GWAS for systemic sclerosis identifies multiple risk loci and highlights fibrotic and vasculopathy pathways.

Systemic sclerosis (SSc) is an autoimmune disease that shows one of the highest mortality rates among rheumatic diseases. We perform a large genome-wide association study (GWAS), and meta-analysis with previous GWASs, in 26,679 individuals and identify 27 independent genome-wide associated signals, including 13 new risk loci. The novel associations nearly double the number of genome-wide hits reported for SSc thus far. We define 95% credible sets of less than 5 likely causal variants in 12 loci. Additionally, we identify specific SSc subtype-associated signals. Functional analysis of high-priority variants shows the potential function of SSc signals, with the identification of 43 robust target genes through HiChIP. Our results point towards molecular pathways potentially involved in vasculopathy and fibrosis, two main hallmarks in SSc, and highlight the spectrum of critical cell types for the disease. This work supports a better understanding of the genetic basis of SSc and provides directions for future functional experiments.

Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases.

OBJECTIVES: There is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases. METHODS: High confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease. RESULTS: Overall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning. CONCLUSION: Capture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

Author Correction: Genotypic variability-based genome-wide association study identifies non-additive loci HLA-C and IL12B for psoriasis.

This article was originally published under a CC BY-NC-SA License, but has now been made available under a CC BY 4.0 License.

Genome-wide meta-analysis reveals shared new loci in systemic seropositive rheumatic diseases.

OBJECTIVE: Immune-mediated inflammatory diseases (IMIDs) are heterogeneous and complex conditions with overlapping clinical symptoms and elevated familial aggregation, which suggests the existence of a shared genetic component. In order to identify this genetic background in a systematic fashion, we performed the first cross-disease genome-wide meta-analysis in systemic seropositive rheumatic diseases, namely, systemic sclerosis, systemic lupus erythematosus, rheumatoid arthritis and idiopathic inflammatory myopathies. METHODS: We meta-analysed ~6.5 million single nucleotide polymorphisms in 11 678 cases and 19 704 non-affected controls of European descent populations. The functional roles of the associated variants were interrogated using publicly available databases. RESULTS: Our analysis revealed five shared genome-wide significant independent loci that had not been previously associated with these diseases: NAB1, KPNA4-ARL14, DGQK, LIMK1 and PRR12. All of these loci are related with immune processes such as interferon and epidermal growth factor signalling, response to methotrexate, cytoskeleton dynamics and coagulation cascade. Remarkably, several of the associated loci are known key players in autoimmunity, which supports the validity of our results. All the associated variants showed significant functional enrichment in DNase hypersensitivity sites, chromatin states and histone marks in relevant immune cells, including shared expression quantitative trait loci. Additionally, our results were significantly enriched in drugs that are being tested for the treatment of the diseases under study. CONCLUSIONS: We have identified shared new risk loci with functional value across diseases and pinpoint new potential candidate loci that could be further investigated. Our results highlight the potential of drug repositioning among related systemic seropositive rheumatic IMIDs.

Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes.

To define potentially causal variants for autoimmune disease, we fine-mapped1,2 76 rheumatoid arthritis (11,475 cases, 15,870 controls)3 and type 1 diabetes loci (9,334 cases, 11,111 controls)4. After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89% of common variants. We defined credible sets of ≤5 causal variants at 5 rheumatoid arthritis and 10 type 1 diabetes loci. We identified potentially causal missense variants at DNASE1L3, PTPN22, SH2B3, and TYK2, and noncoding variants at MEG3, CD28-CTLA4, and IL2RA. We also identified potential candidate causal variants at SIRPG and TNFAIP3. Using functional assays, we confirmed allele-specific protein binding and differential enhancer activity for three variants: the CD28-CTLA4 rs117701653 SNP, MEG3 rs34552516 indel, and TNFAIP3 rs35926684 indel.

Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins.

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

Genotypic variability-based genome-wide association study identifies non-additive loci HLA-C and IL12B for psoriasis.

