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Genome-wide association study meta-analysis identifies five new loci for systemic lupus erythematosus

Genome-wide association study meta-analysis identifies five new loci for systemic lupus... Background: Systemic lupus erythematosus (SLE) is a common systemic autoimmune disease with a complex genetic inheritance. Genome-wide association studies (GWAS) have significantly increased the number of significant loci associated with SLE risk. To date, however, established loci account for less than 30% of the disease heritability and additional risk variants have yet to be identified. Here we performed a GWAS followed by a meta-analysis to identify new genome-wide significant loci for SLE. Methods: We genotyped a cohort of 907 patients with SLE (cases) and 1524 healthy controls from Spain and performed imputation using the 1000 Genomes reference data. We tested for association using logistic regression with correction for the principal components of variation. Meta-analysis of the association results was subsequently performed on 7,110,321 variants using genetic data from a large cohort of 4036 patients with SLE and 6959 controls of Northern European ancestry. Genetic association was also tested at the pathway level after removing the effect of known risk loci using PASCAL software. − 8 Results: We identified five new loci associated with SLE at the genome-wide level of significance (p <5×10 ): GRB2, SMYD3, ST8SIA4, LAT2 and ARHGAP27. Pathway analysis revealed several biological processes significantly associated − 6 − 5 with SLE risk: B cell receptor signaling (p =5.28 ×10 ), CTLA4 co-stimulation during T cell activation (p =3.06 ×10 ), − 5 − 5 interleukin-4 signaling (p =3.97 ×10 ) and cell surface interactions at the vascular wall (p =4.63 ×10 ). Conclusions: Our results identify five novel loci for SLE susceptibility, and biologic pathways associated via multiple low-effect-size loci. Keywords: Systemic lupus erythematosus, Genetic susceptibility, Genome-wide association study, Meta-analysis, Biological pathway analysis * Correspondence: toni.julia@vhir.org; sara.marsal@vhir.org Rheumatology Research Group, Vall d’Hebron Research Institute, 08035 Barcelona, Spain Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 2 of 10 Background biological insights have been gained in different complex Systemic lupus erythematosus (SLE [MIM: 152700]) is a diseases, including autoimmune diseases [12]. common systemic autoimmune disease characterized by The aim of the current work was to identify new the production of autoantibodies and a complex genetic genetic risk loci for SLE by a GWAS meta-analysis inheritance. The prevalence of the disease varies according using a case-control cohort from a previously untar- to the population ancestry, with European populations geted population. After excluding known risk genes, ranging between 30 and 90 cases per 100,000 individuals pathway meta-analysis was also performed to identify [1]. SLE afflicts women at a rate nine times higher than biologic pathways for SLE risk associated by risk loci men, and most often appears during childbearing ages. as yet unaccounted for. Concordance rate studies in monozygotic and dizygotic twins and recurrence risk estimates in siblings of probands Methods (λ ), have clearly shown the importance of genetic factors Study cohorts in the development of the disease [2]. Patients and controls from the Spanish population were Despite the evidence for a strong genetic contribution, recruited through the Immune-Mediated Inflammatory until recently, very few loci were convincingly associated Disease (IMID) Consortium [13]. Patients with SLE were with SLE risk [3]. With the concurrent identification of recruited via the rheumatology departments of 17 uni- common genome variation and the development of versity hospitals in Spain. All included patients fulfilled genome-wide genotyping technologies, genome-wide as- the 1982 revised American College for Rheumatology sociation studies (GWAS) have dramatically changed the diagnosis criteria for SLE [14]. All patients were > 16 years ability to identify risk variants. In SLE, GWAS have old at the time of recruitment, although disease could allowed the identification of more than 50 risk loci at have started earlier. A minimal disease evolution period of − 8 a genome-wide significance level (p value <5 × 10 ) 3 years since diagnosis was also required for inclusion in [4–6]. These findings are of great relevance since they this study. All Patients with SLE were Caucasian with all pinpoint specific biological mechanisms that are four grandparents born in Spain. Patients with an add- relevant for the disease and that otherwise would not itional rheumatologic disease (e.g. rheumatoid arthritis, have been prioritized for research [7]. In a severe systemic sclerosis or mixed connective tissue disease) disease like SLE that is lacking efficacious treatments, except antiphospholipid syndrome or Sjögren’ssyndrome genetic studies provide a unique way to expand the were excluded from the study. Also, patients with number of molecular targets for drug discovery [8]. concomitant psoriasis or inflammatory disease (Crohn’s To date, the explanation for the inherited risk of SLE disease or ulcerative colitis) were also excluded from the is largely unresolved. Including all known risk variants, study. A total of 907 patients with SLE were recruited for less than 30% of disease heritability is currently the GWAS. Additional file 1: Table S1 summarizes the accounted for [9], In order to identify additional risk main features of the Spanish GWAS cohort. variants, GWAS meta-analyses from different countries Healthy control individuals were also recruited have proven to be a highly successful approach [9]. Cur- through the IMID Consortium as described previously rently, most Southern European populations have been [15]. All controls were Caucasian and > 18 years old at underrepresented in GWAS of SLE. In Spain, epidemio- the time of recruitment. Individuals with one or more logical studies have shown that there is an increased grandparents born outside of Spain were excluded. Con- prevalence of the disease compared to other European trols with an autoimmune disease or with a family his- regions [1]. Consequently, the analysis of the genetic tory of autoimmune disease were also excluded from variation in this population could be highly useful to this cohort. A total of 1524 healthy control individuals identify new genetic variation for SLE risk. were finally included in the present GWAS. All the pro- Biological pathways integrate the function of multiple cedures were followed in compliance with the principles genes and, therefore, provide a higher level of detection of the Declaration of Helsinki and informed consent was of the relevant genetic risk [10, 11]. To date, different obtained from all participants. The study and the con- statistical methods have been developed that exploit the sent procedure were reviewed and approved by the local biological knowledge in order to leverage the power of institutional review boards. GWAS. These analysis methods are designed to aggre- GWAS data from European-ancestry cohorts were ob- gate the genetic evidence from multiple risk loci into a tained from a recent meta-analysis [4]. For the present single association statistic. The use of cumulative evidence study, GWAS association data were obtained from 4036 can be a powerful way to detect genetic associations and patients with SLE and 6959 controls of Caucasian European biological mechanisms that otherwise would have been ancestry. The details on the data quality control, imputation missed due to low effect size at the single-marker level. and statistical association analyses have been previously de- Using this complementary GWAS approach, relevant scribed [4]. Association data on a total of 37,577,690 Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 3 of 10 markers from the 22 autosomal chromosomes were avail- for download at http://urr.cat/data/GWAS_SLE_summary- able for meta-analysis. Stats.zip. Association plots for each of the associated loci were prepared using LocusZoom (http://locuszoom.org/). Genotyping, quality control and imputation In the Spain cohort, genome-wide genotyping was per- Genome-wide pathway analysis formed using the Illumina Quad610 Beadchips (Illumina, Several approaches are actually available to perform San Diego, CA, USA) at the National Genotyping Center pathway-based GWAS. However, most of these methods (CeGen, Madrid, Spain). This array genotyping platform do not account for the linkage disequilibrium (LD) includes information on > 550,000 single nucleotide structure in the genome. The variable structure of LD, polymorphisms (SNPs). A whole blood sample (5 mL) particularly of highly correlated chromosomal regions was collected from all patients and genomic DNA containing multiple genes, can negatively impact the re- extracted using the Chemagic Magnetic Separation sults from genome-wide pathway analysis [24]. In order Module I (PerkinElmer, Waltham, MA, USA). Genotyp- to integrate this information into the pathway associ- ing was performed following the protocol recommended ation analysis, we used the method implemented in by Illumina. Genotype calling was performed using the PASCAL [25]. In this approach, genetic markers are first GenomeStudio data analysis software v2011.1 (Illumina, mapped to genes in each pathway (here, all markers in- San Diego, CA, USA). Genotyping quality control was side the gene ± 20 flanking kb). Correlated markers are performed using PLINK genomic analysis software [16]. then identified using the LD structure estimated from a Principal components of variation were estimated using reference population (in this study, from the Caucasian Eigensoft (v4.2) software [17]. The genomic inflation European population from the 1000 Genomes Project factor was λ = 1.16 in the European-ancestry GWAS, (1KG)). Combining the single-marker association values GC and λ = 1.06 in the Spain GWAS (Additional file 1: with the LD structure, association scores are then com- GC Figure S2). After quality control analysis, 864 patients with puted for all genes in the pathway. In those cases where SLE and 1513 controls were available for imputation. genes from the same pathway are located close in a Genome-wide imputation was performed using GUID- chromosome and in strong LD, a joint score is calcu- ANCE, an integrated framework for haplotype phasing lated. Finally, the scores of all genes within a pathway and genotype imputation of genotypes [18]. Markers and are normalized, transformed and integrated to generate samples were first tested for quality control. SNPs a single association statistic that can be used to with a genotyping call rate < 95% or a significant devi- determine the statistical significance of the association ation from Hardy-Weinberg equilibrium in controls between the pathway and the trait of interest. In this − 6 (p value ≤1× 10 ) were removed. Individuals with a study, the default parameter values were used, including genotype call rate < 95% or outlier genetic background maximum number of SNPs per gene (n = 3000). The (i.e. > 6 SD in any of the 10 principal components of SNP p value to gene score estimation was performed variation), were also excluded. After quality control, using the sum gene score approach, and gene score pre-phasing of genotypes was performed using SHA- transforming into the pathway score was performed PEIT2 [19] and genotype imputation