Adjusting for medication status in genome-wide association studies
Chong, A. H. W.; Kintu, C.; Cho, Y.; Fatumo, S.; Torres, J.; Davey Smith, G.; Gaunt, T. R.; Hemani, G.
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When conducting genome-wide association studies, improper handling of medication status that is relevant to the trait of interest can induce biases by opening up different pathways that distort estimates of the true effect. Here, we propose 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 either 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 LDL cholesterol levels, using multi ancestry 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, with an effect of 40% reduction appearing to be consistent across ancestral groups. These findings are consistent with the current clinical guidelines.
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