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Idiopathic inflammatory myopathies (IIM) are rare autoimmune systemic diseases characterized by muscle weakness and the presence of muscle-infiltrating T cells. IIM represent a clinical challenge due to heterogeneity of symptoms and variability of response to immunosuppressive treatment. Here, we performed in-depth single-cell sequencing on muscle-infiltrating T cells and peripheral blood memory T cells in six patients with recently diagnosed IIM. We identified tissue resident memory T-cell (T RM ) signatures including the expression of HOBIT, XCL1 and CXCR6 in the muscle biopsies of all patients with IIM. Clonally expanded T-cell clones were mainly found among cytotoxic and T RM implying their role in the disease pathogenesis. Finally, identical expanded T-cell clones persisting at follow-up in the muscle tissue of two patients suggest their involvement in disease chronicity. Our study reveals a muscle tissue resident memory T-cell signature in patients with IIM and a transcriptomic map to identify novel therapeutic targets in IIM., (© 2023 The Authors. Published under the terms of the CC BY 4.0 license.)

Background: In patients with idiopathic inflammatory myopathies (IIM), autoantibodies are associated with specific clinical phenotypes suggesting a pathogenic role of adaptive immunity. We explored if autoantibody profiles are associated with specific HLA genetic variants and clinical manifestations in IIM., Methods: We included 1348 IIM patients and determined the occurrence of 14 myositis-specific or -associated autoantibodies. We used unsupervised cluster analysis to identify autoantibody-defined subgroups and logistic regression to estimate associations with clinical manifestations, HLA-DRB1, HLA-DQA1, HLA-DQB1 alleles, and amino acids imputed from genetic information of HLA class II and I molecules., Findings: We identified eight subgroups with the following dominant autoantibodies: anti-Ro52, -U1RNP, -PM/Scl, -Mi2, -Jo1, -Jo1/Ro52, -TIF1γ or negative for all analysed autoantibodies. Associations with HLA-DRB1∗11, HLA-DRB1∗15, HLA-DQA1∗03, and HLA-DQB1∗03 were present in the anti-U1RNP-dominated subgroup. HLA-DRB1∗03, HLA-DQA1∗05, and HLA-DQB1∗02 alleles were overrepresented in the anti-PM/Scl and anti-Jo1/Ro52-dominated subgroups. HLA-DRB1∗16, HLA-DRB1∗07 alleles were most frequent in anti-Mi2 and HLA-DRB1∗01 and HLA-DRB1∗07 alleles in the anti-TIF1γ subgroup. The HLA-DRB1∗13, HLA-DQA1∗01 and HLA-DQB1∗06 alleles were overrepresented in the negative subgroup. Significant signals from variations in class I molecules were detected in the subgroups dominated by anti-Mi2, anti-Jo1/Ro52, anti-TIF1γ, and the negative subgroup., Interpretation: Distinct HLA class II and I associations were observed for almost all autoantibody-defined subgroups. The associations support autoantibody profiles use for classifying IIM which would likely reflect underlying pathogenic mechanisms better than classifications based on clinical symptoms and/or histopathological features., Funding: See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript., Competing Interests: Declaration of interests Professor Lundberg has received consulting fees from Corbus Pharmaceuticals, Inc and research grants from Astra Zeneca and has been serving on the advisory board for Astra Zeneca, Bristol Myers Squibb, EMD Serono Research & Development Institute, Argenx, Octapharma, Kezaar, Orphazyme, Pfizer and Janssen and has stock shares in Roche and Novartis. Professor Vencovský has received speaker fees from Abbvie, Biogen, Boehringer, Eli Lilly, Gilead, MSD, Novartis, Pfizer, Roche, Sanofi, UCB, Werfen; and has received consulting fees from Abbvie, Argenx, Boehringer, Eli Lilly, Gilead, Octapharma, Pfizer, UCB. Professor Chinoy has received personal compensation for activities with Novartis, UCB, Eli Lilly, Biogen, Orphazyme, Astra Zeneca, Pfizer, Kezar Life Sciences, Galapagos, Argenx, GSK as a speaker, advisory board member or consultancy; grants via The University of Manchester from Eli Lilly and UCB; travel support from Abbvie and Janssen. Dr Mann has received honoraria from Abbvie, Biogen, BMS, Eli Lilly, MSD, Janssen, Novartis, UCB, Pfizer, SOBI and travel support by Abbvie. Dr Tansley received payment