Oncotarget

Research Papers: Pathology:

Integrative analysis of genome-wide association studies and gene expression analysis identifies pathways associated with rheumatoid arthritis

Mingming Zhang, Hongbo Mu, Hongchao Lv, Lian Duan, Zhenwei Shang, Jin Li, Yongshuai Jiang and Ruijie Zhang _

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Oncotarget. 2016; 7:8580-8589. https://doi.org/10.18632/oncotarget.7390

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Abstract

Mingming Zhang1,*, Hongbo Mu2,*, Hongchao Lv1,*, Lian Duan1,*, Zhenwei Shang1,*, Jin Li1, Yongshuai Jiang1 and Ruijie Zhang1

1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China

2 College of Science, Northeast Forestry University, Harbin, China

* These authors have contributed equally to this work

Correspondence to:

Ruijie Zhang, email:

Keywords: rheumatoid arthritis, pathway analysis, single nucleotide polymorphism, gene expression, Pathology Section

Received: October 22, 2015 Accepted: January 28, 2016 Published: February 14, 2016

Abstract

Rheumatoid arthritis (RA) is a complex and systematic autoimmune disease, which is usually influenced by both genetic and environmental factors. Pathway analyses based on a single data type such as microarray data or SNP data have successfully revealed some biology pathways associated with RA. However, we found that the pathway analysis based on a single data type only provide limited understanding about the pathogenesis of RA. Gene-disease association is usually caused by many ways, such as genotype, gene expression and so on. Therefore, the integrative analysis method combining multiple levels of evidence can more precisely and comprehensively identify the pathway associations. In this study, we performed a pathway analysis by integrating GWAS and gene expression analysis to detect the RA-related pathways. The integrative analysis identified 28 pathways associated with RA. Among these pathways, 18 pathways were also found by both GWAS and gene expression analysis, 7 pathways are novel RA-related pathways, such as B cell receptor signaling pathway, Toll-like receptor signaling pathway, Fc gamma R-mediated phagocytosis and so on. Compared with pathway analyses using only one type genomic data, we found integrative analysis can increase the power to identify the real associations and provided more stable and accurate results. We believe these results will contribute to perform future genetic studies in RA pathogenesis and may promote the development of new therapeutic strategies by targeting these pathways.


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