Zhou Ruixuan Rachel, Wang Liewei, Zhao Sihai Dave
Department of Statistics, University of Illinois at Urbana-Champaign, 725 S. Wright Street, Champaign, Illinois 61820, U.S.A.
Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First St. SW, Rochester, Minnesota 55905, U.S.A.
Biometrika. 2020 Sep;107(3):573-589. doi: 10.1093/biomet/asaa016. Epub 2020 May 4.
Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, and complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect effect estimators. Under complete mediation, where the indirect effect is equivalent to the total effect, we further prove that our approach gives a more powerful test compared to directly testing for the total effect. We confirm our theoretical results in simulations, as well as in an integrative analysis of gene expression and genotype data from a pharmacogenomic study of drug response. We present a novel analysis of gene sets to understand the molecular mechanisms of drug response, and also identify a genome-wide significant noncoding genetic variant that cannot be detected using standard analysis methods.
当潜在中介变量的数量大于样本量时，中介分析会变得困难。在本文中，我们针对线性中介模型在存在高维中介变量的情况下的间接效应提出了新的推断程序。我们针对不完全中介（可能存在直接效应）和完全中介（已知不存在直接效应）分别开发了方法。我们证明了间接效应估计量的一致性和渐近正态性。在完全中介的情况下，间接效应等同于总效应，我们进一步证明，与直接检验总效应相比，我们的方法能给出更具功效的检验。我们在模拟以及对药物反应的药物基因组学研究中的基因表达和基因型数据的综合分析中证实了我们的理论结果。我们对基因集进行了新颖的分析，以了解药物反应的分子机制，还鉴定出了一个全基因组显著的非编码遗传变异，而使用标准分析方法无法检测到该变异。