from Boehringer Ingelheim as a speaker. Dr Rönnblom received support from The Swedish Research Council (2018–02399), Reumatikerförbundet (R-939716) and GV:80 (FAI-2020-0658) foundation. Dr Holmqvist received support from The Swedish Research Council, Stockholm Regional Council (ALF), Reumatikerförbundet, Konung Gustaf V:80 year foundation, The Swedish Cancer Association and was a member of Medical Advisory Board of The Myositis Association. Dr Lamb received support from the Medical Research Council UK and Myositis UK. Other authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: Emerging evidence demonstrates that aPS-PT associate with thrombotic events. Genetic predisposition, including HLA-DRB1 alleles, is known to contribute to the occurrence of conventional aPL [anti-β2glycoprotein-I (anti-β2GPI) and aCL]. We investigated associations between aPS-PT and HLA-DRB1* alleles and thrombosis in SLE. Conventional aPL were included for comparison., Methods: We included 341 consecutive SLE patients, with information on general cardiovascular risk factors, including blood lipids, LA and thrombotic events. aPS/PT, anti-β2GPI and aCL of IgA/G/M isotypes and LA were quantified., Results: aPS/PT antibodies associated positively with HLA-DRB1*13 [odds ratio (OR) 2.7, P = 0.002], whereas anti-β2GPI and aCL antibodies associated primarily with HLA-DRB1*04 (OR 2.5, P = 0.0005). These associations remained after adjustment for age, gender and other HLA-DRB1* alleles. HLA-DRB1*13, but not DRB1*04, remained as an independent risk factor for thrombosis and APS after adjustment for aPL and cardiovascular risk factors. The association between DRB1*13 and thrombosis was mediated by aPS-PT positivity. HLA-DRB1*03, on the other hand, associated negatively with thrombotic events as well as all aPL using both uni- and multivariate analyses. HLA-DRB1*03 had a thrombo-protective effect in aPL-positive patients. Additionally, HLA-DRB1*03 was associated with a favourable lipid profile regarding high-density lipoprotein and triglycerides., Conclusions: HLA-DRB1*13 confers risk for both aPS-PT and thrombotic events in lupus. The association between HLA-DRB1*13 and thrombosis is largely, but not totally, mediated through aPS-PT. HLA-DRB1*03 was negatively associated with aPL and positively with favourable lipid levels. Thus, HLA-DRB1*03 seems to identify a subgroup of SLE patients with reduced vascular risk., (© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Objective: Copy number variation of the C4 complement components, C4A and C4B, has been associated with systemic inflammatory autoimmune diseases. This study was undertaken to investigate whether C4 copy number variation is connected to the autoimmune repertoire in systemic lupus erythematosus (SLE), primary Sjögren's syndrome (SS), or myositis., Methods: Using targeted DNA sequencing, we determined the copy number and genetic variants of C4 in 2,290 well-characterized Scandinavian patients with SLE, primary SS, or myositis and 1,251 healthy controls., Results: A prominent relationship was observed between C4A copy number and the presence of SSA/SSB autoantibodies, which was shared between the 3 diseases. The strongest association was detected in patients with autoantibodies against both SSA and SSB and 0 C4A copies when compared to healthy controls (odds ratio [OR] 18.0 [95% confidence interval (95% CI) 10.2-33.3]), whereas a weaker association was seen in patients without SSA/SSB autoantibodies (OR 3.1 [95% CI 1.7-5.5]). The copy number of C4 correlated positively with C4 plasma levels. Further, a common loss-of-function variant in C4A leading to reduced plasma C4 was more prevalent in SLE patients with a low copy number of C4A. Functionally, we showed that absence of C4A reduced the individuals' capacity to deposit C4b on immune complexes., Conclusion: We show that a low C4A copy number is more strongly associated with the autoantibody repertoire than with the clinically defined disease entities. These findings may have implications for understanding the etiopathogenetic mechanisms of systemic inflammatory autoimmune diseases and for patient stratification when taking the genetic profile into account., (© 2022 The Authors. