Abstract
In pharmacogenomic studies of quantitative change, any association between genetic variants and the pretreatment (baseline) measurement can bias the estimate of effect between those variants and drug response. A putative solution is to adjust for baseline. We conducted a series of genome-wide association studies (GWASs) for low-density lipoprotein cholesterol (LDL-C) response to statin therapy in 34,874 participants of the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort as a case study to investigate the impact of baseline adjustment on results generated from pharmacogenomic studies of quantitative change. Across phenotypes of statin-induced LDL-C change, baseline adjustment identified variants from six loci meeting genome-wide significance (SORT/CELSR2/PSRC1, LPA, SLCO1B1, APOE, APOB, and SMARCA4/LDLR). In contrast, baseline-unadjusted analyses yielded variants from three loci meeting the criteria for genome-wide significance (LPA, APOE, and SLCO1B1). A genome-wide heterogeneity test of baseline versus statin on-treatment LDL-C levels was performed as the definitive test for the true effect of genetic variants on statin-induced LDL-C change. These findings were generally consistent with the models not adjusting for baseline signifying that genome-wide significant hits generated only from baseline-adjusted analyses (SORT/CELSR2/PSRC1, APOB, SMARCA4/LDLR) were likely biased. We then comprehensively reviewed published GWASs of drug-induced quantitative change and discovered that more than half (59%) inappropriately adjusted for baseline. Altogether, we demonstrate that (1) baseline adjustment introduces bias in pharmacogenomic studies of quantitative change and (2) this erroneous methodology is highly prevalent. We conclude that it is critical to avoid this common statistical approach in future pharmacogenomic studies of quantitative change.
Highlights
Pharmacogenomic studies of continuous phenotypes most commonly identify genetic determinants of the change between pretreatment and on-treatment levels from the administration of a therapeutic drug intervention
Population and genetic characteristics A total of 34,874 statin users from the GERA cohort met the criteria for inclusion
The genome-wide association studies (GWASs) of statin low-density lipoprotein cholesterol (LDL-C) response using the Postmus et al definition revealed variants from six loci that met genome-wide significance (SORT/CELSR2/PSRC1, LPA, SLCO1B1, APOE, APOB, and SMARCA4/LDLR; Fig. 1a, Table 1)
Summary
Pharmacogenomic studies of continuous (quantitative) phenotypes most commonly identify genetic determinants of the change between pretreatment (baseline) and on-treatment levels from the administration of a therapeutic drug intervention. This approach has improved statistical power in detecting a genetic effect over using dichotomous outcomes (i.e., case-control design), especially when the dichotomous outcome is rare.[1]. A putative solution is to add the baseline value as a covariate to the linear regression model It has been documented in multiple studies of statistics and epidemiology that this analytical approach (adjusting for baseline) may introduce the bias that it seeks to prevent.[2,3,4,5,6]. We report the results of a comprehensive literature search to determine the prevalence of adjusting for baseline in pharmacogenomic studies
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