using IMPUTE2. using the chi-squared approach. The pathway analysis The 1000G Phase1 integrated haplotypes was used as method implemented in PASCAL has been shown to the reference panel [20, 21]. perform better than other methods, particularly since it A total of 30,038,143 markers were finally imputed from has better control of type I error. the Spain GWAS cohort. From these, after filtering for Pathways and their corresponding gene annotation high imputation quality (info score > 0.8, n = 9,168,673) was obtained from the MSigDB molecular signatures re- and minor allele frequency (MAF) > 1%), 7,195,283 pository (http://software.broadinstitute.org/gsea/msigdb). markers were available for GWAS. Association testing A total of 1077 biological pathways from the Reac- was performed using the logistic regression model imple- tome (n = 674), Kyoto Expression of Genes and Ge- mented in SNPTEST v2 software adjusting for the first nomes (n = 186) and BioCarta (n = 217) databases were two principal components of variation [22]. selected. The association p values obtained using PASCAL Meta-analysis of the common markers between the in the two GWAS cohorts were combined using Fisher’s two GWAS datasets was performed using METAL [23]. method, and the significance was corrected for multiple In this approach, z values are computed to summarize testing using Bonferroni’sadjustment. both the direction of effect and the significance level for In order to capture biologic pathways associated with each genetic marker. These z values are then combined SLE through as yet unaccounted for genetic risk in a weighted sum that incorporates the sample size of variants, all regions previously associated with SLE risk each cohort. The complete results from the Spain were removed from this analysis. For this objective, we GWAS and from the GWAS meta-analysis are available filtered out all markers within ± 250 kb distance from an Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 4 of 10 established SLE risk SNP and with an LD r > 0.2. Given containing 3 protein (SMYD3, rs1780813), and ST8 the strong association between the HLA region and SLE alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 4 risk, we excluded this region from the analysis (chr6: (ST8SIA4, rs55849330). Associated SNPs rs150518861 (bp 28,500,000–33,500,000). and rs114038709 are located in the flanking regions of linker for activation of T cells family member 2 (LAT2) Results and Rho GTPase activating protein 2 (ARHGAP27) GWAS meta-analysis genes, respectively. Figure 2 shows the detailed associ- After quality control, a total of 7,195,283 autosomal ation results for each of the five new SLE risk loci. markers with MAF > 0.01 were available for association testing in the Spain case-control cohort. From these, Genetic pathway association study in SLE 7,110,321 variants were also present in the European After excluding the association signal from known risk ancestry GWAS: 51 of the 52 previously known SLE risk loci, pathway analysis identified 100 and 157 pathways loci were in the same effect-size direction as originally associated with SLE in the Spain and European-ancestry described. From these, 31 had nominal evidence of repli- cohorts, respectively, at the nominal level (p < 0.05). cation (p< 0.05, Additional file 1 Table S2). Four SNPs, From these, 30 pathways (3% of total) were found to be rs1270942 (HLA, OR (95% CI) = 1.96 (1.59–2.42), associated in both cohorts, which is more than would be Spain − 4 OR (95% CI) = 2.53 (2.34–2.74), p value for hetero- expected by chance (p <5 ×10 ). After adjustment for EUR geneity (p ) = 0.00074), rs494003 (RNASEH2C,OR multiple testing, four biologic pathways were significantly Het (95% CI) =1.44 (1.24–1.66), OR (95% CI) =1.16 associated with SLE risk (Table 2). Spain EUR (1.07–1.26); p = 0.0059), rs9652601 (CIITA-SOCS1, Het OR (95% CI) = 0.74 (0.66–0.85), OR (95% CI) =0.85 Conditional and sex-specific association Spain EUR (0.8–0.9, p = 0.026), and rs3024505 (IL10,OR(95%CI) In order to explore the presence of secondary signals Het =1.38 (1.17–1.62), OR (95% CI) = 1.13 (1.04–1.22); at each associated locus, we performed conditional Spain EUR p = 0.028) showed evidence of heterogeneity between the analysis in the Spain cohort. From all five loci, we Het two GWAS cohorts. only identified one independent signal within 1 Mb of Meta-analysis of the two GWAS cohorts identified five the most strongly associated SNP that continued to − 4 new risk loci for SLE (Fig. 1, Table 1). None of the new show evidence of association (conditional p <1 ×10 ). genome-wide significant loci showed evidence of hetero- This independent association was identified at the geneity of effect between the two cohorts (p > 0.05). GRB2 locus and maps to RNF157 gene (SNP − 5 Three of the associated markers are SNPs in introns of rs9891273, p =4.99× 10 , Additional file 1:FigureS3). the genes encoding for growth factor receptor bound The presence of sex-specific associations was tested by protein 2 (GRB2, rs36023980), SET and MYND domain comparing the coefficients for SNP association estimated Fig. 1 Manhattan plot of genome-wide association study meta-analysis results including the Spain cohort. Plot of the -log10 (p values) of association between the 7,110,321 markers after meta-analysis between the European and Spain cohorts. The dashed horizontal line represents − 8 the genome-wide significance threshold (p value = 5 × 10 ). The known regions associated with systemic lupus erythematosus (SLE) risk and genome-wide significant are colored in red. The five new genomic regions associated with SLE risk in this study are colored in green, with the name of the corresponding gene above Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 5 of 10 − 8 Table 1 Novel SNPs for SLE risk showing genome-wide significance (p< 5× 10 ) following meta-analysis of Spain and European ancestry cohorts European Spain Meta-analysis Locus Chr SNP bp RA OR (CI 95%) p value OR (CI 95%) p value OR (CI 95%) p value −7 −8 SMYD3 1 rs1780813 246,444,082 C 0.53 (0.40–0.69) 8.36 × 10 0.61 (0.37–0.98) 0.013 0.55 (0.31–0.79) 3.5 × 10 −7 −8 ST8SIA4 5 rs55849330 100,184,647 A 1.16 (1.10–.123) 8.4 × 10 1.14 (1.01–1.30) 0.019 1.16 (1.11–1.21) 4.9 × 10 −8 LAT2 7 rs150518861 73,566,677 A 1.63 (1.34–1.99) 0.0000015 1.77 (1.23–2.56) 0.0074 1.66 (1.49–1.84) 4.1 × 10 −8 ARHGAP27 17 rs114038709 43,456,728 T 1.15 (1.08–1.22) 0.0000012 1.20 (1.07–1.35) 0.0088 1.16 (1.11–1.22) 3.7 × 10 −9 GRB2 17 rs36023980 73,341,284 C 1.18(1.11–1.25) 0.0000015 1.23 (1.08–1.40) 0.00039 1.18 (1.13–1.24) 4.7 × 10 OR are shown for the minor allele for all five associated polymorphisms Locus closest gene, Chr chromosome, SNP single nucleotide polymorphism, bp base pair, RA risk allele, OR odds ratio independently in women and men. We identified a are in line with the relevance of this causal disease significant difference only for LAT2 SNP rs150518861 mechanism. (p = 0.032). This risk variant was found to be more In a close functional relationship with GRB2, we also strongly associated in the male cohort compared to the found a significant association between linker for activa- female cohort. tion of T cells family member 2 gene (LAT2) locus and − 8 SLE (rs150518861, p = 4.1 × 10 ). LAT2 encodes for an Discussion adaptor molecule that binds GRB2 and, therefore, is also In the present study we have identified five new risk loci involved in BCR signaling [31]. The association at the for systemic lupus erythematosus. Performing a meta- genetic level between SLE and two directly interacting analysis on 4943 patients with SLE and 8483 controls proteins strongly supports the implication of this from different European ancestries, we have identified biological mechanism in SLE risk. B cell dysfunction is a variants at GRB2, SMYD3, ST8SIA4, LAT2, and ARH- hallmark of SLE pathology [9], and our study supports GAP27 loci associated with SLE susceptibility. At the downstream regulation after antigen binding as a crucial pathway level, we have also found four biological path- event in the disease etiology. In the evaluation of sex- ways associated with SLE risk independently of previ- specific effects, we found this locus to be differentially ously known risk genes. associated with SLE risk. The risk variant was associated In the present meta-analysis, we found an association with SLE in men (p = 0.0074, β (95% CI) = 1.3 (0.25–2.2)), between an intronic SNP in the gene encoding for the and it was non-significant in women (p = 0.58, β growth factor receptor-bound protein GRB2 and SLE (95% CI) = 0.13 (− 0.33 to 0.62)). Previous studies − 9 (rs36023980, p = 4.7 × 10 ). Analysis of the tissue- have shown that men require a higher genetic load specific epigenetic data from the NIH Roadmap Epige- to develop the disease [32]. If replicated in an independent nomics Project [26] for rs36023980 SNP showed a cohort, this result would be in line with these findings, strong regulatory activity in different immune cells, in- confirming the importance of sex in mediating the effect cluding enhancer evidence in both T and B lymphocytes of some genetic risk factors in SLE. SMYD3 encodes for (Additional file 1: Table S2). GRB2 encodes for a recep- an H3-Hk histone methyltransferase that has been associ- tor tyrosine-kinase (RTK) adaptor protein composed of ated with increased cell proliferation in cancer [33]. Al- a single SH2 domain and two SH3 domains [27]. SLE is tered epigenetic patterns have been strongly associated a disease characterized by the activation of B cells that with SLE, mostly at the DNA level [34]. More recently, recognize self-antigens via their B cell receptors (BCR). however, methylated histones have also been identified as In B cells, GRB2 functions as an expression adaptor targets of autoantibodies expressed in patients with SLE molecule, attenuating the signals that are transduced by [35]. Similar to other frequent nuclear autoantigens in the BCR [28]. Together with Dok-3 and SHIP1, GRB2 SLE, like double-stranded DNA or ribonucleoproteins, forms a trimer protein complex that binds directly to methylated H3-Hk histones are able to trigger autoreac- the BCR and prevents downstream signaling by inhibit- tive B cells after antigenic-exposure processes like apop- ing PI3K signaling [29]. Gene expression at different tosis. According to the Roadmap Epigenomics Project stages of B cell differentiation shows that GRB2 expres- data, the associated SNP rs1780813 lies in a site that is sion increases in more mature forms, particularly on DNAse hypersensitive for > 30 different tissues, support- antigen-experienced memory B cells (Additional file 1: ing its role in gene regulation. Figure S4). Inadequate control of memory B cell differ- To date little is known about the functional role of entiation into plasma cells has been proposed as a trig- SLE-associated genes STS8IA4 and ARHGAP27. In order ger for autoimmunity in SLE [30]. Our results therefore to infer the potential biological role of these two genes, Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 6 of 10 Fig. 2 Regional association plots from the meta-analysis of the two cohorts for all five genome-wide significant loci: -log10 (p values) for both directly genotyped and imputed single nucleotide polymorphisms (SNPs) are plotted as a function of genomic position (NCBI Build 37). The purple diamond indicates the lead SNP at each locus; the remaining markers are colored based on the LD (r [2]) in relation to the lead SNP. Underlying the image, the estimated recombination rate (cM/Mb) for the CEU panel from 