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Recent metabolomics studies of Rheumatoid Arthritis (RA) reported few metabolites that were associated with the disease, either due to small cohort sizes or limited coverage of metabolic pathways. Our objective is to identify metabolites associated with RA and its cofounders using a new untargeted metabolomics platform. Moreover, to investigate the pathomechanism of RA by identifying correlations between RA-associated metabolites. 132 RA patients and 104 controls were analyzed for 927 metabolites. Metabolites were tested for association with RA using linear regression. OPLS-DA was used to discriminate RA patients from controls. Gaussian Graphical Models (GGMs) were used to identify correlated metabolites. 32 metabolites are identified as significantly (Bonferroni) associated with RA, including the previously reported metabolites as DHEAS, cortisol and androstenedione and extending that to a larger set of metabolites in the steroid pathway. RA classification using metabolic profiles shows a sensitivity of 91% and specificity of 88%. Steroid levels show variation among the RA patients according to the corticosteroid treatment; lowest in those taking the treatment at the time of the study, higher in those who never took the treatment, and highest in those who took it in the past. Finally, the GGM reflects metabolite relations from the steroidogenesis pathway.

Objective Genetic factors underlying susceptibility to rheumatoid arthritis (RA) in Arab populations are largely unknown. This genome-wide association study (GWAS) was undertaken to explore the generalizability of previously reported RA loci to Arab subjects and to discover new Arab-specific genetic loci. Methods The Genetics of Rheumatoid Arthritis in Some Arab States Study was designed to examine the genetics and clinical features of RA patients from Jordan, the Kingdom of Saudi Arabia, Lebanon, Qatar, and the United Arab Emirates. In total, >7 million single-nucleotide polymorphisms (SNPs) were tested for association with RA overall and with seropositive or seronegative RA in 511 RA cases and 352 healthy controls. In addition, replication of 15 signals was attempted in 283 RA cases and 221 healthy controls. A genetic risk score of 68 known RA SNPs was also examined in this study population. Results Three loci (HLA region, intergenic 5q13, and 17p13 at SMTNL2/GGT6) reached genome-wide significance in the analyses of association with RA and with seropositive RA, and for all 3 loci, evidence of independent replication was demonstrated. Consistent with the findings in European and East Asian populations, the association of RA with HLA–DRB1 amino acid position 11 conferred the strongest effect (P = 4.8 × 10−16), and a weighted genetic risk score of previously associated RA loci was found to be associated with RA (P = 3.41 × 10−5) and with seropositive RA (P = 1.48 × 10−6) in this population. In addition, 2 novel associations specific to Arab populations were found at the 5q13 and 17p13 loci. Conclusion This first RA GWAS in Arab populations confirms that established HLA-region and known RA risk alleles contribute strongly to the risk and severity of disease in some Arab groups, suggesting that the genetic architecture of RA is similar across ethnic groups. Moreover, this study identified 2 novel RA risk loci in Arabs, offering further population-specific insights into the pathophysiology of RA.

Item does not contain fulltext OBJECTIVE: In many rheumatoid arthritis (RA) patients, disease is controlled with anti-tumor necrosis factor (anti-TNF) biologic therapies. However, in a significant number of patients, the disease fails to respond to anti-TNF therapy. We undertook the present study to examine the hypothesis that rare and low-frequency genetic variants might influence response to anti-TNF treatment. METHODS: We sequenced the coding region of 750 genes in 1,094 RA patients of European ancestry who were treated with anti-TNF. After quality control, 690 genes were included in the analysis. We applied single-variant association and gene-based association tests to identify variants associated with anti-TNF treatment response. In addition, given the key mechanistic role of TNF, we performed gene set analyses of 27 TNF pathway genes. RESULTS: We identified 14,420 functional variants, of which 6,934 were predicted as nonsynonymous 2,136 of which were further predicted to be "damaging." Despite the fact that the study was well powered, no single variant or gene showed study-wide significant association with change in the outcome measures disease activity or European League Against Rheumatism response. Intriguingly, we observed 3 genes, of 27 with nominal signals of association (P < 0.05), that were involved in the TNF signaling pathway. However, when we performed a rigorous gene set enrichment analysis based on association P value ranking, we observed no evidence of enrichment of association at genes involved in the TNF pathway (Penrichment = 0.15, based on phenotype permutations). CONCLUSION: Our findings suggest that rare and low-frequency protein-coding variants in TNF signaling pathway genes or other genes do not contribute substantially to anti-TNF treatment response in patients with RA.

To reduce costs and improve clinical relevance of genetic studies, there has been increasing interest in performing such studies in hospital-based cohorts by linking phenotypes extracted from electronic medical records (EMRs) to genotypes assessed in routinely collected medical samples. A fundamental difficulty in implementing such studies is extracting accurate information about disease outcomes and important clinical covariates from large numbers of EMRs. Recently, numerous algorithms have been developed to infer phenotypes by combining information from multiple structured and unstructured variables extracted from EMRs. Although these algorithms are quite accurate, they typically do not provide perfect classification due to the difficulty in inferring meaning from the text. Some algorithms can produce for each patient a probability that the patient is a disease case. This probability can be thresholded to define case-control status, and this estimated case-control status has been used to replicate known genetic associations in EMR-based studies. However, using the estimated disease status in place of true disease status results in outcome misclassification, which can diminish test power and bias odds ratio estimates. We propose to instead directly model the algorithm-derived probability of being a case. We demonstrate how our approach improves test power and effect estimation in simulation studies, and we describe its performance in a study of rheumatoid arthritis. Our work provides an easily implemented solution to a major practical challenge that arises in the use of EMR data, which can facilitate the use of EMR infrastructure for more powerful, cost-effective, and diverse genetic studies.

The objective of this study was to study genetic factors that influence quantitative anticyclic citrullinated peptide (anti-CCP) antibody levels in RA patients. We carried out a genome-wide association study (GWAS) meta-analysis using 1975 anti-CCP+ RA patients from three large cohorts, the Brigham Rheumatoid Arthritis Sequential Study (BRASS), North American Rheumatoid Arthritis Consortium (NARAC) and the Epidemiological Investigation of RA (EIRA). We also carried out a genome-wide complex trait analysis (GCTA) to estimate the heritability of anti-CCP levels. GWAS-meta-analysis showed that anti-CCP levels were most strongly associated with the human leukocyte antigen (HLA) region with a P-value of 2 × 10(-11) for rs1980493. There were 112 SNPs in this region that exceeded the genome-wide significance threshold of 5 × 10(-8), and all were in linkage disequilibrium (LD) with the HLA- DRB1*03 allele with LD r(2) in the range of 0.25-0.88. Suggestive novel associations outside of the HLA region were also observed for rs8063248 (near the GP2 gene) with a P-value of 3 × 10(-7). None of the known RA risk alleles (∼52 loci) were associated with anti-CCP level. Heritability analysis estimated that 44% of anti-CCP variation was attributable to genetic factors captured by GWAS variants. In summary, anti-CCP level is a heritable trait, and HLA-DR3 and GP2 are associated with lower anti-CCP levels.

There is limited information on the epidemiology and treatment patterns of rheumatoid arthritis (RA) across the Arab region. We aim in this study to describe the demographic characteristics, clinical profile, and treatment patterns of patients of Arab ancestry with RA.This is a cross sectional study of 895 patients with established rheumatoid arthritis enrolled from five sites (Jordan, Lebanon, Qatar, Kingdom of Saudi Arabia (KSA), and United Arab Emirates). Demographic characteristics, clinical profile, and treatment patterns are compared between the five countries.The majority of our patients are women, have an average disease duration of 10 years, are married and non-smokers, with completed secondary education. We report a high (80%) ever-use of methotrexate (MTX) and steroids among our RA population, while the ever-use of disease modifying anti-rheumatic drugs (DMARDs) and TNF-inhibitors average around 67% and 33%, respectively. There are variations in RA treatment use between the five country sites. Highest utilization of steroids is identified in Jordan and KSA (p-value0.001), while the highest ever-use of TNF-inhibitors is reported in KSA (p-value0.001).Disparities in usage of RA treatments among Arab patients are noted across the five countries. National gross domestic product (GDP), as well as some other unique features in each country likely affect these. Developing treatment guidelines specific to this region could contribute in delivering standardized therapies to RA patients.

