Background: Understanding the relationship between genetic variation and gene expression is a central issue in genetics. Although many studies have identified genetic variations associated with gene expression, it is unclear how they perturb the underlying regulatory network of gene expression.

Objective: To explore how genetic variations perturb potential transcriptional regulation networks of Alzheimer’s disease (AD) to paint a more complete picture of the complex landscape of transcription regulation.

Methods: Fast network component analysis (FastNCA), which can capture the genetic variations in the form of single nucleotide polymorphisms (SNPs), is applied to analyse the expression activities of TFs and their regulatory strengths on TGs using microarray and RNA-seq data of AD. Then, multi-data fusion analysis was used to analyze the different TGs regulated by the same TFs in the different data by constructing the transcriptional regulatory networks of differentially expressed genes.

Results: the common TF regulating TGs are not necessarily identical in different data, they may be involved in the same pathways that are closely related to the pathogenesis of AD, such as immune response, signal transduction and cytokine-cytokine receptor interaction pathways. Even if they are involved in different pathways, these pathways are also confirmed to have a potential link with AD.

Conclusion: The study shows that the pathways of different TGs regulated by the same TFs in different data are all closely related to AD. Multi-data fusion analysis can form a certain complement to some extent and get more comprehensive results in the process of exploring the pathogenesis of AD.

背景：了解遗传变异与基因表达之间的关系是遗传学的核心问题。尽管许多研究已经确定了与基因表达相关的遗传变异，但尚不清楚它们如何干扰基因表达的基础调控网络。

目的：探讨遗传变异如何干扰阿尔茨海默氏病（AD）的潜在转录调控网络，以更完整地描绘复杂的转录调控格局。

方法：利用可捕获单核苷酸多态性（SNPs）形式的遗传变异的快速网络成分分析（FastNCA），利用微阵列和RNA序列数据分析TF的表达活性及其对TG的调控强度。广告。然后，通过构建差异表达基因的转录调控网络，利用多数据融合分析来分析不同数据中相同TFs调控的不同TGs。

结果：常见的TF调节TG在不同数据中不一定相同，它们可能参与与AD发病机理密切相关的相同途径，例如免疫应答，信号转导和细胞因子-细胞因子受体相互作用途径。即使它们参与了不同的途径，这些途径也被证实与AD有潜在的联系。

结论：研究表明，不同数据中相同转录因子调节不同TG的途径均与AD密切相关。多数据融合分析可以在一定程度上形成一定的补充，并在探索AD发病机理的过程中获得更全面的结果。