1000 Genomes is depicted Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 7 of 10 Table 2 Biological pathways significantly associated with SLE risk Biological pathway N genes Spain cohort p value European cohort p value Combined p value Adjusted p value −6 − 6 B cell receptor signaling 75 0.016 2.05 × 10 5.28 × 10 0.0057 −5 CTLA4 co-stimulatory signal during T-cell activation 21 0.0014 0.0016 3.06 × 10 0.033 −5 Interleukin-4 signaling 11 0.00029 0.01 3.97 × 10 0.043 −5 Cell surface interactions at the vascular wall 91 0.0057 0.0006 4.63 × 10 0.049 Biological pathways significantly associated with systemic lupus erythematosus (SLE) after meta-analysis of the Spain and Caucasian European cohorts. P values for each cohort were estimated using PASCAL after removing the previously known risk loci for SLE N genes number of genes in the pathway we used the GeneNetwork approach, a functional-inference implicated in mitogen-activated protein kinase (MAPKi- − 8 method based on the gene co-expression patterns extracted nase) signaling (p value = 3.33 × 10 , Additional file 2: from microarray data from > 80,000 samples [36]. With this Table S5). Both biological processes have been previously approach, we found strong evidence that STS8IA4 is in- associated with SLE etiology, and our results not only − 13 volved in T cell activation (p value = 2.7 × 10 , support their involvement in disease risk but also Additional file 2: Table S4), and that ARHGAP27 is suggest new gene functions. Furthermore, expression Table 3 Top single-marker hits in genes from the four genetic pathways associated with SLE Gene Marker Chr bp MA OR p (Spain) p (EUR) p (meta) Pathway BCL10 rs12084253 1 85,720,326 T 1.11 0.020 0.0015 0.00012 BCR FCER1G rs1136224 1 161,184,097 G 0.91 0.023 0.022 0.0024 VASC FCGR2B rs182968886 1 161,642,985 A 0.86 0.044 0.0018 0.00023 BCR CD247 rs113305799 1 167,416,006 A 1.17 0.0035 0.044 0.0022 CTLA4 PROC rs6740067 2 128,156,366 T 1.16 0.043 0.018 0.0026 VASC CTLA4 rs733618 2 204,730,944 C 1.19 0.026 0.0018 0.00016 CTLA4 PPP3CA rs13120190 4 102,056,663 G 0.93 0.025 0.047 0.0060 BCR IL2 rs45522533 4 123,396,876 T 1.16 0.016 0.0058 0.00044 CTLA4 SLC7A11 rs74843273 4 139,150,464 T 0.81 0.025 0.0066 0.00065 VASC ITK rs60714766 5 156,602,589 T 1.07 0.015 0.043 0.0042 CTLA4 CARD11 rs6461796 7 3,071,195 C 0.94 0.027 0.033 0.0042 BCR LYN rs17812659 8 56,889,862 G 0.86 0.013 2.57E-05 1.17E-06 BCR,VASC ANGPT1 rs79847080 8 108,293,443 G 0.84 0.032 0.0028 0.00031 VASC VAV2 rs2810536 9 136,812,625 G 1.08 0.030 0.011 0.0013 BCR KRAS rs17388587 12 25,389,220 G 1.13 0.036 0.048 0.0075 BCR, VASC PRKCB rs11641223 16 24,020,316 T 1.11 0.041 0.0010 0.00012 BCR CD19 16:28955702:D 16 28,955,702 I 1.06 0.0077 0.047 0.0034 BCR SLC7A6 rs55856208 16 68,324,210 T 1.08 0.045 0.049 0.0086 VASC PLCG2 rs11548656 16 81,916,912 G 1.3 0.014 0.00062 0.000035 BCR ATP1B2 rs1794287 17 7,578,837 A 0.9 0.023 0.024 0.0026 VASC ITGB3 rs75211989 17 45,366,261 G 1.11 0.00014 0.020 0.00020 VASC GRB2 rs36023980 17 73,341,284 T 0.85 0.00039 1.51E-06 4.73E-09 CTLA4, IL4, BCR, VASC NFATC1 rs111354805 18 77,238,078 T 1.21 0.027 6.58E-05 5.26E-06 BCR MAP2K2 rs350913 19 4,096,779 T 0.94 0.029 0.030 0.0039 BCR CD79A rs16975619 19 42,392,441 C 1.52 0.020 0.0099 0.00089 BCR SIRPG rs11696739 20 1,600,925 A 0.92 0.044 0.0050 0.00069 VASC RAC2 rs229566 22 37,602,131 A 1.06 0.041 0.03 0.0047 BCR Suggestive risk variants were identified as those markers showing with the most significant meta-analysis p value (p (meta)), and that are associated in the two genome-wide association study cohorts (p< 0.05) and show the same direction of effect (OR) MA minor allele, OR odds ratio according to minor allele in European ancestry cohort, I insertion allele, Pathway biological pathway/s where the gene has been annotated, BCR B cell receptor pathway, CTLA4 CTLA4 pathway, IL4 interleukin-4 pathway, VASC vascular cell wall pathway Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 8 of 10 quantitative trait locus (eQTL) evidence supports that Two other associated pathways - the CTLA4 co- both SNPs regulate expression of the corresponding stimulatory signal and IL4 pathways - are strongly genes in cis. Whole blood eQTL analysis [37]shows a related to B cell activation. CTLA4 is a co-inhibitory strong association between variation at rs114038709 molecule expressed on activated helper T - TH2 and fol- − 134 and ARHGAP27 expression (p =4.1 ×10 ), and the licular - cells. Inhibition of CTLA4 increases B cell acti- most significant eQTL evidence for rs55849330 is vation after antigen binding, resulting in the production associated to STS8IA4 expression in immortalized B of antibodies [44]. IL-4 is a cytokine that is also − 10 cells [38](p =5.6 ×10 ). expressed in helper T cells and it is essential in the acti- Using a pathway-based analysis we have identified vation of antigen-bound naïve B cells. Similar to the four biological pathways associated with SLE. Since BCR signaling pathway, these two genetically associated the objective was to identify new genetic risk vari- biological processes that are deeply related to B cell acti- ation for SLE, our approach excluded all association vation could be the source of new effective drug targets signals from previously known SLE genes. We showed for the disease [45]. In this regard, a fusion protein in- that by using biological pathway knowledge, it is still cluding the extracellular domain of CTLA4 (abatacept) possible to capture genetic variation that is relevant is being currently evaluated as a therapy for more severe for the disease. One limitation of this approach is forms of SLE [46]. that it relies on the specific knowledge of gene func- tions and pathway definitions, which is still relatively Conclusions low for a substantial fraction of the genome [39]. An- In the present study we have performed a GWAS meta- other limitation is that pathway association is per- analysis approach to identify new genetic variation in SLE. formed on variants within or close to genes. Distant We have found five new genome-wide significant risk loci cis regulation and also trans regulation variants are and four biologic pathways associated with SLE risk. also plausible mechanisms of action [40]. With better Single-marker associations involve BCR downstream sig- knowledge of regulatory effects, particularly on iso- naling mechanisms with disease susceptibility, and auto- lated cell types, pathway-based analysis will become antigen generation and immune cell activity regulation. an even more powerful approach to detect the miss- Pathway-based analysis confirmed the relevance of BCR ing disease heritability. Despite these limitations, our signaling pathway and other B cell activation mechanisms results are robust since they are based on strongly in the disease etiology. The results from this study signifi- supported biological knowledge. Also, we provide stat- cantly expand the knowledge of the biological processes istical evidence of pathway association from two inde- implicated in susceptibility to SLE. pendent GWAS cohorts which, to our knowledge, has not been previously performed in SLE. Additional files The BCR signaling pathway had the strongest associ- ation with SLE. This result is in agreement with the re- Additional file 1: Table S1. Epidemiological features from the Spain GWAS cohort. Figure S1. Principal component analysis of the Spain sults found at the single-marker level, where variants at GWAS cohort. Figure S2. Quantile-quantile(Q-Q) plots of observed and BCR signaling genes GRB2 and LAT2 were found to be expected -log10(p values) of association between SNP genotype and SLE associated with disease susceptibility. Within the BCR risk. Table S2. Epigenetic regulatory data associated with GRB2 risk locus. Figure S3. Regional association plot for the association with SLE risk signaling pathway, however, there are multiple other independent of GRB2 SNP rs36023980. Figure S4. GRB2 gene expression single-marker hits in other genes indicating nominally during human B cell differentiation. (DOCX 627 kb) significant association with disease susceptibility in both Additional file 2: Table S3. Association results for the 52 previously cohorts. Given that they belong to an associated bio- known SLE risk loci in the Spain GWAS. Table S4. Pathway association results after combining the two SLE cohorts (combined raw p value <0.05). logical pathway, these signals are strongly suggestive risk Table S5. List of biological pathways significantly associated with ST8SIA4 variants for SLE (Table 3). Of relevance, several of the gene network. Table S6. List of biological pathways significantly associated proteins encoded by the genes in this pathway, like BTK with ARHGAP27 gene network. (XLSX 48 kb) or CTLA4, are currently being evaluated as therapeutic targets for SLE [41, 42]. Finding an efficacious treatment Abbreviations in SLE has proven extremely difficult and our results BCR: B cell receptor; bp: Base pair; CI: Confidence interval; eQTL: Expression quantitative trait locus; GWAS: Genome-wide association study; HLA: Human support the importance of targeting this pathway. Gen- leukocyte antigen; IMID Consortium: Immune-Mediated Inflammatory etic evidence, either direct or through associated gene Disease Consortium; LD: Linkage disequilibrium; MAF: Minor allele frequency; networks, has been shown to improve drug efficacy pre- OR: Odds ratio; p : P value for heterogeneity on genetic effect; Het SLE: Systemic lupus erythematosus; SNP: Single nucleotide polymorphism diction [43]. Based on the association signals found in the two cohorts, for example, the proteins encoded by Acknowledgements − 6 − 6 LYN (p = 1.17 × 10 ) and NFATC1 (p = 5.26 × 10 ) The technical support group from the Barcelona Supercomputing Center is could also be considered as new drug targets for SLE. gratefully acknowledged. Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 9 of 10 Funding Received: 28 February 2018 Accepted: 23 April 2018 This study was funded by the Spanish Ministry of Economy and Competitiveness (grant numbers: PSE-010000-2006-6 and IPT-010000-2010-36). This work has been also sponsored by the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. This work was References supported by an EFSD/Lilly research fellowship. Josep M. Mercader was 1. Danchenko N, Satia J, Anthony M. Epidemiology of systemic lupus supported by Sara Borrell Fellowship from the Instituto Carlos III. Sílvia erythematosus: a comparison of worldwide disease burden. Lupus. 2006; Bonàs was awarded an FI-DGR Fellowship from FI-DGR 2013 from 15(5):308–18. Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR, Generalitat 2. Harley JB, Alarcon-Riquelme ME, Criswell LA, Jacob CO, Kimberly RP, de Catalunya). The study sponsors had no role in the design of the Moser KL, Tsao BP, Vyse TJ, Langefeld CD, Nath SK, et al. Genome-wide study, collection, analysis or interpretation of the data. association scan in women with systemic lupus erythematosus identifies susceptibility variants in ITGAM, PXK, KIAA1542 and other loci. Nat Genet. 2008;40(2):204–10. Availability of data and materials 3. Gateva V, Sandling JK, Hom G, Taylor KE, Chung SA, Sun X, Ortmann W, The complete results from the Spain GWAS and from the GWAS meta-analysis Kosoy R, Ferreira RC, Nordmark G. A large-scale replication study identifies are available for download at http://urr.cat/data/GWAS_SLE_summaryStats.zip. TNIP1, PRDM1, JAZF1, UHRF1BP1 and IL10 as risk loci for systemic lupus erythematosus. Nat Genet. 2009;41(11):1228–33. 