Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China.
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China.
BMC Genet. 2020 Aug 8;21(1):85. doi: 10.1186/s12863-020-00876-w.
Biological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways.
We propose a path-specific effect statistic (PSE) to detect the differential specific paths under two conditions (e.g. case VS. control groups, exposure Vs. nonexposure groups). In observational studies, the path-specific effect can be obtained by separately calculating the average causal effect of each directed edge through adjusting for the parent nodes of nodes in the specific path and multiplying them under each condition. Theoretical proofs and a series of simulations are conducted to validate the path-specific effect statistic. Applications are also performed to evaluate its practical performances. A series of simulation studies show that the Type I error rates of PSE with Permutation tests are more stable at the nominal level 0.05 and can accurately detect the differential specific paths when comparing with other methods. Specifically, the power reveals an increasing trends with the enlargement of path-specific effects and its effect differences under two conditions. Besides, the power of PSE is robust to the variation of parent or child node of the nodes on specific paths. Application to real data of Glioblastoma Multiforme (GBM), we successfully identified 14 positive specific pathways in mTOR pathway contributing to survival time of patients with GBM. All codes for automatic searching specific paths linking two continuous variables and adjusting set as well as PSE statistic can be found in supplementary materials. CONCLUSION: The proposed PSE statistic can accurately detect the differential specific pathways contributing to complex disease and thus potentially provides new insights and ways to unlock the black box of disease mechanisms.
生物途径在复杂疾病（如癌症）的发生、发展和恢复中起着重要作用，这些疾病是多因素复杂疾病，通常是由多个基因的突变或途径的失调引起的。
我们提出了一种特定路径效应统计量（PSE）来检测两种情况下（例如病例与对照组、暴露与非暴露组）的差异特定路径。在观察性研究中，可以通过分别计算特定路径中节点的父节点调整后的每个有向边的平均因果效应，并在每种情况下相乘，来获得特定路径效应。进行了理论证明和一系列模拟实验来验证特定路径效应统计量。还进行了应用以评估其实际性能。一系列模拟研究表明，置换检验的 PSE 的Ⅰ型错误率在名义水平 0.05 下更加稳定，并且在与其他方法比较时可以准确检测差异特定路径。具体而言，随着两种情况下特定路径效应和效应差异的增大，功效呈现出增加的趋势。此外，PSE 的功效对特定路径上节点的父节点或子节点的变化具有鲁棒性。对胶质母细胞瘤（GBM）的真实数据的应用，我们成功鉴定了 mTOR 途径中的 14 个正向特定途径，这些途径对 GBM 患者的生存时间有影响。自动搜索连接两个连续变量的特定路径、调整集以及 PSE 统计量的所有代码都可以在补充材料中找到。
所提出的 PSE 统计量可以准确检测到对复杂疾病有贡献的差异特定途径，从而为疾病机制的“黑箱”提供新的见解和方法。