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Objective: The heterogeneity of systemic lupus erythematosus (SLE) constitutes clinical and therapeutical challenges. We therefore studied whether unrecognized disease subgroups can be identified by using autoantibody profiling together with HLA-DRB1 alleles and immunological and clinical data., Methods: An unsupervised cluster analysis was performed based on detection of 13 SLE-associated autoantibodies (double-stranded DNA, nucleosomes, ribosomal P, ribonucleoprotein [RNP] 68, RNPA, Smith [Sm], Sm/RNP, Sjögren's syndrome antigen A [SSA]/Ro52, SSA/Ro60, Sjögren's syndrome antigen B [SSB]/La, cardiolipin [CL]-Immunoglobulin G [IgG], CL-Immunoglobulin M [IgM], and β 2 glycoprotein I [β 2 GPI]-IgG) in 911 patients with SLE from two cohorts. We evaluated whether each SLE subgroup is associated with HLA-DRB1 alleles, clinical manifestations (n = 743), and cytokine levels in circulation (n = 446)., Results: Our analysis identified four subgroups among the patients with SLE. Subgroup 1 (29.3%) was dominated by anti-SSA/Ro60/Ro52/SSB autoantibodies and was strongly associated with HLA-DRB1*03 (odds ratio [OR] = 4.73; 95% confidence interval [CI] = 4.52-4.94). Discoid lesions were more common for this disease subgroup (OR = 1.71, 95% CI = 1.18-2.47). Subgroup 2 (28.7%) was dominated by anti-nucleosome/SmRNP/DNA/RNPA autoantibodies and associated with HLA-DRB1*15 (OR = 1.62, 95% CI = 1.41-1.84). Nephritis was most common in this subgroup (OR = 1.61, 95% CI = 1.14-2.26). Subgroup 3 (23.8%) was characterized by anti-ß 2 GPI-IgG/anti-CL-IgG/IgM autoantibodies and a higher frequency of HLA-DRB1*04 compared with the other patients with SLE. Vascular events were more common in Subgroup 3 (OR = 1.74, 95% CI = 1.2-2.5). Subgroup 4 (18.2%) was negative for the investigated autoantibodies, and this subgroup was not associated with HLA-DRB1. Additionally, the levels of eight cytokines significantly differed among the disease subgroups., Conclusion: Our findings suggest that four fairly distinct subgroups can be identified on the basis of the autoantibody profile in SLE. These four SLE subgroups differ regarding associations with HLA-DRB1 alleles and immunological and clinical features, suggesting dissimilar disease pathways., (© 2021 The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Understanding the genetic background of complex diseases requires the expansion of studies beyond univariate associations. Therefore, it is important to use interaction assessments of risk factors in order to discover whether, and how genetic risk variants act together on disease development. The principle of interaction analysis is to explore the magnitude of the combined effect of risk factors on disease causation. In this study, we use simulations to investigate different scenarios of causation to show how the magnitude of the effect of two risk factors interact. We mainly focus on the two most commonly used interaction models, the additive and multiplicative risk scales, since there is often confusion regarding their use and interpretation. Our results show that the combined effect is multiplicative when two risk factors are involved in the same chain of events, an interaction called synergism. Synergism is often described as a deviation from additivity, which is a broader term. Our results also confirm that it is often relevant to estimate additive effect relationships, because they correspond to independent risk factors at low disease prevalence. Importantly, we evaluate the threshold of more than two required risk factors for disease causation, called the multifactorial threshold model. We found a simple mathematical relationship (square root) between the threshold and an additive-to-multiplicative linear effect scale (AMLES), where 0 corresponds to an additive effect and 1 to a multiplicative. We propose AMLES as a metric that could be used to test different effects relationships at the same time, given that it can simultaneously reveal additive, multiplicative and intermediate risk effects relationships. Finally, the utility of our simulation study was demonstrated using real data by analyzing and interpreting gene-gene interaction odds ratios from a rheumatoid arthritis case-control cohort., Competing Interests: The authors have declared that no competing interests exist.

