GEMRA：在全基因组关联分析中校正用药状态的新方法
Adjusting for medication status in genome-wide association studies
研究提出遗传经验用药缩减校正（GEMRA）方法，通过对报告用药参与者的表型值做启发式经验校正，减少用药状态对全基因组关联分析遗传效应估计的偏倚。模拟结果显示，GEMRA 的直接遗传效应估计偏倚更小、统计功效高于将用药作为协变量、仅保留未用药样本或不作校正等常见策略。作者用英国生物样本库与乌干达基因组资源的多人群数据，分别估计他汀类药物对 LDL 胆固醇、降压药对收缩压和舒张压的校正。结果显示相对而非绝对校正更能刻画用药效应，LDL 胆固醇、收缩压和舒张压的降幅分别约为 40%、10% 和 10%，且在不同人群中表现一致；若效应尺度设定错误，会产生虚假的跨人群用药效应差异。
来源：PLOS Genetics · journals.plos.org
by Amanda H. W. Chong, Christopher Kintu, Yoonsu Cho, Segun Fatumo, Jason Torres, George Davey Smith, Tom R. Gaunt, Gibran Hemani
When conducting genome-wide association studies, addressing medication status that is relevant to the trait of interest can prevent biases that could distort estimates of the true genetic effect. Here, we introduce the genetic empirical medication reduction adjustment (GEMRA) method which uses a heuristic search for an empirical adjustment to be applied to phenotypic values of participants reporting medication use. Through simulations we show that the direct genetic effect estimates in the GEMRA approach exhibited less bias and greater statistical power than common strategies such as including medication as a covariate, restricting the sample to unmedicated users or including all samples without adjustment. We then applied the GEMRA approach to estimate statin medication adjustment for analysis of low-density lipoprotein (LDL) cholesterol levels and antihypertensive medication adjustment for analyses of systolic (SBP) and diastolic (DBP) blood pressure using multi-population data from UK Biobank and the Uganda Genome Resource. We found that a relative rather than an absolute adjustment better modelled the effect of medication on LDL cholesterol, SBP and DBP, with an effect of 40%, 10% and 10% reduction appearing to be consistent across populations, respectively. We also show that spurious differences in medication effects across populations can arise when the effect scale is misspecified.