Several regions in the major histocompatibility complex confer risk for anti-CCP-antibody positive rheumatoid arthritis, independent of the DRB1 locus

Autoantibodies in rheumatoid arthritis: rheumatoid factors and anticitrullinated protein antibodies

Rheumatoid arthritis (RA) is a systemic inflammatory autoimmune disease, characterized by chronic, erosive polyarthritis and by the presence of various autoantibodies in serum and synovial fluid. Since rheumatoid factor (RF) was first described, a number of other autoantibodies have been discovered in RA patients. The autoantigens recognized by these autoantibodies include cartilage components, chaperones, enzymes, nuclear proteins and citrullinated proteins. However, the clinical significances and pathogenic roles of these antibodies are largely unknown except for RF and anticitrullinated protein antibodies (ACPAs), whose clinical usefulness has been acknowledged due to their acceptable sensitivities and specificities, and prognostic values. This review presents and discusses the current state of the art regarding RF and ACPA in RA.

Conditional analysis of the major histocompatibility complex in rheumatoid arthritis

We performed a whole-genome association study of rheumatoid arthritis susceptibility using Illumina 550k single-nucleotide polymorphism (SNP) genotypes of 868 cases and 1194 controls from the North American Rheumatoid Arthritis Consortium (NARAC). Structured association analysis with adjustment for potential population stratification yielded 200 SNPs with p < 1 x 10-8 for association with RA, all of which were on chromosome 6 in a 2.7-Mb region of the major histocompatibility complex (MHC). Given the extensive linkage equilibrium in the region and known risk of HLA-DRB1 alleles, we then applied conditional analyses to ascertain independent signals for RA susceptibility among these 200 candidate SNPs. Conditional analyses incorporating risk categories of the HLA-DRB1 "shared epitope" revealed three SNPs having independent associations with RA (conditional p < 0.001). This supports the presence of significant effects on RA susceptibility in the MHC in addition to the shared epitope.

Adjusting for HLA-DRbeta1 in a genome-wide association analysis of rheumatoid arthritis and related biomarkers

Background

There is a long-established association between rheumatoid arthritis and HLA-DRβ1. The shared epitope (SE) allele is an indicator of the presence of any of the HLA-DRβ1 alleles associated with RA. Other autoantibodies are also associated with RA, specifically rheumatoid factor IgM (RFUW) and anti-cyclic citrullinated peptide (anti-CCP).

Methods

Using the Genetic Analysis Workshop 16 North American Rheumatoid Arthritis Consortium genome-wide association data, we sought to find non-HLA-DRβ1 genetic associations by stratifying across SE status, and using the continuous biomarker phenotypes of RFUW and anti-CCP. To evaluate the binary RA phenotype, we applied the recently developed FP test and compared it to logistic regression or a genotype count-based test. We adjusted for multiple testing using the Bonferroni correction, the Q value approach, or permutation-based p-values. A case-only analysis of the biomarkers RFUW and anti-CCP used linear regression and ANOVAs.

Results

The initial genome-wide association analysis using all cases and controls provides substantial evidence of an association on chromosomes 9 and 2 within the immune system-related gene UBXD2. In SE-positive subjects, many single-nucleotide polymorphisms were significant, including some on chromosome 6. Due to very few SE negative cases, we had limited power to detect associations in SE negative subjects. We were also unable to find genetic associations with either RFUW or anti-CCP.

Conclusion

Our analyses have confirmed previous findings for genes PTPN22 and C5. We also identified a novel candidate gene on chromosome 2, UBXD2. Results suggest FP test may be more powerful than the genotype count-based test.

Single versus multiple imputation for genotypic data

Due to the growing need to combine data across multiple studies and to impute untyped markers based on a reference sample, several analytical tools for imputation and analysis of missing genotypes have been developed. Current imputation methods rely on single imputation, which ignores the variation in estimation due to imputation. An alternative to single imputation is multiple imputation. In this paper, we assess the variation in imputation by completing both single and multiple imputations of genotypic data using MACH, a commonly used hidden Markov model imputation method. Using data from the North American Rheumatoid Arthritis Consortium genome-wide study, the use of single and multiple imputation was assessed in four regions of chromosome 1 with varying levels of linkage disequilibrium and association signals. Two scenarios for missing genotypic data were assessed: imputation of untyped markers and combination of genotypic data from two studies. This limited study involving four regions indicates that, contrary to expectations, multiple imputations may not be necessary.