Objective A recent high-density fine-mapping (ImmunoChip) study of genetic associations in rheumatoid arthritis (RA) identified 14 risk loci with validated genome-wide significance, as well as a number of loci showing associations suggestive of significance (P = 5 × 10−5 < 5 × 10−8), but these have yet to be replicated. The aim of this study was to determine whether these potentially significant loci are involved in the pathogenesis of RA, and to explore whether any of the loci are associated with a specific RA serotype. Methods A total of 16 single-nucleotide polymorphisms (SNPs) were selected for genotyping and association analyses in 2 independent validation cohorts, comprising 6,106 RA cases and 4,290 controls. A meta-analysis of the data from the original ImmunoChip discovery cohort and from both validation cohorts was carried out, for a combined total of 17,581 RA cases and 20,160 controls. In addition, stratified analysis of patient subsets, defined according to their anti–cyclic citrullinated peptide (anti-CCP) antibody status, was performed. Results A significant association with RA risk (P < 0.05) was replicated for 6 of the SNPs assessed in the validation cohorts. All SNPs in the validation study had odds ratios (ORs) for RA susceptibility in the same direction as those in the ImmunoChip discovery study. One SNP, rs72928038, mapping to an intron of BACH2, achieved genome-wide significance in the meta-analysis (P = 1.2 × 10−8, OR 1.12), and a second SNP, rs911263, mapping to an intron of RAD51B, was significantly associated in the anti-CCP–positive RA subgroup (P = 4 × 10−8, OR 0.89), confirming that both are RA susceptibility loci. Conclusion This study provides robust evidence for an association of RA susceptibility with genes involved in B cell differentiation (BACH2) and DNA repair (RAD51B). The finding that the RAD51B gene exhibited different associations based on serologic subtype adds to the expanding knowledge base in defining subgroups of RA.

Autoimmune disease results from a loss of tolerance to self-antigens in genetically susceptible individuals. Completely understanding this process requires that targeted antigens be identified, and so a number of techniques have been developed to determine immune receptor specificities. We previously reported the construction of a phage-displayed synthetic human peptidome and a proof-of-principle analysis of antibodies from three patients with neurological autoimmunity. Here we present data from a large-scale screen of 298 independent antibody repertoires, including those from 73 healthy sera, using phage immunoprecipitation sequencing. The resulting database of peptide-antibody interactions characterizes each individual’s unique autoantibody fingerprint, and includes specificities found to occur frequently in the general population as well as those associated with disease. Screening type 1 diabetes (T1D) patients revealed a prematurely polyautoreactive phenotype compared with their matched controls. A collection of cerebrospinal fluids and sera from 63 multiple sclerosis patients uncovered novel, as well as previously reported antibody-peptide interactions. Finally, a screen of synovial fluids and sera from 64 rheumatoid arthritis patients revealed novel disease-associated antibody specificities that were independent of seropositivity status. This work demonstrates the utility of performing PhIP-Seq screens on large numbers of individuals and is another step toward defining the full complement of autoimmunoreactivities in health and disease.

Understanding the implications of genome-wide association studies (GWAS) for disease biology requires both identification of causal variants and definition of how these variants alter gene function. The non-coding triallelic dinucleotide polymorphism CCR6DNP is associated with risk for rheumatoid arthritis, and is considered likely causal because allelic variation correlates with expression of the chemokine receptor CCR6. Using transcription activator-like effector nuclease (TALEN) gene editing, we confirmed that CCR6DNP regulates CCR6. To identify the associated transcription factor, we applied a novel assay, Flanking Restriction Enhanced Pulldown (FREP), to identify specific association of poly (ADP-ribose) polymerase 1 (PARP-1) with CCR6DNP consistent with the established allelic risk hierarchy. Correspondingly, manipulation of PARP-1 expression or activity impaired CCR6 expression in several lineages. These findings show that CCR6DNP is a causal variant through which PARP-1 regulates CCR6, and introduce a highly efficient approach to interrogate non-coding genetic polymorphisms associated with human disease., Author Summary Genome-wide association studies (GWAS) identify loci associated with human disease risk, but bridging the gap between locus and mechanism has proven particularly difficult in cases where associated variants do not alter coding. We aimed to develop a generalizable approach to this problem. Previously, a dual nucleotide polymorphism within the first intron of CCR6 (termed the CCR6DNP) had been associated with risk for rheumatoid arthritis, but the pathway by which this variant altered gene expression could not be determined. Here, we employed sequence perturbation to confirm a regulatory role for the CCR6DNP. Next, using a new technique termed Flanking Restriction Enhanced Pulldown (FREP), we identified PARP-1 as the protein that regulates CCR6 expression through allelic association with the CCR6DNP, a finding confirmed by chromatin immunoprecipitation and functional assays. These findings reveal an unexpected regulatory pathway for CCR6 implicated in rheumatoid arthritis and other disease by human genetics, and more generally introduce a novel approach to identifying regulatory protein-DNA interactions.

Segmental duplications (SDs) comprise about 5% of the human genome and are enriched for immune genes. SD loci often show copy numbers variations (CNV), which are difficult to tag with genotyping methods. CNV in the Fcγ receptor region (FCGR) has been suggested to be associated with rheumatic diseases. The objective of this study was to delineate association of FCGR-CNV with rheumatoid arthritis (RA), coeliac disease and Inflammatory bowel disease incidence. We developed a method to accurately quantify CNV in SD loci based on the intensity values from the Immunochip platform and applied it to the FCGR locus. We determined the method's validity using three independent assays: segregation analysis in families, arrayCGH, and whole genome sequencing. Our data showed the presence of two separate CNVs in the FCGR locus. The first region encodes FCGR2A, FCGR3A and part of FCGR2C gene, the second encodes another part of FCGR2C, FCGR3B and FCGR2B. Analysis of CNV status in 4578 individuals with RA and 5457 controls indicated association of duplications in the FCGR3B gene in antibody-negative RA (P=0.002, OR=1.43). Deletion in FCGR3B was associated with increased risk of antibody-positive RA, consistently with previous reports (P=0.023, OR=1.23). A clear genotype-phenotype relationship was observed: CNV polymorphisms of the FCGR3A gene correlated to CD16A expression (encoded by FCGR3A) on CD8 T-cells. In conclusion, our method allows determining the CNV status of the FCGR locus, we identified association of CNV in FCGR3B to RA and showed a functional relationship between CNV in the FCGR3A gene and CD16A expression.European Journal of Human Genetics advance online publication, 13 May 2015; doi:10.1038/ejhg.2015.95.

Contains fulltext : 118482.pdf (Publisher’s version ) (Open Access) BACKGROUND: Treatment strategies blocking tumour necrosis factor (anti-TNF) have proven very successful in patients with rheumatoid arthritis (RA). However, a significant subset of patients does not respond for unknown reasons. Currently, there are no means of identifying these patients before treatment. This study was aimed at identifying genetic factors predicting anti-TNF treatment outcome in patients with RA using a genome-wide association approach. METHODS: We conducted a multistage, genome-wide association study with a primary analysis of 2 557 253 single-nucleotide polymorphisms (SNPs) in 882 patients with RA receiving anti-TNF therapy included through the Dutch Rheumatoid Arthritis Monitoring (DREAM) registry and the database of Apotheekzorg. Linear regression analysis of changes in the Disease Activity Score in 28 joints after 14 weeks of treatment was performed using an additive model. Markers with p

We have previously shown that rheumatoid arthritis (RA) risk alleles overlap between different ethnic groups. Here, we utilize a multiethnic approach to show that we can effectively discover RA risk alleles. Thirteen putatively associated SNPs that had not yet exceeded genome-wide significance (p < 5 × 10−8) in our previous RA genome-wide association study (GWAS) were analyzed in independent sample sets consisting of 4,366 cases and 17,765 controls of European, African American, and East Asian ancestry. Additionally, we conducted an overall association test across all 65,833 samples (a GWAS meta-analysis plus the replication samples). Of the 13 SNPs investigated, four were significantly below the study-wide Bonferroni corrected p value threshold (p < 0.0038) in the replication samples. Two SNPs (rs3890745 at the 1p36 locus [p = 2.3 × 10−12] and rs2872507 at the 17q12 locus [p = 1.7 × 10−9]) surpassed genome-wide significance in all 16,659 RA cases and 49,174 controls combined. We used available GWAS data to fine map these two loci in Europeans and East Asians, and we found that the same allele conferred risk in both ethnic groups. A series of bioinformatic analyses identified TNFRSF14-MMEL1 at the 1p36 locus and IKZF3-ORMDL3-GSDMB at the 17q12 locus as the genes most likely associated with RA. These findings demonstrate empirically that a multiethnic approach is an effective strategy for discovering RA risk loci, and they suggest that combining GWASs across ethnic groups represents an efficient strategy for gaining statistical power.

