Measles contributes to rheumatoid arthritis: evidence from pathway and network analyses of genome-wide association studies.
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ABSTRACT:
SUBMITTER: Liu G
PROVIDER: S-EPMC3799991 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The current trend in genome-wide association studies is to identify regions where the true disease-causing genes may lie by evaluating thousands of single-nucleotide polymorphisms (SNPs) across the whole genome. However, many challenges exist in detecting disease-causing genes among the thousands of SNPs. Examples include multicollinearity and multiple testing issues, especially when a large number of correlated SNPs are simultaneously tested. Multicollinearity can often occur when predictor variables in a multiple regression model are highly correlated, and can cause imprecise estimation of association. In this study, we propose a simple stepwise procedure that identifies disease-causing SNPs simultaneously by employing elastic-net regularization, a variable selection method that allows one to address multicollinearity. At Step 1, the single-marker association analysis was conducted to screen SNPs. At Step 2, the multiple-marker association was scanned based on the elastic-net regularization. The proposed approach was applied to the rheumatoid arthritis (RA) case-control data set of Genetic Analysis Workshop 16. While the selected SNPs at the screening step are located mostly on chromosome 6, the elastic-net approach identified putative RA-related SNPs on other chromosomes in an increased proportion. For some of those putative RA-related SNPs, we identified the interactions with sex, a well known factor affecting RA susceptibility.
| S-EPMC2795922 | biostudies-literature
Project description:Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.
| S-EPMC10165422 | biostudies-literature
Project description:Genome-wide association studies (GWAS) led to the identification of numerous novel loci for a number of complex diseases. Pathway-based approaches using genotypic data provide tangible leads which cannot be identified by single marker approaches as implemented in GWAS. The available pathway analysis approaches mainly differ in the employed databases and in the applied statistics for determining the significance of the associated disease markers. So far, pathway-based approaches using GWAS data failed to consider the overlapping of genes among different pathways or the influence of protein-interactions. We performed a multistage integrative pathway (MIP) analysis on three common diseases--Crohn's disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D)--incorporating genotypic, pathway, protein- and domain-interaction data to identify novel associations between these diseases and pathways. Additionally, we assessed the sensitivity of our method by studying the influence of the most significant SNPs on the pathway analysis by removing those and comparing the corresponding pathway analysis results. Apart from confirming many previously published associations between pathways and RA, CD and T1D, our MIP approach was able to identify three new associations between disease phenotypes and pathways. This includes a relation between the influenza-A pathway and RA, as well as a relation between T1D and the phagosome and toxoplasmosis pathways. These results provide new leads to understand the molecular underpinnings of these diseases. The developed software herein used is available at http://www.cogsys.cs.uni-tuebingen.de/software/GWASPathwayIdentifier/index.htm.
| S-EPMC3808349 | biostudies-literature
Project description:OBJECTIVES:The molecular mechanism of rheumatoid arthritis (RA) remains elusive. We conducted a protein-protein interaction network-based integrative analysis of genome-wide association studies (GWAS) and gene expression profiles of RA. METHODS:We first performed a dense search of RA-associated gene modules by integrating a large GWAS meta-analysis dataset (containing 5539 RA patients and 20 169 healthy controls), protein interaction network and gene expression profiles of RA synovium and peripheral blood mononuclear cells (PBMCs). Gene ontology (GO) enrichment analysis was conducted by DAVID. The protein association networks of gene modules were generated by STRING. RESULTS:For RA synovium, the top-ranked gene module is HLA-A, containing TAP2, HLA-A, HLA-C, TAPBP and LILRB1 genes. For RA PBMCs, the top-ranked gene module is GRB7, consisting of HLA-DRB5, HLA-DRA, GRB7, CD63 and KIT genes. Functional enrichment analysis identified three significant GO terms for RA synovium, including antigen processing and presentation of peptide antigen via major histocompatibility complex class I (false discovery rate (FDR) = 4.86 × 10 - 4), antigen processing and presentation of peptide antigen (FDR = 2.33 × 10 - 3) and eukaryotic translation initiation factor 4F complex (FDR = 2.52 × 10 - 2). CONCLUSION:This study reported several RA-associated gene modules and their functional association networks.Cite this article: X. Xiao, J. Hao, Y. Wen, W. Wang, X. Guo, F. Zhang. Genome-wide association studies and gene expression profiles of rheumatoid arthritis: an analysis. Bone Joint Res 2016;5:314-319. DOI: 10.1302/2046-3758.57.2000502.
