Jane Worthington

Arthritis Research UK, Chesterfield, England, United Kingdom

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Publications (484)3039.26 Total impact

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    [Show abstract] [Hide abstract] ABSTRACT: Objectives During the last years, genome-wide association studies (GWASs) have identified a number of common genetic risk factors for rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). However, the genetic overlap between these two immune-mediated diseases has not been thoroughly examined so far. The aim of the present study was to identify additional risk loci shared between RA and SLE. Methods We performed a large-scale meta-analysis of GWAS data from RA (3911 cases and 4083 controls) and SLE (2237 cases and 6315 controls). The top-associated polymorphisms in the discovery phase were selected for replication in additional datasets comprising 13 641 RA cases and 31 921 controls and 1957 patients with SLE and 4588 controls. Results The rs9603612 genetic variant, located nearby the COG6 gene, an established susceptibility locus for RA, reached genome-wide significance in the combined analysis including both discovery and replication sets (p value=2.95E−13). In silico expression quantitative trait locus analysis revealed that the associated polymorphism acts as a regulatory variant influencing COG6 expression. Moreover, protein–protein interaction and gene ontology enrichment analyses suggested the existence of overlap with specific biological processes, specially the type I interferon signalling pathway. Finally, genetic correlation and polygenic risk score analyses showed cross-phenotype associations between RA and SLE. Conclusions In conclusion, we have identified a new risk locus shared between RA and SLE through a meta-analysis including GWAS datasets of both diseases. This study represents the first comprehensive large-scale analysis on the genetic overlap between these two complex disorders.
    Full-text · Article · May 2016 · Annals of the Rheumatic Diseases
  • [Show abstract] [Hide abstract] ABSTRACT: There is growing evidence of shared risk alleles for complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing among all individuals (whole-group pleiotropy) or a subset of individuals in a genetically heterogeneous cohort (subgroup heterogeneity). Here we describe the use of a well-powered statistic, BUHMBOX, to distinguish between those two situations using genotype data. We observed a shared genetic basis for 11 autoimmune diseases and type 1 diabetes (T1D; P < 1 × 10−4) and for 11 autoimmune diseases and rheumatoid arthritis (RA; P < 1 × 10−3). This sharing was not explained by subgroup heterogeneity (corrected PBUHMBOX > 0.2; 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (P < 1 × 10−9) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (PBUHMBOX = 0.008; 2,406 seronegative RA cases). We also observed a shared genetic basis for major depressive disorder (MDD) and schizophrenia (P < 1 × 10−4) that was not explained by subgroup heterogeneity (PBUHMBOX = 0.28; 9,238 MDD cases).
    No preview · Technical Report · May 2016
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    [Show abstract] [Hide abstract] ABSTRACT: Objectives: Systemic sclerosis (SSc) and rheumatoid arthritis (RA) are autoimmune diseases that share clinical and immunological characteristics. To date, several shared SSc-RA loci have been identified independently. In this study, we aimed to systematically search for new common SSc-RA loci through an inter-disease meta-GWAS strategy. Methods: We performed a meta-analysis combining GWAS datasets of SSc and RA using a strategy that allowed identification of loci with both same-direction and opposing-direction allelic effects. The top single-nucleotide polymorphisms (SNPs) were followed-up in independent SSc and RA case-control cohorts. This allowed us to increase the sample size to a total of 8,830 SSc patients, 16,870 RA patients and 43,393 controls. Results: The cross-disease meta-analysis of the GWAS datasets identified several loci with nominal association signals (P-value < 5 x 10(-6) ), which also showed evidence of association in the disease-specific GWAS scan. These loci included several genomic regions not previously reported as shared loci, besides risk factors associated with both diseases in previous studies. The follow-up of the putatively new SSc-RA loci identified IRF4 as a shared risk factor for these two diseases (Pcombined = 3.29 x 10(-12) ). In addition, the analysis of the biological relevance of the known SSc-RA shared loci pointed to the type I interferon and the interleukin 12 signaling pathways as the main common etiopathogenic factors. Conclusions: Our study has identified a novel shared locus, IRF4, for SSc and RA and highlighted the usefulness of cross-disease GWAS meta-analysis in the identification of common risk loci. This article is protected by copyright. All rights reserved.
