| Title: | Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations |
| Journal: | International Journal of Epidemiology |
| Published: | 23 Feb 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33619569/ |
| DOI: | https://doi.org/10.1093/ije/dyaa266 |
| URL: | https://academic.oup.com/ije/article-pdf/50/5/1639/41120860/dyaa266.pdf |
Publication 4344
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Abstract
BACKGROUND: Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables.METHODS: We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR.
RESULTS: In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index.
CONCLUSIONS: Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.
5 Authors
- Fernando Pires Hartwig
- Kate Tilling
- George Davey Smith
- Deborah A Lawlor
- Maria Carolina Borges
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