Dataset Information

Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci.

ABSTRACT: Ankylosing spondylitis is a common, highly heritable inflammatory arthritis affecting primarily the spine and pelvis. In addition to HLA-B*27 alleles, 12 loci have previously been identified that are associated with ankylosing spondylitis in populations of European ancestry, and 2 associated loci have been identified in Asians. In this study, we used the Illumina Immunochip microarray to perform a case-control association study involving 10,619 individuals with ankylosing spondylitis (cases) and 15,145 controls. We identified 13 new risk loci and 12 additional ankylosing spondylitis-associated haplotypes at 11 loci. Two ankylosing spondylitis-associated regions have now been identified encoding four aminopeptidases that are involved in peptide processing before major histocompatibility complex (MHC) class I presentation. Protective variants at two of these loci are associated both with reduced aminopeptidase function and with MHC class I cell surface expression.

SUBMITTER: International Genetics of Ankylosing Spondylitis Consortium (IGAS)

PROVIDER: S-EPMC3757343 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci.

Cortes Adrian A Hadler Johanna J Pointon Jenny P JP Robinson Philip C PC Karaderi Tugce T Leo Paul P Cremin Katie K Pryce Karena K Harris Jessica J Lee Seunghun S Joo Kyung Bin KB Shim Seung-Cheol SC Weisman Michael M Ward Michael M Zhou Xiaodong X Garchon Henri-Jean HJ Chiocchia Gilles G Nossent Johannes J Lie Benedicte A BA Førre Øystein Ø Tuomilehto Jaakko J Laiho Kari K Jiang Lei L Liu Yu Y Wu Xin X Bradbury Linda A LA Elewaut Dirk D Burgos-Vargas Ruben R Stebbings Simon S Appleton Louise L Farrah Claire C Lau Jonathan J Kenna Tony J TJ Haroon Nigil N Ferreira Manuel A MA Yang Jian J Mulero Juan J Fernandez-Sueiro Jose Luis JL Gonzalez-Gay Miguel A MA Lopez-Larrea Carlos C Deloukas Panos P Donnelly Peter P Bowness Paul P Gafney Karl K Gaston Hill H Gladman Dafna D DD Rahman Proton P Maksymowych Walter P WP Xu Huji H Crusius J Bart A JB van der Horst-Bruinsma Irene E IE Chou Chung-Tei CT Valle-Oñate Raphael R Romero-Sánchez Consuelo C Hansen Inger Myrnes IM Pimentel-Santos Fernando M FM Inman Robert D RD Videm Vibeke V Martin Javier J Breban Maxime M Reveille John D JD Evans David M DM Kim Tae-Hwan TH Wordsworth Bryan Paul BP Brown Matthew A MA

Nature genetics 20130609 7

Ankylosing spondylitis is a common, highly heritable inflammatory arthritis affecting primarily the spine and pelvis. In addition to HLA-B*27 alleles, 12 loci have previously been identified that are associated with ankylosing spondylitis in populations of European ancestry, and 2 associated loci have been identified in Asians. In this study, we used the Illumina Immunochip microarray to perform a case-control association study involving 10,619 individuals with ankylosing spondylitis (cases) and ...[more]

PMID: 23749187

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Project description:Background and Objective: This study aims to find the key immune genes and mechanisms of low bone mineral density (LBMD) in ankylosing spondylitis (AS) patients. Methods: AS and LBMD datasets were downloaded from the GEO database, and differential expression gene analysis was performed to obtain DEGs. Immune-related genes (IRGs) were obtained from ImmPort. Overlapping DEGs and IRGs got I-DEGs. Pearson coefficients were used to calculate DEGs and IRGs correlations in the AS and LBMD datasets. Louvain community discovery was used to cluster the co-expression network to get gene modules. The module most related to the immune module was defined as the key module. Metascape was used for enrichment analysis of key modules. Further, I-DEGs with the same trend in AS and LBMD were considered key I-DEGs. Multiple machine learning methods were used to construct diagnostic models based on key I-DEGs. IID database was used to find the context of I-DEGs, especially in the skeletal system. Gene–biological process and gene-pathway networks were constructed based on key I-DEGs. In addition, immune infiltration was analyzed on the AS dataset using the CIBERSORT algorithm. Results: A total of 19 genes were identified I-DEGs, of which IFNAR1, PIK3CG, PTGER2, TNF, and CCL3 were considered the key I-DEGs. These key I-DEGs had a good relationship with the hub genes of key modules. Multiple machine learning showed that key I-DEGs, as a signature, had an excellent diagnostic performance in both AS and LBMD, and the SVM model had the highest AUC value. Key I-DEGs were closely linked through bridge genes, especially in the skeletal system. Pathway analysis showed that PIK3CG, IFNAR1, CCL3, and TNF participated in NETs formation through pathways such as the MAPK signaling pathway. Immune infiltration analysis showed neutrophils had the most significant differences between case and control groups and a good correlation with key I-DEG. Conclusion: The key I-DEGs, TNF, CCL3, PIK3CG, PTGER2, and IFNAR1, can be utilized as biomarkers to determine the risk of LBMD in AS patients. They may affect neutrophil infiltration and NETs formation to influence the bone remodeling process in AS.

Dataset Information

Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci.

Publications

Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci.

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