Stephen Eyre

Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci

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 × 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.

Genetic variants at CD28, PRDM1, and CD2/CD58 are associated with rheumatoid arthritis risk

To discover new rheumatoid arthritis (RA) risk loci, we systematically examined 370 SNPs from 179 independent loci with P < 0.001 in a published meta-analysis of RA genome-wide association studies (GWAS) of 3,393 cases and 12,462 controls. We used Gene Relationships Across Implicated Loci (GRAIL), a computational method that applies statistical text mining to PubMed abstracts, to score these 179 loci for functional relationships to genes in 16 established RA disease loci. We identified 22 loci with a significant degree of functional connectivity. We genotyped 22 representative SNPs in an independent set of 7,957 cases and 11,958 matched controls. Three were convincingly validated: CD2-CD58 (rs11586238, P = 1 × 10−6 replication, P = 1 × 10−9 overall), CD28 (rs1980422, P = 5 × 10−6 replication, P = 1 × 10−9 overall) and PRDM1 (rs548234, P = 1 × 10−5 replication, P = 2 × 10−8 overall). An additional four were replicated (P < 0.0023): TAGAP (rs394581, P = 0.0002 replication, P = 4 × 10−7 overall), PTPRC (rs10919563, P = 0.0003 replication, P = 7 × 10−7 overall), TRAF6-RAG1 (rs540386, P = 0.0008 replication, P = 4 × 10−6 overall) and FCGR2A (rs12746613, P = 0.0022 replication, P = 2 × 10−5 overall). Many of these loci are also associated to other immunologic diseases.

Dense genotyping of immune-related disease regions identifies 14 new susceptibility loci for juvenile idiopathic arthritis

We used the Immunochip array to analyze 2,816 individuals with juvenile idiopathic arthritis (JIA), comprising the most common subtypes (oligoarticular and rheumatoid factor–negative polyarticular JIA), and 13,056 controls. We confirmed association of 3 known JIA risk loci (the human leukocyte antigen (HLA) region, PTPN22 and PTPN2) and identified 14 loci reaching genome-wide significance (P < 5 × 10−8) for the first time. Eleven additional new regions showed suggestive evidence of association with JIA (P < 1 × 10−6). Dense mapping of loci along with bioinformatics analysis refined the associations to one gene in each of eight regions, highlighting crucial pathways, including the interleukin (IL)-2 pathway, in JIA disease pathogenesis. The entire Immunochip content, the HLA region and the top 27 loci (P < 1 × 10−6) explain an estimated 18, 13 and 6% of the risk of JIA, respectively. In summary, this is the largest collection of JIA cases investigated so far and provides new insight into the genetic basis of this childhood autoimmune disease.

EULAR recommendations for terminology and research in individuals at risk of rheumatoid arthritis: report from the Study Group for Risk Factors for Rheumatoid Arthritis

The Study Group for Risk Factors for Rheumatoid Arthritis was established by the EULAR Standing Committee on Investigative Rheumatology to facilitate research into the preclinical and earliest clinically apparent phases of rheumatoid arthritis (RA). This report describes the recommendation for terminology to be used to define specific subgroups during different phases of disease, and defines the priorities for research in this area. Terminology was discussed by way of a three-stage structured process: A provisional list of descriptors for each of the possible phases preceding the diagnosis of RA were circulated to members of the study group for review and feedback. Anonymised comments from the members on this list were fed back to participants before a 2-day meeting. 18 participants met to discuss these data, agree terminologies and prioritise important research questions. The study group recommended that, in prospective studies, individuals without RA are described as having: genetic risk factors for RA; environmental risk factors for RA; systemic autoimmunity associated with RA; symptoms without clinical arthritis; unclassified arthritis; which may be used in a combinatorial manner. It was recommended that the prefix ‘pre-RA with:’ could be used before any/any combination of the five points above but only to describe retrospectively a phase that an individual had progressed through once it was known that they have developed RA. An approach to dating disease onset was recommended. In addition, important areas for research were proposed, including research of other tissues in which an adaptive immune response may be initiated, and the identification of additional risk factors and biomarkers for the development of RA, its progression and the development of extra-articular features. These recommendations provide guidance on approaches to describe phases before the development of RA that will facilitate communication between researchers and comparisons between studies. A number of research questions have been defined, requiring new cohorts to be established and new techniques to be developed to image and collect material from different sites.