4. Bentham J, Morris DL, Cunninghame Graham DS, Pinder CL, Tombleson P, Behrens TW, Martin J, Fairfax BP, Knight JC, Chen L, et al. Genetic association Authors’ contributions analyses implicate aberrant regulation of innate and adaptive immunity AJ, FLL, SM and AFN conceived the study. SBG, JMM and DT performed genes in the pathogenesis of systemic lupus erythematosus. Nat Genet. genetic data imputation. AJ and DA performed statistical analysis. FJL, JPV, 2015;47(12):1457–64. AO, JLA, MAZ, PV, JMN, JLM, AZ, JMP, MF, ED, ERA, PC, RB, VMT, MLL, MLC 5. Graham RR, Cotsapas C, Davies L, Hackett R, Lessard CJ, Leon JM, Burtt and SM provided Spanish GWAS samples. AJ and DA performed genotyping. NP, Guiducci C, Parkin M, Gates C. Genetic variants near TNFAIP3 on AJ, SBG, JMM and DA wrote the first draft of the manuscript. All authors 6q23 are associated with systemic lupus erythematosus. Nat Genet. contributed to revision of the manuscript. All authors read and approved the 2008;40(9):1059–61. final manuscript. 6. Yang W, Shen N, Ye D-Q, Liu Q, Zhang Y, Qian X-X, Hirankarn N, Ying D, Pan H-F, Mok CC. Genome-wide association study in Asian populations identifies variants in ETS1 and WDFY4 associated with systemic lupus erythematosus. Ethics approval and consent to participate PLoS Genet. 2010;6(2):e1000841. All the procedures were followed in compliance with the principles of the 7. Tsokos GC, Lo MS, Reis PC, Sullivan KE. New insights into the Declaration of Helsinki and informed consent was obtained from all immunopathogenesis of systemic lupus erythematosus. Nat Rev Rheumatol. participants. The study and the consent procedure were reviewed and 2016;12(12):716–30. approved by the local institutional review boards. 8. Plenge RM, Scolnick EM, Altshuler D. Validating therapeutic targets through human genetics. Nat Rev Drug Discov. 2013;12(8):581–94. 9. Morris DL, Sheng Y, Zhang Y, Wang Y-F, Zhu Z, Tombleson P, Chen L, Competing interests Graham DSC, Bentham J, Roberts AL. Genome-wide association meta- The authors declare that they have no competing interests. analysis in Chinese and European individuals identifies ten new loci associated with systemic lupus erythematosus. Nat Genet. 2016;48(8):940–6. 10. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. Publisher’sNote 2015;44.D1:D457–D462. Springer Nature remains neutral with regard to jurisdictional claims in 11. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, published maps and institutional affiliations. Gillespie M, Kamdar MR. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014;42(D1):D472–7. Author details 12. Aterido A, Julia A, Ferrandiz C, Puig L, Fonseca E, Fernandez-Lopez E, Rheumatology Research Group, Vall d’Hebron Research Institute, 08035 Dauden E, Sanchez-Carazo JL, Lopez-Estebaranz JL, Moreno-Ramirez D, et al. Barcelona, Spain. Department of Rheumatology, Hospital Universitario Genome-wide pathway analysis identifies genetic pathways associated with Gregorio Marañón, 28007 Madrid, Spain. Department of Rheumatology, psoriasis. J Invest Dermatol. 2016;136(3):593–602. Hospital del SAS de Jerez de la Frontera, 11407 Cádiz, Spain. Barcelona 13. Julia A, Pinto JA, Gratacos J, Queiro R, Ferrandiz C, Fonseca E, Montilla C, Supercomputing Center. Joint BSC-CRG-IRB Research Program in Torre-Alonso JC, Puig L, Perez Venegas JJ, et al. A deletion at ADAMTS9- Computational Biology, 08034 Barcelona, Spain. Department of MAGI1 locus is associated with psoriatic arthritis risk. Ann Rheum Dis. 2015; Rheumatology, Hospital Universitari Germans Trias i Pujol, 08916 Badalona, 74(10):1875–81. Spain. Department of Rheumatology, Hospital Universitario Puerta de Hierro, 14. Tan EM, Cohen AS, Fries JF, Masi AT, Mcshane DJ, Rothfield NF, Schaller JG, 28222 Madrid, Spain. Department of Rheumatology, Hospital Universitario Talal N, Winchester RJ. The 1982 revised criteria for the classification of Reina Sofía, 14004 Córdoba, Spain. Department of Rheumatology, Hospital systemic lupus erythematosus. Arthritis Rheumatol. 1982;25(11):1271–7. General Universitario de Alicante, 03010 Alicante, Spain. Department of 15. Julia A, Gonzalez I, Fernandez-Nebro A, Blanco F, Rodriguez L, Gonzalez A, Rheumatology, Hospital Universitari de Bellvitge, 08907 Barcelona, Spain. Canete JD, Maymo J, Alperi-Lopez M, Olive A, et al. A genome-wide Department of Rheumatology, Hospital de Valme, 41007 Sevilla, Spain. association study identifies SLC8A3 as a susceptibility locus for ACPA- Department of Rheumatology, Hospital Universitario Ramón y Cajal, 28034 positive rheumatoid arthritis. Rheumatology (Oxford). 2016;55(6):1106–11. Madrid, Spain. Department of Rheumatology, Hospital do Meixoeiro, Grupo IRIDIS, Instituto de Investigación sanitaria Galicia Sur (IISGS), 36312 Vigo, 16. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Spain. Department of Rheumatology, Hospital Universitario A Coruña, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome 15006 A Coruña, Spain. Department of Rheumatology, Hospital Complejo association and population-based linkage analyses. Am J Hum Genet. 2007; Asistencial Universitario de León, 24071 León, Spain. Department of 81(3):559–75. Rheumatology, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain. 17. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Department of Rheumatology, Hospital Universitario Marqués de Valdecilla, Principal components analysis corrects for stratification in genome-wide 39008 Santander, Spain. Institució Catalana de Recerca i Estudis Avançats association studies. Nat Genet. 2006;38(8):904–9. (ICREA), 08010 Barcelona, Spain. HudsonAlpha Institute for Biotechnology, 18. Bonàs-Guarch S, Guindo-Martínez M, Miguel-Escalada I, Grarup N, Sebastian Huntsville, AL 35806, USA. Department of Rheumatology, Hospital Regional D, Rodriguez-Fos E, et al. Re-analysis of public genetic data reveals a rare Universitario de Málaga, Instituto de Investigación Biomédica de Málaga, X-chromosomal variant associated with type 2 diabetes. Nat Commun. 29011 Málaga, Spain. 2018;9(1):321. Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 10 of 10 19. Delaneau O, Zagury JF, Marchini J. Improved whole-chromosome phasing 41. Rankin AL, Seth N, Keegan S, Andreyeva T, Cook TA, Edmonds J, Mathialagan for disease and population genetic studies. Nat Methods. 2013;10(1):5–6. N, Benson MJ, Syed J, Zhan Y. Selective inhibition of BTK prevents murine 20. Consortium GP. A map of human genome variation from population-scale lupus and antibody-mediated glomerulonephritis. J Immunol. 2013;191(9): sequencing. Nature. 2010;467(7319):1061–73. 4540–50. 42. Merrill J, Burgos-Vargas R, Westhovens R, Chalmers A, D'cruz D, Wallace D, 21. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Bae S, Sigal L, Becker JC, Kelly S. The efficacy and safety of abatacept in Kang HM, Marth GT, McVean GA. An integrated map of genetic variation patients with non–life-threatening manifestations of systemic lupus from 1,092 human genomes. Nature. 2012;491(7422):56–65. erythematosus: results of a twelve-month, multicenter, exploratory, phase 22. Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint IIb, randomized, double-blind, placebo-controlled trial. Arthritis Rheum. method for genome-wide association studies by imputation of genotypes. 2010;62(10):3077–87. Nat Genet. 2007;39(7):906–13. 43. Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, Floratos A, 23. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of Sham PC, Li MJ, Wang J. The support of human genetic evidence for genomewide association scans. Bioinformatics. 2010;26(17):2190–1. approved drug indications. Nat Genet. 2015;47(8):856. 24. Cantor RM, Lange K, Sinsheimer JS. Prioritizing GWAS results: a review of 44. Kwek SS, Cha E, Fong L. Unmasking the immune recognition of prostate statistical methods and recommendations for their application. Am J Hum cancer with CTLA4 blockade. Nat Rev Cancer. 2012;12(4):289. Genet. 2010;86(1):6–22. 45. Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, Kochi Y, Ohmura K, Suzuki 25. Lamparter D, Marbach D, Rueedi R, Kutalik Z, Bergmann S. Fast and rigorous A, Yoshida S, et al. Genetics of rheumatoid arthritis contributes to biology computation of gene and pathway scores from SNP-based summary and drug discovery. Nature. 2014;506(7488):376–81. statistics. PLoS Comput Biol. 2016;12(1):e1004714. 46. Pimentel-Quiroz VR, Ugarte-Gil MF, Alarcón GS. Abatacept for the 26. Romanoski CE, Glass CK, Stunnenberg HG, Wilson L, Almouzni G. treatment of systemic lupus erythematosus. Expert Opin Investig Drugs. Epigenomics: Roadmap for regulation. Nature. 2015;518(7539):314–6. 2016;25(4):493–9. 27. Ogura K, Okamura H. Conformational change of Sos-derived proline-rich peptide upon binding Grb2 N-terminal SH3 domain probed by NMR. Sci Rep. 2013;3:2913. 28. Lösing M, Goldbeck I, Manno B, Oellerich T, Schnyder T, Bohnenberger H, Stork B, Urlaub H, Batista FD, Wienands J. The Dok-3/Grb2 protein signal module attenuates Lyn kinase-dependent activation of Syk kinase in B cell antigen receptor microclusters. J Biol Chem. 2013;288(4):2303–13. 29. Reth M, Gold MR. What goes up must come down: a tripartite Dok-3/Grb2/ SHIP1 inhibitory module limits BCR signaling. Eur J Immunol. 2016;46(11): 2507–11. 30. Doreau A, Belot A, Bastid J, Riche B, Trescol-Biemont M-C, Ranchin B, Fabien N, Cochat P, Pouteil-Noble C, Trolliet P. Interleukin 17 acts in synergy with B cell–activating factor to influence B cell biology and the pathophysiology of systemic lupus erythematosus. Nat Immunol. 2009;10(7):778. 31. Janssen E, Zhu M, Zhang W, Koonpaew S, Zhang W. LAB: a new membrane- associated adaptor molecule in B cell activation. Nat Immunol. 2003;4(2): 117–23. 32. Hughes T, Adler A, Merrill JT, Kelly JA, Kaufman KM, Williams A, Langefeld CD, Gilkeson GS, Sanchez E, Martin J. Analysis of autosomal genes reveals gene–sex interactions and higher total genetic risk in men with systemic lupus erythematosus. Ann Rheum Dis. 2012;71:694–9. 33. Hamamoto R, Furukawa Y, Morita M, Iimura Y, Silva FP, Li M, Yagyu R, Nakamura Y. SMYD3 encodes a histone methyltransferase involved in the proliferation of cancer cells. Nat Cell Biol. 2004;6(8):731–40. 34. Absher DM, Li X, Waite LL, Gibson A, Roberts K, Edberg J, Chatham WW, Kimberly RP. Genome-wide DNA methylation analysis of systemic lupus erythematosus reveals persistent hypomethylation of interferon genes and compositional changes to CD4+ T-cell populations. PLoS Genet. 2013;9(8): e1003678. 35. van Bavel CC, Dieker JW, Kroeze Y, Tamboer WP, Voll R, Muller S, Berden JH, van der Vlag J. Apoptosis-induced histone H3 methylation is targeted by autoantibodies in systemic lupus erythematosus. Ann Rheum Dis. 2011; 70(1):201–7. 36. Fehrmann RS, Karjalainen JM, Krajewska M, Westra H-J, Maloney D, Simeonov A, Pers TH, Hirschhorn JN, Jansen RC, Schultes EA. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat Genet. 2015;47(2):115–25. 37. Westra H-J, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, Christiansen MW, Fairfax BP, Schramm K, Powell JE. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45(10):1238–43. 38. Lappalainen T, Sammeth M, Friedländer MR, AC’t Hoen P, Monlong J, Rivas MA, Gonzalez-Porta M, Kurbatova N, Griebel T, Ferreira PG. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501(7468):506. 39. McCarthy MI, MacArthur DG. Human disease genomics: from variants to biology. BioMed Central. 2017;18(1):20. 40. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 10 Years of GWAS discovery: biology, function, and translation. 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Genome-wide association study meta-analysis identifies five new loci for systemic lupus erythematosus

