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.
Key messages
Summary genetic associations from large genome-wide associations studies (GWAS) have been increasingly used in two-sample Mendelian randomization (MR) analyses.
Many GWAS adjust for heritable covariates in an attempt to estimate direct genetic effects on the trait of interest.
In an extensive simulation study, we demonstrate that using covariable-adjusted summary associations may bias MR analyses.
The bias largely depends on the underlying causal structure, specially the presence of unmeasured common causes between the covariable and the outcome.
Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided.
Competing Interest Statement
No competing interests: FPH, KT, GDS, MCB. Competing interests: DAL has received funding from several national and international government and charitable organisations, Roche Diagnostics and Medtronic for research unrelated to that presented in this paper.