A common allele at the TAGAP gene locus demonstrates a suggestive, but not conclusive association with risk of rheumatoid arthritis (RA). To fine map the locus, we conducted comprehensive imputation of CEU HapMap single-nucleotide polymorphisms (SNPs) in a genome-wide association study (GWAS) of 5500 RA cases and 22 621 controls (all of European ancestry). After controlling for population stratification with principal components analysis, the strongest signal of association was to an imputed SNP, rs212389 (P 3.9 x 10(-8), odds ratio 0.87). This SNP remained highly significant upon conditioning on the previous RA risk variant (rs394581, P = 2.2 x 10(-5)) or on a SNP previously associated with celiac disease and type I diabetes (rs1738074, P = 1.7 x 10(-4)). Our study has refined the TAGAP signal of association to a single haplotype in RA, and in doing so provides conclusive statistical evidence that the TAGAP locus is associated with RA risk. Our study also underscores the utility of comprehensive imputation in large GWAS data sets to fine map disease risk alleles. Genes and Immunity (2011) 12, 314-318; doi:10.1038/gene.2011.8; published online 10 March 2011

The objective of this review is to report on the progress of the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study (BRASS) Registry data collection and summarize previous research in understanding therapeutic response to DMARDs using clinical and genetic data. The BRASS Registry, established in 2003, is a large, single-centre, prospective and observational cohort of 1100 RA patients. Patients with either new-onset or established RA disease are recruited from the practices of rheumatologists. Annual visits collect information on demographics, 28-joint DAS-CRP3 (DAS-28-CRP3), medication use, comorbidities and functional status (Modified Health Assessment Questionnaire, Short Form Health Survey 12). Two published studies have utilized BRASS to examine genetic predictors of treatment response. In a cross-sectional study, examining the association between candidate single nucleotide polymorphisms (SNPs) and disease activity in a subset of 120 RA patients on MTX monotherapy, the minor allele of ATIC rs4673993 was associated with low disease activity (P=0.01, DAS-28-CRP3≤3.2). In an international collaboration, 55 BRASS patients receiving anti-TNF therapy were genotyped for 31 SNPs associated with the risk of RA. With our collaborators, we discovered an SNP at the protein tyrosine phosphatase, receptor type, C (PTPRC) gene locus that was associated with EULAR 'good response'. With accurate data collection and the capacity to run genome-wide association studies and SNP analyses, the BRASS Registry has the ability to determine the contribution of genetic variants to disease onset and to assess their usefulness as biomarkers for treatment response and drug toxicity.

Objective: Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with an algorithm using codified EMR data alone. Methods: Subjects with ≥1 International Classification of Diseases, Ninth Revision RA code (714.xx) or who had anti–cyclic citrullinated peptide (anti-CCP) checked in the EMR of 2 large academic centers were included in an “RA Mart” (n = 29,432). For all 29,432 subjects, we extracted narrative (using natural language processing) and codified RA clinical information. In a training set of 96 RA and 404 non-RA cases from the RA Mart classified by medical record review, we used narrative and codified data to develop classification algorithms using logistic regression. These algorithms were applied to the entire RA Mart. We calculated and compared the positive predictive value (PPV) of these algorithms by reviewing the records of an additional 400 subjects classified as having RA by the algorithms. Results: A complete algorithm (narrative and codified data) classified RA subjects with a significantly higher PPV of 94% than an algorithm with codified data alone (PPV of 88%). Characteristics of the RA cohort identified by the complete algorithm were comparable to existing RA cohorts (80% women, 63% anti-CCP positive, and 59% positive for erosions). Conclusion: We demonstrate the ability to utilize complete EMR data to define an RA cohort with a PPV of 94%, which was superior to an algorithm using codified data alone., National Library of Medicine (U.S.) (Award U54LM008748), National Institutes of Health (U.S.). i2b2 (Informatics for Integrating Biology and the Bedside) (Grant U54-LM008748)

We carried out a genome-wide association study of genetic predictors of anti-cyclic citrullinated peptide antibody (anti-CCP) level in 531 self-reported non-Hispanic Caucasian Rheumatoid Arthritis (RA) patients enrolled in the Brigham Rheumatoid Arthritis Sequential Study (BRASS). For replication, we then analyzed 289 single nucleotide polymorphisms (SNPs) with P0.001 in BRASS in an independent population of 849 RA patients from the North American Rheumatoid Arthritis Consortium (NARAC). BRASS and NARAC samples were genotyped using the Affymetrix 100K and Illumina 550K platforms respectively. Association between SNPs and anti-CCP titer was tested using general linear models. The five most significant SNPs from BRASS all were within the major histocompatibility complex (MHC) region (Por = 3.5 x 10(-6)). After controlling for the human leukocyte antigen shared epitope (HLA-SE), the top SNPs still yielded P values0.0002. In NARAC, a single SNP from the MHC region near BTNL2 and HLA-DRA, rs1980493 (r(2) = 0.85 with the top five SNPs from BRASS), was associated significantly with CCP titer (P = 6.1 x 10(-5)) even after adjustment for the HLA-SE (P = 0.0002). The top SNPs found in BRASS and NARAC had r(2) = 0.46 and 0.64, respectively, to HLA-DRB1 DR3 alleles. These results confirm that the most significant genome region affecting anti-CCP titers in RA is the MHC region. We identified a SNP in moderate linkage disequilibrium (LD) with HLA-DR3, which may influence anti-CCP titer independently of the HLA-SE.

Recent evidence suggests that additional risk loci for RA are present in the major histocompatibility complex (MHC), independent of the class II HLA-DRB1 locus. We have now tested a total of 1,769 SNPs across 7.5Mb of the MHC located from 6p22.2 (26.03 Mb) to 6p21.32 (33.59 Mb) derived from the Illumina 550K Beadchip (Illumina, San Diego, CA, USA). For an initial analysis in the whole dataset (869 RA CCP + cases, 1,193 controls), the strongest association signal was observed in markers near the HLA-DRB1 locus, with additional evidence for association extending out into the Class I HLA region. To avoid confounding that may arise due to linkage disequilibrium with DRB1 alleles, we analyzed a subset of the data by matching cases and controls by DRB1 genotype (both alleles matched 1:1), yielding a set of 372 cases with 372 controls. This analysis revealed the presence of at least two regions of association with RA in the Class I region, independent of DRB1 genotype. SNP alleles found on the conserved A1-B8-DR3 (8.1) haplotype show the strongest evidence of positive association (P ~ 0.00005) clustered in the region around the HLA-C locus. In addition, we identified risk alleles that are not present on the 8.1 haplotype, with maximal association signals (P ~ 0.001-0.0027) located near the ZNF311 locus. This latter association is enriched in DRB1*0404 individuals. Finally, several additional association signals were found in the extreme centromeric portion of the MHC, in regions containing the DOB1, TAP2, DPB1, and COL11A2 genes. These data emphasize that further analysis of the MHC is likely to reveal genetic risk factors for rheumatoid arthritis that are independent of the DRB1 shared epitope alleles.