    Full-text · Article · Apr 2016 · Arthritis and Rheumatology
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    [Show abstract] [Hide abstract] ABSTRACT: 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.
    Preview · Article · Apr 2016 · Scientific Reports
  • [Show abstract] [Hide abstract] ABSTRACT: Introduction: Genetic polymorphisms within the HLA region explain only a modest proportion of anti-CCP negative rheumatoid arthritis (RA) heritability. However, few non-HLA markers have been identified so far. Therefore, we aimed to replicate non-HLA markers from a previous study. Methods: The Rheumatoid Arthritis Consortium International (RACI) densely genotyped 186 autoimmune-related regions in 3,297 anti-CCP negative RA patients and 15,870 controls across 6 different populations using the Illumina ImmunoChip array. Here, we performed a case-control replication study of the most associated anti-CCP negative markers from this discovery study, in an independent cohort of anti-CCP negative UK RA patients; individuals from the ArcOGEN and Wellcome Trust Case Control consortia were used as controls. Genotyping in cases was performed using Sequenom MassArray technology. Genome-wide data from controls was imputed using the 1000 Genomes Phase I integrated variant call set release (v3) as reference panel. Results: After genotyping and imputation quality control procedures, data was available for 15 non-HLA single nucleotide polymorphism (SNPs) in 1024 cases and 6348 controls. We confirmed known markers (ANKRD55, meta-analysis odds ratio (OR): 0.80, p-value=2.8E-13; BLK, OR: 1.13, p=7.0E-06) and identified new and specific markers of anti-CCP negative RA (Prolactin PRL, OR: 1.13, p=2.1E-06; NFIA, OR: 0.85, p=2.5E-06). Neither of these loci is associated with other common, complex auto-immune disease. Conclusion: Anti-CCP negative and positive RA are two genetically different disease subsets, only partially sharing susceptibility factors. Genetic polymorphism located near the PRL and NFIA genes represent examples of genetic susceptibility factors specific for anti-CCP negative RA. This article is protected by copyright. All rights reserved.
    No preview · Article · Feb 2016 · Arthritis and Rheumatology
  • W.-H. Wei · C.-Y. Loh · Jane Worthington · Stephen Eyre
    [Show abstract] [Hide abstract] ABSTRACT: Objective: Studying statistical gene-gene interactions (epistasis) has been limited by the difficulties in performance, both statistically and computationally, in large enough sample numbers to gain sufficient power. Three large Immunochip datasets from cohort samples recruited in the United Kingdom, United States, and Sweden with European ancestry were used to examine epistasis in rheumatoid arthritis (RA). Methods: A full pairwise search was conducted in the UK cohort using a high-throughput tool and the resultant significant epistatic signals were tested for replication in the United States and Swedish cohorts. A forward selection approach was applied to remove redundant signals, while conditioning on the preidentified additive effects. Results: We detected abundant genome-wide significant (p < 1.0e-13) epistatic signals, all within the MHC region. These signals were reduced substantially, but a proportion remained significant (p < 1.0e-03) in conditional tests. We identified 11 independent epistatic interactions across the entire MHC, each explaining on average 0.12% of the phenotypic variance, nearly all replicated in both replication cohorts. We also identified non-MHC epistatic interactions between RA susceptible loci LOC100506023 and IRF5 with Immunochip-wide significance (p < 1.1e-08) and between 2 neighboring single-nucleotide polymorphism near PTPN22 that were in low linkage disequilibrium with independent interaction (p < 1.0e-05). Both non-MHC epistatic interactions were statistically replicated with a similar interaction pattern in the US cohort only. Conclusion: There are multiple but relatively weak interactions independent of the additive effects in RA and a larger sample number is required to confidently assign additional non-MHC epistasis.
