Transcriptional regulation analysis of alzheimer’s disease based on fastNCA algorithm

Qianni Sun, Wei Kong, Xiaoyang Mou, Shuaiqun Wang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)771-782
Number of pages12
JournalCurrent Bioinformatics
Issue number8
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computational Mathematics


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