| S-EPMC5005471 | biostudies-literature
Project description:Rheumatoid arthritis (RA) is a multifactorial chronic autoimmune disease, which involves a complex interplay of environmental triggers and genetic components in its etiology. It has been shown that genetics only explain about half of the liability to develop RA, leaving a large room for non-genetic factors. Indeed, several environmental exposures including smoking, drinking, obesity, and dietary patterns (and more) have been identified to be associated with RA risk, yet the observational nature of conventional epidemiological investigation hampers causal inference, as the validity of results could be plagued by measurement error, confounding, and/or reverse causality. Mendelian randomization (MR) is a novel statistical approach that uses genetic variants as instrumental variables (IV) to make causal inferences from observational data. The current genetic discoveries in the many heritable and modifiable human complex traits have provided an exceptional opportunity to evaluate a putative causal relationship between exposure and outcome in the absence of high-quality experimental or intervention studies, through a MR design. In the current review, we detail the contribution of MR studies hitherto conducted for modifiable environmental exposures with the risk of RA to understand the role of these factors in RA pathogenesis. We start with a brief introduction of each study, follow by a summarization of shortcomings and conclude by highlighting future directions. The application of MR design in the field of rheumatology remains limited. Only a few MR studies have examined the causal roles of vitamin D, cigarette smoking, alcohol consumption, coffee consumption, and levels of education in RA, where, no consistent evidence for a causal relationship has been found. Most studies lacked sensitivity analyses to verify MR model assumptions and to guarantee the validity of results. Almost all studies are likely to bias the strength of association towards a null value, since they used IVs from earlier GWAS(s) of exposures with a small sample size (i.e., few genetic markers). As the magnitudes of GWAS expand rapidly, additional trait-associated loci have been discovered. Incorporating these loci would greatly improve the strength of genetic instruments, as well as both the accuracy and precision of MR estimates. To conclude, there is a need for an update and a huge space for improvement of future MR studies in RA.
| S-EPMC7310443 | biostudies-literature
Project description:Rheumatoid arthritis (RA, MIM 180300) is a chronic and complex autoimmune disease. Using the North American Rheumatoid Arthritis Consortium (NARAC) data set provided in Genetic Analysis Workshop 16 (GAW16), we used the genotype-trait distortion (GTD) scores and proposed analysis procedures to capture the gene-gene interaction effects of multiple susceptibility gene regions on RA. In this paper, we focused on 27 RA candidate gene regions (531 SNPs) based on a literature search. Statistical significance was evaluated using 1000 permutations. HLADRB1 was found to have strong marginal association with RA. We identified 14 significant interactions (p < 0.01), which were aggregated into an association network among 12 selected candidate genes PADI4, FCGR3, TNFRSF1B, ITGAV, BTLA, SLC22A4, IL3, VEGF, TNF, NFKBIL1, TRAF1-C5, and MIF. Based on our and other contributors' findings during the GAW16 conference, we further studied 24 candidate regions with 336 SNPs. We found 23 significant interactions (p-value < 0.01), nine interactions in addition to our initial findings, and the association network was extended to include candidate genes HLA-A, HLA-B, HLA-C, CTLA4, and IL6. As we will discuss in this paper, the reported possible interactions between genes may suggest potential biological activities of RA.