Background: Fine-mapping of human leukocyte antigen (HLA) region for rheumatoid arthritis (RA) risk factors has identified several HLA alleles and its corresponding amino acid residues as independent signals (i.e., HLA-A, HLA-B, HLA-DPB1, and HLA-DQA1 genes), in addition to the well-established genetic factor in HLA-DRB1 gene. However, this was mainly performed in the Caucasian and East Asian populations, and data from different Asian regions is less represented. We aimed to evaluate whether there are independent RA risk variants in both anti-citrullinated protein antibody (ACPA)-positive and ACPA-negative RA patients from the multi-ethnic Malaysian population, using the fine-mapping of HLA region strategy., Methods: We imputed the classical HLA alleles, amino acids, and haplotypes using the Immunochip genotyping data of 1260 RA cases (i.e., 530 Malays, 259 Chinese, 412 Indians, and 59 mixed ethnicities) and 1571 controls (i.e., 981 Malays, 205 Chinese, 297 Indians, and 87 mixed ethnicities) from the Malaysian Epidemiological Investigation of Rheumatoid Arthritis (MyEIRA) population-based case-control study. Stepwise logistic regression was performed to identify the independent genetic risk factors for RA within the HLA region., Results: We confirmed that the HLA-DRB1 amino acid at position 11 with valine residue conferred the strongest risk effect for ACPA-positive RA (OR = 4.26, 95% CI = 3.30-5.49, P GWAS  = 7.22 × 10 -29 ) in the Malays. Our study also revealed that HLA-DRB1 amino acid at position 96 with histidine residue was negatively associated with the risk of developing ACPA-positive RA in the Indians (OR = 0.48, 95% CI = 0.37-0.62, P GWAS  = 2.58 × 10 -08 ). Interestingly, we observed that HLA-DQB1*03:02 allele was inversely related to the risk of developing ACPA-positive RA in the Malays (OR = 0.17, 95% CI = 0.09-0.30, P GWAS  = 1.60 × 10 -09 ). No association was observed between the HLA variants and risk of developing ACPA-negative RA in any of the three major ethnic groups in Malaysia., Conclusions: Our results demonstrate that the RA-associated genetic factors in the multi-ethnic Malaysian population are similar to those in the Caucasian population, despite significant differences in the genetic architecture of HLA region across populations. A novel and distinct independent association between the HLA-DQB1*03:02 allele and ACPA-positive RA was observed in the Malays. In common with the Caucasian population, there is little risk from HLA region for ACPA-negative RA.

The influence and effect of cigarette smoking in sarcoidosis is unclear. Here, we evaluated gene-environment interaction between multiple genetic variants including HLA genes and smoking in sarcoidosis defined by two clinical phenotypes, Löfgren's syndrome (LS) and patients without Löfgren's syndrome (non-LS). To quantify smoking effects in sarcoidosis, we performed a gene-environment interaction study in a Swedish population-based case-control study consisting of 3,713 individuals. Cases and controls were classified according to their cigarette smoking status and genotypes by Immunochip platform. Gene-smoking interactions were quantified by an additive interaction model using a logistic regression adjusted by sex, age and first two principal components. The estimated attributable proportion (AP) was used to quantify the interaction effect. Assessment of smoking effects with inclusion of genetic information revealed 53 (in LS) and 34 (in non-LS) SNP-smoking additive interactions at false discovery rate (FDR) below 5%. The lead signals interacting with smoking were rs12132140 (AP = 0.56, 95% CI = 0.22-0.90), p = 1.28e-03) in FCRL1 for LS and rs61780312 (AP = 0.62, 95% CI = 0.28-0.90), p = 3e-04) in IL23R for non-LS. We further identified 16 genomic loci (in LS) and 13 (in non-LS) that interact with cigarette smoking. These findings suggest that sarcoidosis risk is modulated by smoking due to genetic susceptibility. Therefore, patients having certain gene variants, are at a higher risk for the disease. Consideration of individual's genetic predisposition is crucial to quantify effects of smoking in sarcoidosis.