    No preview · Article · Feb 2016 · The Journal of Rheumatology
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    [Show abstract] [Hide abstract] ABSTRACT: Family studies have provided overwhelming evidence for an underlying genetic component to psoriasis. Toll-like receptors (TLRs) are key transmembrane proteins in both the innate and adaptive immune responses which are known to be integral processes in psoriasis. Recent functional studies support this notion having suggested a role for TLR4 in the pathogenesis of psoriasis. Furthermore a missense polymorphism in the TLR4 gene has been associated with a number of autoimmune conditions, including Crohn diseases, making TLR4 a viable candidate gene for investigation. The aim of this study was to investigate polymorphisms across the TLR4 region with a high-density single nucleotide polymorphism (SNP) panel in a large cohort of patients with chronic plaque type psoriasis. Twenty SNPs were successfully genotyped using Sequenom iPLEX Gold platform in 2826 UK chronic plaque type psoriasis patients including subgroup data on presence of confirmed psoriatic arthritis (n = 1839) and early-onset psoriasis (n = 1466) was available. Allele frequencies for psoriasis patients were compared against imputed Wellcome Trust Case Control Consortium controls (n = 4861). Significant association was observed between a missense variant rs4986790 of TLR4 (Asp229Gly) and plaque type psoriasis (p = 2 × 10−4) which was also notable in those with psoriatic arthritis (p = 2 × 10−4) and early-onset psoriasis (p = 8 × 10−4). We present data suggestive of an association between a functional variant and an intronic variant of TLR4 and chronic plaque type psoriasis and psoriatic arthritis. However, validation of this association in independent cohorts will be necessary.
    Full-text · Article · Feb 2016 · Archives for Dermatological Research
  • D Plant · A Webster · N Nair · J Oliver · S Smith · S Eyre · KL Hyrich · AG Wilson · AW Morgan · J Isaacs · J Worthington · A Barton
    [Show abstract] [Hide abstract] ABSTRACT: Background Biologic drug therapies represent a huge advance in the treatment of rheumatoid arthritis (RA). However, very good disease control is only achieved in 30% of patients, making identification of biomarkers of response a research priority. We therefore hypothesise that differential DNA methylation patterns may provide biomarkers predictive of response to TNF-inhibitor (TNFi) therapy in patients with RA. Methods An epigenome-wide association study was performed on pre-treatment whole blood DNA from patients with RA. Patients who displayed good response (n = 36) or no response (n = 36) to etanercept therapy at 3-months were selected. Differentially methylated positions (DMPs) were identified using linear regression. Variance of methylation at DMPs was assessed for correlation with cis-acting SNPs. A replication experiment for prioritised SNPs was performed in an independent cohort of 1,204 RA patients. Results Five DMPs between responders groups were identified with a FDR <5%. The top two DMPs mapped to exon 7 of the LRPAP1 gene on chromosome 4 (cg04857395, p = 1.39E-08 and cg26401028, 1.96E-08). The A allele of the SNP rs3468 was correlated with higher levels of methylation for both of the top two DMPs (2.63E-07 and 1.05E-06, respectively). Further the A allele of rs3468 was correlated with EULAR non-response in the discovery cohort (n = 56; p = 0.03) and in the independent replication cohort (n = 1.204; p = 0.003). Conclusions We identify DNA methylation as a potential biomarker of TNFi response and report the association between response and the LRPAP1 gene which encodes a chaperone of low-density lipoprotein receptor-related protein-1. Additional replication experiments in independent sample collections are now required.
