Liu Yang, Sun Wei, Hsu Li, He Qianchuan
Program of Biostatistics, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.
Department of Mathematics and Statistics, Wright State University, Dayton, Ohio, U.S.A.
Comput Stat Data Anal. 2022 May;169. doi: 10.1016/j.csda.2021.107418. Epub 2022 Jan 13.
Pathway analysis, i.e., grouping analysis, has important applications in genomic studies. Existing pathway analysis approaches are mostly focused on a single response and are not suitable for analyzing complex diseases that are often related with multiple response variables. Although a handful of approaches have been developed for multiple responses, these methods are mainly designed for pathways with a moderate number of features. A multi-response pathway analysis approach that is able to conduct statistical inference when the dimension is potentially higher than sample size is introduced. Asymptotical properties of the test statistic are established and theoretical investigation of the statistical power is conducted. Simulation studies and real data analysis show that the proposed approach performs well in identifying important pathways that influence multiple expression quantitative trait loci (eQTL).
通路分析，即分组分析，在基因组研究中具有重要应用。现有的通路分析方法大多聚焦于单一反应，不适用于分析通常与多个反应变量相关的复杂疾病。尽管已经开发了一些用于多反应的方法，但这些方法主要是为具有中等数量特征的通路设计的。本文介绍了一种多反应通路分析方法，该方法能够在维度可能高于样本量时进行统计推断。建立了检验统计量的渐近性质，并对统计功效进行了理论研究。模拟研究和实际数据分析表明，所提出的方法在识别影响多个表达数量性状位点（eQTL）的重要通路方面表现良好。