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Medicine & Public Health; Rheumatology; Orthopedics
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Abstract

Background: Systemic lupus erythematosus (SLE) is a common systemic autoimmune disease with a complex genetic inheritance. Genome-wide association studies (GWAS) have significantly increased the number of significant loci associated with SLE risk. To date, however, established loci account for less than 30% of the disease heritability and additional risk variants have yet to be identified. Here we performed a GWAS followed by a meta-analysis to identify new genome-wide significant loci for SLE. Methods: We genotyped a cohort of 907 patients with SLE (cases) and 1524 healthy controls from Spain and performed imputation using the 1000 Genomes reference data. We tested for association using logistic regression with correction for the principal components of variation. Meta-analysis of the association results was subsequently performed on 7,110,321 variants using genetic data from a large cohort of 4036 patients with SLE and 6959 controls of Northern European ancestry. Genetic association was also tested at the pathway level after removing the effect of known risk loci using PASCAL software. − 8 Results: We identified five new loci associated with SLE at the genome-wide level of significance (p <5×10 ): GRB2, SMYD3, ST8SIA4, LAT2 and ARHGAP27. Pathway analysis revealed several biological processes significantly associated − 6 − 5 with SLE risk: B cell receptor signaling (p =5.28 ×10 ), CTLA4 co-stimulation during T cell activation (p =3.06 ×10 ), − 5 − 5 interleukin-4 signaling (p =3.97 ×10 ) and cell surface interactions at the vascular wall (p =4.63 ×10 ). Conclusions: Our results identify five novel loci for SLE susceptibility, and biologic pathways associated via multiple low-effect-size loci. Keywords: Systemic lupus erythematosus, Genetic susceptibility, Genome-wide association study, Meta-analysis, Biological pathway analysis * Correspondence: toni.julia@vhir.org; sara.marsal@vhir.org Rheumatology Research Group, Vall d’Hebron Research Institute, 08035 Barcelona, Spain Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 2 of 10 Background biological insights have been gained in different complex Systemic lupus erythematosus (SLE [MIM: 152700]) is a diseases, including autoimmune diseases [12]. common systemic autoimmune disease characterized by The aim of the current work was to identify new the production of autoantibodies and a complex genetic genetic risk loci for SLE by a GWAS meta-analysis inheritance. The prevalence of the disease varies according using a case-control cohort from a previously untar- to the population ancestry, with European populations geted population. After excluding known risk genes, ranging between 30 and 90 cases per 100,000 individuals pathway meta-analysis was also performed to identify [1]. SLE afflicts women at a rate nine times higher than biologic pathways for SLE risk associated by risk loci men, and most often appears during childbearing ages. as yet unaccounted for. Concordance rate studies in monozygotic and dizygotic twins and recurrence risk estimates in siblings of probands Methods (λ ), have clearly shown the importance of genetic factors Study cohorts in the development of the disease [2]. Patients and controls from the Spanish population were Despite the evidence for a strong genetic contribution, recruited through the Immune-Mediated Inflammatory until recently, very few loci were convincingly associated Disease (IMID) Consortium [13]. Patients with SLE were with SLE risk [3]. With the concurrent identification of recruited via the rheumatology departments of 17 uni- common genome variation and the development of versity hospitals in Spain. All included patients fulfilled genome-wide genotyping technologies, genome-wide as- the 1982 revised American College for Rheumatology sociation studies (GWAS) have dramatically changed the diagnosis criteria for SLE [14]. All patients were > 16 years ability to identify risk variants. In SLE, GWAS have old at the time of recruitment, although disease could allowed the identification of more than 50 risk loci at have started earlier. A minimal disease evolution period of − 8 a genome-wide significance level (p value <5 × 10 ) 3 years since diagnosis was also required for inclusion in [4–6]. These findings are of great relevance since they this study. All Patients with SLE were Caucasian with all pinpoint specific biological mechanisms that are four grandparents born in Spain. Patients with an add- relevant for the disease and that otherwise would not itional rheumatologic disease (e.g. rheumatoid arthritis, have been prioritized for research [7]. In a severe systemic sclerosis or mixed connective tissue disease) disease like SLE that is lacking efficacious treatments, except antiphospholipid syndrome or Sjögren’ssyndrome genetic studies provide a unique way to expand the were excluded from the study. Also, patients with number of molecular targets for drug discovery [8]. concomitant psoriasis or inflammatory disease (Crohn’s To date, the explanation for the inherited risk of SLE disease or ulcerative colitis) were also excluded from the is largely unresolved. Including all known risk variants, study. A total of 907 patients with SLE were recruited for less than 30% of disease heritability is currently the GWAS. Additional file 1: Table S1 summarizes the accounted for [9], In order to identify additional risk main features of the Spanish GWAS cohort. variants, GWAS meta-analyses from different countries Healthy control individuals were also recruited have proven to be a highly successful approach [9]. Cur- through the IMID Consortium as described previously rently, most Southern European populations have been [15]. All controls were Caucasian and > 18 years old at underrepresented in GWAS of SLE. In Spain, epidemio- the time of recruitment. Individuals with one or more logical studies have shown that there is an increased grandparents born outside of Spain were excluded. Con- prevalence of the disease compared to other European trols with an autoimmune disease or with a family his- regions [1]. Consequently, the analysis of the genetic tory of autoimmune disease were also excluded from variation in this population could be highly useful to this cohort. A total of 1524 healthy control individuals identify new genetic variation for SLE risk. were finally included in the present GWAS. All the pro- Biological pathways integrate the function of multiple cedures were followed in compliance with the principles genes and, therefore, provide a higher level of detection of the Declaration of Helsinki and informed consent was of the relevant genetic risk [10, 11]. To date, different obtained from all participants. The study and the con- statistical methods have been developed that exploit the sent procedure were reviewed and approved by the local biological knowledge in order to leverage the power of institutional review boards. GWAS. These analysis methods are designed to aggre- GWAS data from European-ancestry cohorts were ob- gate the genetic evidence from multiple risk loci into a tained from a recent meta-analysis [4]. For the present single association statistic. The use of cumulative evidence study, GWAS association data were obtained from 4036 can be a powerful way to detect genetic associations and patients with SLE and 6959 controls of Caucasian European biological mechanisms that otherwise would have been ancestry. The details on the data quality control, imputation missed due to low effect size at the single-marker level. and statistical association analyses have been previously de- Using this complementary GWAS approach, relevant scribed [4]. Association data on a total of 37,577,690 Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 3 of 10 markers from the 22 autosomal chromosomes were avail- for download at http://urr.cat/data/GWAS_SLE_summary- able for meta-analysis. Stats.zip. Association plots for each of the associated loci were prepared using LocusZoom (http://locuszoom.org/). Genotyping, quality control and imputation In the Spain cohort, genome-wide genotyping was per- Genome-wide pathway analysis formed using the Illumina Quad610 Beadchips (Illumina, Several approaches are actually available to perform San Diego, CA, USA) at the National Genotyping Center pathway-based GWAS. However, most of these methods (CeGen, Madrid, Spain). This array genotyping platform do not account for the linkage disequilibrium (LD) includes information on > 550,000 single nucleotide structure in the genome. The variable structure of LD, polymorphisms (SNPs). A whole blood sample (5 mL) particularly of highly correlated chromosomal regions was collected from all patients and genomic DNA containing multiple genes, can negatively impact the re- extracted using the Chemagic Magnetic Separation sults from genome-wide pathway analysis [24]. In order Module I (PerkinElmer, Waltham, MA, USA). Genotyp- to integrate this information into the pathway associ- ing was performed following the protocol recommended ation analysis, we used the method implemented in by Illumina. Genotype calling was performed using the PASCAL [25]. In this approach, genetic markers are first GenomeStudio data analysis software v2011.1 (Illumina, mapped to genes in each pathway (here, all markers in- San Diego, CA, USA). Genotyping quality control was side the gene ± 20 flanking kb). Correlated markers are performed using PLINK genomic analysis software [16]. then identified using the LD structure estimated from a Principal components of variation were estimated using reference population (in this study, from the Caucasian Eigensoft (v4.2) software [17]. The genomic inflation European population from the 1000 Genomes Project factor was λ = 1.16 in the European-ancestry GWAS, (1KG)). Combining the single-marker association values GC and λ = 1.06 in the Spain GWAS (Additional file 1: with the LD structure, association scores are then com- GC Figure S2). After quality control analysis, 864 patients with puted for all genes in the pathway. In those cases where SLE and 1513 controls were available for imputation. genes from the same pathway are located close in a Genome-wide imputation was performed using GUID- chromosome and in strong LD, a joint score is calcu- ANCE, an integrated framework for haplotype phasing lated. Finally, the scores of all genes within a pathway and genotype imputation of genotypes [18]. Markers and are normalized, transformed and integrated to generate samples were first tested for quality control. SNPs a single association statistic that can be used to with a genotyping call rate < 95% or a significant devi- determine the statistical significance of the association ation from Hardy-Weinberg equilibrium in controls between the pathway and the trait of interest. In this − 6 (p value ≤1× 10 ) were removed. Individuals with a study, the default parameter values were used, including genotype call rate < 95% or outlier genetic background maximum number of SNPs per gene (n = 3000). The (i.e. > 6 SD in any of the 10 principal components of SNP p value to gene score estimation was performed variation), were also excluded. After quality control, using the sum gene score approach, and gene score pre-phasing of genotypes was performed using SHA- transforming into the pathway score was performed PEIT2 [19] and genotype imputation using IMPUTE2. using the chi-squared