No evidence that genetic predictors of susceptibility predict changes in core outcomes in JIA.

OBJECTIVES: The clinical progression of JIA is unpredictable. Knowing who will develop severe disease could facilitate rapid intensification of therapies. We use genetic variants conferring susceptibility to JIA to predict disease outcome measures. METHODS: A total of 713 JIA patients with genotype data and core outcome variables (COVs) at diagnosis (baseline) and 1 year follow-up were identified from the Childhood Arthritis Prospective Study (CAPS). A weighted genetic risk score (GRS) was generated, including all single nucleotide polymorphisms (SNPs) previously associated with JIA susceptibility (P-value < 5×10-08). We used multivariable linear regression to test the GRS for association with COVS (limited joint count, active joint count, physician global assessment, parent/patient general evaluation, childhood HAQ and ESR) at baseline and change in COVS from baseline to 1 year, adjusting for baseline COV and International League of Associations of Rheumatology (ILAR) category. The GRS was split into quintiles to identify high (quintile 5) and low (quintile 1) risk groups. RESULTS: Patients in the high-risk group for the GRS had a younger age at presentation (median low risk 7.79, median high risk 3.51). No association was observed between the GRS and any outcome measures at 1 year follow-up or baseline. CONCLUSION: For the first time we have used all known JIA genetic susceptibility loci (P=<5×10-08) in a GRS to predict changes in disease outcome measured over time. Genetic susceptibility variants are poor predictors of changes in core outcome measures, it is likely that genetic factors predicting disease outcome are independent to those predicting susceptibility. The next step will be to conduct a genome-wide association analysis of JIA outcome.

Combined genetic analysis of juvenile idiopathic arthritis clinical subtypes identifies novel risk loci, target genes and key regulatory mechanisms.

OBJECTIVES: Juvenile idiopathic arthritis (JIA) is the most prevalent form of juvenile rheumatic disease. Our understanding of the genetic risk factors for this disease is limited due to low disease prevalence and extensive clinical heterogeneity. The objective of this research is to identify novel JIA susceptibility variants and link these variants to target genes, which is essential to facilitate the translation of genetic discoveries to clinical benefit. METHODS: We performed a genome-wide association study (GWAS) in 3305 patients and 9196 healthy controls, and used a Bayesian model selection approach to systematically investigate specificity and sharing of associated loci across JIA clinical subtypes. Suggestive signals were followed-up for meta-analysis with a previous GWAS (2751 cases/15 886 controls). We tested for enrichment of association signals in a broad range of functional annotations, and integrated statistical fine-mapping and experimental data to identify target genes. RESULTS: Our analysis provides evidence to support joint analysis of all JIA subtypes with the identification of five novel significant loci. Fine-mapping nominated causal single nucleotide polymorphisms with posterior inclusion probabilities ≥50% in five JIA loci. Enrichment analysis identified RELA and EBF1 as key transcription factors contributing to disease risk. Our integrative approach provided compelling evidence to prioritise target genes at six loci, highlighting mechanistic insights for the disease biology and IL6ST as a potential drug target. CONCLUSIONS: In a large JIA GWAS, we identify five novel risk loci and describe potential function of JIA association signals that will be informative for future experimental works and therapeutic strategies.

Mapping DNA interaction landscapes in psoriasis susceptibility loci highlights KLF4 as a target gene in 9q31.

BACKGROUND: Genome-wide association studies (GWAS) have uncovered many genetic risk loci for psoriasis, yet many remain uncharacterised in terms of the causal gene and their biological mechanism in disease. This is largely a result of the findings that over 90% of GWAS variants map outside of protein-coding DNA and instead are enriched in cell type- and stimulation-specific gene regulatory regions. RESULTS: Here, we use a disease-focused Capture Hi-C (CHi-C) experiment to link psoriasis-associated variants with their target genes in psoriasis-relevant cell lines (HaCaT keratinocytes and My-La CD8+ T cells). We confirm previously assigned genes, suggest novel candidates and provide evidence for complexity at psoriasis GWAS loci. For one locus, uniquely, we combine further epigenomic evidence to demonstrate how a psoriasis-associated region forms a functional interaction with the distant (> 500 kb) KLF4 gene. This interaction occurs between the gene and active enhancers in HaCaT cells, but not in My-La cells. We go on to investigate this long-distance interaction further with Cas9 fusion protein-mediated chromatin modification (CRISPR activation) coupled with RNA-seq, demonstrating how activation of the psoriasis-associated enhancer upregulates KLF4 and its downstream targets, relevant to skin cells and apoptosis. CONCLUSIONS: This approach utilises multiple functional genomic techniques to follow up GWAS-associated variants implicating relevant cell types and causal genes in each locus; these are vital next steps for the translation of genetic findings into clinical benefit.

