IL1RN Variation Influences Both Disease Susceptibility and Response to Recombinant Human Interleukin-1 Receptor Antagonist Therapy in Systemic Juvenile Idiopathic Arthritis.

OBJECTIVE: To determine whether systemic juvenile idiopathic arthritis (JIA) susceptibility loci that were identified by candidate gene studies demonstrate association with systemic JIA in the largest study population assembled to date. METHODS: Single-nucleotide polymorphisms (SNPs) from 11 previously reported systemic JIA risk loci were examined for association in 9 populations, including 770 patients with systemic JIA and 6,947 controls. The effect of systemic JIA-associated SNPs on gene expression was evaluated in silico in paired whole genome and RNA sequencing data from the lymphoblastoid cell lines (LCLs) of 373 European subjects from the 1000 Genomes Project. Responses of systemic JIA-associated SNPs to anakinra treatment were evaluated in 38 US patients for whom treatment response data were available. RESULTS: We found no association between the previously reported 26 SNPs and systemic JIA. Expanded analysis of the regions containing the 26 SNPs revealed only 1 significant association: the promoter region of IL1RN (P < 1 × 10-4 ). Systemic JIA-associated SNPs correlated with IL1RN expression in LCLs, with an inverse correlation between systemic JIA risk and IL1RN expression. The presence of homozygous IL1RN high expression alleles correlated strongly with a lack of response to anakinra therapy (odds ratio 28.7 [95% confidence interval 3.2-255.8]). CONCLUSION: In our study, IL1RN was the only candidate locus associated with systemic JIA. The implicated SNPs are among the strongest known determinants of IL1RN and interleukin-1 receptor antagonist levels, linking low expression with increased systemic JIA risk. Homozygous high expression alleles predicted nonresponsiveness to anakinra therapy, making them ideal candidate biomarkers to guide systemic JIA treatment. This study is an important first step toward the personalized treatment of systemic JIA.

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 architecture distinguishes systemic juvenile idiopathic arthritis from other forms of juvenile idiopathic arthritis: clinical and therapeutic implications.

OBJECTIVES: Juvenile idiopathic arthritis (JIA) is a heterogeneous group of conditions unified by the presence of chronic childhood arthritis without an identifiable cause. Systemic JIA (sJIA) is a rare form of JIA characterised by systemic inflammation. sJIA is distinguished from other forms of JIA by unique clinical features and treatment responses that are similar to autoinflammatory diseases. However, approximately half of children with sJIA develop destructive, long-standing arthritis that appears similar to other forms of JIA. Using genomic approaches, we sought to gain novel insights into the pathophysiology of sJIA and its relationship with other forms of JIA. METHODS: We performed a genome-wide association study of 770 children with sJIA collected in nine countries by the International Childhood Arthritis Genetics Consortium. Single nucleotide polymorphisms were tested for association with sJIA. Weighted genetic risk scores were used to compare the genetic architecture of sJIA with other JIA subtypes. RESULTS: The major histocompatibility complex locus and a locus on chromosome 1 each showed association with sJIA exceeding the threshold for genome-wide significance, while 23 other novel loci were suggestive of association with sJIA. Using a combination of genetic and statistical approaches, we found no evidence of shared genetic architecture between sJIA and other common JIA subtypes. CONCLUSIONS: The lack of shared genetic risk factors between sJIA and other JIA subtypes supports the hypothesis that sJIA is a unique disease process and argues for a different classification framework. Research to improve sJIA therapy should target its unique genetics and specific pathophysiological pathways.

HLA-DRB1*11 and variants of the MHC class II locus are strong risk factors for systemic juvenile idiopathic arthritis.