Genome-wide association studies (GWASs) have identified a number of loci for psoriasis but largely ignored non-additive effects. We report a genotypic variability-based GWAS (vGWAS) that can prioritize non-additive loci without requiring prior knowledge of interaction types or interacting factors in two steps, using a mixed model to partition dichotomous phenotypes into an additive component and non-additive environmental residuals on the liability scale and then the Levene's (Brown-Forsythe) test to assess equality of the residual variances across genotype groups genome widely. The vGWAS identified two genome-wide significant (P < 5.0e-08) non-additive loci HLA-C and IL12B that were also genome-wide significant in an accompanying GWAS in the discovery cohort. Both loci were statistically replicated in vGWAS of an independent cohort with a small sample size. HLA-C and IL12B were reported in moderate gene-gene and/or gene-environment interactions in several occasions. We found a moderate interaction with age-of-onset of psoriasis, which was replicated indirectly. The vGWAS also revealed five suggestive loci (P < 6.76e-05) including FUT2 that was associated with psoriasis with environmental aspects triggered by virus infection and/or metabolic factors. Replication and functional investigation are needed to validate the suggestive vGWAS loci.

An MIF Promoter Polymorphism Is Associated with Susceptibility to Pulmonary Arterial Hypertension in Diffuse Cutaneous Systemic Sclerosis.

OBJECTIVE: Systemic sclerosis (SSc) is a fibrotic immune-mediated disease of unknown etiology. Among its clinical manifestations, pulmonary involvement is the leading cause of mortality in patients with SSc. However, the genetic factors involved in lung complication are not well defined. We aimed to review the association of the MIF gene, which encodes a cytokine implicated in idiopathic pulmonary hypertension among other diseases, with the susceptibility and clinical expression of SSc, in addition to testing the association of this polymorphism with SSc-related pulmonary involvement. METHODS: A total of 4392 patients with SSc and 16,591 unaffected controls from 6 cohorts of European origin were genotyped for the MIF promoter variant rs755622. An inverse variance method was used to metaanalyze the data. RESULTS: A statistically significant increase of the MIF rs755622*C allele frequency compared with controls was observed in the subgroups of patients with diffuse cutaneous SSc (dcSSc) and with pulmonary arterial hypertension (PAH) independently (dcSSc: p = 3.20E-2, OR 1.13; PAH: p = 2.19E-02, OR 1.32). However, our data revealed a stronger effect size with the subset of patients with SSc showing both clinical manifestations (dcSSc with PAH: p = 6.91E-3, OR 2.05). CONCLUSION: We reviewed the association of the MIF rs755622*C allele with SSc and described a phenotype-specific association of this variant with the susceptibility to develop PAH in patients with dcSSc.

Genotypic variability based association identifies novel non-additive loci DHCR7 and IRF4 in sero-negative rheumatoid arthritis.

Sero-negative rheumatoid arthritis (RA) is a highly heterogeneous disorder with only a few additive loci identified to date. We report a genotypic variability-based genome-wide association study (vGWAS) of six cohorts of sero-negative RA recruited in Europe and the US that were genotyped with the Immunochip. A two-stage approach was used: (1) a mixed model to partition dichotomous phenotypes into an additive component and non-additive residuals on the liability scale and (2) the Levene's test to assess equality of the residual variances across genotype groups. The vGWAS identified rs2852853 (P = 1.3e-08, DHCR7) and rs62389423 (P = 1.8e-05, near IRF4) in addition to two previously identified loci (HLA-DQB1 and ANKRD55), which were all statistically validated using cross validation. DHCR7 encodes an enzyme important in cutaneous synthesis of vitamin D and DHCR7 mutations are believed to be important for early humans to adapt to Northern Europe where residents have reduced ultraviolet-B exposure and tend to have light skin color. IRF4 is a key locus responsible for skin color, with a vitamin D receptor-binding interval. These vGWAS results together suggest that vitamin D deficiency is potentially causal of sero-negative RA and provide new insights into the pathogenesis of the disorder.

The genetics revolution in rheumatology: large scale genomic arrays and genetic mapping.

Susceptibility to rheumatic diseases, such as osteoarthritis, rheumatoid arthritis, ankylosing spondylitis, systemic lupus erythematosus, juvenile idiopathic arthritis and psoriatic arthritis, includes a large genetic component. Understanding how an individual's genetic background influences disease onset and outcome can lead to a better understanding of disease biology, improved diagnosis and treatment, and, ultimately, to disease prevention or cure. The past decade has seen great progress in the identification of genetic variants that influence the risk of rheumatic diseases. The challenging task of unravelling the function of these variants is ongoing. In this Review, the major insights from genetic studies, gained from advances in technology, bioinformatics and study design, are discussed in the context of rheumatic disease. In addition, pivotal genetic studies in the main rheumatic diseases are highlighted, with insights into how these studies have changed the way we view these conditions in terms of disease overlap, pathways of disease and potential new therapeutic targets. Finally, the limitations of genetic studies, gaps in our knowledge and ways in which current genetic knowledge can be fully translated into clinical benefit are examined.

