The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change

Akinyemi Oni-Orisan; Tanushree Haldar; Dilrini K Ranatunga; Marisa W Medina; Catherine Schaefer; Ronald M Krauss; Carlos Iribarren; Neil Risch; Thomas J Hoffmann

doi:10.1038/s41525-019-0109-4

The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change

NPJ Genom Med. 2020 Jan 16:5:1. doi: 10.1038/s41525-019-0109-4. eCollection 2020.

Authors

Akinyemi Oni-Orisan^{1

2}, Tanushree Haldar², Dilrini K Ranatunga³, Marisa W Medina⁴, Catherine Schaefer³, Ronald M Krauss^{4

5}, Carlos Iribarren³, Neil Risch^#^{2

3

6}, Thomas J Hoffmann^#^{2

6}

Affiliations

¹ 1Department of Clinical Pharmacy, University of California, San Francisco, CA 94143 USA.
² 2Institute for Human Genetics, University of California, San Francisco, CA 94143 USA.
³ 3Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612 USA.
⁴ 4Children's Hospital Oakland Research Institute, Oakland, CA 94609 USA.
⁵ 5Department of Medicine, University of California, San Francisco, CA 94143 USA.
⁶ 6Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158 USA.

^# Contributed equally.

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.

Keywords: Dyslipidaemias; Genetics research; Pharmacogenetics.

Abstract

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