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Multiple sclerosis risk pathways differ in Caucasian and Chinese populations

  • Guiyou Liu
  • , Fang Zhang
  • , Yang Hu
  • , Yongshuai Jiang
  • , Zhongying Gong
  • , Shoufeng Liu
  • , Xiuju Chen
  • , Qinghua Jiang*
  • , Junwei Hao
  • *Corresponding author for this work
  • School of Life Science and Technology, Harbin Institute of Technology
  • Tianjin Medical University
  • Harbin Medical University
  • Tianjin First Central Hospital
  • Tianjin Huanhu Hospital
  • Nankai Hospital

Research output: Contribution to journalArticlepeer-review

Abstract

Large-scale genome-wide association study (GWAS) datasets provide strong support for investigations of the mechanisms underlying multiple sclerosis (MS) by using pathway analysis methods. In our recent study, we conducted a three-stage pathway analysis of GWAS and expression datasets. After identifying 15 shared MS pathways in separate MS GWAS datasets, we found that dysregulated MS genes were significantly enriched in 10 of the 15 MS risk pathways. Evidence showed that 17%–30% of genes are differentially expressed among individual ethnic populations. We then verified the potential disruption of genes in the 10 MS risk pathways cited above in Chinese MS patients. Here, we investigated potential up- and down-regulation of 42 MS genes in these 10 MS risk pathways using 132 Chinese MS patients and 76 healthy control subjects. We then identified 31 differentially expressed genes in Chinese MS patients compared to healthy control subjects. Moreover, the expression patterns of 28 of these genes were consistent with those obtained from Caucasian (European and American) MS patients, although 14 genes differed from the latter group's. Our results provide clinically useful clues about the link between these risk genes and MS susceptibility in the Chinese population.

Original languageEnglish
Pages (from-to)63-68
Number of pages6
JournalJournal of Neuroimmunology
Volume307
DOIs
StatePublished - 15 Jun 2017
Externally publishedYes

Keywords

  • Chinese population
  • Genome-wide association study
  • Multiple sclerosis
  • Pathway analysis

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