    No preview · Article · Feb 2016 · Annals of the Rheumatic Diseases
  • [Show abstract] [Hide abstract] ABSTRACT: Objective: Biologic drug therapies represent a huge advance in the treatment of rheumatoid arthritis (RA). However, very good disease control is achieved in only 30% of patients, making identification of biomarkers of response a research priority. We undertook this study to test our hypothesis that differential DNA methylation patterns may provide biomarkers predictive of response to tumor necrosis factor inhibitor (TNFi) therapy in patients with RA. Methods: An epigenome-wide association study was performed on pretreatment whole blood DNA from patients with RA. Patients who displayed good response (n=36) or no response (n=36) to etanercept therapy at 3 months were selected. Differentially methylated positions were identified using linear regression. Variance of methylation at differentially methylated positions was assessed for correlation with cis-acting single-nucleotide polymorphisms (SNPs). A replication experiment for prioritized SNPs was performed in an independent cohort of 1,204 RA patients. Results: Five positions that were differentially methylated between responder groups were identified, with a false discovery rate of <5%. The top 2 differentially methylated positions mapped to exon 7 of the LRPAP1 gene on chromosome 4 (cg04857395, P=1.39 × 10-8 and cg26401028, P=1.69 × 10-8). The A allele of the SNP rs3468 was correlated with higher levels of methylation for both of the top 2 differentially methylated positions (P=2.63 × 10-7 and P=1.05 × 10-6, respectively). Furthermore, the A allele of rs3468 was correlated with European League Against Rheumatism nonresponse in the discovery cohort (P=0.03; n=56) and in the independent replication cohort (P=0.003; n=1,204). Conclusion: We identify DNA methylation as a potential biomarker of response to TNFi therapy, and we report the association between response and the LRPAP1 gene, which encodes a chaperone of low-density lipoprotein receptor-related protein 1. Additional replication experiments in independent sample collections are now needed. © 2016 The Authors. Arthritis & Rheumatology published by Wiley Periodicals, Inc. on behalf of the American College of Rheumatology.
    No preview · Article · Jan 2016
  • AP Webster · S L Smith · J Worthington · A Barton · D Plant
    No preview · Article · Dec 2015 · Scandinavian Journal of Rheumatology
  • [Show abstract] [Hide abstract] ABSTRACT: 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).
    No preview · Article · Nov 2015 · Nature Communications
  • [Show abstract] [Hide abstract] ABSTRACT: Background: Understanding the genetic basis of airflow obstruction and smoking behaviour is key to determining the pathophysiology of chronic obstructive pulmonary disease (COPD). We used UK Biobank data to study the genetic causes of smoking behaviour and lung health. Methods: We sampled individuals of European ancestry from UK Biobank, from the middle and extremes of the forced expiratory volume in 1 s (FEV1) distribution among heavy smokers (mean 35 pack-years) and never smokers. We developed a custom array for UK Biobank to provide optimum genome-wide coverage of common and low-frequency variants, dense coverage of genomic regions already implicated in lung health and disease, and to assay rare coding variants relevant to the UK population. We investigated whether there were shared genetic causes between different phenotypes defined by extremes of FEV1. We also looked for novel variants associated with extremes of FEV1 and smoking behaviour and assessed regions of the genome that had already shown evidence for a role in lung health and disease. We set genome-wide significance at p<5 × 10-8. Findings: UK Biobank participants were recruited from March 15, 2006, to July 7, 2010. Sample selection for the UK BiLEVE study started on Nov 22, 2012, and was completed on Dec 20, 2012. We selected 50 008 unique samples: 10 002 individuals with low FEV1, 10 000 with average FEV1, and 5002 with high FEV1 from each of the heavy smoker and never smoker groups. We noted a substantial sharing of genetic causes of low FEV1 between heavy smokers and never smokers (p=2·29 × 10-16) and between individuals with and without doctor-diagnosed asthma (p=6·06 × 10-11). We discovered six novel genome-wide significant signals of association with extremes of FEV1, including signals at four novel loci (KANSL1, TSEN54, TET2, and RBM19/TBX5) and independent signals at two previously reported loci (NPNT and HLA-DQB1/HLA-DQA2). These variants also showed association with COPD, including in individuals with no history of smoking. The number of copies of a 150 kb region containing the 5' end of KANSL1, a gene that is important for epigenetic gene regulation, was associated with extremes of FEV1. We also discovered five new genome-wide significant signals for smoking behaviour, including a variant in NCAM1 (chromosome 11) and a variant on chromosome 2 (between TEX41 and PABPC1P2) that has a trans effect on expression of NCAM1 in brain tissue. Interpretation: By sampling from the extremes of the lung function distribution in UK Biobank, we identified novel genetic causes of lung function and smoking behaviour. These results provide new insight into the specific mechanisms underlying airflow obstruction, COPD, and tobacco addiction, and show substantial shared genetic architecture underlying airflow obstruction across individuals, irrespective of smoking behaviour and other airway disease. Funding: Medical Research Council. © 2015 Wain et al. Open Access article distributed under the terms of CC BY.
