Association Study Of Rheumatoid Arthritis Research Articles

Effect of Polymorphisms in the FCN1, FCN2, and FCN3 Genes on the Susceptibility to Develop Rheumatoid Arthritis: A Systematic Review.

Genetic association studies in rheumatoid arthritis conducted in various populations have yielded heterogeneous results. The present systematic review was conducted to synthesize the results of the studies in order to establish the impact of polymorphisms in the ficolin-coding genes FCN1, FCN2, and FCN3 on the susceptibility to develop rheumatoid arthritis. A systematic literature review was performed using the following keywords "gene (FCN1/FCN2/FCN3)", "Polymorphism/Genetic Variant", and "rheumatoid arthritis" in different databases until January 2022. Authors assessed articles by title/abstract and then assessed by full text for data extraction. The risk of bias was assessed using the Newcastle-Ottawa scale. Data synthesis was performed qualitatively and quantitatively. A total of 1519 articles were eligible for inclusion in this review, 3 were identified as relevant for the quantitative synthesis with 670 patients and 1019 controls. For the FCN1 gene, an association was found in the dominant and recessive genetic models of the variants rs2989727 (genotype TT = OR: 0.577, 95% CI: 0.430-0.769) and rs1071583 (genotype GG = OR: 1.537, 95% CI: 1.153-2.049, p = 0.0032) with the development of rheumatoid arthritis as a protective or susceptibility factor. FCN2 and FCN3 genes did not show association with disease development. The FCN1 gene variants rs2989727 and rs1071583 are associated with the risk of developing rheumatoid arthritis in populations from Brazil and Belgium, but not in FCN2 and FCN3 gene variants.

Rheumatoid arthritis decreases risk for Parkinson\u2019s disease: a Mendelian randomization study

Epidemiological and clinical studies have suggested comorbidity between rheumatoid arthritis and Parkinson’s disease (PD), but whether there exists a causal association and the effect direction of rheumatoid arthritis on PD is controversial and elusive. To evaluate the causal relationship, we first estimated the genetic correlation between rheumatoid arthritis and PD, and then performed a two-sample Mendelian randomization analysis based on summary statistics from large genome-wide association studies of rheumatoid arthritis (N = 47,580) and PD (N = 482,703). We identified negative and significant correlation between rheumatoid arthritis and PD (genetic correlation: −0.10, P = 0.0033). Meanwhile, one standard deviation increase in rheumatoid arthritis risk was associated with a lower risk of PD (OR: 0.904, 95% CI: 0.866–0.943, P: 2.95E–06). The result was robust under all sensitivity analyses. Our results provide evidence supporting a protective role of rheumatoid arthritis on PD. A deeper understanding of the inflammation and immune response is likely to elucidate the potential pathogenesis of PD and identify therapeutic targets for PD.

The genetic basis for the inverse relationship between rheumatoid arthritis and schizophrenia.

IntroductionRheumatoid arthritis is a common autoimmune disease and schizophrenia is a relatively common and debilitating neurological disorder. There are several common features between rheumatoid arthritis and schizophrenia. The inverse relationship between rheumatoid arthritis and schizophrenia has been replicated in several studies. Despite evidence for an inverse epidemiological relationship and negative correlations for risk between rheumatoid arthritis and schizophrenia, there are no biological data that directly support this inverse relationship.Materials and Methods’We meta‐analyzed the genome‐wide association studies to investigate the shared association loci between rheumatoid arthritis and schizophrenia at the genome‐wide scale. Rheumatoid arthritis‐ and schizophrenia‐associated loci in most recent genome‐wide association studies of rheumatoid arthritis and schizophrenia were tested. Genetic risk score analysis was also conducted to investigate the collective contribution of schizophrenia risk loci to rheumatoid arthritis risk.ResultsRheumatoid arthritis and schizophrenia meta‐genome‐wide association study showed a significant peak at the major histocompatibility complex locus on chromosome 6 in both rheumatoid arthritis‐schizophrenia meta‐genome‐wide association study and inverted meta‐genome‐wide association study datasets. Testing rheumatoid arthritis‐ and schizophrenia‐associated loci outside the human leukocyte antigen region showed no association with both rheumatoid arthritis and schizophrenia at a genome‐wide level of significance. Weighted genetic risk scores showed no evidence for a statistically significant association between rheumatoid arthritis and schizophrenia.ConclusionThe finding of our study is consistent with the role of the major histocompatibility complex locus in the genetic correlation between rheumatoid arthritis and schizophrenia, and suggests that either schizophrenia has an autoimmune basis and/or rheumatoid arthritis has an active neurological component.

Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins

BackgroundRheumatoid arthritis is a common autoimmune disorder influenced by both genetic and environmental factors. Epigenome-wide association studies can identify environmentally mediated epigenetic changes such as altered DNA methylation, which may also be influenced by genetic factors. To investigate possible contributions of DNA methylation to the aetiology of rheumatoid arthritis with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs.MethodsGenome-wide DNA methylation was assessed in 79 monozygotic twin pairs discordant for rheumatoid arthritis using the HumanMethylation450 BeadChip array (Illumina). Discordant twins were tested for both differential DNA methylation and methylation variability between rheumatoid arthritis and healthy twins. The methylation variability signature was then compared with methylation variants from studies of other autoimmune diseases and with an independent healthy population.ResultsWe have identified a differentially variable DNA methylation signature that suggests multiple stress response pathways may be involved in the aetiology of the disease. This methylation variability signature also highlighted potential epigenetic disruption of multiple RUNX3 transcription factor binding sites as being associated with disease development. Comparison with previously performed epigenome-wide association studies of rheumatoid arthritis and type 1 diabetes identified shared pathways for autoimmune disorders, suggesting that epigenetics plays a role in autoimmunity and offering the possibility of identifying new targets for intervention.ConclusionsThrough genome-wide analysis of DNA methylation in disease-discordant monozygotic twins, we have identified a differentially variable DNA methylation signature, in the absence of differential methylation in rheumatoid arthritis. This finding supports the importance of epigenetic variability as an emerging component in autoimmune disorders.

Epigenome-wide association study of rheumatoid arthritis identifies differentially methylated loci in B cells

Epigenetic regulation of immune cell types could be critical for the development and maintenance of autoimmune diseases like rheumatoid arthritis (RA). B cells are highly relevant in RA, since patients express autoantibodies and depleting this cell type is a successful therapeutic approach. Epigenetic variation, such as DNA methylation, may mediate the pathogenic activity of B cells. In this study, we performed an epigenome-wide association study (EWAS) for RA with three different replication cohorts, to identify disease-specific alterations in DNA methylation in B cells. CpG methylation in isolated B lymphocytes was assayed on the Illumina HumanMethylation450 BeadChip in a discovery cohort of RA patients (N = 50) and controls (N = 75). Differential methylation was observed in 64 CpG sites (q < 0.05). Six biological pathways were also differentially methylated in RA B cells. Analysis in an independent cohort of patients (N = 15) and controls (N = 15) validated the association of 10 CpG sites located on 8 genes CD1C, TNFSF10, PARVG, NID1, DHRS12, ITPK1, ACSF3 and TNFRSF13C, and 2 intergenic regions. Differential methylation at the CBL signaling pathway was replicated. Using an additional case-control cohort (N = 24), the association between RA risk and CpGs cg18972751 at CD1C (P = 2.26 × 10-9) and cg03055671 at TNFSF10 (P = 1.67 × 10-8) genes was further validated. Differential methylation at genes CD1C, TNFSF10, PARVG, NID1, DHRS12, ITPK1, ACSF3, TNFRSF13C and intergenic region chr10p12.31 was replicated in a cohort of systemic lupus erythematosus (SLE) patients (N = 47) and controls (N = 56). Our results highlight genes that may drive the pathogenic activity of B cells in RA and suggest shared methylation patterns with SLE.

Rheumatoid Arthritis Naive T Cells Share Hypermethylation Sites With Synoviocytes.

ObjectiveTo determine whether differentially methylated CpGs in synovium‐derived fibroblast‐like synoviocytes (FLS) of patients with rheumatoid arthritis (RA) were also differentially methylated in RA peripheral blood (PB) samples.MethodsFor this study, 371 genome‐wide DNA methylation profiles were measured using Illumina HumanMethylation450 BeadChips in PB samples from 63 patients with RA and 31 unaffected control subjects, specifically in the cell subsets of CD14+ monocytes, CD19+ B cells, CD4+ memory T cells, and CD4+ naive T cells.ResultsOf 5,532 hypermethylated FLS candidate CpGs, 1,056 were hypermethylated in CD4+ naive T cells from RA PB compared to control PB. In analyses of a second set of CpG candidates based on single‐nucleotide polymorphisms from a genome‐wide association study of RA, 1 significantly hypermethylated CpG in CD4+ memory T cells and 18 significant CpGs (6 hypomethylated, 12 hypermethylated) in CD4+ naive T cells were found. A prediction score based on the hypermethylated FLS candidates had an area under the curve of 0.73 for association with RA case status, which compared favorably to the association of RA with the HLA–DRB1 shared epitope risk allele and with a validated RA genetic risk score.ConclusionFLS‐representative DNA methylation signatures derived from the PB may prove to be valuable biomarkers for the risk of RA or for disease status.

