Oni-Orisan Akinyemi, Haldar Tanushree, Ranatunga Dilrini K, Medina Marisa W, Schaefer Catherine, Krauss Ronald M, Iribarren Carlos, Risch Neil, Hoffmann Thomas J
1Department of Clinical Pharmacy, University of California, San Francisco, CA 94143 USA.
2Institute for Human Genetics, University of California, San Francisco, CA 94143 USA.
NPJ Genom Med. 2020 Jan 16;5:1. doi: 10.1038/s41525-019-0109-4. eCollection 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 (, and /). In contrast, baseline-unadjusted analyses yielded variants from three loci meeting the criteria for genome-wide significance (, , and ). 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 (/) 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.
在药物基因组学定量变化研究中，基因变异与治疗前（基线）测量值之间的任何关联都可能使这些变异与药物反应之间的效应估计产生偏差。一种可能的解决方法是对基线进行调整。我们对成人健康与衰老遗传流行病学研究（GERA）队列中的34874名参与者进行了一系列全基因组关联研究（GWAS），以研究他汀类药物治疗低密度脂蛋白胆固醇（LDL-C）反应，作为一个案例研究来调查基线调整对药物基因组学定量变化研究结果的影响。在他汀类药物诱导的LDL-C变化的各种表型中，基线调整确定了来自六个位点的变异达到全基因组显著性水平（，以及/）。相比之下，未进行基线调整的分析得出了来自三个位点的变异符合全基因组显著性标准（，，和）。对基线与他汀类药物治疗期间LDL-C水平进行了全基因组异质性检验，作为基因变异对他汀类药物诱导的LDL-C变化真实效应的决定性检验。这些发现总体上与未对基线进行调整的模型一致，这表明仅从基线调整分析（/）中产生的全基因组显著命中结果可能存在偏差。然后，我们全面回顾了已发表的药物诱导定量变化的GWAS，发现超过一半（59%）的研究对基线进行了不适当的调整。总之，我们证明了（1）基线调整在药物基因组学定量变化研究中引入了偏差，以及（2）这种错误的方法非常普遍。我们得出结论，在未来药物基因组学定量变化研究中避免这种常见的统计方法至关重要。