Systemic juvenile idiopathic arthritis (sJIA) is an often severe, potentially life-threatening childhood inflammatory disease, the pathophysiology of which is poorly understood. To determine whether genetic variation within the MHC locus on chromosome 6 influences sJIA susceptibility, we performed an association study of 982 children with sJIA and 8,010 healthy control subjects from nine countries. Using meta-analysis of directly observed and imputed SNP genotypes and imputed classic HLA types, we identified the MHC locus as a bona fide susceptibility locus with effects on sJIA risk that transcended geographically defined strata. The strongest sJIA-associated SNP, rs151043342 [P = 2.8 × 10(-17), odds ratio (OR) 2.6 (2.1, 3.3)], was part of a cluster of 482 sJIA-associated SNPs that spanned a 400-kb region and included the class II HLA region. Conditional analysis controlling for the effect of rs151043342 found that rs12722051 independently influenced sJIA risk [P = 1.0 × 10(-5), OR 0.7 (0.6, 0.8)]. Meta-analysis of imputed classic HLA-type associations in six study populations of Western European ancestry revealed that HLA-DRB1*11 and its defining amino acid residue, glutamate 58, were strongly associated with sJIA [P = 2.7 × 10(-16), OR 2.3 (1.9, 2.8)], as was the HLA-DRB1*11-HLA-DQA1*05-HLA-DQB1*03 haplotype [6.4 × 10(-17), OR 2.3 (1.9, 2.9)]. By examining the MHC locus in the largest collection of sJIA patients assembled to date, this study solidifies the relationship between the class II HLA region and sJIA, implicating adaptive immune molecules in the pathogenesis of sJIA.

The genetics of juvenile idiopathic arthritis: current understanding and future prospects.

Understanding the genetic risk of JIA, a relatively rare chronic disease, is a challenging task, but recent research in this field has shown great advances. This review summarizes the current understanding of the genetic architecture of JIA susceptibility and proposes where this work is heading in the coming years. Insights into how we might progress this relatively understudied field will be provided, highlighting how the field will move towards the ultimate goals of predicting long-term disease outcomes at onset, predicting drug response, and move towards more targeted treatment options for children with JIA.

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 factor's impact on prediction. Each model's 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 smoking's 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.

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.

Autoinflammatory gene polymorphisms and susceptibility to UK juvenile idiopathic arthritis.

BACKGROUND: To investigate the autoinflammatory hereditary periodic fever syndrome genes MVK and TNFRSF1A, and the NLRP1 and IL1 genes, for association with juvenile idiopathic arthritis (JIA). METHODS: For MVK, TNFRSF1A and NLRP1 pair-wise tagging SNPs across each gene were selected and for IL1A SNPs from a prior meta-analysis were included. 1054 UK Caucasian JIA patients were genotyped by Sequenom iPlex MassARRAY and allele and genotype frequencies compared with 5380 unrelated healthy UK Caucasian controls. RESULTS: Four SNPs were significantly associated with UK JIA: rs2071374 within intron 4 of IL1A (ptrend=0.006), rs2228576 3' of TNFRSF1A (ptrend=0.009) and 2 SNPs, rs11836136 and rs7957619, within MVK (ptrend=0.006, ptrend=0.005 respectively). In all cases the association appeared to be driven by the systemic-onset JIA (SoJIA) subtype. Genotype data for the two MVK SNPs was available in a validation cohort of 814 JIA (oligoarticular and RF negative polyarticular) cases and 3058 controls from the US. Replication was not confirmed, however, further suggesting that this association is specific to SoJIA. CONCLUSIONS: These findings extend the observations of the relevance of studying monogenic loci as candidates for complex diseases. We provide novel evidence of association of MVK and TNFRSF1A with UK JIA, specifically driven by association with SoJIA and further confirm that the IL1A SNP association with SoJIA is subtype specific. Replication is required in independent cohorts.

Association of the IL-10 gene family locus on chromosome 1 with juvenile idiopathic arthritis (JIA).