Rheumatoid arthritis-associated RBPJ polymorphism alters memory CD4+ T cells.

Notch signaling has recently emerged as an important regulator of immune responses in autoimmune diseases. The recombination signal-binding protein for immunoglobulin kappa J region (RBPJ) is a transcriptional repressor, but converts into a transcriptional activator upon activation of the canonical Notch pathway. Genome-wide association studies of rheumatoid arthritis (RA) identified a susceptibility locus, rs874040(CC), which implicated the RBPJ gene. Here, chromatin state mapping generated using the chromHMM algorithm reveals strong enhancer regions containing DNase I hypersensitive sites overlapping the rs874040 linkage disequilibrium block in human memory, but not in naïve CD4(+) T cells. The rs874040 overlapping this chromatin state was associated with increased RBPJ expression in stimulated memory CD4(+) T cells from healthy subjects homozygous for the risk allele (CC) compared with memory CD4(+) T cells bearing the protective allele (GG). Transcriptomic analysis of rs874040(CC) memory T cells showed a repression of canonical Notch target genes IL (interleukin)-9, IL-17 and interferon (IFN)γ in the basal state. Interestingly, activation of the Notch pathway using soluble Notch ligand, Jagged2-Fc, induced IL-9 and IL-17A while delta-like 4Fc, another Notch ligand, induced higher IFNγ expression in the rs874040(CC) memory CD4(+) T cells compared with their rs874040(GG) counterparts. In RA, RBPJ expression is elevated in memory T cells from RA patients compared with control subjects, and this was associated with induced inflammatory cytokines IL-9, IL-17A and IFNγ in response to Notch ligation in vitro. These findings demonstrate that the rs874040(CC) allele skews memory T cells toward a pro-inflammatory phenotype involving Notch signaling, thus increasing the susceptibility to develop RA.

A genome-wide association study of rheumatoid arthritis without antibodies against citrullinated peptides

IntroductionRheumatoid arthritis (RA) patients can be classified based on presence or absence of anticitrullinated peptide antibodies (ACPA) in their serum. This heterogeneity among patients may reflect important biological differences underlying...

Emerging patterns of genetic overlap across autoimmune disorders

Most of the recently identified autoimmunity loci are shared among multiple autoimmune diseases. The pattern of genetic association with autoimmune phenotypes varies, suggesting that certain subgroups of autoimmune diseases are likely to share etiological similarities and underlying mechanisms of disease. In this review, we summarize the major findings from recent studies that have sought to refine genotype-phenotype associations in autoimmune disease by identifying both shared and distinct autoimmunity loci. More specifically, we focus on information from recent genome-wide association studies of rheumatoid arthritis, ankylosing spondylitis, celiac disease, multiple sclerosis, systemic lupus erythematosus, type 1 diabetes and inflammatory bowel disease. Additional work in this area is warranted given both the opportunity it provides to elucidate pathogenic mechanisms in autoimmunity and its potential to inform the development of improved diagnostic and therapeutic tools for this group on complex human disorders.

Genome‐wide association study of rheumatoid arthritis in Koreans: Population‐specific loci as well as overlap with European susceptibility loci

To perform a genome-wide association study (GWAS) in Koreans in order to identify susceptibility loci for rheumatoid arthritis (RA). We generated high-quality genotypes for 441,398 single-nucleotide polymorphisms (SNPs) in 801 RA cases and 757 controls. We then tested 79 markers from 46 loci for replication in an independent sample of 718 RA cases and 719 controls. Genome-wide significance (P < 5 × 10(-08) ) was attained by markers from the major histocompatibility complex region and from the PADI4 gene. The replication data showed nominal association signals (P < 5 × 10(-02) ) for markers from 11 of the 46 replicated loci, greatly exceeding random expectation. Genes that were most significant in the replication stage and in the combined analysis include the known European RA loci BLK, AFF3, and CCL21. Thus, in addition to the previously associated STAT4 alleles, variants at these three loci may contribute to RA not only among Europeans, but also among Asians. In addition, we observed replication signals near the genes PTPN2, FLI1, ARHGEF3, LCP2, GPR137B, TRHDE, and CGA1. Based on the excess of small P values in the replication stage study, we estimate that more than half of these loci are genuine RA susceptibility genes. Finally, we systematically analyzed the presence of association signals in Koreans at established European RA loci, which showed a significant enrichment of European RA loci among the Korean RA loci. Genetic risk for RA involves both population-specific loci as well as many shared genetic susceptibility loci in comparisons of Asian and European populations.