A combined large-scale meta-analysis identifies COG6 as a novel shared risk locus for rheumatoid arthritis and systemic lupus erythematosus.

OBJECTIVES: During the last years, genome-wide association studies (GWASs) have identified a number of common genetic risk factors for rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). However, the genetic overlap between these two immune-mediated diseases has not been thoroughly examined so far. The aim of the present study was to identify additional risk loci shared between RA and SLE. METHODS: We performed a large-scale meta-analysis of GWAS data from RA (3911 cases and 4083 controls) and SLE (2237 cases and 6315 controls). The top-associated polymorphisms in the discovery phase were selected for replication in additional datasets comprising 13 641 RA cases and 31 921 controls and 1957 patients with SLE and 4588 controls. RESULTS: The rs9603612 genetic variant, located nearby the COG6 gene, an established susceptibility locus for RA, reached genome-wide significance in the combined analysis including both discovery and replication sets (p value=2.95E-13). In silico expression quantitative trait locus analysis revealed that the associated polymorphism acts as a regulatory variant influencing COG6 expression. Moreover, protein-protein interaction and gene ontology enrichment analyses suggested the existence of overlap with specific biological processes, specially the type I interferon signalling pathway. Finally, genetic correlation and polygenic risk score analyses showed cross-phenotype associations between RA and SLE. CONCLUSIONS: In conclusion, we have identified a new risk locus shared between RA and SLE through a meta-analysis including GWAS datasets of both diseases. This study represents the first comprehensive large-scale analysis on the genetic overlap between these two complex disorders.

Capture Hi-C identifies a novel causal gene, IL20RA, in the pan-autoimmune genetic susceptibility region 6q23.

BACKGROUND: The identification of causal genes from genome-wide association studies (GWAS) is the next important step for the translation of genetic findings into biologically meaningful mechanisms of disease and potential therapeutic targets. Using novel chromatin interaction detection techniques and allele specific assays in T and B cell lines, we provide compelling evidence that redefines causal genes at the 6q23 locus, one of the most important loci that confers autoimmunity risk. RESULTS: Although the function of disease-associated non-coding single nucleotide polymorphisms (SNPs) at 6q23 is unknown, the association is generally assigned to TNFAIP3, the closest gene. However, the DNA fragment containing the associated SNPs interacts through chromatin looping not only with TNFAIP3, but also with IL20RA, located 680 kb upstream. The risk allele of the most likely causal SNP, rs6927172, is correlated with both a higher frequency of interactions and increased expression of IL20RA, along with a stronger binding of both the NFκB transcription factor and chromatin marks characteristic of active enhancers in T-cells. CONCLUSIONS: Our results highlight the importance of gene assignment for translating GWAS findings into biologically meaningful mechanisms of disease and potential therapeutic targets; indeed, monoclonal antibody therapy targeting IL-20 is effective in the treatment of rheumatoid arthritis and psoriasis, both with strong GWAS associations to this region.

Identifying Causal Genes at the Multiple Sclerosis Associated Region 6q23 Using Capture Hi-C.

BACKGROUND: The chromosomal region 6q23 has been found to be associated with multiple sclerosis (MS) predisposition through genome wide association studies (GWAS). There are four independent single nucleotide polymorphisms (SNPs) associated with MS in this region, which spans around 2.5 Mb. Most GWAS variants associated with complex traits, including these four MS associated SNPs, are non-coding and their function is currently unknown. However, GWAS variants have been found to be enriched in enhancers and there is evidence that they may be involved in transcriptional regulation of their distant target genes through long range chromatin looping. AIM: The aim of this work is to identify causal disease genes in the 6q23 locus by studying long range chromatin interactions, using the recently developed Capture Hi-C method in human T and B-cell lines. Interactions involving four independent associations unique to MS, tagged by rs11154801, rs17066096, rs7769192 and rs67297943 were analysed using Capture Hi-C Analysis of Genomic Organisation (CHiCAGO). RESULTS: We found that the pattern of chromatin looping interactions in the MS 6q23 associated region is complex. Interactions cluster in two regions, the first involving the rs11154801 region and a second containing the rs17066096, rs7769192 and rs67297943 SNPs. Firstly, SNPs located within the AHI1 gene, tagged by rs11154801, are correlated with expression of AHI1 and interact with its promoter. These SNPs also interact with other potential candidate genes such as SGK1 and BCLAF1. Secondly, the rs17066096, rs7769192 and rs67297943 SNPs interact with each other and with immune-related genes such as IL20RA, IL22RA2, IFNGR1 and TNFAIP3. Finally, the above-mentioned regions interact with each other and therefore, may co-regulate these target genes. CONCLUSION: These results suggest that the four 6q23 variants, independently associated with MS, are involved in the regulation of several genes, including immune genes. These findings could help understand mechanisms of disease and suggest potential novel therapeutic targets.