To identify rheumatoid arthritis risk loci in European populations, we conducted a meta-analysis of two published genome-wide association (GWA) studies totaling 3,393 cases and 12,462 controls. We genotyped 31 top-ranked SNPs not previously associated with rheumatoid arthritis in an independent replication of 3,929 autoantibody-positive rheumatoid arthritis cases and 5,807 matched controls from eight separate collections. We identified a common variant at the CD40 gene locus (rs4810485, P = 0.0032 replication, P = 8.2 x 10(-9) overall, OR = 0.87). Along with other associations near TRAF1 (refs. 2,3) and TNFAIP3 (refs. 4,5), this implies a central role for the CD40 signaling pathway in rheumatoid arthritis pathogenesis. We also identified association at the CCL21 gene locus (rs2812378, P = 0.00097 replication, P = 2.8 x 10(-7) overall), a gene involved in lymphocyte trafficking. Finally, we identified evidence of association at four additional gene loci: MMEL1-TNFRSF14 (rs3890745, P = 0.0035 replication, P = 1.1 x 10(-7) overall), CDK6 (rs42041, P = 0.010 replication, P = 4.0 x 10(-6) overall), PRKCQ (rs4750316, P = 0.0078 replication, P = 4.4 x 10(-6) overall), and KIF5A-PIP4K2C (rs1678542, P = 0.0026 replication, P = 8.8 x 10(-8) overall).

Gene-gene and gene-environment interactions are key features in the development of rheumatoid arthritis (RA) and other complex diseases. The aim of this study was to use and compare three different definitions of interaction between the two major genetic risk factors of RA--the HLA-DRB1 shared epitope (SE) alleles and the PTPN22 R620W allele--in three large case-control studies: the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA) study, the North American RA Consortium (NARAC) study, and the Dutch Leiden Early Arthritis Clinic study (in total, 1,977 cases and 2,405 controls). The EIRA study was also used to analyze interactions between smoking and the two genes. "Interaction" was defined either as a departure from additivity, as interaction in a multiplicative model, or in terms of linkage disequilibrium--for example, deviation from independence of penetrance of two unlinked loci. Consistent interaction, defined as departure from additivity, between HLA-DRB1 SE alleles and the A allele of PTPN22 R620W was seen in all three studies regarding anti-CCP-positive RA. Testing for multiplicative interactions demonstrated an interaction between the two genes only when the three studies were pooled. The linkage disequilibrium approach indicated a gene-gene interaction in EIRA and NARAC, as well as in the pooled analysis. No interaction was seen between smoking and PTPN22 R620W. A new pattern of interactions is described between the two major known genetic risk factors and the major environmental risk factor concerning the risk of developing anti-CCP-positive RA. The data extend the basis for a pathogenetic hypothesis for RA involving genetic and environmental factors. The study also raises and illustrates principal questions concerning ways to define interactions in complex diseases.

Background: A long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders. Methods: We use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born. Results: We estimate a small but significant negative SNP-genetic correlation between SZ and RA (−0.046, s.e. 0.026, P = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (−0.174, s.e. 0.071, P = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P = 0.00090). Conclusions: Our results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context. Refereed/Peer-reviewed

BackgroundThere is currently great interest in the incorporation of genetic susceptibility loci into screening models to identify individuals at high risk of disease. Here, we present the first risk prediction model including all 46 known genetic loci associated with rheumatoid arthritis (RA).MethodsA weighted genetic risk score (wGRS) was created using 45 RA non-human leucocyte antigen (HLA) susceptibility loci, imputed amino acids at HLA-DRB1 (11, 71 and 74), HLA-DPB1 (position 9) HLA-B (position 9) and gender. The wGRS was tested in 11 366 RA cases and 15 489 healthy controls. The risk of developing RA was estimated using logistic regression by dividing the wGRS into quintiles. The ability of the wGRS to discriminate between cases and controls was assessed by receiver operator characteristic analysis and discrimination improvement tests.ResultsIndividuals in the highest risk group showed significantly increased odds of developing anti-cyclic citrullinated peptide-positive RA compared to the lowest risk group (OR 27.13, 95% CI 23.70 to 31.05). The wGRS was validated in an independent cohort that showed similar results (area under the curve 0.78, OR 18.00, 95% CI 13.67 to 23.71). Comparison of the full wGRS with a wGRS in which HLA amino acids were replaced by a HLA tag single-nucleotide polymorphism showed a significant loss of sensitivity and specificity.ConclusionsOur study suggests that in RA, even when using all known genetic susceptibility variants, prediction performance remains modest; while this is insufficiently accurate for general population screening, it may prove of more use in targeted studies. Our study has also highlighted the importance of including HLA variation in risk prediction models.

Copyright © 2015 Helena Canhão et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited., Background: The aim of our work was to replicate, in a Southern European population, the association reported in Northern populations between PTPRC locus and response to anti-tumor necrosis factor (anti-TNF) treatment in rheumatoid arthritis (RA). We also looked at associations between five RA risk alleles and treatment response. Methods: We evaluated associations between anti-TNF treatment responses assessed by DAS28 change and by EULAR response at six months in 383 Portuguese patients. Univariate and multivariate linear and logistic regression analyses were performed. In a second step to confirm our findings, we pooled our population with 265 Spanish patients. Results: No association was found between PTPRC rs10919563 allele and anti-TNF treatment response, neither in Portuguese modeling for several clinical variables nor in the overall population combining Portuguese and Spanish patients. The minor allele for RA susceptibility, rs3761847 SNP in TRAF1/C5 region, was associated with a poor response in linear and logistic univariate and multivariate regression analyses. No association was observed with the other allellic variants. Results were confirmed in the pooled analysis. Conclusion: This study did not replicate the association between PTPRC and the response to anti-TNF treatment in our Southern European population. We found that TRAF1/C5 risk RA variants potentially influence anti-TNF treatment response., This work was supported by a grant from Harvard-Portugal Program HMSP-ICS/SAU-ICT/0002/2010. Daniel H. Solomon received support for this work from the NIH (K24-AR-055989). Elizabeth W. Karlson received support for this work from NIH (K24-AR-AR0524). Reuma.pt received unrestricted grants from Abbott, Bristol-Myers Squibb, Merck Sharp and Dohme, Pfizer, Roche, and UCB Pharma.

Candidate-gene association studies in rheumatoid arthritis (RA) have lead to encouraging yet apparently inconsistent results. One explanation for the inconsistency is insufficient power to detect modest effects in the context of a low prior probability of a true effect. To overcome this limitation, we selected alleles with an increased probability of a disease association, on the basis of a review of the literature on RA and other autoimmune diseases, and tested them for association with RA susceptibility in a sample collection powered to detect modest genetic effects. We tested 17 alleles from 14 genes in 2,370 RA cases and 1,757 controls from the North American Rheumatoid Arthritis Consortium (NARAC) and the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA) collections. We found strong evidence of an association of PTPN22 with the development of anti-citrulline antibody-positive RA (odds ratio [OR] 1.49; P=.00002), using previously untested EIRA samples. We provide support for an association of CTLA4 (CT60 allele, OR 1.23; P=.001) and PADI4 (PADI4_94, OR 1.24; P=.001) with the development of RA, but only in the NARAC cohort. The CTLA4 association is stronger in patients with RA from both cohorts who are seropositive for anti-citrulline antibodies (P=.0006). Exploration of our data set with clinically relevant subsets of RA reveals that PTPN22 is associated with an earlier age at disease onset (P=.004) and that PTPN22 has a stronger effect in males than in females (P=.03). A meta-analysis failed to demonstrate an association of the remaining alleles with RA susceptibility, suggesting that the previously published associations may represent false-positive results. Given the strong statistical power to replicate a true-positive association in this study, our results provide support for PTPN22, CTLA4, and PADI4 as RA susceptibility genes and demonstrate novel associations with clinically relevant subsets of RA.

To evaluate HLA-DRB1 genetic risk of rheumatoid arthritis (RA) in African Americans by 3 validated allele classification systems and by amino acid position and residue, and to compare genetic risk between African American and European ancestries.Four-digit HLA-DRB1 genotyping was performed on 561 autoantibody-positive African American cases and 776 African American controls. Association analysis was performed on Tezenas du Montcel (TdM), de Vries (DV), and Mattey classification system alleles and separately by amino acid position and individual residues.TdM S2 and S3P alleles were associated with RA (odds ratio [95% confidence interval] 2.8 [2.0-3.9] and 2.1 [1.7-2.7], respectively). The DV (P = 3.2 × 10(-12)) and Mattey (P = 6.5 × 10(-13)) system alleles were both protective in African Americans. Amino acid position 11 (permutation P0.00001) accounted for nearly all variability explained by HLA-DRB1, although conditional analysis demonstrated that position 57 was also significant (0.01 ≤ permutation P ≤ 0.05). The valine and aspartic acid residues at position 11 conferred the highest risk of RA in African Americans.With some exceptions, the genetic risk conferred by HLA-DRB1 in African Americans is similar to that in individuals of European ancestry at multiple levels: classification system (e.g., TdM), amino acid position (e.g., 11), and residue (Val11). Unlike that reported for individuals of European ancestry, amino acid position 57 was associated with RA in African Americans, but positions 71 and 74 were not. Asp11 (odds ratio 1 in European ancestry) corresponds to the 4-digit classical allele *09:01, which is also a risk allele for RA in Koreans.

