| Title: | Detecting and correcting for bias in Mendelian randomization analyses using Gene-by-Environment interactions |
| Journal: | International Journal of Epidemiology |
| Published: | 20 Nov 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/30462199/ |
| DOI: | https://doi.org/10.1093/ije/dyy204 |
| URL: | https://academic.oup.com/ije/article-pdf/48/3/702/29005148/dyy204.pdf |
| Citations: | 73 (15 in last 2 years) as of 8 Aug 2024 |
Publication 4653
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Abstract
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.4 Authors
- Wes Spiller
- David Slichter
- Jack Bowden
- George Davey Smith
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