using data from the UK Biobank and a single weighted allelic score informed by data from the GIANT consortium. We find MRGxE produces findings in agreement with two-sample summary MR approaches. Further, we perform simulations highlighting the utility of the approach even when the MRGxE assumptions are violated. Conclusions: By utilizing instrument-covariate interactions in MR analyses implemented within a linear-regression framework, it is possible to identify and correct for horizontal pleiotropic bias, provided the average magnitude of pleiotropy is constant across interaction-covariate subgroups.
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