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.

Loci associated with N-glycosylation of human IgG are not associated with rheumatoid arthritis: a Mendelian randomisation study.

OBJECTIVES: A recent study identified 16 genetic variants associated with N-glycosylation of human IgG. Several of the genomic regions where these single nucleotide polymorphisms (SNPs) reside have also been associated with autoimmune disease (AID) susceptibility, suggesting there may be pleiotropy (genetic sharing) between loci controlling both N-glycosylation and AIDs. We investigated this by testing variants associated with levels of IgG N-glycosylation for association with rheumatoid arthritis (RA) susceptibility using a Mendelian randomisation study, and testing a subset of these variants in a less well-powered study of treatment response and severity. METHODS: SNPs showing association with IgG N-glycosylation were analysed for association with RA susceptibility in 14 361 RA cases and 43 923 controls. Five SNPs were tested for association with response to anti-tumour necrosis factor (TNF) therapy in 1081 RA patient samples and for association with radiological disease severity in 342 patients. RESULTS: Only one SNP (rs9296009) associated with N-glycosylation showed an association (p=6.92×10(-266)) with RA susceptibility, although this was due to linkage disequilibrium with causal human leukocyte antigen (HLA) variants. Four regions of the genome harboured SNPs associated with both traits (shared loci); although statistical analysis indicated that the associations observed for the two traits are independent. No SNPs showed association with response to anti-TNF therapy. One SNP rs12342831 was modestly associated with Larsen score (p=0.05). CONCLUSIONS: In a large, well-powered cohort of RA patients, we show SNPs driving levels of N-glycosylation have no association with RA susceptibility, indicating colocalisation of associated SNPs are not necessarily indicative of a shared genetic background or a role for glycosylation in disease susceptibility.

The genetics of rheumatoid arthritis: risk and protection in different stages of the evolution of RA.

There is now a general consensus that RA has a spectrum of disease stages that can begin many years before the onset of clinical symptoms. It is widely thought that understanding the complex interplay between genetics and environment, and their role in pathogenesis, is essential in gaining further insight into the mechanisms that drive disease development and progression. More than 100 genetic susceptibility loci have now been identified for RA through studies that have focused on patients with established RA compared with healthy controls. Studying the early preclinical phases of disease will provide valuable insights into the biological events that precede disease and could potentially identify biomarkers to predict disease onset and future therapeutic targets. In this review we will cover recent advances in the knowledge of genetic and environmental risk factors and speculate on how these factors may influence the transition from one stage of disease to another.

A weighted genetic risk score using all known susceptibility variants to estimate rheumatoid arthritis risk.

BACKGROUND: There 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). METHODS: A 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. RESULTS: Individuals 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. CONCLUSIONS: Our 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.

Association of HLA-DRB1 haplotypes with rheumatoid arthritis severity, mortality, and treatment response.

