GWAS meta-analysis of Axial spondyloarthritis and Behçet's disease identifies CXCR6 as a novel MHC-I-opathy gene

GWAS meta-analysis of Axial spondyloarthritis and Behçet's disease identifies CXCR6 as a novel MHC-I-opathy gene | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article GWAS meta-analysis of Axial spondyloarthritis and Behçet's disease identifies CXCR6 as a novel MHC-I-opathy gene Mohammad Saeed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5959154/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: Axial spondyloarthritis (AxSpA) and Behçet's disease (BD) have clinical and HLA locus overlap and have been grouped under MHC-I-opathy. This study aimed to identify overlapping loci between AxSpA and BD to help elucidate MHC-I-opathy pathogenesis. Methods: Association clustering methods, such as OASIS, reduce the multiple-testing burden and are more powerful than single variant analysis for identifying modest genetic effects. Two large publically available genome-wide association studies (GWAS) of AxSpA (921 cases, 907 controls) and BD (1215 cases and 1278 healthy controls) from Turkiÿe, were subjected to OASIS meta-analyses to identify common non-HLA loci. Statistics used to identify significant loci included the novel OASIS locus index (OLI). Expression analysis was performed using GEO datasets, GSE181364 for AxSpA and GSE209567 for BD. STRING network analysis was performed. Results: GWAS for both diseases had the highest significance at the HLA-I locus. Of the 234 independent modestly significant non-HLA loci, there were 15 loci common to both AxSpA and BD. These included known MHC-I-opathy loci, 1p31.3 for IL23R ( P = 5.37x10 -6 , OLI=52.7) and 13q14.11 for LACC1 ( P =7.41x10 -6 , OLI=65.3). A novel locus identified in this study is 3p21.31 containing CXCR6 ( P =2.46x10 -5 , OLI=25.8). The locus 3p22.3 had the highest overall OLI (81.3) and the most significant SNP at this locus (rs2291897; P =1.82x10 -5 ), is an intronic variant in the gene FBXL2 . However, this association was specific for BD only. Conclusion: Several loci containing pathologically relevant genes for MHC-I-opathy were identified here, using a cluster-based approach in AxSpA and BD GWAS, with CXCR6 being a novel target. MHC-I-opathy AxSpA Behçet GWAS OASIS Figures Figure 1 Key Message MHC-I-opathy immunopathogenesis is incompletely understood and challenged by risk genes of modest effect. OASIS, a clustering algorithm, can help identify genes of modest significance for MHC-I-opathy, rapidly and cost-effectively using publically available GWAS datasets. This meta-analysis identified 15 loci common to both AxSpA and BD. Major candidate genes for MHC-I-opathy identified here are IL23R , LACC1 and CXCR6 . FBXL2 associated strongly with BD by OASIS analysis Introduction The ’MHC-I-opathy’ describes a family of inflammatory disorders with a strong genetic link to the major histocompatibility complex class I antigen presentation pathway. Classical MHC-I-opathies include Axial spondyloarthritis (AxSpA), Behçet’s disease (BD), psoriasis, and birdshot uveitis (McGonagle, Aydin, Gül, Mahr, & Direskeneli, 2015 ). AxSpA is strongly associated with HLA-B27 while BD with HLA-B51. MHC-1-opathies represent an intermediate between aberrant innate and adaptive immunity and therefore lie on the spectrum of autoinflammatory and autoimmune disorders. They have overlapping clinical manifestations. BD and SpA, both have skin, eye, joint and gastrointestinal system involvement, however axial features are rare in BD while neurological and vascular features are absent in SpA (McGonagle et al., 2015 ). MHC-1-opathies are characterized by enthesitis, i.e. inflammation at sites of contact with the environment (oral mucosa, gut and skin) and physical stress, including in the eye, valves and vessel walls. There is substantial immunopathogenetic link between SpA and BD. Though the MHC-I genetic associations were discovered about 