title: Integrative analysis of genome-wide association studies and gene expression analysis identifies pathways associated with rheumatoid arthritis.
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.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Mingming
authors.initials: M
authors.lastname: Zhang
authors.affiliation: College of Science, Northeast Forestry University, Harbin, China.
authors.forename: Hongbo
authors.initials: H
authors.lastname: Mu
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Hongchao
authors.initials: H
authors.lastname: Lv
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Lian
authors.initials: L
authors.lastname: Duan
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Zhenwei
authors.initials: Z
authors.lastname: Shang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Li
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Yongshuai
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Ruijie
authors.initials: R
authors.lastname: Zhang
mesh_terms: D001172:Arthritis, Rheumatoid / Q000235:genetics*; D015415:Biomarkers / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods*; D005838:Genotype; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D015398:Signal Transduction; D049490:Systems Biology
chemical_list: D015415:Biomarkers
keywords: Pathology Section; gene expression; pathway analysis; rheumatoid arthritis; single nucleotide polymorphism