Despite progress in defining human leukocyte antigen (HLA) alleles for anti-citrullinated-protein-autoantibody-positive (ACPA(+)) rheumatoid arthritis (RA), identifying HLA alleles for ACPA-negative (ACPA(-)) RA has been challenging because of clinical heterogeneity within clinical cohorts. We imputed 8,961 classical HLA alleles, amino acids, and SNPs from Immunochip data in a discovery set of 2,406 ACPA(-) RA case and 13,930 control individuals. We developed a statistical approach to identify and adjust for clinical heterogeneity within ACPA RA and observed independent associations for serine and leucine at position 11 in HLA-DR beta 1 (p = 1.4 x 10 (13), odds ratio [OR] = 1.30) and for aspartate at position 9 in HLA-B (p = 2.7 x 10(-12), OR = 1.39) within the peptide binding grooves. These amino acid positions induced associations at HLA-DRB1*03 (encoding serine at 11) and HLA-B*08 (encoding aspartate at 9). We validated these findings in an independent set of 427 ACPA(-) case subjects, carefully phenotyped with a highly sensitive ACPA assay, and 1,691 control subjects (HLA-DR beta 1 Ser11+Leu11: p = 5.8 x 10(-4), OR = 1.28; HLA-B Asp9: p = 2.6 x 10(-3), OR = 1.34). Although both amino acid sites drove risk of ACPA(+) and ACPA(-) disease, the effects of individual residues at HLA-DR beta 1 position 11 were distinct (p

Purpose of review A significant number of loci implicated in rheumatoid arthritis (RA) susceptibility have been highlighted by genome-wide association studies (GWAS). Here, we review the recent advances of GWAS in understanding the genetic architecture of RA, and place these findings in the context of RA pathogenesis. Recent findings Although the interpretation of GWAS findings in the context of the disease biology remains challenging, interesting observations can be highlighted. Integration of GWAS results with cell-type specific gene expression or epigenetic marks have highlighted regulatory T cells and CD4 memory T cells as critical cell types in RA. In addition, many genes in RA loci are involved in the nuclear factor-kappaB signaling pathway or the Janus kinase (JAK)-signal transducers and activators of transcription (STAT) signaling pathway. The observation that these pathways are targeted by several approved drugs used to treat the symptoms of RA highlights the promises of human genetics to provide insights in the disease biology, and help identify new therapeutic targets. Summary These findings highlight the promises and need of future studies investigating causal genes and underlined mechanisms in GWAS loci to advance our understanding of RA.

Although genetic and non-genetic studies in mouse and human implicate the CD40 pathway in rheumatoid arthritis (RA), there are no approved drugs that inhibit CD40 signaling for clinical care in RA or any other disease. Here, we sought to understand the biological consequences of a CD40 risk variant in RA discovered by a previous genome-wide association study (GWAS) and to perform a high-throughput drug screen for modulators of CD40 signaling based on human genetic findings. First, we fine-map the CD40 risk locus in 7,222 seropositive RA patients and 15,870 controls, together with deep sequencing of CD40 coding exons in 500 RA cases and 650 controls, to identify a single SNP that explains the entire signal of association (rs4810485, P = 1.4×10−9). Second, we demonstrate that subjects homozygous for the RA risk allele have ∼33% more CD40 on the surface of primary human CD19+ B lymphocytes than subjects homozygous for the non-risk allele (P = 10−9), a finding corroborated by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells from 1,469 healthy control individuals. Third, we use retroviral shRNA infection to perturb the amount of CD40 on the surface of a human B lymphocyte cell line (BL2) and observe a direct correlation between amount of CD40 protein and phosphorylation of RelA (p65), a subunit of the NF-κB transcription factor. Finally, we develop a high-throughput NF-κB luciferase reporter assay in BL2 cells activated with trimerized CD40 ligand (tCD40L) and conduct an HTS of 1,982 chemical compounds and FDA–approved drugs. After a series of counter-screens and testing in primary human CD19+ B cells, we identify 2 novel chemical inhibitors not previously implicated in inflammation or CD40-mediated NF-κB signaling. Our study demonstrates proof-of-concept that human genetics can be used to guide the development of phenotype-based, high-throughput small-molecule screens to identify potential novel therapies in complex traits such as RA., Author Summary A current challenge in human genetics is to follow-up “hits” from genome-wide association studies (GWAS) to guide drug discovery for complex traits. Previously, we identified a common variant in the CD40 locus as associated with risk of rheumatoid arthritis (RA). Here, we fine-map the CD40 signal of association through a combination of dense genotyping and exonic sequencing in large patient collections. Further, we demonstrate that the RA risk allele is a gain-of-function allele that increases the amount of CD40 on the surface of primary human B lymphocyte cells from healthy control individuals. Based on these observations, we develop a high-throughput assay to recapitulate the biology of the RA risk allele in a system suitable for a small molecule drug screen. After a series of primary screens and counter screens, we identify small molecules that inhibit CD40-mediated NF-kB signaling in human B cells. While this is only the first step towards a more comprehensive effort to identify CD40-specific inhibitors that may be used to treat RA, our study demonstrates a successful strategy to progress from a GWAS to a drug screen for complex traits such as RA.

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 X more heritability than GWAS-associated SNPs on average (P = 3.3 X 10[superscript -5]). For some diseases, this increase was individually significant:2.07 X for Multiple Sclerosis (MS) (P = 6.5 X 10 [superscript -9]) and for Crohn's Disease (CD) (P = 1.3 X 10[superscript -3]); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 X more MS heritability than known MS SNPs (P < 1.0 X 10[superscript -16]) and more CD heritability than known CD SNPs (P = 6.1 X 10[superscript -9]), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of > 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 X more heritability from all SNPs at GWAS loci (P = 2.3 X 10[superscript -6]) and more heritability from all autoimmune disease loci (P < 1 X 10[superscript -16]) compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture., National Institutes of Health (U.S.) (Grant R03HG006731), National Institutes of Health (U.S.) (Fellowship F32GM106584)

Objective The significance of non–rheumatoid arthritis (RA) autoantibodies in patients with RA is unclear. The aim of this study was to assess associations of autoantibodies with autoimmune risk alleles and with clinical diagnoses from the electronic medical records (EMRs) among RA cases and non-RA controls. Methods Data on 1,290 RA cases and 1,236 non-RA controls of European genetic ancestry were obtained from the EMRs of 2 large academic centers. The levels of anti–citrullinated protein antibodies (ACPAs), antinuclear antibodies (ANAs), anti–tissue transglutaminase antibodies (AGTAs), and anti–thyroid peroxidase (anti-TPO) antibodies were measured. All subjects were genotyped for autoimmune risk alleles, and the association between number of autoimmune risk alleles present and number of types of autoantibodies present was studied. A phenome-wide association study (PheWAS) was conducted to study potential associations between autoantibodies and clinical diagnoses among RA cases and non-RA controls. Results The mean ages were 60.7 years in RA cases and 64.6 years in non-RA controls. The proportion of female subjects was 79% in each group. The prevalence of ACPAs and ANAs was higher in RA cases compared to controls (each P < 0.0001); there were no differences in the prevalence of anti-TPO antibodies and AGTAs. Carriage of higher numbers of autoimmune risk alleles was associated with increasing numbers of autoantibody types in RA cases (P = 2.1 × 10−5) and non-RA controls (P = 5.0 × 10−3). From the PheWAS, the presence of ANAs was significantly associated with a diagnosis of Sjogren's/sicca syndrome in RA cases. Conclusion The increased frequency of autoantibodies in RA cases and non-RA controls was associated with the number of autoimmune risk alleles carried by an individual. PheWAS of EMR data, with linkage to laboratory data obtained from blood samples, provide a novel method to test for the clinical significance of biomarkers in disease.