A genetic marker at the OLIG3/TNFAIP3 locus associates with methotrexate continuation in early inflammatory polyarthritis: results from the Norfolk Arthritis Register

Whole-genome association studies in rheumatoid arthritis have identified single-nucleotide polymorphisms (SNPs) predisposing to disease with moderate risk. We aimed to investigate the role of these markers in predicting methotrexate (MTX) response, measured by continuation on MTX monotherapy in patients with recent onset inflammatory polyarthritis (IP). In all, 19 SNPs were genotyped in 736 patients treated with MTX following registration, or not more than 3 months before registration, to the Norfolk Arthritis Register. The association of SNPs with MTX continuation by year 1 and by year 2 was investigated using Cox proportional hazard regression models. A SNP within the OLIG3/TNFAIP3 locus (rs6920220) was associated with being less likely to maintain MTX monotherapy at year 1, hazards ratio (HR) 1.73 (1.18, 2.52) and year 2, HR 1.49 (1.11, 2.00); correlating with an increased in adverse events. Weak evidence for an effect at the PTPN22 locus was also observed. These findings require replication in other large datasets.

Locus category based analysis of a large genome-wide association study of rheumatoid arthritis.

To pinpoint true positive single-nucleotide polymorphism (SNP) associations in a genome-wide association study (GWAS) of rheumatoid arthritis (RA), we categorize genetic loci by external knowledge. We test both the 'enrichment of associated loci' in a locus category and the 'combined association' of a locus category. The former is quantified by the odds ratio for the presence of SNP associations at the loci of a category, whereas the latter is quantified by the number of loci in a category that have SNP associations. These measures are compared with their expected values as obtained from the permutation of the affection status. To account for linkage disequilibrium (LD) among SNPs, we view each LD block as a genetic locus. Positional candidates were defined as loci implicated by earlier GWAS results, whereas functional candidates were defined by annotations regarding the molecular roles of genes, such as gene ontology categories. As expected, immune-related categories show the largest enrichment signal, although it is not very strong. The intersection of positional and functional candidate information predicts novel RA loci near the genes TEC/TXK, MBL2 and PIK3R1/CD180. Notably, a combined association signal is not only produced by immune-related categories, but also by most other categories and even randomly defined categories. The unspecific quality of these signals limits the possible conclusions from combined association tests. It also reduces the magnitude of enrichment test results. These unspecific signals might result from common variants of small effect and hardly concentrated in candidate categories, or an inflated size of associated regions from weak LD with infrequent mutations.

Dissection of the FCGR3A association with RA: increased association in men and with autoantibody positive disease

ObjectivesGenome-wide association studies in rheumatoid arthritis (RA) have failed to examine the FCGR gene cluster because of the confounding effects of segmental duplication. This study aimed to replicate previous candidate...

A regulatory variant in CCR6 is associated with rheumatoid arthritis susceptibility

Rheumatoid arthritis is a common autoimmune disease with a complex genetic etiology. Here, through a genome-wide association study of rheumatoid arthritis, we identified a polymorphism in CCR6, the gene encoding chemokine (C-C motif) receptor 6 (a surface marker for Th17 cells) at 6q27, that was associated with rheumatoid arthritis susceptibility and was validated in two independent replication cohorts from Japan (rs3093024, a total of 7,069 individuals with rheumatoid arthritis (cases) and 20,727 controls, overall odds ratio = 1.19, P = 7.7 x 10(-19)). We identified a triallelic dinucleotide polymorphism of CCR6 (CCR6DNP) in strong linkage disequilibrium with rs3093024 that showed effects on gene transcription. The CCR6DNP genotype was correlated with the expression level of CCR6 and was associated with the presence of interleukin-17 (IL-17) in the sera of subjects with rheumatoid arthritis. Moreover, CCR6DNP was associated with susceptibility to Graves' and Crohn's diseases. These results suggest that CCR6 is critically involved in IL-17-driven autoimmunity in human diseases.