approach. The pathway analysis The 1000G Phase1 integrated haplotypes was used as method implemented in PASCAL has been shown to the reference panel [20, 21]. perform better than other methods, particularly since it A total of 30,038,143 markers were finally imputed from has better control of type I error. the Spain GWAS cohort. From these, after filtering for Pathways and their corresponding gene annotation high imputation quality (info score > 0.8, n = 9,168,673) was obtained from the MSigDB molecular signatures re- and minor allele frequency (MAF) > 1%), 7,195,283 pository (http://software.broadinstitute.org/gsea/msigdb). markers were available for GWAS. Association testing A total of 1077 biological pathways from the Reac- was performed using the logistic regression model imple- tome (n = 674), Kyoto Expression of Genes and Ge- mented in SNPTEST v2 software adjusting for the first nomes (n = 186) and BioCarta (n = 217) databases were two principal components of variation [22]. selected. The association p values obtained using PASCAL Meta-analysis of the common markers between the in the two GWAS cohorts were combined using Fisher’s two GWAS datasets was performed using METAL [23]. method, and the significance was corrected for multiple In this approach, z values are computed to summarize testing using Bonferroni’sadjustment. both the direction of effect and the significance level for In order to capture biologic pathways associated with each genetic marker. These z values are then combined SLE through as yet unaccounted for genetic risk in a weighted sum that incorporates the sample size of variants, all regions previously associated with SLE risk each cohort. The complete results from the Spain were removed from this analysis. For this objective, we GWAS and from the GWAS meta-analysis are available filtered out all markers within ± 250 kb distance from an Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 4 of 10 established SLE risk SNP and with an LD r > 0.2. Given containing 3 protein (SMYD3, rs1780813), and ST8 the strong association between the HLA region and SLE alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 4 risk, we excluded this region from the analysis (chr6: (ST8SIA4, rs55849330). Associated SNPs rs150518861 (bp 28,500,000–33,500,000). and rs114038709 are located in the flanking regions of linker for activation of T cells family member 2 (LAT2) Results and Rho GTPase activating protein 2 (ARHGAP27) GWAS meta-analysis genes, respectively. Figure 2 shows the detailed associ- After quality control, a total of 7,195,283 autosomal ation results for each of the five new SLE risk loci. markers with MAF > 0.01 were available for association testing in the Spain case-control cohort. From these, Genetic pathway association study in SLE 7,110,321 variants were also present in the European After excluding the association signal from known risk ancestry GWAS: 51 of the 52 previously known SLE risk loci, pathway analysis identified 100 and 157 pathways loci were in the same effect-size direction as originally associated with SLE in the Spain and European-ancestry described. From these, 31 had nominal evidence of repli- cohorts, respectively, at the nominal level (p < 0.05). cation (p< 0.05, Additional file 1 Table S2). Four SNPs, From these, 30 pathways (3% of total) were found to be rs1270942 (HLA, OR (95% CI) = 1.96 (1.59–2.42), associated in both cohorts, which is more than would be Spain − 4 OR (95% CI) = 2.53 (2.34–2.74), p value for hetero- expected by chance (p <5 ×10 ). After adjustment for EUR geneity (p ) = 0.00074), rs494003 (RNASEH2C,OR multiple testing, four biologic pathways were significantly Het (95% CI) =1.44 (1.24–1.66), OR (95% CI) =1.16 associated with SLE risk (Table 2). Spain EUR (1.07–1.26); p = 0.0059), rs9652601 (CIITA-SOCS1, Het OR (95% CI) = 0.74 (0.66–0.85), OR (95% CI) =0.85 Conditional and sex-specific association Spain EUR (0.8–0.9, p = 0.026), and rs3024505 (IL10,OR(95%CI) In order to explore the presence of secondary signals Het =1.38 (1.17–1.62), OR (95% CI) = 1.13 (1.04–1.22); at each associated locus, we performed conditional Spain EUR p = 0.028) showed evidence of heterogeneity between the analysis in the Spain cohort. From all five loci, we Het two GWAS cohorts. only identified one independent signal within 1 Mb of Meta-analysis of the two GWAS cohorts identified five the most strongly associated SNP that continued to − 4 new risk loci for SLE (Fig. 1, Table 1). None of the new show evidence of association (conditional p <1 ×10 ). genome-wide significant loci showed evidence of hetero- This independent association was identified at the geneity of effect between the two cohorts (p > 0.05). GRB2 locus and maps to RNF157 gene (SNP − 5 Three of the associated markers are SNPs in introns of rs9891273, p =4.99× 10 , Additional file 1:FigureS3). the genes encoding for growth factor receptor bound The presence of sex-specific associations was tested by protein 2 (GRB2, rs36023980), SET and MYND domain comparing the coefficients for SNP association estimated Fig. 1 Manhattan plot of genome-wide association study meta-analysis results including the Spain cohort. Plot of the -log10 (p values) of association between the 7,110,321 markers after meta-analysis between the European and Spain cohorts. The dashed horizontal line represents − 8 the genome-wide significance threshold (p value = 5 × 10 ). The known regions associated with systemic lupus erythematosus (SLE) risk and genome-wide significant are colored in red. The five new genomic regions associated with SLE risk in this study are colored in green, with the name of the corresponding gene above Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 5 of 10 − 8 Table 1 Novel SNPs for SLE risk showing genome-wide significance (p< 5× 10 ) following meta-analysis of Spain and European ancestry cohorts European Spain Meta-analysis Locus Chr SNP bp RA OR (CI 95%) p value OR (CI 95%) p value OR (CI 95%) p value −7 −8 SMYD3 1 rs1780813 246,444,082 C 0.53 (0.40–0.69) 8.36 × 10 0.61 (0.37–0.98) 0.013 0.55 (0.31–0.79) 3.5 × 10 −7 −8 ST8SIA4 5 rs55849330 100,184,647 A 1.16 (1.10–.123) 8.4 × 10 1.14 (1.01–1.30) 0.019 1.16 (1.11–1.21) 4.9 × 10 −8 LAT2 7 rs150518861 73,566,677 A 1.63 (1.34–1.99) 0.0000015 1.77 (1.23–2.56) 0.0074 1.66 (1.49–1.84) 4.1 × 10 −8 ARHGAP27 17 rs114038709 43,456,728 T 1.15 (1.08–1.22) 0.0000012 1.20 (1.07–1.35) 0.0088 1.16 (1.11–1.22) 3.7 × 10 −9 GRB2 17 rs36023980 73,341,284 C 1.18(1.11–1.25) 0.0000015 1.23 (1.08–1.40) 0.00039 1.18 (1.13–1.24) 4.7 × 10 OR are shown for the minor allele for all five associated polymorphisms Locus closest gene, Chr chromosome, SNP single nucleotide polymorphism, bp base pair, RA risk allele, OR odds ratio independently in women and men. We identified a are in line with the relevance of this causal disease significant difference only for LAT2 SNP rs150518861 mechanism. (p = 0.032). This risk variant was found to be more In a close functional relationship with GRB2, we also strongly associated in the male cohort compared to the found a significant association between linker for activa- female cohort. tion of T cells family member 2 gene (LAT2) locus and − 8 SLE (rs150518861, p = 4.1 × 10 ). LAT2 encodes for an Discussion adaptor molecule that binds GRB2 and, therefore, is also In the present study we have identified five new risk loci involved in BCR signaling [31]. The association at the for systemic lupus erythematosus. Performing a meta- genetic level between SLE and two directly interacting analysis on 4943 patients with SLE and 8483 controls proteins strongly supports the implication of this from different European ancestries, we have identified biological mechanism in SLE risk. B cell dysfunction is a variants at GRB2, SMYD3, ST8SIA4, LAT2, and ARH- hallmark of SLE pathology [9], and our study supports GAP27 loci associated with SLE susceptibility. At the downstream regulation after antigen binding as a crucial pathway level, we have also found four biological path- event in the disease etiology. In the evaluation of sex- ways associated with SLE risk independently of previ- specific effects, we found this locus to be differentially ously known risk genes. associated with SLE risk. The risk variant was associated In the present meta-analysis, we found an association with SLE in men (p = 0.0074, β (95% CI) = 1.3 (0.25–2.2)), between an intronic SNP in the gene encoding for the and it was non-significant in women (p = 0.58, β growth factor receptor-bound protein GRB2 and SLE (95% CI) = 0.13 (− 0.33 to 0.62)). Previous studies − 9 (rs36023980, p = 4.7 × 10 ). Analysis of the tissue- have shown that men require a higher genetic load specific epigenetic data from the NIH Roadmap Epige- to develop the disease [32]. If replicated in an independent nomics Project [26] for rs36023980 SNP showed a cohort, this result would be in line with these findings, strong regulatory activity in different immune cells, in- confirming the importance of sex in mediating the effect cluding enhancer evidence in both T and B lymphocytes of some genetic risk factors in SLE. SMYD3 encodes for (Additional file 1: Table S2). GRB2 encodes for a recep- an H3-Hk histone methyltransferase that has been associ- tor tyrosine-kinase (RTK) adaptor protein composed of ated with increased cell proliferation in cancer [33]. Al- a single SH2 domain and two SH3 domains [27]. SLE is tered epigenetic patterns have been strongly associated a disease characterized by the activation of B cells that with SLE, mostly at the DNA level [34]. More recently, recognize self-antigens via their B cell receptors (BCR). however, methylated histones have also been identified as In B cells, GRB2 functions as an expression adaptor targets of autoantibodies expressed in patients with SLE molecule, attenuating the signals that are transduced by [35]. Similar to other frequent nuclear autoantigens in the BCR [28]. Together with Dok-3 and SHIP1, GRB2 SLE, like double-stranded DNA or ribonucleoproteins, forms a trimer protein complex that binds directly to methylated H3-Hk histones are able to trigger autoreac- the BCR and prevents downstream signaling by inhibit- tive B cells after antigenic-exposure processes like apop- ing PI3K signaling [29]. Gene expression at different tosis. According to the Roadmap Epigenomics Project stages of B cell differentiation shows that GRB2 expres- data, the associated SNP rs1780813 lies in a site that is sion increases in more mature forms, particularly on DNAse hypersensitive for > 30 different tissues, support- antigen-experienced memory B cells (Additional file 1: ing its role in gene regulation. Figure S4). Inadequate control of memory B cell differ- To date little is known about the functional role of entiation into plasma cells has been proposed as a trig- SLE-associated genes STS8IA4 and ARHGAP27. In order ger for autoimmunity in SLE [30]. Our results therefore to infer the potential biological role of these two genes, Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 6 of 10 Fig. 2 Regional association plots from the meta-analysis of the two cohorts for all five genome-wide significant loci: -log10 (p values) for both directly genotyped and imputed single nucleotide polymorphisms (SNPs) are plotted as a function of genomic position (NCBI Build 37). The purple diamond indicates the lead SNP at each locus; the remaining markers are colored based on the LD (r [2]) in relation to the lead SNP. Underlying the image, the estimated recombination rate (cM/Mb) for the CEU panel from 1000 Genomes is