BACKGROUND: The cytokine IL-10 and its family members have been implicated in autoimmune diseases and we have previously reported that genetic variants in IL-10 were associated with a rare group of diseases called juvenile idiopathic arthritis (JIA). The aim of this study was to fine map genetic variants within the IL-10 cytokine family cluster on chromosome 1 using linkage disequilibrium (LD)-tagging single nucleotide polymorphisms (tSNPs) approach with imputation and conditional analysis to test for disease associations. METHODOLOGY/PRINCIPAL FINDINGS: Fifty-three tSNPs were tested for association between Caucasian paediatric cohorts [219 systemic JIA (sJIA), 187 persistent oligoarticular JIA (pOJIA), and 139 extended OJIA (eOJIA) patients], and controls (Wellcome Trust control cohort, WTCCC2). Significant association with sJIA was detected at rs1400986 in the promoter of IL-20 (odds ratio 1.53; 95% CI 1.21-1.93; p = 0.0004), but in no other subtypes. Imputation analysis identified additional associated SNPs for pOJIA at IL-20 and IL-24, including a rare, functional, missense variant at IL-24 with a p = 0.0002. Penalised logistic regression analysis with HyperLasso and conditional analysis identified several further associations with JIA subtypes. In particular, haplotype analysis refined the sJIA association, with a joint effect at rs1400986 and rs4129024 in intron 1 of MAPKAPK2 (p = 3.2E-5). For pOJIA, a 3-SNP haplotype including rs1878672 in intron 3 of IL-10 showed evidence for association (p = 0.0018). In eOJIA, rs10863962 (3'UTR of FCAMR) and rs12409577 (intron of IL-19) haplotype showed some evidence of association (p = 0.0003). CONCLUSIONS: This study supports previous association of IL-20 with sJIA. Haplotype analyses provided stronger association signals than single point analyses, while a penalised logistic regression approach also suggested multiple independent association signals. Replication studies are required to confirm or refute these findings. The results indicate that combined effects with unknown/rare variants remain to be characterised in JIA, and represent a possible example of synthetic association in this region.

Genome-wide association analysis of juvenile idiopathic arthritis identifies a new susceptibility locus at chromosomal region 3q13.

OBJECTIVE: In a genome-wide association study of Caucasian patients with juvenile idiopathic arthritis (JIA), we have previously described findings limited to autoimmunity loci shared by JIA and other diseases. The present study was undertaken to identify novel JIA-predisposing loci using genome-wide approaches. METHODS: The discovery cohort consisted of Caucasian JIA cases (n = 814) and local controls (n = 658) genotyped on the Affymetrix Genome-Wide SNP 6.0 Array, along with 2,400 out-of-study controls. In a replication study, we genotyped 10 single-nucleotide polymorphisms (SNPs) in 1,744 cases and 7,010 controls from the US and Europe. RESULTS: Analysis within the discovery cohort provided evidence of associations at 3q13 within C3orf1 and near CD80 (rs4688011) (odds ratio [OR] 1.37, P = 1.88 × 10(-6) ) and at 10q21 near JMJD1C (rs647989 [OR 1.59, P = 6.1 × 10(-8) ], rs12411988 [OR 1.57, P = 1.16 × 10(-7) ], and rs10995450 [OR 1.31, P = 6.74 × 10(-5) ]). Meta-analysis provided further evidence of association for these 4 SNPs (P = 3.6 × 10(-7) for rs4688011, P = 4.33 × 10(-5) for rs6479891, P = 2.71 × 10(-5) for rs12411988, and P = 5.39 × 10(-5) for rs10995450). Gene expression data on 68 JIA cases and 23 local controls showed cis expression quantitative trait locus associations for C3orf1 SNP rs4688011 (P = 0.024 or P = 0.034, depending on the probe set) and JMJD1C SNPs rs6479891 and rs12411988 (P = 0.01 or P = 0.04, depending on the probe set and P = 0.008, respectively). Using a variance component liability model, it was estimated that common SNP variation accounts for approximately one-third of JIA susceptibility. CONCLUSION: Genetic association results and correlated gene expression findings provide evidence of JIA association at 3q13 and suggest novel genes as plausible candidates in disease pathology.

Investigation of rheumatoid arthritis susceptibility loci in juvenile idiopathic arthritis confirms high degree of overlap.