    No preview · Article · Oct 2015 · The Lancet Respiratory Medicine
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    [Show abstract] [Hide abstract] ABSTRACT: 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.
    Preview · Article · Sep 2015 · Annals of the rheumatic diseases
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    [Show abstract] [Hide abstract] ABSTRACT: TYK2 is a common genetic risk factor for several autoimmune diseases. This gene encodes a protein kinase involved in interleukin 12 (IL-12) pathway, which is a well-known player in the pathogenesis of systemic sclerosis (SSc). Therefore, we aimed to assess the possible role of this locus in SSc. This study comprised a total of 7103 patients with SSc and 12 220 healthy controls of European ancestry from Spain, USA, Germany, the Netherlands, Italy and the UK. Four TYK2 single-nucleotide polymorphisms (V362F (rs2304256), P1104A (rs34536443), I684S (rs12720356) and A928V (rs35018800)) were selected for follow-up based on the results of an Immunochip screening phase of the locus. Association and dependence analyses were performed by the means of logistic regression and conditional logistic regression. Meta-analyses were performed using the inverse variance method. Genome-wide significance level was reached for TYK2 V362F common variant in our pooled analysis (p=3.08×10(-13), OR=0.83), while the association of P1104A, A928V and I684S rare and low-frequency missense variants remained significant with nominal signals (p=2.28×10(-3), OR=0.80; p=1.27×10(-3), OR=0.59; p=2.63×10(-5), OR=0.83, respectively). Interestingly, dependence and allelic combination analyses showed that the strong association observed for V362F with SSc, corresponded to a synthetic association dependent on the effect of the three previously mentioned TYK2 missense variants. We report for the first time the association of TYK2 with SSc and reinforce the relevance of the IL-12 pathway in SSc pathophysiology. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
    Full-text · Article · Sep 2015 · Annals of the rheumatic diseases
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    [Show abstract] [Hide abstract] ABSTRACT: Objectives: The current knowledge of the influence of systemic sclerosis (SSc) risk loci in the clinical sub-phenotypes is still limited. The main limitation lies in the low frequency of some sub-phenotypes which could be solved by replication studies in independent cohorts and meta-analysis between studies. In this regard, CCR6 gene variants have been recently associated with anti-topoisomerase I positive (ATA+) production in SSc patients in a candidate gene study. This gene has been proposed to have a critical role in IL-17-driven autoimmunity in human diseases. Methods: In order to confirm the association between CCR6 and ATA+ SSc patients, we performed an independent replication study in populations of European ancestry. We studied two CCR6 genetic variants (rs968334 and rs3093024) in a total of 901 ATA+ SSc cases, 3,258 ATA- SSc cases and 7,865 healthy controls and compared allelic frequencies for those SNPs in ATA+ SSc with healthy controls and also with ATA- SSc patients. Results: The comparison performed between ATA+ SSc patients and healthy controls showed significant association with SNP rs968334 (p=4.88 x 10-2, OR=1.11). When we compared ATA+ SSc cases with ATA- SSc, both SNPs, rs3093024 and rs968334, showed significant associations (p=2.89 x 10-2, OR=1.13; p=1.69 x 10-2, OR=1.15). Finally, in order to increase even more sample size and statistical power, we meta-analysed our study with the previous reported and found a significant association between SNP rs3093024 and ATA+ SSc patients (p=1.00 x 10-4, OR=1.16) comparing with healthy controls. Conclusions: Our work confirms the association of CCR6 gene and ATA+ SSc patients.
    Full-text · Article · Aug 2015 · Clinical and experimental rheumatology
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    [Show abstract] [Hide abstract] ABSTRACT: A long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders. We use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born. We estimate a small but significant negative SNP-genetic correlation between SZ and RA (-0.046, s.e. 0.026, P = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (-0.174, s.e. 0.071, P = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P = 0.00090). Our results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context. © The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.
    Full-text · Article · Aug 2015 · International Journal of Epidemiology
  • [Show abstract] [Hide abstract] ABSTRACT: 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 heterozygote's 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.