Background: Here we integrate verified signals from previous genetic association studies with gene expression and pathway analysis for discovery of new candidate genes and signaling networks, relevant for rheumatoid arthritis (RA)., Method: RNA-sequencing-(RNA-seq)-based expression analysis of 377 genes from previously verified RA-associated loci was performed in blood cells from 5 newly diagnosed, non-treated patients with RA, 7 patients with treated RA and 12 healthy controls. Differentially expressed genes sharing a similar expression pattern in treated and untreated RA sub-groups were selected for pathway analysis. A set of "connector" genes derived from pathway analysis was tested for differential expression in the initial discovery cohort and validated in blood cells from 73 patients with RA and in 35 healthy controls., Results: There were 11 qualifying genes selected for pathway analysis and these were grouped into two evidence-based functional networks, containing 29 and 27 additional connector molecules. The expression of genes, corresponding to connector molecules was then tested in the initial RNA-seq data. Differences in the expression of ERBB2, TP53 and THOP1 were similar in both treated and non-treated patients with RA and an additional nine genes were differentially expressed in at least one group of patients compared to healthy controls. The ERBB2, TP53. THOP1 expression profile was successfully replicated in RNA-seq data from peripheral blood mononuclear cells from healthy controls and non-treated patients with RA, in an independent collection of samples., Conclusion: Integration of RNA-seq data with findings from association studies, and consequent pathway analysis implicate new candidate genes, ERBB2, TP53 and THOP1 in the pathogenesis of RA.

Objective: In rheumatoid arthritis (RA) several recent efforts have sought to discover means of predicting which patients would benefit from treatment. However, results have been discrepant with few successful replications. Our objective was to build a biobank with DNA, RNA and protein measurements to test the claim that the current state-of-the-art precision medicine will benefit RA patients., Methods: We collected 451 blood samples from 61 healthy individuals and 185 RA patients initiating treatment, before treatment initiation and at a 3 month follow-up time. All samples were subjected to high-throughput RNA sequencing, DNA genotyping, extensive proteomics and flow cytometry measurements, as well as comprehensive clinical phenotyping. Literature review identified 2 proteins, 52 single-nucleotide polymorphisms (SNPs) and 72 gene-expression biomarkers that had previously been proposed as predictors of TNF inhibitor response (∆DAS28-CRP)., Results: From these published TNFi biomarkers we found that 2 protein, 2 SNP and 8 mRNA biomarkers could be replicated in the 59 TNF initiating patients. Combining these replicated biomarkers into a single signature we found that we could explain 51% of the variation in ∆DAS28-CRP. This corresponds to a sensitivity of 0.73 and specificity of 0.78 for the prediction of three month ∆DAS28-CRP better than -1.2., Conclusions: The COMBINE biobank is currently the largest collection of multi-omics data from RA patients with high potential for discovery and replication. Taking advantage of this we surveyed the current state-of-the-art of drug-response stratification in RA, and identified a small set of previously published biomarkers available in peripheral blood which predicts clinical response to TNF blockade in this independent cohort.