depicted Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 7 of 10 Table 2 Biological pathways significantly associated with SLE risk Biological pathway N genes Spain cohort p value European cohort p value Combined p value Adjusted p value −6 − 6 B cell receptor signaling 75 0.016 2.05 × 10 5.28 × 10 0.0057 −5 CTLA4 co-stimulatory signal during T-cell activation 21 0.0014 0.0016 3.06 × 10 0.033 −5 Interleukin-4 signaling 11 0.00029 0.01 3.97 × 10 0.043 −5 Cell surface interactions at the vascular wall 91 0.0057 0.0006 4.63 × 10 0.049 Biological pathways significantly associated with systemic lupus erythematosus (SLE) after meta-analysis of the Spain and Caucasian European cohorts. P values for each cohort were estimated using PASCAL after removing the previously known risk loci for SLE N genes number of genes in the pathway we used the GeneNetwork approach, a functional-inference implicated in mitogen-activated protein kinase (MAPKi- − 8 method based on the gene co-expression patterns extracted nase) signaling (p value = 3.33 × 10 , Additional file 2: from microarray data from > 80,000 samples [36]. With this Table S5). Both biological processes have been previously approach, we found strong evidence that STS8IA4 is in- associated with SLE etiology, and our results not only − 13 volved in T cell activation (p value = 2.7 × 10 , support their involvement in disease risk but also Additional file 2: Table S4), and that ARHGAP27 is suggest new gene functions. Furthermore, expression Table 3 Top single-marker hits in genes from the four genetic pathways associated with SLE Gene Marker Chr bp MA OR p (Spain) p (EUR) p (meta) Pathway BCL10 rs12084253 1 85,720,326 T 1.11 0.020 0.0015 0.00012 BCR FCER1G rs1136224 1 161,184,097 G 0.91 0.023 0.022 0.0024 VASC FCGR2B rs182968886 1 161,642,985 A 0.86 0.044 0.0018 0.00023 BCR CD247 rs113305799 1 167,416,006 A 1.17 0.0035 0.044 0.0022 CTLA4 PROC rs6740067 2 128,156,366 T 1.16 0.043 0.018 0.0026 VASC CTLA4 rs733618 2 204,730,944 C 1.19 0.026 0.0018 0.00016 CTLA4 PPP3CA rs13120190 4 102,056,663 G 0.93 0.025 0.047 0.0060 BCR IL2 rs45522533 4 123,396,876 T 1.16 0.016 0.0058 0.00044 CTLA4 SLC7A11 rs74843273 4 139,150,464 T 0.81 0.025 0.0066 0.00065 VASC ITK rs60714766 5 156,602,589 T 1.07 0.015 0.043 0.0042 CTLA4 CARD11 rs6461796 7 3,071,195 C 0.94 0.027 0.033 0.0042 BCR LYN rs17812659 8 56,889,862 G 0.86 0.013 2.57E-05 1.17E-06 BCR,VASC ANGPT1 rs79847080 8 108,293,443 G 0.84 0.032 0.0028 0.00031 VASC VAV2 rs2810536 9 136,812,625 G 1.08 0.030 0.011 0.0013 BCR KRAS rs17388587 12 25,389,220 G 1.13 0.036 0.048 0.0075 BCR, VASC PRKCB rs11641223 16 24,020,316 T 1.11 0.041 0.0010 0.00012 BCR CD19 16:28955702:D 16 28,955,702 I 1.06 0.0077 0.047 0.0034 BCR SLC7A6 rs55856208 16 68,324,210 T 1.08 0.045 0.049 0.0086 VASC PLCG2 rs11548656 16 81,916,912 G 1.3 0.014 0.00062 0.000035 BCR ATP1B2 rs1794287 17 7,578,837 A 0.9 0.023 0.024 0.0026 VASC ITGB3 rs75211989 17 45,366,261 G 1.11 0.00014 0.020 0.00020 VASC GRB2 rs36023980 17 73,341,284 T 0.85 0.00039 1.51E-06 4.73E-09 CTLA4, IL4, BCR, VASC NFATC1 rs111354805 18 77,238,078 T 1.21 0.027 6.58E-05 5.26E-06 BCR MAP2K2 rs350913 19 4,096,779 T 0.94 0.029 0.030 0.0039 BCR CD79A rs16975619 19 42,392,441 C 1.52 0.020 0.0099 0.00089 BCR SIRPG rs11696739 20 1,600,925 A 0.92 0.044 0.0050 0.00069 VASC RAC2 rs229566 22 37,602,131 A 1.06 0.041 0.03 0.0047 BCR Suggestive risk variants were identified as those markers showing with the most significant meta-analysis p value (p (meta)), and that are associated in the two genome-wide association study cohorts (p< 0.05) and show the same direction of effect (OR) MA minor allele, OR odds ratio according to minor allele in European ancestry cohort, I insertion allele, Pathway biological pathway/s where the gene has been annotated, BCR B cell receptor pathway, CTLA4 CTLA4 pathway, IL4 interleukin-4 pathway, VASC vascular cell wall pathway Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 8 of 10 quantitative trait locus (eQTL) evidence supports that Two other associated pathways - the CTLA4 co- both SNPs regulate expression of the corresponding stimulatory signal and IL4 pathways - are strongly genes in cis. Whole blood eQTL analysis [37]shows a related to B cell activation. CTLA4 is a co-inhibitory strong association between variation at rs114038709 molecule expressed on activated helper T - TH2 and fol- − 134 and ARHGAP27 expression (p =4.1 ×10 ), and the licular - cells. Inhibition of CTLA4 increases B cell acti- most significant eQTL evidence for rs55849330 is vation after antigen binding, resulting in the production associated to STS8IA4 expression in immortalized B of antibodies [44]. IL-4 is a cytokine that is also − 10 cells [38](p =5.6 ×10 ). expressed in helper T cells and it is essential in the acti- Using a pathway-based analysis we have identified vation of antigen-bound naïve B cells. Similar to the four biological pathways associated with SLE. Since BCR signaling pathway, these two genetically associated the objective was to identify new genetic risk vari- biological processes that are deeply related to B cell acti- ation for SLE, our approach excluded all association vation could be the source of new effective drug targets signals from previously known SLE genes. We showed for the disease [45]. In this regard, a fusion protein in- that by using biological pathway knowledge, it is still cluding the extracellular domain of CTLA4 (abatacept) possible to capture genetic variation that is relevant is being currently evaluated as a therapy for more severe for the disease. One limitation of this approach is forms of SLE [46]. that it relies on the specific knowledge of gene func- tions and pathway definitions, which is still relatively Conclusions low for a substantial fraction of the genome [39]. An- In the present study we have performed a GWAS meta- other limitation is that pathway association is per- analysis approach to identify new genetic variation in SLE. formed on variants within or close to genes. Distant We have found five new genome-wide significant risk loci cis regulation and also trans regulation variants are and four biologic pathways associated with SLE risk. also plausible mechanisms of action [40]. With better Single-marker associations involve BCR downstream sig- knowledge of regulatory effects, particularly on iso- naling mechanisms with disease susceptibility, and auto- lated cell types, pathway-based analysis will become antigen generation and immune cell activity regulation. an even more powerful approach to detect the miss- Pathway-based analysis confirmed the relevance of BCR ing disease heritability. Despite these limitations, our signaling pathway and other B cell activation mechanisms results are robust since they are based on strongly in the disease etiology. The results from this study signifi- supported biological knowledge. Also, we provide stat- cantly expand the knowledge of the biological processes istical evidence of pathway association from two inde- implicated in susceptibility to SLE. pendent GWAS cohorts which, to our knowledge, has not been previously performed in SLE. Additional files The BCR signaling pathway had the strongest associ- ation with SLE. This result is in agreement with the re- Additional file 1: Table S1. Epidemiological features from the Spain GWAS cohort. Figure S1. Principal component analysis of the Spain sults found at the single-marker level, where variants at GWAS cohort. Figure S2. Quantile-quantile(Q-Q) plots of observed and BCR signaling genes GRB2 and LAT2 were found to be expected -log10(p values) of association between SNP genotype and SLE associated with disease susceptibility. Within the BCR risk. Table S2. Epigenetic regulatory data associated with GRB2 risk locus. Figure S3. Regional association plot for the association with SLE risk signaling pathway, however, there are multiple other independent of GRB2 SNP rs36023980. Figure S4. GRB2 gene expression single-marker hits in other genes indicating nominally during human B cell differentiation. (DOCX 627 kb) significant association with disease susceptibility in both Additional file 2: Table S3. Association results for the 52 previously cohorts. Given that they belong to an associated bio- known SLE risk loci in the Spain GWAS. Table S4. Pathway association results after combining the two SLE cohorts (combined raw p value <0.05). logical pathway, these signals are strongly suggestive risk Table S5. List of biological pathways significantly associated with ST8SIA4 variants for SLE (Table 3). Of relevance, several of the gene network. Table S6. List of biological pathways significantly associated proteins encoded by the genes in this pathway, like BTK with ARHGAP27 gene network. (XLSX 48 kb) or CTLA4, are currently being evaluated as therapeutic targets for SLE [41, 42]. Finding an efficacious treatment Abbreviations in SLE has proven extremely difficult and our results BCR: B cell receptor; bp: Base pair; CI: Confidence interval; eQTL: Expression quantitative trait locus; GWAS: Genome-wide association study; HLA: Human support the importance of targeting this pathway. Gen- leukocyte antigen; IMID Consortium: Immune-Mediated Inflammatory etic evidence, either direct or through associated gene Disease Consortium; LD: Linkage disequilibrium; MAF: Minor allele frequency; networks, has been shown to improve drug efficacy pre- OR: Odds ratio; p : P value for heterogeneity on genetic effect; Het SLE: Systemic lupus erythematosus; SNP: Single nucleotide polymorphism diction [43]. Based on the association signals found in the two cohorts, for example, the proteins encoded by Acknowledgements − 6 − 6 LYN (p = 1.17 × 10 ) and NFATC1 (p = 5.26 × 10 ) The technical support group from the Barcelona Supercomputing Center is could also be considered as new drug targets for SLE. gratefully acknowledged. Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 9 of 10 Funding Received: 28 February 2018 Accepted: 23 April 2018 This study was funded by the Spanish Ministry of Economy and Competitiveness (grant numbers: PSE-010000-2006-6 and IPT-010000-2010-36). This work has been also sponsored by the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. This work was References supported by an EFSD/Lilly research fellowship. Josep M. Mercader was 1. Danchenko N, Satia J, Anthony M. Epidemiology of systemic lupus supported by Sara Borrell Fellowship from the Instituto Carlos III. Sílvia erythematosus: a comparison of worldwide disease burden. Lupus. 2006; Bonàs was awarded an FI-DGR Fellowship from FI-DGR 2013 from 15(5):308–18. Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR, Generalitat 2. Harley JB, Alarcon-Riquelme ME, Criswell LA, Jacob CO, Kimberly RP, de Catalunya). The study sponsors had no role in the design of the Moser KL, Tsao BP, Vyse TJ, Langefeld CD, Nath SK, et al. Genome-wide study, collection, analysis or interpretation of the data. association scan in women with systemic lupus erythematosus identifies susceptibility variants in ITGAM, PXK, KIAA1542 and other loci. Nat Genet. 2008;40(2):204–10. Availability of data and materials 3. Gateva V, Sandling JK, Hom G, Taylor KE, Chung SA, Sun X, Ortmann W, The complete results from the Spain GWAS and from the GWAS meta-analysis Kosoy R, Ferreira RC, Nordmark G. A large-scale replication study identifies are available for download at http://urr.cat/data/GWAS_SLE_summaryStats.zip. TNIP1, PRDM1, JAZF1, UHRF1BP1 and IL10 as risk loci for systemic lupus erythematosus. Nat Genet. 2009;41(11):1228–33. 