A principal-components-based clustering method to identify multiple variants associated with rheumatoid arthritis and arthritis-related autoantibodies

Multivariate techniques are an important area of investigation for studying contributions of multiple genetic variants to disease onset and pathology. We analyzed the Genetic Analysis Workshop 16 North American Rheumatoid Arthritis Consortium (NARAC) data using a principal-components analysis (PCA) with an orthoblique rotation to identify specific subsets of single-nucleotide polymorphisms (SNP) in the major histocompatibility complex (MHC) region associated with rheumatoid arthritis (RA) and rheumatoid factor IgM (RFUW), and compared this method with a traditional PC approach. Using the orthoblique PC-based clustering method, we identified new clusters of SNPs across the MHC region associated with RA and RFUW, and replicated known SNP cluster associations with RA, such as those in the HLA-DRB region.

Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases

Although the introduction of genome-wide association studies (GWAS) have greatly increased the number of genes associated with common diseases, only a small proportion of the predicted genetic contribution has so far been elucidated. Studying the cumulative variation of polymorphisms in multiple genes acting in functional pathways may provide a complementary approach to the more common single SNP association approach in understanding genetic determinants of common disease. We developed a novel pathway-based method to assess the combined contribution of multiple genetic variants acting within canonical biological pathways and applied it to data from 14,000 UK individuals with 7 common diseases. We tested inflammatory pathways for association with Crohn's disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) with 4 non-inflammatory diseases as controls. Using a variable selection algorithm, we identified variants responsible for the pathway association and evaluated their use for disease prediction using a 10 fold cross-validation framework in order to calculate out-of-sample area under the Receiver Operating Curve (AUC). The generalisability of these predictive models was tested on an independent birth cohort from Northern Finland. Multiple canonical inflammatory pathways showed highly significant associations (p 10(-3)-10(-20)) with CD, T1D and RA. Variable selection identified on average a set of 205 SNPs (149 genes) for T1D, 350 SNPs (189 genes) for RA and 493 SNPs (277 genes) for CD. The pattern of polymorphisms at these SNPS were found to be highly predictive of T1D (91% AUC) and RA (85% AUC), and weakly predictive of CD (60% AUC). The predictive ability of the T1D model (without any parameter refitting) had good predictive ability (79% AUC) in the Finnish cohort. Our analysis suggests that genetic contribution to common inflammatory diseases operates through multiple genes interacting in functional pathways.

Mapping of multiple susceptibility variants within the MHC region for 7 immune-mediated diseases

The human MHC represents the strongest susceptibility locus for autoimmune diseases. However, the identification of the true predisposing gene(s) has been handicapped by the strong linkage disequilibrium across the region. Furthermore, most studies to date have been limited to the examination of a subset of the HLA and non-HLA genes with a marker density and sample size insufficient for mapping all independent association signals. We genotyped a panel of 1,472 SNPs to capture the common genomic variation across the 3.44 megabase (Mb) classic MHC region in 10,576 DNA samples derived from patients with systemic lupus erythematosus, Crohn's disease, ulcerative colitis, rheumatoid arthritis, myasthenia gravis, selective IgA deficiency, multiple sclerosis, and appropriate control samples. We identified the primary association signals for each disease and performed conditional regression to identify independent secondary signals. The data demonstrate that MHC associations with autoimmune diseases result from complex, multilocus effects that span the entire region.

Unraveling the genetics of complex diseases: susceptibility genes for rheumatoid arthritis and psoriasis

Talk of numerous genetic risk factors for rheumatoid arthritis (RA) and psoriasis has been percolating for years, but with the exception of the human leukocyte antigen (HLA) region, none have been definitively identified. Recently the results of multiple, well powered, genetic case-control studies have begun to appear providing convincing statistical evidence for at least ten non-HLA related risk genes or loci (C5/TRAF1, CD40, CTLA4, KIF5A/PIP4K2C, MMEL1/TNFRSF14, PADI4, PRKCQ, PTPN22, STAT4, and TNFAIP3/OLIG3) for RA and six (IL12B, IL13, IL23R, STAT2/IL23A, TNFAIP3, and TNIP1) for psoriasis. These initial, novel findings are beginning to shed light on the molecular pathways pertinent to the individual diseases and highlight the pleiotropic effects of several risk factors as well as the allelic heterogeneity underlying susceptibility to these and other autoimmune diseases.

