Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.
Institute for Medical Dataology, Shandong University, Jinan 250003, China.
Genes (Basel). 2023 Feb 25;14(3):586. doi: 10.3390/genes14030586.
Transcriptome-wide association studies (TWASs) aim to detect associations between genetically predicted gene expression and complex diseases or traits through integrating genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) mapping studies. Most current TWAS methods analyze one gene at a time, ignoring the correlations between multiple genes. Few of the existing TWAS methods focus on survival outcomes. Here, we propose a novel method, namely a COx proportional hazards model for NEtwork regression in TWAS (CoNet), that is applicable for identifying the association between one given network and the survival time. CoNet considers the general relationship among the predicted gene expression as edges of the network and quantifies it through pointwise mutual information (PMI), which is under a two-stage TWAS. Extensive simulation studies illustrate that CoNet can not only achieve type I error calibration control in testing both the node effect and edge effect, but it can also gain more power compared with currently available methods. In addition, it demonstrates superior performance in real data application, namely utilizing the breast cancer survival data of UK Biobank. CoNet effectively accounts for network structure and can simultaneously identify the potential effecting nodes and edges that are related to survival outcomes in TWAS.
转录组关联研究（TWAS）旨在通过整合全基因组关联研究（GWAS）和表达数量性状基因座（eQTL）图谱研究，检测遗传预测的基因表达与复杂疾病或特征之间的关联。大多数当前的 TWAS 方法一次分析一个基因，忽略了多个基因之间的相关性。现有的 TWAS 方法很少关注生存结局。在这里，我们提出了一种新的方法，即 TWAS 中网络回归的 COx 比例风险模型（CoNet），该方法适用于识别给定网络与生存时间之间的关联。CoNet 考虑了预测基因表达之间的一般关系作为网络的边缘，并通过两点互信息（PMI）对其进行量化，这是在两阶段 TWAS 下进行的。广泛的模拟研究表明，CoNet 不仅可以在测试节点效应和边缘效应时实现 I 型错误校准控制，而且与现有的方法相比，它还可以获得更多的功效。此外，它在真实数据应用中表现出优越的性能，即在利用英国生物库的乳腺癌生存数据时。CoNet 有效地考虑了网络结构，可以同时识别与 TWAS 中生存结局相关的潜在作用节点和边缘。
