Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac376.
Mendelian randomization is a versatile tool to identify the possible causal relationship between an omics biomarker and disease outcome using genetic variants as instrumental variables. A key theme is the prioritization of genes whose omics readouts can be used as predictors of the disease outcome through analyzing GWAS and QTL summary data. However, there is a dearth of study of the best practice in probing the effects of multiple -omics biomarkers annotated to the same gene of interest. To bridge this gap, we propose powerful combination tests that integrate multiple correlated $P$-values without assuming the dependence structure between the exposures. Our extensive simulation experiments demonstrate the superiority of our proposed approach compared with existing methods that are adapted to the setting of our interest. The top hits of the analyses of multi-omics Alzheimer's disease datasets include genes ABCA7 and ATP1B1.
孟德尔随机化是一种通用工具，可利用遗传变异作为工具变量，确定组学生物标志物与疾病结局之间可能的因果关系。一个关键主题是优先考虑那些可以通过分析 GWAS 和 QTL 汇总数据将其组学读数用作疾病结局预测因子的基因。然而，对于探究注释到同一感兴趣基因的多个组学生物标志物的效果的最佳实践研究还很少。为了弥补这一空白，我们提出了强大的组合检验方法，可以整合多个相关的$P$值，而无需假设暴露之间的依赖结构。我们广泛的模拟实验表明，与适应我们关注的环境的现有方法相比，我们提出的方法具有优越性。对多组学阿尔茨海默病数据集进行分析的结果包括 ABCA7 和 ATP1B1 等基因。
