Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, 63110, USA.
Center for Applied Mathematics, Tianjin University, Tianjin, 300072, China.
BMC Bioinformatics. 2022 Jul 25;23(1):296. doi: 10.1186/s12859-022-04748-1.
Mediation analysis plays a major role in identifying significant mediators in the pathway between environmental exposures and health outcomes. With advanced data collection technology for large-scale studies, there has been growing research interest in developing methodology for high-dimensional mediation analysis. In this paper we present HIMA2, an extension of the HIMA method (Zhang in Bioinformatics 32:3150-3154, 2016). First, the proposed HIMA2 reduces the dimension of mediators to a manageable level based on the sure independence screening (SIS) method (Fan in J R Stat Soc Ser B 70:849-911, 2008). Second, a de-biased Lasso procedure is implemented for estimating regression parameters. Third, we use a multiple-testing procedure to accurately control the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. We demonstrate its practical performance using Monte Carlo simulation studies and apply our method to identify DNA methylation markers which mediate the pathway from smoking to reduced lung function in the Coronary Artery Risk Development in Young Adults (CARDIA) Study.
中介分析在确定环境暴露与健康结果之间途径中的重要中介因素方面起着重要作用。随着大规模研究的先进数据收集技术的发展，人们越来越有兴趣开发高维中介分析的方法。在本文中，我们提出了 HIMA2，这是 HIMA 方法的扩展（Zhang 在 Bioinformatics 32:3150-3154, 2016）。首先，所提出的 HIMA2 基于 Sure Independence Screening (SIS) 方法（Fan 在 J R Stat Soc Ser B 70:849-911, 2008）将中介的维度降低到可管理的水平。其次，实施了去偏置 Lasso 程序来估计回归参数。第三，我们使用多重检验程序来准确控制高维中介假设检验的错误发现率 (FDR)。我们使用蒙特卡罗模拟研究证明了其实用性能，并将我们的方法应用于识别 DNA 甲基化标记，这些标记在冠状动脉风险发展在年轻人（CARDIA）研究中从吸烟到肺功能降低的途径中起中介作用。