OBJECTIVES: Rheumatoid arthritis (RA) shares some similar clinical and pathological features with juvenile idiopathic arthritis (JIA); indeed, the strategy of investigating whether RA susceptibility loci also confer susceptibility to JIA has already proved highly successful in identifying novel JIA loci. A plethora of newly validated RA loci has been reported in the past year. Therefore, the aim of this study was to investigate these single nucleotide polymorphisms (SNP) to determine if they were also associated with JIA. METHODS: Thirty-four SNP that showed validated association with RA and had not been investigated previously in the UK JIA cohort were genotyped in JIA cases (n=1242), healthy controls (n=4281), and data were extracted for approximately 5380 UK Caucasian controls from the Wellcome Trust Case-Control Consortium 2. Genotype and allele frequencies were compared between cases with JIA and controls using PLINK. A replication cohort of 813 JIA cases and 3058 controls from the USA was available for validation of any significant findings. RESULTS: Thirteen SNP showed significant association (p<0.05) with JIA and for all but one the direction of association was the same as in RA. Of the eight loci that were tested, three showed significant association in the US cohort. CONCLUSIONS: A novel JIA susceptibility locus was identified, CD247, which represents another JIA susceptibility gene whose protein product is important in T-cell activation and signalling. The authors have also confirmed association of the PTPN2 and IL2RA genes with JIA, both reaching genome-wide significance in the combined analysis.

Association of the 5-aminoimidazole-4-carboxamide ribonucleotide transformylase gene with response to methotrexate in juvenile idiopathic arthritis.

OBJECTIVES: Methotrexate (MTX) is the mainstay treatment for juvenile idiopathic arthritis (JIA), however approximately 30% of children will fail to respond to the drug. Identification of genetic predictors of response to MTX would be invaluable in developing optimal treatment strategies for JIA. Using a candidate gene approach, single nucleotide polymorphisms (SNPs) within genes in the metabolic pathway of MTX, were investigated for association with response to treatment in JIA cases. METHODS: Tagging SNPs were selected across 13 MTX metabolic pathway genes and were genotyped using Sequenom genotyping technology in subjects recruited from the Sparks Childhood Arthritis Response to Medication Study. Response to MTX was defined using the American College of Rheumatology (ACR) paediatric response criteria and SNP genotype frequencies were compared between the worst and best responders (ACR-Ped70) to MTX. An independent cohort of US JIA cases was available for validation of initial findings. RESULTS: One SNP within the inosine triphosphate pyrophosphatase gene (ITPA) and two SNPs within 5-aminoimidazole-4-carboxamide ribonucleotide transformylase gene (ATIC) were significantly associated with a poor response to MTX. One of the ATIC SNPs showed a trend towards association with MTX response in an independent cohort of US JIA cases. Meta-analysis of the two studies strengthened this association (combined p value=0.002). CONCLUSIONS: This study presents association of a SNP in the ATIC gene with response to MTX in JIA. There is now growing evidence to support a role of the ATIC gene with response to MTX treatment. These results could contribute towards a better understanding of and ability to predict MTX response in JIA.

Subtype specific genetic associations for juvenile idiopathic arthritis: ERAP1 with the enthesitis related arthritis subtype and IL23R with juvenile psoriatic arthritis.

