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Exploring epistasis in candidate genes for rheumatoid arthritis.

ABSTRACT: The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data.

SUBMITTER: Ritchie MD

PROVIDER: S-EPMC2367541 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Exploring epistasis in candidate genes for rheumatoid arthritis.

Ritchie Marylyn D MD Bartlett Jacquelaine J Bush William S WS Edwards Todd L TL Motsinger Alison A AA Torstenson Eric S ES

BMC proceedings 20071218

The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflamm ...[more]

PMID: 18466572

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Project description:Rheumatoid arthritis (RA), which normally manifests as a multi‑joint inflammatory reaction, is a common immunological disease in clinical practice. However, the pathogenesis of RA has not yet been fully elucidated. Rituximab (RTX) is an effective drug in the treatment of RA, however its therapeutic efficacy and mechanism of action require further investigation. Thus, the present study aimed to screen the candidate key regulatory genes and explain the potential mechanisms of RA. Gene chips of RA and normal joint tissues were analyzed and, gene chips of RTX before and after treatment were investigated. In the present study, strong evidence supporting the pathogenesis of RA and mechanism of action of RTX were also revealed. Differentially expressed genes (DEGs) were analyzed using the limma package of RStudio software. A total of 1,150 DEGs were detected in RA compared with normal joint tissues. The upregulated genes were enriched in 'interleukin‑12 production', 'I‑κB kinase/NF‑κB signaling', 'regulation of cytokine production involved in immune response' and 'cytokine metabolic process'. Functional enrichment analysis showed that RTX was primarily involved in the inhibition of 'adaptive immune response', 'B cell activation involved in immune response' and 'immune effector process'. Subsequently, leukocyte immunoglobulin‑like receptor subfamily B member 1 (LILRB1), a hub gene with high connectivity degree, was selected, and traditional Chinese medicine libraries were molecularly screened according to the structure of the LILRB1 protein. The results indicated that kaempferol 3‑O‑β‑D‑glucosyl‑(1→2)‑β‑D‑glucoside exhibited the highest docking score. In the present study, the DEGs and their biological functions in RA and the pharmacological mechanism of RTX action were determined. Taken together, the results suggested that LILRB1 may be used as a molecular target for RA treatment, and kaempferol 3‑O‑β‑D‑glucosyl‑(1→2)‑β‑D‑glucoside may inhibit the pathological process of RA.

Project description:Objective Rheumatoid arthritis (RA) is one of the most prevalent inflammatory arthritis worldwide. However, the genes and pathways associated with macrophages from synovial fluids in RA patients still remain unclear. This study aims to screen and verify differentially expressed genes (DEGs) related to identifying candidate genes related to synovial macrophages in rheumatoid arthritis by bioinformatics analysis. Methods We searched the Gene Expression Omnibus (GEO) database, and GSE97779 and GSE10500 with synovial macrophages expression profiling from multiple RA microarray dataset were selected to conduct a systematic analysis. GSE97779 included nine macrophage samples from synovial fluids of RA patients and five macrophage samples from primary human blood of HC. GSE10500 included five macrophage samples from synovial fluids of RA patients and three macrophage samples from primary human blood of HC. Functional annotation of DEGs was performed, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein–protein interaction (PPI) network of DEGs was established using the STRING database. CytoHubba was used to identify hub genes. MCODE was used to determine gene clusters in the interactive network. Results There were 2638 DEGs (1425 upregulated genes and 1213 downregulated ones) and 889 DEGs (438 upregulated genes and 451 downregulated ones) selected from GSE97779 and GSE10500, respectively. Venn diagrams showed that 173 genes were upregulated and 106 downregulated in both two datasets. The top 10 hub genes, including FN1, VEGFA, HGF, SERPINA1, MMP9, PPBP, CD44, FPR2, IGF1, and ITGAM, were identified using the PPI network. Conclusion This study provides new insights for the potential biomarkers and the relevant molecular mechanisms in RA patients. Our findings suggest that the 10 candidate genes might be used in diagnosis, prognosis, and therapy of RA in the future. However, further studies are required to confirm the expression of these genes in synovial macrophages in RA and control specimen.

Project description:Rheumatoid arthritis (RA) is a chronic autoimmune, inflammatory disease, characterized by synovitis in small- and medium-sized joints and, if not treated early and efficiently, joint damage, and destruction. RA is a heterogeneous disease with a plethora of treatment options. The pro-inflammatory cytokine tumor necrosis factor (TNF) plays a central role in the pathogenesis of RA, and TNF inhibitors effectively repress inflammatory activity in RA. Currently, treatment decisions are primarily based on empirics and economic considerations. However, the considerable interpatient variability in response to treatment is a challenge. Markers for a more exact patient classification and stratification are lacking. The objective of this study was to identify markers in immune cell populations that distinguish RA patients from healthy donors with an emphasis on TNF signaling. We employed mass cytometry (CyTOF) with a panel of 13 phenotyping and 10 functional markers to explore signaling in unstimulated and TNF-stimulated peripheral blood mononuclear cells from 20 newly diagnosed, untreated RA patients and 20 healthy donors. The resulting high-dimensional data were analyzed in three independent analysis pipelines, characterized by differences in both data clean-up, identification of cell subsets/clustering and statistical approaches. All three analysis pipelines identified p-p38, IkBa, p-cJun, p-NFkB, and CD86 in cells of both the innate arm (myeloid dendritic cells and classical monocytes) and the adaptive arm (memory CD4+ T cells) of the immune system as markers for differentiation between RA patients and healthy donors. Inclusion of the markers p-Akt and CD120b resulted in the correct classification of 18 of 20 RA patients and 17 of 20 healthy donors in regression modeling based on a combined model of basal and TNF-induced signal. Expression patterns in a set of functional markers and specific immune cell subsets were distinct in RA patients compared to healthy individuals. These signatures may support studies of disease pathogenesis, provide candidate markers for response, and non-response to TNF inhibitor treatment, and aid the identification of future therapeutic targets.

Dataset Information

Exploring epistasis in candidate genes for rheumatoid arthritis.

Publications

Exploring epistasis in candidate genes for rheumatoid arthritis.

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