    No preview · Article · Aug 2015 · Nature Genetics
  • No preview · Conference Paper · Aug 2015
  • [Show abstract] [Hide abstract] ABSTRACT: Determining whether potential causal variants for related diseases are shared can identify overlapping etiologies of multifactorial disorders. Colocalization methods disentangle shared and distinct causal variants. However, existing approaches require independent data sets. Here we extend two colocalization methods to allow for the shared-control design commonly used in comparison of genome-wide association study results across diseases. Our analysis of four autoimmune diseases-type 1 diabetes (T1D), rheumatoid arthritis, celiac disease and multiple sclerosis-identified 90 regions that were associated with at least one disease, 33 (37%) of which were associated with 2 or more disorders. Nevertheless, for 14 of these 33 shared regions, there was evidence that the causal variants differed. We identified new disease associations in 11 regions previously associated with one or more of the other 3 disorders. Four of eight T1D-specific regions contained known type 2 diabetes (T2D) candidate genes (COBL, GLIS3, RNLS and BCAR1), suggesting a shared cellular etiology.
    No preview · Article · Jun 2015 · Nature Genetics
  • [Show abstract] [Hide abstract] ABSTRACT: Background Systemic sclerosis (SSc) is a chronic connective tissue disorder with the highest mortality of any autoimmune disease. SSc is a complex disease with a clear genetic component. This genetic susceptibility has been supported by a number of well-powered genetic association studies. In this regard, it is remarkable that several SSc genetic markers have been reported in the IL-12 pathway (IL12A, STAT4, IL12RB2, IL12RB1). Furthermore, multiple clinical and experimental evidences have shown that this pathway is altered in SSc patients. TYK2 encodes the Tyrosine kinase 2 enzyme, which mediates the signaling of IL-12 receptor. Objectives We aimed to analyze the association of the TYK2 locus with SSc susceptibility. Methods The complete set of individuals included in this study reached 4,985 SSc patients and 11,621 healthy controls of European ancestry from Spain, Germany, The Netherlands, USA, Italy and United Kingdom. Initially, we analyzed all the polymorphisms located in the region that encompasses the TYK2 coding sequence and 20 kb up and downstream, in a previously published Immunochip-based dense genotyping study. We identified a common single nucleotide polymorphism (SNP), the V362F variant (rs2304256) and three rare variants: P1104A (rs34536443), A928V (rs35018800) and I684S (rs12720356), as the genetic markers that better explained the observed association in the region. Then, these variants were followed up in additional cohorts. Association and dependence relations were tested using logistic regression and conditional logistic regression, and pooled analyses were performed using the inverse variance method. Results Our pooled analysis showed that V362F variant reached the genome-wide significance level (P=2.00x10-10, OR =0.84), while P1104A (rs34536443), A928V (rs35018800) and I684S (rs12720356) remained significant (P=8.61x10-3, OR=0.79; P=1.95x10-4, OR=0.49; Prandom=0.016, OR=0.84, respectively). The analyses carried out for the main clinical features revealed that the observed association signals relied on the whole disease. Furthermore, our results revealed that the association of TYK2 with SSc was dependent on the interaction between the P1104A, A928V and I684S rare variants and that the previously observed association for the autoimmune related V362F variant, corresponded to a synthetic association dependent on the association of the three previously mentioned rare variants. Conclusions We report for the first time the association of TYK2 variants with SSc. Moreover, our data support that the highly significant association of a previously known autoimmune disease marker, the V362F variant (rs2304256), is dependent on the effect of three non-synonymous rare variants in this locus. Disclosure of Interest None declared
    No preview · Article · Jun 2015 · Annals of the Rheumatic Diseases

Publication Stats

20k Citations
3,039.26 Total Impact Points


  • 2014
    • Arthritis Research UK
      Chesterfield, England, United Kingdom
  • 2010
    • University of Milan
      Milano, Lombardy, Italy
  • 2006
    • University of Oxford
      Oxford, England, United Kingdom
  • 2001
    • Trinity College Dublin
      • Department of Psychiatry
      Dublin, Leinster, Ireland