Genetic susceptibility to rheumatoid arthritis and its implications for novel drug discovery.

INTRODUCTION: Over 100 susceptibility loci have now been identified for rheumatoid arthritis (RA), several of which are already the targets of approved RA therapies providing proof of concept for the use of genetics in novel drug development for RA. Determining how these loci contribute to disease will be key to elucidating the mechanisms driving disease development, which has the potential for major impact on therapeutic development. AREAS COVERED: Here the authors review the use of genetics in drug discovery, including the use of 'omics' data to prioritise potential drug targets at susceptibility loci using RA as an exemplar. They discuss the current state of RA genetics its impact on stratified medicine, and how the findings from RA genetics studies can be used to inform drug discovery. EXPERT OPINION: It is anticipated that functional characterisation of disease variants will provide biological validation of a gene as a drug target, providing safer targets, with an increased likelihood of efficacy. In the future, techniques such as genome editing may represent a plausible option for RA therapy. Technologies such as genome-wide chromatin conformation capture Hi-C and CRISPR will be crucial to inform our understanding of how diseases develop and in developing new treatments.

A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases.

There is growing evidence of shared risk alleles for complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing among all individuals (whole-group pleiotropy) or a subset of individuals in a genetically heterogeneous cohort (subgroup heterogeneity). Here we describe the use of a well-powered statistic, BUHMBOX, to distinguish between those two situations using genotype data. We observed a shared genetic basis for 11 autoimmune diseases and type 1 diabetes (T1D; P < 1 × 10(-4)) and for 11 autoimmune diseases and rheumatoid arthritis (RA; P < 1 × 10(-3)). This sharing was not explained by subgroup heterogeneity (corrected PBUHMBOX > 0.2; 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (P < 1 × 10(-9)) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (PBUHMBOX = 0.008; 2,406 seronegative RA cases). We also observed a shared genetic basis for major depressive disorder (MDD) and schizophrenia (P < 1 × 10(-4)) that was not explained by subgroup heterogeneity (PBUHMBOX = 0.28; 9,238 MDD cases).

Brief Report: IRF4 Newly Identified as a Common Susceptibility Locus for Systemic Sclerosis and Rheumatoid Arthritis in a Cross-Disease Meta-Analysis of Genome-Wide Association Studies.

OBJECTIVE: Systemic sclerosis (SSc) and rheumatoid arthritis (RA) are autoimmune diseases that have similar clinical and immunologic characteristics. To date, several shared SSc-RA genetic loci have been identified independently. The aim of the current study was to systematically search for new common SSc-RA loci through an interdisease meta-genome-wide association (meta-GWAS) strategy. METHODS: The study was designed as a meta-analysis combining GWAS data sets of patients with SSc and patients with RA, using a strategy that allowed identification of loci with both same-direction and opposite-direction allelic effects. The top single-nucleotide polymorphisms were followed up in independent SSc and RA case-control cohorts. This allowed an increase in the sample size to a total of 8,830 patients with SSc, 16,870 patients with RA, and 43,393 healthy controls. RESULTS: This cross-disease meta-analysis of the GWAS data sets identified several loci with nominal association signals (P < 5 × 10(-6) ) that also showed evidence of association in the disease-specific GWAS scans. These loci included several genomic regions not previously reported as shared loci, as well as several risk factors that were previously found to be associated with both diseases. Follow-up analyses of the putatively new SSc-RA loci identified IRF4 as a shared risk factor for these 2 diseases (Pcombined = 3.29 × 10(-12) ). Analysis of the biologic relevance of the known SSc-RA shared loci identified the type I interferon and interleukin-12 signaling pathways as the main common etiologic factors. CONCLUSION: This study identified a novel shared locus, IRF4, for the risk of SSc and RA, and highlighted the usefulness of a cross-disease GWAS meta-analysis strategy in the identification of common risk loci.

Major histocompatibility complex harbors widespread genotypic variability of non-additive risk of rheumatoid arthritis including epistasis.