Association of MICA with rheumatoid arthritis independent of known HLA-DRB1 risk alleles in a family-based and a case control study

INTRODUCTION

The gene MICA encodes the protein major histocompatibility complex class I polypeptide-related sequence A. It is expressed in synovium of patients with rheumatoid arthritis (RA) and its implication in autoimmunity is discussed. We analyzed the association of genetic variants of MICA with susceptibility to RA.

METHODS

Initially, 300 French Caucasian individuals belonging to 100 RA trio families were studied. An additional 100 independent RA trio families and a German Caucasian case-control cohort (90/182 individuals) were available for replication. As MICA is situated in proximity to known risk alleles of the HLA-DRB1 locus, our analysis accounted for linkage disequilibrium either by analyzing the subgroup consisting of parents not carrying HLA-DRB1 risk alleles with transmission disequilibrium test (TDT) or by implementing a regression model including all available data. Analysis included a microsatellite polymorphism (GCT)n and single-nucleotide polymorphisms (SNPs) rs3763288 and rs1051794.

RESULTS

In contrast to the other investigated polymorphisms, the non-synonymously coding SNP MICA-250 (rs1051794, Lys196Glu) was strongly associated in the first family cohort (TDT: P = 0.014; regression model: odds ratio [OR] 0.46, 95% confidence interval [CI] 0.25 to 0.82, P = 0.007). Although the replication family sample showed only a trend, combined family data remained consistent with the hypothesis of MICA-250 association independent from shared epitope (SE) alleles (TDT: P = 0.027; regression model: OR 0.56, 95% CI 0.38 to 0.83, P = 0.003). We also replicated the protective association of MICA-250A within a German Caucasian cohort (OR 0.31, 95% CI 0.1 to 0.7, P = 0.005; regression model: OR 0.6, 95% CI 0.37 to 0.96, P = 0.032). We showed complete linkage disequilibrium of MICA-250 (D' = 1, r2= 1) with the functional MICA variant rs1051792 (D' = 1, r2= 1). As rs1051792 confers differential allelic affinity of MICA to the receptor NKG2D, this provides a possible functional explanation for the observed association.

CONCLUSIONS

We present evidence for linkage and association of MICA-250 (rs1051794) with RA independent of known HLA-DRB1 risk alleles, suggesting MICA as an RA susceptibility gene. However, more studies within other populations are necessary to prove the general relevance of this polymorphism for RA.

Variation in the ATP-binding cassette transporter 2 gene is a separate risk factor for systemic lupus erythematosus within the MHC

The ATP-binding cassette transporter (TAP) proteins are functionally relevant candidates for predisposition to systemic lupus erythematosus (SLE) by virtue of their role in autoantigen presentation and location in the major histocompatibility complex (MHC). We tested if variation in the TAP genes (TAP1 and TAP2) is associated with SLE. We genotyped tag single nucleotide polymorphisms (SNPs) and performed family-based association analysis on 390 Caucasian pedigrees. We found significant evidence of association between TAP2 and SLE (rs241453, P=1.33 x 10(-6)). Conditional logistic regression analysis suggests that this TAP2 effect is separate from the HLA-DRB1 alleles. Our analyses show that both rs241453 (P=1.6 x 10(-4)) and HLA-DRB1*03xx (P=2.3 x 10(-4)) have significant autonomous effects not due to linkage disequilibrium. Moreover, these loci exhibit a significant statistical interaction (P<1.0 x 10(-6)), demonstrated by an increase in the odds ratio for the TAP2 association from OR=2.00 (95% confidence interval (CI)=1.17-3.42) in HLA-DRB1*03xx-negative subjects to OR=4.29 (CI=1.88-9.76) in the subjects with at least one HLA-DRB1*03xx allele group. We report the largest association study of the TAP genes with SLE to date, and the first to test for its separate effect and interaction with the HLA alleles consistently associated with SLE.

