The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change.
Akinyemi Oni-OrisanTanushree HaldarDilrini K RanatungaMarisa W MedinaCatherine SchaeferRonald M KraussCarlos IribarrenNeil RischThomas J HoffmannPublished in: NPJ genomic medicine (2020)
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.
Keyphrases
- genome wide
- drug induced
- genome wide association
- copy number
- dna methylation
- high resolution
- liver injury
- cardiovascular disease
- low density lipoprotein
- coronary artery disease
- healthcare
- clinical decision support
- stem cells
- magnetic resonance
- mental health
- public health
- magnetic resonance imaging
- systematic review
- climate change
- skeletal muscle
- randomized controlled trial
- radiation therapy
- high fat diet
- cell therapy
- mass spectrometry
- single cell
- cognitive decline
- smoking cessation
- rectal cancer
- contrast enhanced
- stress induced
- replacement therapy