Differences in lipid levels associated with cardiovascular (CV) risk between rheumatoid arthritis (RA) patients and the general population remain unclear. Determining these differences is important in understanding the role of lipids in CV risk in RA.We studied 2,005 RA subjects from 2 large academic medical centers. We extracted electronic medical record data on the first low-density lipoprotein (LDL) measurement, and total cholesterol and high-density lipoprotein (HDL) measurements within 1 year of the LDL measurement. Subjects with an electronic statin prescription prior to the first LDL measurement were excluded. We compared lipid levels in RA patients to recently published levels from the general US population using the t-test and stratifying by published parameters, i.e., 2007-2010, and women. We determined lipid trends using separate linear regression models for total cholesterol, LDL cholesterol, and HDL cholesterol, testing the association between year of measurement (1989-2010) and lipid level, adjusted by age and sex. Lipid trends in RA were qualitatively compared to the published general population trends.Women with RA had a significantly lower total cholesterol (186 versus 200 mg/dl; P = 0.002) and LDL cholesterol (105 versus 118 mg/dl; P = 0.001) compared to the general population (2007-2010). HDL cholesterol was not significantly different in the 2 groups. In the RA cohort, total cholesterol and LDL cholesterol significantly decreased each year, while HDL cholesterol increased (all with P0.0001), consistent with overall trends observed in a previous study.RA patients appear to have an overall lower total cholesterol and LDL cholesterol than the general population despite the general overall risk of CV disease in RA from observational studies.

The extent to which variants in the protein-coding sequence of genes contribute to risk of rheumatoid arthritis (RA) is unknown. In this study, we addressed this issue by deep exon sequencing and large-scale genotyping of 25 biological candidate genes located within RA risk loci discovered by genome-wide association studies (GWASs). First, we assessed the contribution of rare coding variants in the 25 genes to the risk of RA in a pooled sequencing study of 500 RA cases and 650 controls of European ancestry. We observed an accumulation of rare nonsynonymous variants exclusive to RA cases in IL2RA and IL2RB (burden test: p = 0.007 and p = 0.018, respectively). Next, we assessed the aggregate contribution of low-frequency and common coding variants to the risk of RA by dense genotyping of the 25 gene loci in 10,609 RA cases and 35,605 controls. We observed a strong enrichment of coding variants with a nominal signal of association with RA (p A [p.His266Gln]), and a noncoding variant, rs624988, reside on distinct haplotypes and independently contribute to the risk of RA (p = 4.6 x 10(-6)). Overall, our results indicate that variants (distributed across the allele-frequency spectrum) within the protein-coding portion of a subset of biological candidate genes identified by GWASs contribute to the risk of RA. Further, we have demonstrated that very large sample sizes will be required for comprehensively identifying the independent alleles contributing to the missing heritability of RA.

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8×10−8), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3′ UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1×10−11 in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry., Author Summary There are no genetic predictors of response to one of the most widely used classes of drugs in the treatment of rheumatoid arthritis—biological modifiers of the inflammatory cytokine tumor necrosis factor-alpha (or anti-TNF therapy). To identify genetic predictors, we performed the largest genome-wide association study (GWAS) to date as part of an international collaboration. In our study, which included 2,706 RA patients treated with one of three anti-TNF drugs, the most significant finding was restricted to RA patients treated with etanercept (P = 8×10−8), a drug that acts as a soluble receptor to bind circulating cytokine and prevents TNF from binding to its cell surface receptor. The associated variant influences expression of a nearby immune-related gene, CD84, whose expression is correlated with disease activity in RA patients. Together, our data support a model in which genomic factors related to CD84 expression serve as a predictor of disease activity and response to etanercept therapy among RA patients of European ancestry, but not anti-TNF therapies that act through different biological mechanisms or potentially in RA patients of other genetic ancestries.

Crowdsourcing genetic prediction of clinical utility in the Rheumatoid Arthritis Responder Challenge

In this study, we investigated whether genetic variants known to be related with susceptibility to rheumatoid arthritis (RA) are also associated with response to therapy with anti-tumor necrosis factor (anti-TNF) biologics; 233 patients enrolled in the Consortium of Rheumatology Researchers of North America (CORRONA) RA registry were studied. Findings were combined with results from an international collaborative study (N = 1,283) in a meta-analysis (N = 1,516). Multivariate models investigating the association between single nucleotide polymorphisms (SNPs) and change in RA disease activity were adjusted for age, gender, concomitant methotrexate, and baseline disease activity. In the CORRONA cohort, nominal associations with disease activity improvement were observed for the rs1980422 SNP of the CD28 gene in multivariate models (coefficient −0.377, p = 0.005) but were not significant after adjustment for multiple comparisons (q = 0.10). In the meta-analysis, the only SNP with nominal associations with change in DAS28 was the rs2812378 SNP of the CCL21 gene (coefficient 1.9195, p = 0.0068). This association was not significant after adjustment for multiple comparisons (q = 0.143). We conclude that the established RA risk alleles studied were not significantly associated with response to anti-TNF biologics in the CORRONA cohort or the meta-analysis.

Rheumatoid arthritis is a common autoimmune disease characterized by chronic inflammation. We report a meta-analysis of genome-wide association studies (GWAS) in a Japanese population including 4,074 individuals with rheumatoid arthritis (cases) and 16,891 controls, followed by a replication in 5,277 rheumatoid arthritis cases and 21,684 controls. Our study identified nine loci newly associated with rheumatoid arthritis at a threshold of P < 5.0 × 10(-8), including B3GNT2, ANXA3, CSF2, CD83, NFKBIE, ARID5B, PDE2A-ARAP1, PLD4 and PTPN2. ANXA3 was also associated with susceptibility to systemic lupus erythematosus (P = 0.0040), and B3GNT2 and ARID5B were associated with Graves' disease (P = 3.5 × 10(-4) and 2.9 × 10(-4), respectively). We conducted a multi-ancestry comparative analysis with a previous meta-analysis in individuals of European descent (5,539 rheumatoid arthritis cases and 20,169 controls). This provided evidence of shared genetic risks of rheumatoid arthritis between the populations.

Objective To examine the association of previously identified autoimmune disease susceptibility loci with granulomatosis with polyangiitis (Wegener's) (GPA), and to determine whether the genetic susceptibility profiles of other autoimmune diseases are associated with those of GPA. Methods Genetic data from 2 cohorts were meta-analyzed. Genotypes for 168 previously identified single-nucleotide polymorphisms (SNPs) associated with susceptibility to different autoimmune diseases were ascertained in a total of 880 patients with GPA and 1,969 control subjects of European descent. Single-marker associations were identified using additive logistic regression models. Associations of multiple SNPs with GPA were assessed using genetic risk scores based on susceptibility loci for Crohn's disease, type 1 diabetes, systemic lupus erythematosus, rheumatoid arthritis (RA), celiac disease, and ulcerative colitis. Adjustment for population substructure was performed in all analyses, using ancestry-informative markers and principal components analysis. Results Genetic polymorphisms in CTLA4 were significantly associated with GPA in the single-marker meta-analysis (odds ratio [OR] 0.79, 95% confidence interval [95% CI] 0.70–0.89, P = 9.8 × 10−5). The genetic risk score for RA susceptibility markers was significantly associated with GPA (OR 1.05 per 1-unit increase in genetic risk score, 95% CI 1.02–1.08, P = 5.1 × 10−5). Conclusion RA and GPA may arise from a similar genetic predisposition. Aside from CTLA4, other loci previously found to be associated with common autoimmune diseases were not statistically significantly associated with GPA in this study.

Objectives Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A ratelimiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems. Materials and Methods Physicians reviewed charts from three institutions to identify patients with RA. Each institution compiled attributes from various sources in the EHR, including codified data and clinical narratives, which were searched using one of two natural language processing (NLP) systems. The performance of the published model was compared with locally retrained models. Results Applying the previously published model from Partners Healthcare to datasets from Northwestern and Vanderbilt Universities, the area under the receiver operating characteristic curve was found to be 92% for Northwestern and 95% for Vanderbilt, compared with 97% at Partners. Retraining the model improved the average sensitivity at a specificity of 97% to 72% from the original 65%. Both the original logistic regression models and locally retrained models were superior to simple billing code count thresholds. Discussion These results show that a previously published algorithm for RA is portable to two external hospitals using different EHR systems, different NLP systems, and different target NLP vocabularies. Retraining the algorithm primarily increased the sensitivity at each site. Conclusion Electronic phenotype algorithms allow rapid identification of case populations in multiple sites with little retraining.