IMPORTANCE: Advances have been made in identifying genetic susceptibility loci for autoimmune diseases, but evidence is needed regarding their association with prognosis and treatment response. OBJECTIVE: To assess whether specific HLA-DRB1 haplotypes associated with rheumatoid arthritis (RA) susceptibility are also associated with radiological severity, mortality, and response to tumor necrosis factor (TNF) inhibitor drugs. DESIGN, SETTING, AND PARTICIPANTS: The Norfolk Arthritis Register (NOAR; 1691 patients and 2811 radiographs; recruitment: 1989-2008; 2008 as final follow-up) was used as a discovery cohort and the Early Rheumatoid Arthritis Study (421 patients and 3758 radiographs; recruitment: 1986-1999; 2005 as final follow-up) as an independent replication cohort for studies of radiographic outcome. Mortality studies were performed in the NOAR cohort (2432 patients; recruitment: 1990-2007; 2011 as final follow-up) and studies of treatment response in the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate cohort (1846 patients enrolled at initiation of TNF inhibitor; recruitment: 2006-2010; 2011 as final follow-up). Longitudinal statistical modeling was performed to integrate multiple radiograph records per patient over time. All patients were from the United Kingdom and had self-reported white ancestry. EXPOSURES: Sixteen HLA-DRB1 haplotypes defined by amino acids at positions 11, 71, and 74. MAIN OUTCOMES AND MEASURES: Radiological outcome using the Larsen score (range: 0 [none] to 200 [severe joint damage]) and erosions of the hands and feet on radiographs, all-cause mortality, and treatment response measured by change in Disease Activity Score based on 28 joint counts and European League Against Rheumatism (EULAR) response. RESULTS: Patients with RA and valine at position 11 of HLA-DRB1 had the strongest association with radiological damage (OR, 1.75 [95% CI, 1.51-2.05], P = 4.6E-13). By year 5, the percentages of patients with erosions of the hands and feet were 48% of noncarriers (150/314) of valine at position 11, 61% of heterozygote carriers (130/213), and 74% of homozygote carriers (43/58). Valine at position 11 also was associated with higher all-cause mortality in patients with inflammatory polyarthritis (hazard ratio, 1.16 [95% CI, 1.03-1.31], P = .01) (noncarriers: 319 deaths in 1398 patients over 17,196 person-years, mortality rate of 1.9% per year; carriers: 324 deaths in 1116 patients in 13,208 person-years, mortality rate of 2.5% per year) and with better EULAR response to TNF inhibitor therapy (OR, 1.14 [95% CI, 1.01-1.30], P = .04) (noncarriers: 78% [439/561 patients] with moderate or good EULAR response; heterozygote carriers: 81% [698/866]; and homozygote carriers: 86% [277/322]). The risk hierarchy defined by HLA-DRB1 haplotypes was correlated between disease susceptibility, severity, and mortality, but inversely correlated with TNF inhibitor treatment response. CONCLUSIONS AND RELEVANCE: Among patients with RA, the HLA-DRB1 locus, which is associated with disease susceptibility, was also associated with radiological severity, mortality, and treatment response. Replication of these findings in other cohorts is needed as a next step in evaluating the role of HLA-DRB1 haplotype analysis for management of RA.

Identification of BACH2 and RAD51B as rheumatoid arthritis susceptibility loci in a meta-analysis of genome-wide data.

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.

Chromatin Looping Links Target Genes with Genetic Risk Loci for Dermatological Traits.

Chromatin looping between regulatory elements and gene promoters presents a potential mechanism whereby disease risk variants affect their target genes. In this study, we use H3K27ac HiChIP, a method for assaying the active chromatin interactome in two cell lines: keratinocytes and skin lymphoma-derived CD8+ T cells. We integrate public datasets for a lymphoblastoid cell line and primary CD4+ T cells and identify gene targets at risk loci for skin-related disorders. Interacting genes enrich for pathways of known importance in each trait, such as cytokine response (psoriatic arthritis and psoriasis) and replicative senescence (melanoma). We show examples of how our analysis can inform changes in the current understanding of multiple psoriasis-associated risk loci. For example, the variant rs10794648, which is generally assigned to IFNLR1, was linked to GRHL3, a gene essential in skin repair and development, in our dataset. Our findings, therefore, indicate a renewed importance of skin-related factors in the risk of disease.

Diversity of peripheral blood human NK cells identified by single-cell RNA sequencing.

Human natural killer (NK) cells in peripheral blood perform many functions, and classification of specific subsets has been a longstanding goal. We report single-cell RNA sequencing of NK cells, comparing gene expression in unstimulated and interleukin (IL)-2-activated cells from healthy cytomegalovirus (CMV)-negative donors. Three NK cell subsets resembled well-described populations; CD56brightCD16-, CD56dimCD16+CD57-, and CD56dimCD16+CD57+. CD56dimCD16+CD57- cells subdivided to include a population with higher chemokine mRNA and increased frequency of killer-cell immunoglobulin-like receptor expression. Three novel human blood NK cell populations were identified: a population of type I interferon-responding NK cells that were CD56neg; a population exhibiting a cytokine-induced memory-like phenotype, including increased granzyme B mRNA in response to IL-2; and finally, a small population, with low ribosomal expression, downregulation of oxidative phosphorylation, and high levels of immediate early response genes indicative of cellular activation. Analysis of CMV+ donors established that CMV altered the proportion of NK cells in each subset, especially an increase in adaptive NK cells, as well as gene regulation within each subset. Together, these data establish an unexpected diversity in blood NK cells and provide a new framework for analyzing NK cell responses in health and disease.

Brief Report: The Genetic Profile of Rheumatoid Factor-Positive Polyarticular Juvenile Idiopathic Arthritis Resembles That of Adult Rheumatoid Arthritis.