INTRODUCTION: Juvenile idiopathic arthritis (JIA) is an umbrella term for all chronic childhood arthropathies and can be divided into seven subtypes. It includes the enthesitis related arthritis (ERA) subtype which displays symptoms similar to ankylosing spondylitis (AS) and juvenile-onset psoriatic arthritis which has similarities to psoriatic arthritis (PsA) and psoriasis (Ps). We, therefore, hypothesized that two well-established susceptibility loci for AS and Ps, ERAP1 and IL23R, could also confer susceptibility to these JIA subtypes. METHODS: Single nucleotide polymorphisms (SNPs) in ERAP1 (rs30187) and IL23R (rs11209026) were genotyped in JIA cases (n = 1,054) and healthy controls (n = 5,200). Genotype frequencies were compared between all JIA cases and controls using the Cochrane-Armitage trend test implemented in PLINK. Stratified analysis by ILAR subtype was performed. RESULTS: The ERA subtype showed strong association with ERAP1 SNP (P trend = 0.005). The IL23R SNP showed significant association in the PsA subtype (P trend = 0.04). The SNPs were not associated with JIA overall or with any other subtype. CONCLUSIONS: We present evidence for subtype specific association of the ERAP1 gene with ERA JIA and the IL23R gene with juvenile-onset PsA. The findings will require validation in independent JIA datasets. These results suggest distinct pathogenic pathways in these subtypes.

Study of the common genetic background for rheumatoid arthritis and systemic lupus erythematosus.

BACKGROUND: Evidence is beginning to emerge that there may be susceptibility loci for rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) that are common to both diseases. OBJECTIVE: To investigate single nucleotide polymorphisms that have been reported to be associated with SLE in a UK cohort of patients with RA and controls. METHODS: 3962 patients with RA and 9275 controls were included in the study. Eleven SNPs mapping to confirmed SLE loci were investigated. These mapped to the TNFSF4, BANK1, TNIP1, PTTG1, UHRF1BP1, ATG5, JAZF1, BLK, KIAA1542, ITGAM and UBE2L3 loci. Genotype frequencies were compared between patients with RA and controls using the trend test. RESULTS: The SNPs mapping to the BLK and UBE2L3 loci showed significant evidence for association with RA. Two other SNPs, mapping to ATG5 and KIAA1542, showed nominal evidence for association with RA (p=0.02 and p=0.02, respectively) but these were not significant after applying a Bonferroni correction. Additionally, a significant global enrichment in carriage of SLE alleles in patients with RA compared with controls (p=9.1×10(-7)) was found. Meta-analysis of this and previous studies confirmed the association of the BLK and UBE2L3 gene with RA at genome-wide significance levels (p<5×10(-8)). Together, the authors estimate that the SLE and RA overlapping loci, excluding HLA-DRB1 alleles, identified so far explain ∼5.8% of the genetic susceptibility to RA as a whole. CONCLUSION: The findings confirm the association of the BLK and UBE2L3 loci with RA, thus adding to the list of loci showing overlap between RA and SLE.

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

INTRODUCTION: Genome 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). METHODS: We 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. RESULTS: We 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. CONCLUSIONS: In 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.

Generation of novel pharmacogenomic candidates in response to methotrexate in juvenile idiopathic arthritis: correlation between gene expression and genotype.

OBJECTIVES: Little is known about the mechanisms of efficacy of methotrexate (MTX) in childhood arthritis, or genetic influences upon response to MTX. The aims of this study were to use gene expression profiling to identify novel pathways/genes altered by MTX and then investigate these genes for genotype associations with response to MTX treatment. METHODS: Gene expression profiling before and after MTX treatment was performed on 11 children with juvenile idiopathic arthritis (JIA) treated with MTX, in whom response at 6 months of treatment was defined. Genes showing the most differential gene expression after the treatment were selected for single nucleotide polymorphism (SNP) genotyping. Genotype frequencies were compared between nonresponders and responders (ACR-Ped70). An independent cohort was available for validation. RESULTS: Gene expression profiling before and after MTX treatment revealed 1222 differentially expressed probes sets (fold change >1.7, P<0.05) and 1065 when restricted to full responder cases only. Six highly differentially expressed genes were analyzed for genetic association in response to MTX. Three SNPs in the SLC16A7 gene showed significant association with MTX response. One SNP showed validated association in an independent cohort. CONCLUSION: This study is the first, to our knowledge, to evaluate gene expression profiles in children with JIA before and after MTX, and to analyze genetic variation in differentially expressed genes. We have identified a gene, which may contribute to genetic variability in MTX response in JIA, and established as proof of principle that genes that are differentially expressed at mRNA level after drug administration may also be good candidates for genetic analysis.