Genotypic variability based genome-wide association studies (vGWASs) can identify potentially interacting loci without prior knowledge of the interacting factors. We report a two-stage approach to make vGWAS applicable to diseases: firstly using a mixed model approach to partition dichotomous phenotypes into additive risk and non-additive environmental residuals on the liability scale and secondly using the Levene's (Brown-Forsythe) test to assess equality of the residual variances across genotype groups per marker. We found widespread significant (P < 2.5e-05) vGWAS signals within the major histocompatibility complex (MHC) across all three study cohorts of rheumatoid arthritis. We further identified 10 epistatic interactions between the vGWAS signals independent of the MHC additive effects, each with a weak effect but jointly explained 1.9% of phenotypic variance. PTPN22 was also identified in the discovery cohort but replicated in only one independent cohort. Combining the three cohorts boosted power of vGWAS and additionally identified TYK2 and ANKRD55. Both PTPN22 and TYK2 had evidence of interactions reported elsewhere. We conclude that vGWAS can help discover interacting loci for complex diseases but require large samples to find additional signals.

Replication of Associations of Genetic Loci Outside the HLA Region With Susceptibility to Anti-Cyclic Citrullinated Peptide-Negative Rheumatoid Arthritis.

OBJECTIVE: Genetic polymorphisms within the HLA region explain only a modest proportion of anti-cyclic citrullinated peptide (anti-CCP)-negative rheumatoid arthritis (RA) heritability. However, few non-HLA markers have been identified so far. This study was undertaken to replicate the associations of anti-CCP-negative RA with non-HLA genetic polymorphisms demonstrated in a previous study. METHODS: The Rheumatoid Arthritis Consortium International densely genotyped 186 autoimmune-related regions in 3,339 anti-CCP-negative RA patients and 15,870 controls across 6 different populations using the Illumina ImmunoChip array. We performed a case-control replication study of the anti-CCP-negative markers with the strongest associations in that discovery study, in an independent cohort of anti-CCP-negative UK RA patients. Individuals from the arcOGEN Consortium and Wellcome Trust Case Control Consortium were used as controls. Genotyping in cases was performed using Sequenom MassArray technology. Genome-wide data from controls were imputed using the 1000 Genomes Phase I integrated variant call set release version 3 as a reference panel. RESULTS: After genotyping and imputation quality control procedures, data were available for 15 non-HLA single-nucleotide polymorphisms in 1,024 cases and 6,348 controls. We confirmed the known markers ANKRD55 (meta-analysis odds ratio [OR] 0.80; P = 2.8 × 10(-13) ) and BLK (OR 1.13; P = 7.0 × 10(-6) ) and identified new and specific markers of anti-CCP-negative RA (prolactin [PRL] [OR 1.13; P = 2.1 × 10(-6) ] and NFIA [OR 0.85; P = 2.5 × 10(-6) ]). Neither of these loci is associated with other common, complex autoimmune diseases. CONCLUSION: Anti-CCP-negative RA and anti-CCP-positive RA are genetically different disease subsets that only partially share susceptibility factors. Genetic polymorphisms located near the PRL and NFIA genes represent examples of genetic susceptibility factors specific for anti-CCP-negative RA.

Immunochip Analyses of Epistasis in Rheumatoid Arthritis Confirm Multiple Interactions within MHC and Suggest Novel Non-MHC Epistatic Signals.

OBJECTIVE: Studying statistical gene-gene interactions (epistasis) has been limited by the difficulties in performance, both statistically and computationally, in large enough sample numbers to gain sufficient power. Three large Immunochip datasets from cohort samples recruited in the United Kingdom, United States, and Sweden with European ancestry were used to examine epistasis in rheumatoid arthritis (RA). METHODS: A full pairwise search was conducted in the UK cohort using a high-throughput tool and the resultant significant epistatic signals were tested for replication in the United States and Swedish cohorts. A forward selection approach was applied to remove redundant signals, while conditioning on the preidentified additive effects. RESULTS: We detected abundant genome-wide significant (p < 1.0e-13) epistatic signals, all within the MHC region. These signals were reduced substantially, but a proportion remained significant (p < 1.0e-03) in conditional tests. We identified 11 independent epistatic interactions across the entire MHC, each explaining on average 0.12% of the phenotypic variance, nearly all replicated in both replication cohorts. We also identified non-MHC epistatic interactions between RA susceptible loci LOC100506023 and IRF5 with Immunochip-wide significance (p < 1.1e-08) and between 2 neighboring single-nucleotide polymorphism near PTPN22 that were in low linkage disequilibrium with independent interaction (p < 1.0e-05). Both non-MHC epistatic interactions were statistically replicated with a similar interaction pattern in the US cohort only. CONCLUSION: There are multiple but relatively weak interactions independent of the additive effects in RA and a larger sample number is required to confidently assign additional non-MHC epistasis.