Different patterns of associations with anti-citrullinated protein antibody-positive and anti-citrullinated protein antibody-negative rheumatoid arthritis in the extended major histocompatibility complex region

OBJECTIVE

To identify additional variants in the major histocompatibility complex (MHC) region that independently contribute to risk in 2 disease subsets of rheumatoid arthritis (RA) defined according to the presence or absence of antibodies to citrullinated protein antigens (ACPAs).

METHODS

In a multistep analytical strategy using unmatched as well as matched analyses to adjust for HLA-DRB1 genotype, we analyzed 2,221 single-nucleotide polymorphisms (SNPs) spanning 10.7 Mb, from 6p22.2 to 6p21.31, across the MHC. For ACPA-positive RA, we analyzed samples from the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA) and the North American Rheumatoid Arthritis Consortium (NARAC) studies (totaling 1,255 cases and 1,719 controls). For ACPA-negative RA, we used samples from the EIRA study (640 cases and 670 controls). Plink and SAS statistical packages were used to conduct all statistical analyses.

RESULTS

A total of 299 SNPs reached locus-wide significance (P<2.3x10(-5)) for ACPA-positive RA, whereas surprisingly, no SNPs reached this significance for ACPA-negative RA. For ACPA-positive RA, we adjusted for known DRB1 risk alleles and identified additional independent associations with SNPs near HLA-DPB1 (rs3117213; odds ratio 1.42 [95% confidence interval 1.17-1.73], Pcombined=0.0003 for the strongest association).

CONCLUSION

There are distinct genetic patterns of MHC associations in the 2 disease subsets of RA defined according to ACPA status. HLA-DPB1 is an independent risk locus for ACPA-positive RA. We did not identify any associations with SNPs within the MHC for ACPA-negative RA.

Rheumatoid arthritis

Rheumatoid arthritis is a systemic, inflammatory, autoimmune disorder. Enhanced understanding of molecular pathogenesis has enabled development of innovative biological agents that target specific parts of the immune system. These treatments have changed the course and face of rheumatoid arthritis and outcomes for patients and society. New knowledge has emerged of how environmental factors interact with susceptibility genes and the immune system in the pathogenesis of a major subset of rheumatoid arthritis. Research undertaken on the longitudinal disease process and molecular pathology of joint inflammation has led to new therapeutic strategies that promote early use of disease-modifying drugs with tight disease control and distinct and quantifiable treatment goals. Today, such approaches can halt most cases of joint destruction but not all instances of joint inflammation and comorbidity. Understanding the cause and pathogenesis of different rheumatoid arthritis subsets will lead not only to individualised treatments during early phases of the illness but also, possibly, to disease prevention.

Recent advances in the genetics of autoimmune disease

Extraordinary technical advances in the field of human genetics over the past few years have catalyzed an explosion of new information about the genetics of human autoimmunity. In particular, the ability to scan the entire genome for common polymorphisms that associate with disease has led to the identification of numerous new risk genes involved in autoimmune phenotypes. Several themes are emerging. Autoimmune disorders have a complex genetic basis; multiple genes contribute to disease risk, each with generally modest effects independently. In addition, it is now clear that common genes underlie multiple autoimmune disorders. There is also heterogeneity among subphenotypes within a disease and across major racial groups. The current crop of genetic associations are only the start of a complete catalog of genetic factors for autoimmunity, and it remains unclear to what extent common variation versus multiple rare variants contribute to disease susceptibility. The current review focuses on recent discoveries within functionally related groups of genes that provide clues to novel pathways of pathogenesis for human autoimmunity.

Rheumatoid arthritis: a view of the current genetic landscape

The field of genetics and autoimmune diseases is undergoing a rapid and unprecedented expansion with new genetic findings being reported at an astounding pace. It is now clear that multiple genes contribute to each of the major autoimmune disorders, with significant genetic overlaps among them. Rheumatoid arthritis (RA) is no exception to this, and emerging data are beginning to reveal the outlines of new diagnostic subgroups, complex overlapping relationships with other autoimmune disorders and potential new targets for therapy. This review describes the evolving genetic landscape of RA, with the full knowledge that our current view is far from complete. However, with the first round of genome-wide association scans now completed, it is reasonable to begin to take stock of the direction in which the major common genetic risk factors are leading us.