Mutations of the gene encoding four-and-a-half LIM domain 1 (FHL1) are the causative factor of several X-linked hereditary myopathies that are collectively termed FHL1-related myopathies. These disorders are characterized by severe muscle dysfunction and damage. Here, we have shown that patients with idiopathic inflammatory myopathies (IIMs) develop autoimmunity to FHL1, which is a muscle-specific protein. Anti-FHL1 autoantibodies were detected in 25% of IIM patients, while patients with other autoimmune diseases or muscular dystrophies were largely anti-FHL1 negative. Anti-FHL1 reactivity was predictive for muscle atrophy, dysphagia, pronounced muscle fiber damage, and vasculitis. FHL1 showed an altered expression pattern, with focal accumulation in the muscle fibers of autoantibody-positive patients compared with a homogeneous expression in anti-FHL1-negative patients and healthy controls. We determined that FHL1 is a target of the cytotoxic protease granzyme B, indicating that the generation of FHL1 fragments may initiate FHL1 autoimmunity. Moreover, immunization of myositis-prone mice with FHL1 aggravated muscle weakness and increased mortality, suggesting a direct link between anti-FHL1 responses and muscle damage. Together, our findings provide evidence that FHL1 may be involved in the pathogenesis not only of genetic FHL1-related myopathies but also of autoimmune IIM. Importantly, these results indicate that anti-FHL1 autoantibodies in peripheral blood have promising potential as a biomarker to identify a subset of severe IIM.

The ubiquitin associated and Src-homology 3 (SH3) domain containing A (UBASH3a) is a suppressor of T-cell receptor signaling, underscoring antigen presentation to T-cells as a critical shared mechanism of diseases pathogenesis. The aim of the present study was to determine whether the UBASH3a gene influence the susceptibility to systemic lupus erythematosus (SLE) in Caucasian populations. We evaluated five UBASH3a polymorphisms (rs2277798, rs2277800, rs9976767, rs13048049 and rs17114930), using TaqMan® allelic discrimination assays, in a discovery cohort that included 906 SLE patients and 1165 healthy controls from Spain. The SNPs that exhibit statistical significance difference were evaluated in a German replication cohort of 360 SLE patients and 379 healthy controls. The case-control analysis in the Spanish population showed a significant association between the rs9976767 and SLE (Pc = 9.9E-03 OR = 1.21 95%CI = 1.07-1.37) and a trend of association for the rs2277798 analysis (P = 0.09 OR = 0.9 95%CI = 0.79-1.02). The replication in a German cohort and the meta-analysis confirmed that the rs9976767 (Pc = 0.02; Pc = 2.4E-04, for German cohort and meta-analysis, respectively) and rs2277798 (Pc = 0.013; Pc = 4.7E-03, for German cohort and meta-analysis, respectively) UBASH3a variants are susceptibility factors for SLE. Finally, a conditional regression analysis suggested that the most likely genetic variation responsible for the association was the rs9976767 polymorphism. Our results suggest that UBASH3a gene plays a role in the susceptibility to SLE. Moreover, our study indicates that UBASH3a can be considered as a common genetic factor in autoimmune diseases.

Objectives: C1858T single nucleotide polymorphism in PTPN22 encoding the R620W allele variant of Lyp-PTPN22 (a protein phosphatase negatively regulating T-cell activation) has been associated with autoimmunity. This work has investigated the possible association between PTPN22 C1858T (rs2476601) polymorphism and rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) in a Colombian population., Methods: A case-control study included 1,042 samples from 413 RA, 94 SLE and 101 SSc patients and 434 healthy controls. The TaqMan allele discrimination assay was used for genotyping., Results: The case-control study provided robust evidence of association between allele 1858T and RA (p=5E-05), as well as between 1858T and SLE (p=0.004). These observations were confirmed for both diseases by meta-analysis (p=2E-04, pooled OR 1.9; 1.3-2.7 95% CI for RA; p<0.0001, pooled OR 2.8, 1.8-4.5 95% CI for SLE). No significant association was observed between 1858T and SSc (p=0.98, OR 1.11, 0.46-2.65 95% CI)., Conclusions: The study suggested that the PTPN22 1858T variant influences RA and SLE genetic background but not that of SSc in the Colombian population.

