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Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations.

Fernando Pires Hartwig1,2, Kate Tilling2,3, George Davey Smith2,3

  • 1Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil.

International Journal of Epidemiology
|February 23, 2021
PubMed
Summary

Using covariable-adjusted data in two-sample Mendelian randomization (MR) can introduce bias, even without horizontal pleiotropy. Researchers should generally avoid covariable-adjusted summary associations for causal effect estimation in MR studies.

Keywords:
Two-sample Mendelian randomizationbiasgenetic pleiotropygenome-wide association studysummary results

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Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Two-sample Mendelian randomization (MR) uses genome-wide association studies (GWAS) summary results to infer causal effects.
  • GWAS may adjust for heritable covariables to estimate direct genetic effects.
  • The adjustment status of exposure and outcome GWAS can vary.

Purpose of the Study:

  • To evaluate the impact of using covariable-adjusted summary association results in two-sample MR.
  • To assess bias introduced by covariable adjustment under different confounding scenarios.

Main Methods:

  • Conducted a simulation study with various covariable adjustment scenarios.
  • Analyzed real-world data from large consortia (GIANT, UK Biobank).
  • Assessed causal effect estimates in two-sample MR using adjusted and unadjusted GWAS data.

Main Results:

  • Covariable adjustment eliminated bias from horizontal pleiotropy when residual confounding was absent.
  • Bias was introduced by covariable adjustment in the presence of residual confounding, particularly between the covariable and outcome.
  • Real data analysis showed a change in the causal effect direction of waist circumference on blood pressure after adjustment for body mass index.

Conclusions:

  • Generally, avoid using covariable-adjusted summary associations in two-sample MR.
  • Careful consideration of causal pathways and potential unmeasured confounders is crucial when adjustment is unavoidable.
  • Sensitivity analyses and cautious interpretation are necessary when using adjusted data.