Overlapping genetic susceptibility variants between three autoimmune disorders: rheumatoid arthritis, type 1 diabetes and coeliac disease

IntroductionGenome wide association studies, replicated by numerous well powered validation studies, have revealed a large number of loci likely to play a role in susceptibility to many multifactorial diseases. It is now well established that some of these loci are shared between diseases with similar aetiology. For example, a number of autoimmune diseases have been associated with variants in the PTPN22, TNFAIP3 and CTLA4 genes. Here we have attempted to define overlapping genetic variants between rheumatoid arthritis (RA), type 1 diabetes (T1D) and coeliac disease (CeD).MethodsWe selected eight SNPs previously identified as being associated with CeD and six T1D-associated SNPs for validation in a sample of 3,962 RA patients and 3,531 controls. Genotyping was performed using the Sequenom MassArray platform and comparison of genotype and allele frequencies between cases and controls was undertaken. A trend test P-value < 0.004 was regarded as significant.ResultsWe found statistically significant evidence for association of the TAGAP locus with RA (P = 5.0 × 10-4). A marker at one other locus, C1QTNF6, previously associated with T1D, showed nominal association with RA in the current study but did not remain statistically significant at the corrected threshold.ConclusionsIn exploring the overlap between T1D, CeD and RA, there is strong evidence that variation within the TAGAP gene is associated with all three autoimmune diseases. Interestingly a number of loci appear to be specific to one of the three diseases currently studied suggesting that they may play a role in determining the particular autoimmune phenotype at presentation.

Genetic markers of rheumatoid arthritis susceptibility in anti-citrullinated peptide antibody negative patients

Introduction There are now over 30 confirmed loci predisposing to rheumatoid arthritis (RA). Studies have been largely undertaken in patients with anticyclic citrullinated peptide (anti-CCP) positive RA, and some genetic associations appear stronger in this subgroup than in anti-CCP negative disease, although few studies have had adequate power to address the question. The authors therefore investigated confirmed RA susceptibility loci in a large cohort of anti-CCP negative RA subjects. Methods RA patients and controls, with serological and genetic data, were available from UK Caucasian patients (n=4068 anti-CCP positive, 2040 anti-CCP negative RA) and 13,009 healthy controls. HLA-DRB1 genotypes and 36 single nucleotide polymorphisms were tested for association between controls and anti-CCP positive or negative RA. Results The shared epitope (SE) showed a strong association with anti-CCP positive and negative RA, although the effect size was significantly lower in the latter (effect size ratio=3.18, p<1.0E-96). A non-intronic marker at TNFAIP3, GIN1/C5orf30, STAT4, ANKRD55/IL6ST, BLK and PTPN22 showed association with RA susceptibility, irrespective of the serological status, the latter three markers remaining significantly associated with anti-CCP negative RA, after correction for multiple testing. No significant association with anti-CCP negative RA was detected for other markers (eg, AFF3, CD28, intronic marker at TNFAIP3), though the study power for those markers was over 80%. Discussion In the largest sample size studied to date, the authors have shown that the strength of association, the effect size and the number of known RA susceptibility loci associated with disease is different in the two disease serotypes, confirming the hypothesis that they might be two genetically different subsets.

Widespread non-additive and interaction effects within HLA loci modulate the risk of autoimmune diseases.

Human leukocyte antigen (HLA) genes confer substantial risk for autoimmune diseases on a log-additive scale. Here we speculated that differences in autoantigen-binding repertoires between a heterozygotes two expressed HLA variants might result in additional non-additive risk effects. We tested the non-additive disease contributions of classical HLA alleles in patients and matched controls for five common autoimmune diseases: rheumatoid arthritis (ncases = 5,337), type 1 diabetes (T1D; ncases = 5,567), psoriasis vulgaris (ncases = 3,089), idiopathic achalasia (ncases = 727) and celiac disease (ncases = 11,115). In four of the five diseases, we observed highly significant, non-additive dominance effects (rheumatoid arthritis, P = 2.5 × 10−12; T1D, P = 2.4 × 10−10; psoriasis, P = 5.9 × 10−6; celiac disease, P = 1.2 × 10−87). In three of these diseases, the non-additive dominance effects were explained by interactions between specific classical HLA alleles (rheumatoid arthritis, P = 1.8 × 10−3; T1D, P = 8.6 × 10−27; celiac disease, P = 6.0 × 10−100). These interactions generally increased disease risk and explained moderate but significant fractions of phenotypic variance (rheumatoid arthritis, 1.4%; T1D, 4.0%; celiac disease, 4.1%) beyond a simple additive model.

