A statistical perspective on baseline adjustment in pharmacogenomic genome-wide association studies of quantitative change
Hong Zhang, Aparna Chhibber, Peter Shaw, Devan V. Mehrotra, Judong Shen
Abstract
In pharmacogenetic (PGx) studies, drug response phenotypes are often measured in the form of change in a quantitative trait before and after treatment. There is some debate in recent literature regarding baseline adjustment, or inclusion of pre-treatment or baseline value as a covariate, in PGx genome-wide association studies (GWAS) analysis. Here, we provide a clear statistical perspective on this baseline adjustment issue by running extensive simulations based on nine statistical models to evaluate the influence of baseline adjustment on type I error and power. We then apply these nine models to analyzing the change in low-density lipoprotein cholesterol (LDL-C) levels with ezetimibe + simvastatin combination therapy compared with simvastatin monotherapy therapy in the 5661 participants of the IMPROVE-IT (IMProved Reduction of Outcomes: Vytroin Efficacy International Trial) PGx GWAS, supporting the conclusions drawn from our simulations. Both simulations and GWAS analyses consistently show that baseline-unadjusted models inflate type I error for the variants associated with baseline value if the baseline value is also associated with change from baseline (e.g., when baseline value is a mediator between a variant and change from baseline), while baseline-adjusted models can control type I error in various scenarios. We thus recommend performing baseline-adjusted analyses in PGx GWASs of quantitative change.