Adjusting for medication status in genome-wide association studies

When conducting genome-wide association studies, addressing medication status that is relevant to the trait of interest can prevent biases that could distort estimates of the true genetic effect. Here, we introduce the genetic empirical medication reduction adjustment (GEMRA) method which uses a heuristic search for an empirical adjustment to be applied to phenotypic values of participants reporting medication use. Through simulations we show that the direct genetic effect estimates in the GEMRA approach exhibited less bias and greater statistical power than common strategies such as including medication as a covariate, restricting the sample to unmedicated users or including all samples without adjustment. We then applied the GEMRA approach to estimate statin medication adjustment for analysis of low-density lipoprotein (LDL) cholesterol levels and antihypertensive medication adjustment for analyses of systolic (SBP) and diastolic (DBP) blood pressure using multi-population data from UK Biobank and the Uganda Genome Resource. We found that a relative rather than an absolute adjustment better modelled the effect of medication on LDL cholesterol, SBP and DBP, with an effect of 40%, 10% and 10% reduction appearing to be consistent across populations, respectively. We also show that spurious differences in medication effects across populations can arise when the effect scale is misspecified.