OBJECTIVE: Juvenile idiopathic arthritis (JIA) comprises 7 heterogeneous categories of chronic childhood arthritides. Approximately 5% of children with JIA have rheumatoid factor (RF)-positive arthritis, which phenotypically resembles adult rheumatoid arthritis (RA). Our objective was to compare and contrast the genetics of RF-positive polyarticular JIA with those of RA and selected other JIA categories, to more fully understand the pathophysiologic relationships of inflammatory arthropathies. METHODS: Patients with RF-positive polyarticular JIA (n = 340) and controls (n = 14,412) were genotyped using the Immunochip array. Single-nucleotide polymorphisms were tested for association using a logistic regression model adjusting for admixture proportions. We calculated weighted genetic risk scores (wGRS) of reported RA and JIA risk loci, and we compared the ability of these wGRS to predict RF-positive polyarticular JIA. RESULTS: As expected, the HLA region was strongly associated with RF-positive polyarticular JIA (P = 5.51 × 10-31 ). Nineteen of 44 RA risk loci and 6 of 27 oligoarticular/RF-negative polyarticular JIA risk loci were associated with RF-positive polyarticular JIA (P < 0.05). The RA wGRS predicted RF-positive polyarticular JIA (area under the curve [AUC] 0.71) better than did the oligoarticular/RF-negative polyarticular JIA wGRS (AUC 0.59). The genetic profile of patients with RF-positive polyarticular JIA was more similar to that of RA patients with age at onset 16-29 years than to that of RA patients with age at onset ≥70 years. CONCLUSION: RF-positive polyarticular JIA is genetically more similar to adult RA than to the most common JIA categories and thus appears to be a childhood-onset presentation of autoantibody-positive RA. These findings suggest common disease mechanisms, which could lead to novel therapeutic targets and shared treatment strategies.

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.

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.

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.

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.

Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci.

Genome-wide association studies have been tremendously successful in identifying genetic variants associated with complex diseases. The majority of association signals are intergenic and evidence is accumulating that a high proportion of signals lie in enhancer regions. We use Capture Hi-C to investigate, for the first time, the interactions between associated variants for four autoimmune diseases and their functional targets in B- and T-cell lines. Here we report numerous looping interactions and provide evidence that only a minority of interactions are common to both B- and T-cell lines, suggesting interactions may be highly cell-type specific; some disease-associated SNPs do not interact with the nearest gene but with more compelling candidate genes (for example, FOXO1, AZI2) often situated several megabases away; and finally, regions associated with different autoimmune diseases interact with each other and the same promoter suggesting common autoimmune gene targets (for example, PTPRC, DEXI and ZFP36L1).

Investigating CD11c expression as a potential genomic biomarker of response to TNF inhibitor biologics in whole blood rheumatoid arthritis samples.

INTRODUCTION: Gene expression profiling is rapidly becoming a useful and informative tool in a much needed area of research. Identifying patients as to whether they will respond or not to a given treatment before prescription is not only essential to optimise treatment outcome but also to lessen the economic burden that such drugs can have on healthcare resources. In rheumatoid arthritis (RA), there is of yet no genetic/genomic biomarker which can accurately predict response to TNF inhibitor biologics prior to treatment, despite much interest in this area. Multiple studies have reported findings on potential candidate genes; however, due to relatively small sample sizes or lack of sufficient validation, results have been disappointingly inconsistent. The aim of this research was to further explore the predictive value of a previously reported association between CD11c expression and response to the TNF inhibitor biologics, adalimumab and etanercept. METHODS: Real-time qPCR was performed using whole blood RNA samples obtained from seventy-five rheumatoid arthritis patients about to commence treatment with a TNF inhibitor biologic drug, whose response status was determined at 3-month follow-up using the EULAR classification criteria. Relative quantification of CD11c using the comparative CT method outputted differential expression between good-responders and non-responders as a fold-change. RESULTS: Relative expression of CD11c in patients receiving TNF inhibitor biologics yielded a decrease of 1.025 fold in good-responders as compared to non-responders (p-value = 0.36). Upon stratification of patients dependent upon the specific drug administered, adalimumab or etanercept, similar findings to the full cohort were observed, decreases of 1.015 (p-value = 0.33) and 1.032 fold (p-value = 0.13) in good-responders compared to non-responders, respectively. CONCLUSION: The results from this study reveal that CD11c expression does not correlate with response to TNF inhibitor biologics when tested for within pre-treatment whole blood samples of rheumatoid arthritis patients.