| S-EPMC2795977 | biostudies-literature
Project description:ObjectivesFatigue is a disabling symptom in people with RA. This study aims to describe the prevalence, risk factors and longitudinal course of fatigue in early RA.MethodsDemographic, clinical, quality of life (QoL), comorbidities and laboratory data were from the Early RA Network (ERAN), a UK multicentre inception cohort of people with RA. Fatigue was measured using the vitality subscale of the 36-item Short Form Health Survey, where higher values represent better QoL. Baseline prevalences of fatigue classifications were age and sex standardized. Linear regression, hierarchical growth curve modelling and group-based trajectory modelling (GBTM) were utilized.ResultsAt baseline (n = 1236, 67% female, mean age 57 years), the mean vitality was 41 (s.d. 11) and disease duration was 11 months (interquartile range 7-18). Age- and sex-standardized prevalence rates of fatigue and severe fatigue were 44% (95% CI 39, 50) and 19% (95% CI 15, 23), respectively. Fatigue changed little over 3 years and five measurement occasions β = -0.13 (95% CI -0.23, -0.02). GBTM identified two subgroups, which we named 'Fatigue' (53%) and 'No-fatigue' (47%). Female sex, worse pain, mental health and functional ability were associated with greater fatigue and predicted Fatigue group membership (area under the receiver operating characteristics curve = 0.81). Objective measures of inflammation-swollen joint count and ESR-were not significantly associated with fatigue.ConclusionsFatigue is prevalent and persistent in early RA. Diverse characteristics indicative of central mechanisms are associated with persistent fatigue. Management of fatigue might require interventions targeted at central mechanisms in addition to inflammatory disease modification. People who require such interventions might be identified at presentation with early RA.
| S-EPMC9434222 | biostudies-literature
Project description:Rheumatoid arthritis (RA) is a multifactorial disease with complex genetic etiology, about which little is known. Here, we apply a two-stage procedure in which a quick first-stage analysis was used to narrow down targets for a more thorough and detailed testing for gene x gene interaction. Potentially interesting regions were first identified by testing for major gene effects using non-parametric linkage methods. To select regions of interest, we first tested for linkage to three different RA-related traits one at a time: RA affection status and the quantitative phenotypes rheumatoid factor IgM and anti-cyclic citrullinated peptide levels. These linkage analyses identified regions on chromosomes 3, 5, 6, 8, 16, 18, 19, and 20. We subsequently analyzed the selected regions in a pairwise manner to detect gene x gene interactions influencing RA using a recently developed two-dimensional linkage method. We found evidence of interacting loci on chromosomes 5, 6, and 18.
| S-EPMC2367460 | biostudies-literature
Project description:Evaluation of the association between single-nucleotide polymorphisms (SNPs) and disease outcomes is widely used to identify genetic risk factors for complex diseases. Although this analysis paradigm has made significant progress in many genetic studies, many challenges remain, such as the requirement of a large sample size to achieve adequate power. Here we use rheumatoid arthritis (RA) as an example and explore a new analysis strategy: pathway-based analysis to search for related genes and SNPs contributing to the disease.We first propose the application of measure of explained variation to quantify the predictive ability of a given SNP. We then use gene set enrichment analysis to evaluate enrichment of specific pathways, where pathways, are considered enriched if they consist of genes that are associated with the phenotype of interest above and beyond is expected by chance. The results are also compared with score tests for association analysis by adjusting for population stratification.Our study identified some significantly enriched pathways, such as "cell adhesion molecules," which are known to play a key role in RA. Our results showed that pathway-based analysis may identify other biologically interesting loci (e.g., rs1018361) related to RA: the gene (CTLA4) closest to this marker has previously been shown to be associated with RA and the gene is in the significant pathways we identified, even though the marker has not reached genome-wide significance in univariate single-marker analysis.
| S-EPMC2795901 | biostudies-literature
Project description:The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Currently, single-nucleotide polymorphism (SNP) association analysis is the most widely used method of genome-wide association data, but recent research shows that multi-marker tests of association may provide greater power, especially when more than one mutation is present within a gene and the mutations are in low linkage disequilibrium with each other. Here we use a multi-marker association test based on regression to SNPs located within known genes to obtain a gene-level score of association. We then perform pathway analysis using this score as a measure of gene importance. We use two tests of pathway enrichment - a binomial test and a random set method. By utilizing publicly available gene and pathway information, we identify B cell, cytokine and inflammation response, and antigen presentation pathways as being associated with rheumatoid arthritis. These results confirm known biological mechanisms for auto-immunity disorders, of which rheumatoid arthritis is one.
| S-EPMC2795995 | biostudies-literature