4. Bentham J, Morris DL, Cunninghame Graham DS, Pinder CL, Tombleson P, Behrens TW, Martin J, Fairfax BP, Knight JC, Chen L, et al. Genetic association Authors’ contributions analyses implicate aberrant regulation of innate and adaptive immunity AJ, FLL, SM and AFN conceived the study. SBG, JMM and DT performed genes in the pathogenesis of systemic lupus erythematosus. Nat Genet. genetic data imputation. AJ and DA performed statistical analysis. FJL, JPV, 2015;47(12):1457–64. AO, JLA, MAZ, PV, JMN, JLM, AZ, JMP, MF, ED, ERA, PC, RB, VMT, MLL, MLC 5. Graham RR, Cotsapas C, Davies L, Hackett R, Lessard CJ, Leon JM, Burtt and SM provided Spanish GWAS samples. AJ and DA performed genotyping. NP, Guiducci C, Parkin M, Gates C. Genetic variants near TNFAIP3 on AJ, SBG, JMM and DA wrote the first draft of the manuscript. All authors 6q23 are associated with systemic lupus erythematosus. Nat Genet. contributed to revision of the manuscript. All authors read and approved the 2008;40(9):1059–61. final manuscript. 6. Yang W, Shen N, Ye D-Q, Liu Q, Zhang Y, Qian X-X, Hirankarn N, Ying D, Pan H-F, Mok CC. Genome-wide association study in Asian populations identifies variants in ETS1 and WDFY4 associated with systemic lupus erythematosus. Ethics approval and consent to participate PLoS Genet. 2010;6(2):e1000841. All the procedures were followed in compliance with the principles of the 7. Tsokos GC, Lo MS, Reis PC, Sullivan KE. New insights into the Declaration of Helsinki and informed consent was obtained from all immunopathogenesis of systemic lupus erythematosus. Nat Rev Rheumatol. participants. The study and the consent procedure were reviewed and 2016;12(12):716–30. approved by the local institutional review boards. 8. Plenge RM, Scolnick EM, Altshuler D. Validating therapeutic targets through human genetics. Nat Rev Drug Discov. 2013;12(8):581–94. 9. Morris DL, Sheng Y, Zhang Y, Wang Y-F, Zhu Z, Tombleson P, Chen L, Competing interests Graham DSC, Bentham J, Roberts AL. Genome-wide association meta- The authors declare that they have no competing interests. analysis in Chinese and European individuals identifies ten new loci associated with systemic lupus erythematosus. Nat Genet. 2016;48(8):940–6. 10. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. Publisher’sNote 2015;44.D1:D457–D462. Springer Nature remains neutral with regard to jurisdictional claims in 11. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, published maps and institutional affiliations. Gillespie M, Kamdar MR. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014;42(D1):D472–7. Author details 12. Aterido A, Julia A, Ferrandiz C, Puig L, Fonseca E, Fernandez-Lopez E, Rheumatology Research Group, Vall d’Hebron Research Institute, 08035 Dauden E, Sanchez-Carazo JL, Lopez-Estebaranz JL, Moreno-Ramirez D, et al. Barcelona, Spain. Department of Rheumatology, Hospital Universitario Genome-wide pathway analysis identifies genetic pathways associated with Gregorio Marañón, 28007 Madrid, Spain. Department of Rheumatology, psoriasis. J Invest Dermatol. 2016;136(3):593–602. Hospital del SAS de Jerez de la Frontera, 11407 Cádiz, Spain. Barcelona 13. Julia A, Pinto JA, Gratacos J, Queiro R, Ferrandiz C, Fonseca E, Montilla C, Supercomputing Center. Joint BSC-CRG-IRB Research Program in Torre-Alonso JC, Puig L, Perez Venegas JJ, et al. A deletion at ADAMTS9- Computational Biology, 08034 Barcelona, Spain. Department of MAGI1 locus is associated with psoriatic arthritis risk. Ann Rheum Dis. 2015; Rheumatology, Hospital Universitari Germans Trias i Pujol, 08916 Badalona, 74(10):1875–81. Spain. Department of Rheumatology, Hospital Universitario Puerta de Hierro, 14. Tan EM, Cohen AS, Fries JF, Masi AT, Mcshane DJ, Rothfield NF, Schaller JG, 28222 Madrid, Spain. Department of Rheumatology, Hospital Universitario Talal N, Winchester RJ. The 1982 revised criteria for the classification of Reina Sofía, 14004 Córdoba, Spain. Department of Rheumatology, Hospital systemic lupus erythematosus. Arthritis Rheumatol. 1982;25(11):1271–7. General Universitario de Alicante, 03010 Alicante, Spain. Department of 15. Julia A, Gonzalez I, Fernandez-Nebro A, Blanco F, Rodriguez L, Gonzalez A, Rheumatology, Hospital Universitari de Bellvitge, 08907 Barcelona, Spain. Canete JD, Maymo J, Alperi-Lopez M, Olive A, et al. A genome-wide Department of Rheumatology, Hospital de Valme, 41007 Sevilla, Spain. association study identifies SLC8A3 as a susceptibility locus for ACPA- Department of Rheumatology, Hospital Universitario Ramón y Cajal, 28034 positive rheumatoid arthritis. Rheumatology (Oxford). 2016;55(6):1106–11. Madrid, Spain. Department of Rheumatology, Hospital do Meixoeiro, Grupo IRIDIS, Instituto de Investigación sanitaria Galicia Sur (IISGS), 36312 Vigo, 16. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Spain. Department of Rheumatology, Hospital Universitario A Coruña, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome 15006 A Coruña, Spain. Department of Rheumatology, Hospital Complejo association and population-based linkage analyses. Am J Hum Genet. 2007; Asistencial Universitario de León, 24071 León, Spain. Department of 81(3):559–75. Rheumatology, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain. 17. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Department of Rheumatology, Hospital Universitario Marqués de Valdecilla, Principal components analysis corrects for stratification in genome-wide 39008 Santander, Spain. Institució Catalana de Recerca i Estudis Avançats association studies. Nat Genet. 2006;38(8):904–9. (ICREA), 08010 Barcelona, Spain. HudsonAlpha Institute for Biotechnology, 18. Bonàs-Guarch S, Guindo-Martínez M, Miguel-Escalada I, Grarup N, Sebastian Huntsville, AL 35806, USA. Department of Rheumatology, Hospital Regional D, Rodriguez-Fos E, et al. Re-analysis of public genetic data reveals a rare Universitario de Málaga, Instituto de Investigación Biomédica de Málaga, X-chromosomal variant associated with type 2 diabetes. Nat Commun. 29011 Málaga, Spain. 2018;9(1):321. Julià et al. Arthritis Research & Therapy (2018) 20:100 Page 10 of 10 19. Delaneau O, Zagury JF, Marchini J. Improved whole-chromosome phasing 41. Rankin AL, Seth N, Keegan S, Andreyeva T, Cook TA, Edmonds J, Mathialagan for disease and population genetic studies. Nat Methods. 2013;10(1):5–6. N, Benson MJ, Syed J, Zhan Y. Selective inhibition of BTK prevents murine 20. Consortium GP. A map of human genome variation from population-scale lupus and antibody-mediated glomerulonephritis. J Immunol. 2013;191(9): sequencing. Nature. 2010;467(7319):1061–73. 4540–50. 42. Merrill J, Burgos-Vargas R, Westhovens R, Chalmers A, D'cruz D, Wallace D, 21. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Bae S, Sigal L, Becker JC, Kelly S. The efficacy and safety of abatacept in Kang HM, Marth GT, McVean GA. An integrated map of genetic variation patients with non–life-threatening manifestations of systemic lupus from 1,092 human genomes. Nature. 2012;491(7422):56–65. erythematosus: results of a twelve-month, multicenter, exploratory, phase 22. Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint IIb, randomized, double-blind, placebo-controlled trial. Arthritis Rheum. method for genome-wide association studies by imputation of genotypes. 2010;62(10):3077–87. Nat Genet. 2007;39(7):906–13. 43. Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, Floratos A, 23. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of Sham PC, Li MJ, Wang J. The support of human genetic evidence for genomewide association scans. Bioinformatics. 2010;26(17):2190–1. approved drug indications. Nat Genet. 2015;47(8):856. 24. Cantor RM, Lange K, Sinsheimer JS. Prioritizing GWAS results: a review of 44. Kwek SS, Cha E, Fong L. Unmasking the immune recognition of prostate statistical methods and recommendations for their application. Am J Hum cancer with CTLA4 blockade. Nat Rev Cancer. 2012;12(4):289. Genet. 2010;86(1):6–22. 45. Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, Kochi Y, Ohmura K, Suzuki 25. Lamparter D, Marbach D, Rueedi R, Kutalik Z, Bergmann S. Fast and rigorous A, Yoshida S, et al. Genetics of rheumatoid arthritis contributes to biology computation of gene and pathway scores from SNP-based summary and drug discovery. Nature. 2014;506(7488):376–81. statistics. PLoS Comput Biol. 2016;12(1):e1004714. 46. Pimentel-Quiroz VR, Ugarte-Gil MF, Alarcón GS. Abatacept for the 26. Romanoski CE, Glass CK, Stunnenberg HG, Wilson L, Almouzni G. treatment of systemic lupus erythematosus. Expert Opin Investig Drugs. Epigenomics: Roadmap for regulation. Nature. 2015;518(7539):314–6. 2016;25(4):493–9. 27. Ogura K, Okamura H. Conformational change of Sos-derived proline-rich peptide upon binding Grb2 N-terminal SH3 domain probed by NMR. Sci Rep. 2013;3:2913. 28. Lösing M, Goldbeck I, Manno B, Oellerich T, Schnyder T, Bohnenberger H, Stork B, Urlaub H, Batista FD, Wienands J. The Dok-3/Grb2 protein signal module attenuates Lyn kinase-dependent activation of Syk kinase in B cell antigen receptor microclusters. J Biol Chem. 2013;288(4):2303–13. 29. Reth M, Gold MR. What goes up must come down: a tripartite Dok-3/Grb2/ SHIP1 inhibitory module limits BCR signaling. Eur J Immunol. 2016;46(11): 2507–11. 30. Doreau A, Belot A, Bastid J, Riche B, Trescol-Biemont M-C, Ranchin B, Fabien N, Cochat P, Pouteil-Noble C, Trolliet P. Interleukin 17 acts in synergy with B cell–activating factor to influence B cell biology and the pathophysiology of systemic lupus erythematosus. Nat Immunol. 2009;10(7):778. 31. Janssen E, Zhu M, Zhang W, Koonpaew S, Zhang W. LAB: a new membrane- associated adaptor molecule in B cell activation. Nat Immunol. 2003;4(2): 117–23. 32. Hughes T, Adler A, Merrill JT, Kelly JA, Kaufman KM, Williams A, Langefeld CD, Gilkeson GS, Sanchez E, Martin J. Analysis of autosomal genes reveals gene–sex interactions and higher total genetic risk in men with systemic lupus erythematosus. Ann Rheum Dis. 2012;71:694–9. 33. Hamamoto R, Furukawa Y, Morita M, Iimura Y, Silva FP, Li M, Yagyu R, Nakamura Y. SMYD3 encodes a histone methyltransferase involved in the proliferation of cancer cells. Nat Cell Biol. 2004;6(8):731–40. 34. Absher DM, Li X, Waite LL, Gibson A, Roberts K, Edberg J, Chatham WW, Kimberly RP. Genome-wide DNA methylation analysis of systemic lupus erythematosus reveals persistent hypomethylation of interferon genes and compositional changes to CD4+ T-cell populations. PLoS Genet. 2013;9(8): e1003678. 35. van Bavel CC, Dieker JW, Kroeze Y, Tamboer WP, Voll R, Muller S, Berden JH, van der Vlag J. Apoptosis-induced histone H3 methylation is targeted by autoantibodies in systemic lupus erythematosus. Ann Rheum Dis. 2011; 70(1):201–7. 36. Fehrmann RS, Karjalainen JM, Krajewska M, Westra H-J, Maloney D, Simeonov A, Pers TH, Hirschhorn JN, Jansen RC, Schultes EA. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat Genet. 2015;47(2):115–25. 37. Westra H-J, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, Christiansen MW, Fairfax BP, Schramm K, Powell JE. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45(10):1238–43. 38. Lappalainen T, Sammeth M, Friedländer MR, AC’t Hoen P, Monlong J, Rivas MA, Gonzalez-Porta M, Kurbatova N, Griebel T, Ferreira PG. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501(7468):506. 39. McCarthy MI, MacArthur DG. Human disease genomics: from variants to biology. BioMed Central. 2017;18(1):20. 40. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101(1):5–22.

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Arthritis Research & Therapy – Springer Journals

Published: May 30, 2018

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