Genome-wide association study of rheumatoid arthritis by a score test based on wavelet transformation

We have conducted a genome-wide association study on the Genetic Analysis Workshop (GAW) 16 rheumatoid arthritis data using a multilocus score test based on wavelet transform proposed recently by the authors. The wavelet-based test automatically adjusts for the amount of noise suppressed from the data. The power of the test is also increased by using the genetic information contained in the spatial ordering of single-nucleotide polymorphisms on a chromosome. After adjusting for the effect of population stratification, the test identified some previously discovered rheumatoid arthritis susceptibility loci (HLA-DRB1 and rs3761847) as well as some loci (rs2076530 and rs3130340) known to have association with sarcoidosis and bone mineral density. It was previously reported that patients with rheumatoid arthritis have elevated prevalence of sarcoidosis and have reduced bone mass. This new test provides a useful tool in genome-wide association studies.

Comparison between the stochastic search variable selection and the least absolute shrinkage and selection operator for genome-wide association studies of rheumatoid arthritis

Because multiple loci control complex diseases, there is great interest in testing markers simultaneously instead of one by one. In this paper, we applied two model selection algorithms: the stochastic search variable selection (SSVS) and the least absolute shrinkage and selection operator (LASSO) to two quantitative phenotypes related to rheumatoid arthritis (RA). The Genetic Analysis Workshop 16 data includes 2,062 unrelated individuals and 545,080 single-nucleotide polymorphism markers from the Illumina 550 k chip. We performed our analyses on the cases as the quantitative phenotype data was not provided for the controls. The performance of the two algorithms was compared. Using sure independence screening as the prescreening procedure, both SSVS and LASSO give small models. No markers are identified in the human leukocyte antigen region of chromosome 6 that was shown to be associated with RA. SSVS and LASSO identify seven common loci, and some of them are on genes LRRC8D, LRP1B, and COLEC12. These genes have not been reported to be associated with RA. LASSO also identified a common locus on gene KTCD21 for the two phenotypes (marker rs230662 and rs483731, respectively). SSVS outperforms LASSO in simulation studies. Both SSVS and LASSO give small models on the RA data, however this depends on model parameters. We also demonstrate the ability of both LASSO and SSVS to handle more markers than the number of samples.

Pathway-based analysis of a genome-wide case-control association study of rheumatoid arthritis

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.

Elastic-net regularization approaches for genome-wide association studies of rheumatoid arthritis

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.

Comparison of methods for correcting population stratification in a genome-wide association study of rheumatoid arthritis: principal-component analysis versus multidimensional scaling

Population stratification (PS) represents a major challenge in genome-wide association studies. Using the Genetic Analysis Workshop 16 Problem 1 data, which include samples of rheumatoid arthritis patients and healthy controls, we compared two methods that can be used to evaluate population structure and correct PS in genome-wide association studies: the principal-component analysis method and the multidimensional-scaling method. While both methods identified similar population structures in this dataset, principal-component analysis performed slightly better than the multidimensional-scaling method in correcting for PS in genome-wide association analysis of this dataset.

Application of imputation methods to the analysis of rheumatoid arthritis data in genome-wide association studies

Most genetic association studies only genotype a small proportion of cataloged single-nucleotide polymorphisms (SNPs) in regions of interest. With the catalogs of high-density SNP data available (e.g., HapMap) to researchers today, it has become possible to impute genotypes at untyped SNPs. This in turn allows us to test those untyped SNPs, the motivation being to increase power in association studies. Several imputation methods and corresponding software packages have been developed for this purpose. The objective of our study is to apply three widely used imputation methods and corresponding software packages to a data from a genome-wide association study of rheumatoid arthritis from the North American Rheumatoid Arthritis Consortium in Genetic Analysis Workshop 16, to compare the performances of the three methods, to evaluate their strengths and weaknesses, and to identify additional susceptibility loci underlying rheumatoid arthritis. The software packages used in this paper included a program for Bayesian imputation-based association mapping (BIMBAM), a program for imputing unobserved genotypes in case-control association studies (IMPUTE), and a program for testing untyped alleles (TUNA). We found some untyped SNP that showed significant association with rheumatoid arthritis. Among them, a few of these were not located near any typed SNP that was found to be significant and thus may be worth further investigation.