The genetic association of the major histocompatibility complex (MHC) to rheumatoid arthritis risk has commonly been attributed to alleles in HLA-DRB1. However, debate persists about the identity of the causal variants in HLA-DRB1 and the presence of independent effects elsewhere in the MHC. Using existing genome-wide SNP data in 5,018 individuals with seropositive rheumatoid arthritis (cases) and 14,974 unaffected controls, we imputed and tested classical alleles and amino acid polymorphisms in HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1 and HLA-DRB1, as well as 3,117 SNPs across the MHC. Conditional and haplotype analyses identified that three amino acid positions (11, 71 and 74) in HLA-DRβ1 and single-amino-acid polymorphisms in HLA-B (at position 9) and HLA-DPβ1 (at position 9), which are all located in peptide-binding grooves, almost completely explain the MHC association to rheumatoid arthritis risk. This study shows how imputation of functional variation from large reference panels can help fine map association signals in the MHC.

Epidemiology and candidate gene studies indicate a shared genetic basis for celiac disease (CD) and rheumatoid arthritis (RA), but the extent of this sharing has not been systematically explored. Previous studies demonstrate that 6 of the established non-HLA CD and RA risk loci (out of 26 loci for each disease) are shared between both diseases. We hypothesized that there are additional shared risk alleles and that combining genome-wide association study (GWAS) data from each disease would increase power to identify these shared risk alleles. We performed a meta-analysis of two published GWAS on CD (4,533 cases and 10,750 controls) and RA (5,539 cases and 17,231 controls). After genotyping the top associated SNPs in 2,169 CD cases and 2,255 controls, and 2,845 RA cases and 4,944 controls, 8 additional SNPs demonstrated P, Author Summary Celiac disease (CD) and rheumatoid arthritis (RA) are two autoimmune diseases characterized by distinct clinical features but increased co-occurrence in families and individuals. Genome-wide association studies (GWAS) performed in CD and RA have identified the HLA region and 26 non-HLA genetic risk loci in each disease. Of the 26 CD and 26 RA risk loci, previous studies have shown that six are shared between the two diseases. In this study we aimed to identify additional shared risk alleles and, in doing so, gain more insight into shared disease pathogenesis. We first empirically investigated the distribution of putative risk alleles from GWAS across both diseases (after removing known risk loci for both diseases). We found that CD risk alleles are non-randomly distributed in the RA GWAS (and vice versa), indicating that CD risk alleles have an increased prior probability of being associated with RA (and vice versa). Next, we performed a GWAS meta-analysis to search for shared risk alleles by combing the RA and CD GWAS, performing both directional and opposite allelic effect analyses, followed by replication testing in independent case-control datasets in both diseases. In addition to the already established six non-HLA shared risk loci, we observed statistically robust associations at eight SNPs, thereby increasing the number of shared non-HLA risk loci to fourteen. Finally, we used gene expression studies and pathway analysis tools to identify the plausible candidate genes in the fourteen associated loci. We observed remarkable overrepresentation of T-cell signaling molecules among the shared genes.

Background Cumulative genetic profiles can help identify individuals at high-risk for developing RA. We examined the impact of 39 validated genetic risk alleles on the risk of RA phenotypes characterized by serologic and erosive status. Methods/Principal Findings We evaluated single nucleotide polymorphisms at 31 validated RA risk loci and 8 Human Leukocyte Antigen alleles among 542 Caucasian RA cases and 551 Caucasian controls from Nurses' Health Study and Nurses' Health Study II. We created a weighted genetic risk score (GRS) and evaluated it as 7 ordinal groups using logistic regression (adjusting for age and smoking) to assess the relationship between GRS group and odds of developing seronegative (RF− and CCP−), seropositive (RF+ or CCP+), erosive, and seropositive, erosive RA phenotypes. In separate case only analyses, we assessed the relationships between GRS and age of symptom onset. In 542 RA cases, 317 (58%) were seropositive, 163 (30%) had erosions and 105 (19%) were seropositive with erosions. Comparing the highest GRS risk group to the median group, we found an OR of 1.2 (95% CI = 0.8–2.1) for seronegative RA, 3.0 (95% CI = 1.9–4.7) for seropositive RA, 3.2 (95% CI = 1.8–5.6) for erosive RA, and 7.6 (95% CI = 3.6–16.3) for seropositive, erosive RA. No significant relationship was seen between GRS and age of onset. Conclusions/Significance Results suggest that seronegative and seropositive/erosive RA have different genetic architecture and support the importance of considering RA phenotypes in RA genetic studies.

Discovering and following up on genetic associations with complex phenotypes require large patient cohorts. This is particularly true for patient cohorts of diverse ancestry and clinically relevant subsets of disease. The ability to mine the electronic health records (EHRs) of patients followed as part of routine clinical care provides a potential opportunity to efficiently identify affected cases and unaffected controls for appropriate-sized genetic studies. Here, we demonstrate proof-of-concept that it is possible to use EHR data linked with biospecimens to establish a multi-ethnic case-control cohort for genetic research of a complex disease, rheumatoid arthritis (RA). In 1,515 EHR-derived RA cases and 1,480 controls matched for both genetic ancestry and disease-specific autoantibodies (anti-citrullinated protein antibodies [ACPA]), we demonstrate that the odds ratios and aggregate genetic risk score (GRS) of known RA risk alleles measured in individuals of European ancestry within our EHR cohort are nearly identical to those derived from a genome-wide association study (GWAS) of 5,539 autoantibody-positive RA cases and 20,169 controls. We extend this approach to other ethnic groups and identify a large overlap in the GRS among individuals of European, African, East Asian, and Hispanic ancestry. We also demonstrate that the distribution of a GRS based on 28 non-HLA risk alleles in ACPA+ cases partially overlaps with ACPA- subgroup of RA cases. Our study demonstrates that the genetic basis of rheumatoid arthritis risk is similar among cases of diverse ancestry divided into subsets based on ACPA status and emphasizes the utility of linking EHR clinical data with biospecimens for genetic studies.

Large-scale genetic association studies have identified20 rheumatoid arthritis (RA) risk alleles among individuals of European ancestry. The influence of these risk alleles has not been comprehensively studied in African Americans. We therefore sought to examine whether these validated RA risk alleles are associated with RA risk in an African American population.Twenty-seven candidate single-nucleotide polymorphisms (SNPs) were genotyped in 556 autoantibody-positive African Americans with RA and 791 healthy African American control subjects. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for each SNP were compared with previously published ORs for RA patients of European ancestry. We then calculated a composite genetic risk score (GRS) for each individual based on the sum of all risk alleles.Overlap of the ORs and 95% CIs between the European and African American populations was observed for 24 of the 27 candidate SNPs. Conversely, 3 of the 27 SNPs (CCR6 rs3093023, TAGAP rs394581, and TNFAIP3 rs6920220) demonstrated ORs in the opposite direction from those reported for RA patients of European ancestry. The GRS analysis indicated a small but highly significant probability that African American patients relative to control subjects were enriched for the risk alleles validated in European RA patients (P = 0.00005).The majority of RA risk alleles previously validated for RA patients of European ancestry showed similar ORs in our population of African Americans with RA. Furthermore, the aggregate GRS supports the hypothesis that these SNPs are risk alleles for RA in the African American population. Future large-scale genetic studies are needed to validate these risk alleles and identify novel RA risk alleles in African Americans.

The purpose of this article is to place these genetic discoveries in the context of current and future therapeutic strategies for patients with RA. More specifically, this article focuses on (1) a brief overview of genetic studies, (2) human genetics as an approach to identify the Achilles heel of disease pathways, (3) humans as the model organism for functional studies of human mutations, (4) pharmacogenetic studies to gain insight into the mechanism of action of drugs, and (5) next-generation patient registries to enable large-scale genotype-phenotype studies.