Genetic variation in the protein tyrosine phosphatase non-receptor type gene 22 (PTPN22, encoding lymphoid tyrosine phosphatase, LYP) influences the risk of developing multiple autoimmune diseases, but the underlying mechanisms are not completely understood. In a recent study published in Genome Medicine, Ronninger et al. showed that there are differences in the expression of PTPN22 isoforms between peripheral blood mononuclear cells from rheumatoid arthritis patients and those of healthy controls. This study provides new insights into the role of PTPN22 in autoimmune diseases.

Impaired innate inflammatory response has a key role in the Crohn's disease (CD) pathogenesis. The aim of this study was to investigate the possible role of the TLR10-TLR1-TLR6 gene cluster in CD susceptibility. A total of 508 CD patients (284, cohort 1 and 224, cohort 2) and 576 controls were included. TLR10-TLR1-TLR6 cluster single-nucleotide polymorphisms genotyping, NOD2 mutations and TLR10 mRNA quantification were performed using TaqMan assays. Nucleotide-binding oligomerization domain containing 2 (NOD2) and Toll-like receptor (TLR) loci interaction was analyzed by logistic regression and multifactor-dimensionality reduction (MDR). Entropy-based analysis was used to interpret combination effects. One TLR10 haplotype (TLR10(GGGG)) was found associated with CD susceptibility in both cohorts, individuals with two copies had approximately twofold more risk of CD susceptibility than individuals having no copies (odds ratio=1.89, P-value=0.0002). No differences in the mRNA levels were observed among the genotypes. The strongest model for predicting CD risk according to the MDR analysis was a two-locus model including NOD2 mutations and TLR10(GGGG) haplotype (P(c)<0.0001). The interaction gain attributed to the combination of both genes was negative (IG=-2.36%), indicating redundancy or independent effects. Our results support association of the TLR10 gene with CD susceptibility. The effect of TLR10 would be independent of NOD2, suggesting different signaling pathways for both genes.

The aim of this study was to determine, through a genome-wide association study (GWAS), the genetic components contributing to different clinical sub-phenotypes of systemic sclerosis (SSc). We considered limited (lcSSc) and diffuse (dcSSc) cutaneous involvement, and the relationships with presence of the SSc-specific auto-antibodies, anti-centromere (ACA), and anti-topoisomerase I (ATA). Four GWAS cohorts, comprising 2,296 SSc patients and 5,171 healthy controls, were meta-analyzed looking for associations in the selected subgroups. Eighteen polymorphisms were further tested in nine independent cohorts comprising an additional 3,175 SSc patients and 4,971 controls. Conditional analysis for associated SNPs in the HLA region was performed to explore their independent association in antibody subgroups. Overall analysis showed that non-HLA polymorphism rs11642873 in IRF8 gene to be associated at GWAS level with lcSSc (P = 2.32×10(-12), OR = 0.75). Also, rs12540874 in GRB10 gene (P = 1.27 × 10(-6), OR = 1.15) and rs11047102 in SOX5 gene (P = 1.39×10(-7), OR = 1.36) showed a suggestive association with lcSSc and ACA subgroups respectively. In the HLA region, we observed highly associated allelic combinations in the HLA-DQB1 locus with ACA (P = 1.79×10(-61), OR = 2.48), in the HLA-DPA1/B1 loci with ATA (P = 4.57×10(-76), OR = 8.84), and in NOTCH4 with ACA P = 8.84×10(-21), OR = 0.55) and ATA (P = 1.14×10(-8), OR = 0.54). We have identified three new non-HLA genes (IRF8, GRB10, and SOX5) associated with SSc clinical and auto-antibody subgroups. Within the HLA region, HLA-DQB1, HLA-DPA1/B1, and NOTCH4 associations with SSc are likely confined to specific auto-antibodies. These data emphasize the differential genetic components of subphenotypes of SSc., Competing Interests: The authors have declared that no competing interests exist.