Validation in a multiple urology practice cohort of the prostate cancer prevention trial calculator for predicting prostate cancer detection.

PURPOSE The Prostate Cancer Prevention Trial prostate cancer risk calculator was developed in a clinical trial cohort that does not represent men routinely referred for prostate biopsy. We assessed the generalizability of the Prostate Cancer Prevention Trial calculator in a cohort more representative of patients referred for consideration of prostate biopsy in American urology practice. MATERIALS AND METHODS Patients undergoing prostate biopsy by 12 urologists at 5 sites were enrolled in an Early Detection Research Network cohort. The Prostate Cancer Prevention Trial risk calculator was validated by examining area underneath the receiver operating characteristic curve, sensitivity, specificity and calibration comparing observed vs predicted risk of prostate cancer detection. RESULTS Cancer incidence was greater (43% vs 22%, p = 0.001) in the Early Detection Research Network validation cohort (645) compared to the Prostate Cancer Prevention Trial group (5,519). Early Detection Research Network participants were younger and more racially diverse, and had more abnormal digital rectal examinations and higher prostate specific antigen than Prostate Cancer Prevention Trial participants (all p <0.001). Cancer severity was worse in the Early Detection Research Network cohort than in the Prostate Cancer Prevention Trial (Gleason 7 or higher 60% vs 21%, p <0.001). Nevertheless, the Prostate Cancer Prevention Trial risk calculator was superior to prostate specific antigen alone for predicting cancer in the Early Detection Research Network (AUC 0.691 vs 0.655, p = 0.009) and calibration confirmed that the Prostate Cancer Prevention Trial risk score accurately predicted individual risks in the Early Detection Research Network cohort. CONCLUSIONS Differences between the Early Detection Research Network validation cohort and the Prostate Cancer Prevention Trial cohort underscore the importance of validating calculator performance in the multicenter urology practice setting. Our findings extend the applicability of the Prostate Cancer Prevention Trial calculator for measuring the risk of prostate cancer detection on biopsy to the routine American urology practice setting.

TYK2 protein-coding variants protect against rheumatoid arthritis and autoimmunity, with no evidence of major pleiotropic effects on non-autoimmune complex traits

Despite the success of genome-wide association studies (GWAS) in detecting a large number of loci for complex phenotypes such as rheumatoid arthritis (RA) susceptibility, the lack of information on the causal genes leaves important challenges to interpret GWAS results in the context of the disease biology. Here, we genetically fine-map the RA risk locus at 19p13 to define causal variants, and explore the pleiotropic effects of these same variants in other complex traits. First, we combined Immunochip dense genotyping (n = 23,092 case/control samples), Exomechip genotyping (n = 18,409 case/control samples) and targeted exon-sequencing (n = 2,236 case/controls samples) to demonstrate that three protein-coding variants in TYK2 (tyrosine kinase 2) independently protect against RA: P1104A (rs34536443, OR = 0.66, P = 2.3x10-21), A928V (rs35018800, OR = 0.53, P = 1.2x10-9), and I684S (rs12720356, OR = 0.86, P = 4.6x10-7). Second, we show that the same three TYK2 variants protect against systemic lupus erythematosus (SLE, Pomnibus = 6x10-18), and provide suggestive evidence that two of the TYK2 variants (P1104A and A928V) may also protect against inflammatory bowel disease (IBD; Pomnibus = 0.005). Finally, in a phenome-wide association study (PheWAS) assessing >500 phenotypes using electronic medical records (EMR) in >29,000 subjects, we found no convincing evidence for association of P1104A and A928V with complex phenotypes other than autoimmune diseases such as RA, SLE and IBD. Together, our results demonstrate the role of TYK2 in the pathogenesis of RA, SLE and IBD, and provide supporting evidence for TYK2 as a promising drug target for the treatment of autoimmune diseases.

Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking

The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA). Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factors impact on prediction. Each models ability to discriminate anti-citrullinated protein antibody (ACPA)-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls); UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls). HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC) value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG). Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001); ever-smoking associated with older onset disease. This latter finding suggests smokings impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.