Contains fulltext : 88498.pdf (Publisher’s version ) (Closed access) To identify new genetic risk factors for rheumatoid arthritis, we conducted a genome-wide association study meta-analysis of 5,539 autoantibody-positive individuals with rheumatoid arthritis (cases) and 20,169 controls of European descent, followed by replication in an independent set of 6,768 rheumatoid arthritis cases and 8,806 controls. Of 34 SNPs selected for replication, 7 new rheumatoid arthritis risk alleles were identified at genome-wide significance (P < 5 x 10(-8)) in an analysis of all 41,282 samples. The associated SNPs are near genes of known immune function, including IL6ST, SPRED2, RBPJ, CCR6, IRF5 and PXK. We also refined associations at two established rheumatoid arthritis risk loci (IL2RA and CCL21) and confirmed the association at AFF3. These new associations bring the total number of confirmed rheumatoid arthritis risk loci to 31 among individuals of European ancestry. An additional 11 SNPs replicated at P < 0.05, many of which are validated autoimmune risk alleles, suggesting that most represent genuine rheumatoid arthritis risk alleles. 01 juni 2010

OBJECTIVE: Anti-tumor necrosis factor alpha (anti-TNF) therapy is a mainstay of treatment in rheumatoid arthritis (RA). The aim of the present study was to test established RA genetic risk factors to determine whether the same alleles also influence the response to anti-TNF therapy. METHODS: A total of 1,283 RA patients receiving etanercept, infliximab, or adalimumab therapy were studied from among an international collaborative consortium of 9 different RA cohorts. The primary end point compared RA patients with a good treatment response according to the European League Against Rheumatism (EULAR) response criteria (n = 505) with RA patients considered to be nonresponders (n = 316). The secondary end point was the change from baseline in the level of disease activity according to the Disease Activity Score in 28 joints (triangle upDAS28). Clinical factors such as age, sex, and concomitant medications were tested as possible correlates of treatment response. Thirty-one single-nucleotide polymorphisms (SNPs) associated with the risk of RA were genotyped and tested for any association with treatment response, using univariate and multivariate logistic regression models. RESULTS: Of the 31 RA-associated risk alleles, a SNP at the PTPRC (also known as CD45) gene locus (rs10919563) was associated with the primary end point, a EULAR good response versus no response (odds ratio [OR] 0.55, P = 0.0001 in the multivariate model). Similar results were obtained using the secondary end point, the triangle upDAS28 (P = 0.0002). There was suggestive evidence of a stronger association in autoantibody-positive patients with RA (OR 0.55, 95% confidence interval [95% CI] 0.39-0.76) as compared with autoantibody-negative patients (OR 0.90, 95% CI 0.41-1.99). CONCLUSION: Statistically significant associations were observed between the response to anti-TNF therapy and an RA risk allele at the PTPRC gene locus. Additional studies will be required to replicate this finding in additional patient collections

Recent human genetic discoveries have increased our understanding of rheumatoid arthritis (RA) susceptibility. Genome-wide association studies have expanded the number of validated RA risk loci beyond HLA-DRB1 "shared epitope" alleles to include additional major histocompatibility complex (MHC) risk alleles and more than 10 regions outside the MHC. The newly discovered risk alleles are common in the general population, have a modest effect on RA risk, and together explain less than 5% of the variance in disease risk. Whereas the actual causal mutation and causal gene for most loci remain to be determined, these studies are beginning to reveal general themes: many risk loci are associated with other autoimmune diseases; many genes fall within discrete biological pathways (eg, the nuclear factor kappa-light-chain-enhancer of activated B cells signaling pathway); and human genetics can group diseases into clinically meaningful subset categories (eg, presence or absence of autoantibodies). This review discusses recent RA genetic discoveries in terms of their potential to improve patient care.

Objective The co-occurrence of autoimmune diseases such as rheumatoid arthritis (RA) and type 1 diabetes mellitus (DM) has been reported in individuals and families. In this study, the strength and nature of this association were investigated at the population level in a Swedish case–control cohort. Methods For this case–control study, 1,419 patients with incident RA diagnosed between 1996 and 2003 were recruited from university, public, and private rheumatology units throughout Sweden; 1,674 matched control subjects were recruited from the Swedish national population registry. Sera from the subjects were tested for the presence of antibodies to cyclic citrullinated peptide (anti-CCP), rheumatoid factor (RF), and the 620W PTPN22 allele. Information on a history of diabetes was obtained by questionnaire, telephone interview, and/or medical record review. The prevalence of type 1 DM and type 2 DM was compared between patients with incident RA and control subjects and further stratified for the presence of anti-CCP, RF, and the PTPN22 risk allele. Results Type 1 DM was associated with an increased risk of RA (odds ratio [OR] 4.9, 95% confidence interval [95% CI] 1.8–13.1), and this association was specific for anti-CCP–positive RA (OR 7.3, 95% CI 2.7–20.0), but not anti-CCP–negative RA. Further adjustment for the presence of PTPN22 attenuated the risk of anti-CCP–positive RA in patients with type 1 DM to an OR of 5.3 (95% CI 1.5–18.7). No association between RA and type 2 DM was observed. Conclusion The association between type 1 DM and RA is specific for a particular RA subset, anti-CCP–positive RA. The risk of developing RA later in life in patients with type 1 DM may be attributed, in part, to the presence of the 620W PTPN22 allele, suggesting that this risk factor may represent a common pathway for the pathogenesis of these 2 diseases.

Systemic lupus erythematosus (SLE) is an autoimmune disease influenced by genetic and environmental factors. We carried out a genome-wide association scan and replication study and found an association between SLE and a variant in TNFAIP3 (rs5029939, meta-analysis P = 2.89 x 10(-12), OR = 2.29). We also found evidence of two independent signals near TNFAIP3 associated with SLE, including one previously associated with rheumatoid arthritis (RA). These results establish that variants near TNFAIP3 contribute to differential risk of SLE and RA.

Objective It has been suggested that polymorphisms in IL1 are correlated with severe and/or erosive rheumatoid arthritis (RA), but the implicated alleles have differed among studies. The aim of this study was to perform a broad and well-powered search for association between allelic polymorphism in IL1A and IL1B and the susceptibility to or severity of RA. Methods Key coding and regulatory regions in IL1A and IL1B were sequenced in 24 patients with RA, revealing 4 novel single-nucleotide polymorphisms (SNPs) in IL1B. These and a comprehensive set of 24 SNPs tagging most of the underlying genetic diversity were genotyped in 3 independent RA case–control sample sets and 1 longitudinal RA cohort, totaling 3,561 patients and 3,062 control subjects. Results No fully significant associations were observed. Analysis of the discovery case–control sample sets indicated a potential association of IL1B promoter region SNPs with susceptibility to RA (for RA3/A, odds ratio [OR] 1.27, P = 0.0021) or with the incidence of radiographic erosions (for RA4/C, OR 1.56, P = 0.036), but these findings were not replicated in independent case–control samples. No association with rheumatoid factor, anti–cyclic citrullinated peptide, or the Disease Activity Score in 28 joints was found. None of the associations previously observed in other studies were replicated here. Conclusion In spite of a broad and highly powered study, we observed no robust, reproducible association between IL1A/B variants and the susceptibility to or severity of RA in white individuals of European descent. Our results provide evidence that, in the majority of cases, polymorphism in IL1A and IL1B is not a major contributor to genetic susceptibility to RA.

PURPOSE OF REVIEW Tumor necrosis factor alpha (TNF) inhibitors are a mainstay of treatment in rheumatoid arthritis, yet there are no effective clinical or biomarker predictors of which patients will respond. Here we review genetic association studies conducted to search for DNA biomarkers of response to anti-TNF therapy. RECENT FINDINGS The entirety of genetic association studies to date that focus on response to anti-TNF therapy has been limited to a small number of genetic variants within a few candidate genes (primarily within the major histocompatibility complex region). Moreover, these studies have been conducted in a relatively small number of rheumatoid arthritis patients (approximately 1000 patients across all studies combined). From these studies, no single genetic factor is associated unequivocally with treatment response, although some studies suggest that alleles within the major histocompatibility complex may influence response. SUMMARY Additional studies are required to investigate the genetic basis of response to anti-TNF therapy. These studies should include an unbiased search of DNA variation across the human genome--now feasible through cost-effective genome-wide association studies--and be conducted in large patient collections powered to detect modest effect sizes.