Investigation of type 1 diabetes and coeliac disease susceptibility loci for association with juvenile idiopathic arthritis.

BACKGROUND: There is strong evidence suggesting that juvenile idiopathic arthritis (JIA) shares many susceptibility loci with other autoimmune diseases. OBJECTIVE: To investigate variants robustly associated with type 1 diabetes (T1D) or coeliac disease (CD) for association with JIA. METHODS: Sixteen single-nucleotide polymorphisms (SNPs) already identified as susceptibility loci for T1D/CD were selected for genotyping in patients with JIA (n=1054) and healthy controls (n=3129). Genotype and allele frequencies were compared using the Cochrane-Armitage trend test implemented in PLINK. RESULTS: One SNP in the LPP gene, rs1464510, showed significant association with JIA (p(trend)=0.002, OR=1.18, 95% CI 1.06 to 1.30). A second SNP, rs653178 in ATXN2, also showed nominal evidence for association with JIA (p(trend)=0.02, OR=1.13, 95% CI 1.02 to 1.25). The SNP, rs17810546, in IL12A showed subtype-specific association with enthesitis-related arthritis (ERA) subtype (p(trend)=0.005, OR=1.88, 95% CI 1.2 to 2.94). CONCLUSIONS: Evidence for a novel JIA susceptibility locus, LPP, is presented. Association at the SH2B3/ATXN2 locus, previously reported to be associated with JIA in a US series, also supports this region as contributing to JIA susceptibility. In addition, a subtype-specific association of IL12A with ERA is identified. All findings will require validation in independent JIA cohorts.

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 x 10(-8)) in an analysis of all 41,282 samples. The associated SNPs are near genes of known immune function, including IL6ST, SPRED2, RBPJ, CCR6, IRF5 and PXK. We also refined associations at two established rheumatoid arthritis risk loci (IL2RA and CCL21) and confirmed the association at AFF3. These new associations bring the total number of confirmed rheumatoid arthritis risk loci to 31 among individuals of European ancestry. An additional 11 SNPs replicated at P < 0.05, many of which are validated autoimmune risk alleles, suggesting that most represent genuine rheumatoid arthritis risk alleles.

Overlap of disease susceptibility loci for rheumatoid arthritis and juvenile idiopathic arthritis.

BACKGROUND: Genome-wide association studies (GWAS) have been extremely successful in the search for susceptibility risk factors for complex genetic autoimmune diseases. As more studies are published, evidence is emerging of considerable overlap of loci between these diseases. In juvenile idiopathic arthritis (JIA), another complex genetic autoimmune disease, the strategy of using information from autoimmune disease GWAS or candidate gene studies to help in the search for novel JIA susceptibility loci has been successful, with confirmed association with two genes, PTPN22 and IL2RA. Rheumatoid arthritis (RA) is an autoimmune disease that shares similar clinical and pathological features with JIA and, therefore, recently identified confirmed RA susceptibility loci are also excellent JIA candidate loci. OBJECTIVE: To determine the overlap of disease susceptibility loci for RA and JIA. METHODS: /st> Fifteen single nucleotide polymorphisms (SNPs) at nine RA-associated loci were genotyped in Caucasian patients with JIA (n=1054) and controls (n=3531) and tested for association with JIA. Allele and genotype frequencies were compared between cases and controls using the genetic analysis software, PLINK. RESULTS: Two JIA susceptibility loci were identified, one of which was a novel JIA association (STAT4) and the second confirmed previously published associations of the TRAF1/C5 locus with JIA. Weak evidence of association of JIA with three additional loci (Chr6q23, KIF5A and PRKCQ) was also obtained, which warrants further investigation. CONCLUSION: All these loci are good candidates in view of the known pathogenesis of JIA, as genes within these regions (TRAF1, STAT4, TNFAIP3, PRKCQ) are known to be involved in T-cell receptor signalling or activation pathways.