50-years ago the pathogenesis of these disorders is not fully understood (Kuiper et al., 2023 ). In order to unravel the immunogenetic pathways and identify common genes for MHC-I-opathies, a cluster-based meta-analysis of AxSpA and BD genome-wide association studies (GWAS) was conducted. Both selected GWAS were from Turkiÿe to minimize population stratification effects (Li et al., 2019 ; Remmers et al., 2010 ). They consisted of genotypes of 4,321 subjects. OASIS ( O bjective A ssimilation of S NPs I nteracting in S ynchrony) is an association clustering algorithm that takes into account all SNPs at a locus, and the OASIS Locus Index (OLI) invokes a weighting procedure to select significant loci (Saeed, 2017 ; Saeed, 2023 ). OLI merges two aspects of the linkage disequilibrium (LD) phenomenon, strength of association and the number of surrounding significant SNPs, into a single statistic (Saeed, 2023 ). By binning variants in loci, OASIS (Saeed, 2017 ) reduces multiple-testing burden and provides an alternative to increasing sample size for GWAS to detect modest associations (Saeed, 2018 ; Saeed et al., 2021 ). Methods OASIS Algorithm OASIS is a binning method that functions in a manner akin to gene- or pathway-based tests (Christoforou et al., 2012 ; Neale & Sham, 2004 ). The algorithm has been previously described in detail (Saeed, 2017 ; Saeed et al., 2021 ). Briefly, publically available GWAS summary statistics are used to identify overlapping loci (2Mbp). The first variant with a P ≤ 0.05 is considered the start of a new locus and all SNPs with P ≤ 0.05, located within 200kbp of this initial SNP, are counted to define the OASIS score . The lowest P -value at this locus (-log.P max ) is noted. Expected number of significant SNPs (SNP Expected ) is calculated as 5% of the number of genotyped SNPs at the locus. OLI is defined as -log.P max x (OASIS score / SNP Expected ) (Saeed, 2023 ). Datasets GWAS datasets were downloaded from the publically available dbGAP and the GWAS Catalog repositories. Two large GWAS from Turkiÿe were subjected to OASIS meta-analysis to identify common non-HLA loci. The AxSpA dataset had 921 cases and 907 controls (Li et al., 2019 ) whereas, the BD GWAS had 1215 cases and 1278 healthy controls (Remmers et al., 2010 ). MHC-I-opathy pathway analysis was performed using protein interactions with STRING (Szklarczyk et al., 2015 ). All candidate genes identified by OASIS that were common between AxSpA and BD GWAS were evaluated (Table 1 ). Gene relationships that were based only on experimentation or co-expression were included. In order to allow maximum interactions to surface, low confidence (0.15) network was initially selected. Most significant SNPs in AxSpA and BD common loci were tested using GTEx Portal to identify eQTLs (expression quantitative trait loci) ("The GTEx Consortium atlas of genetic regulatory effects across human tissues," 2020). Expression of candidate genes at MHC-I-opathy loci were evaluated using Gene Expression Omnibus (GEO) datasets, GSE181364 for AxSpA and GSE209567 for BD (Edgar, Domrachev, & Lash, 2002 ). Table 1 Overlapping AxSpA and BD loci of modest significance (P > 10 − 8 ) Serial Locus GWAS Max SNP Max -log(P) OLI Candidate Genes eQTL GTEx 1 1p31.3 AxSpA rs3753368 5.31 22.03 IL23R GADD45A 1p31.3 BD rs924080 5.27 52.7 xxxx 2 2q11.2 AxSpA rs79915040 4.51 4.09 AFF3, LONRF2 AFF3 2q11.2 BD rs1519662 3.83 57.45 PDCL3 3 3p21.31 AxSpA rs75057315 4.61 2.64 CCR9, CXCR6, LIMD1, SACM1L xxxx 3p21.31 BD rs12639224 4.13 25.81 CXCR6 4 3q22.3 AxSpA rs55999829 4.27 15.8 CLDN18, ARMC8 xxxx 3q22.3 BD rs2622694 2.09 31.35 A4GNT, DBR1 5 4q28.1 AxSpA rs546994 3.92 21.21 FGF2, SPRY1, NUDT6, IL21 xxxx 4q27-4q28.1 BD rs1519238 3.93 29.48 LINC01091 6 5p15.31 AxSpA rs13354547 4.39 8.42 TENT4A, SRD5A1 LOC105374645 5p15.31 BD rs563624 4.21 11.48 UBE2QL1 5p15.31 BD rs6864374 2.89 37.4 xxxx 7 5q11.2 AxSpA rs286008 4.31 6.85 ESM1 DDX4 5q11.2 BD rs10940434 4.49 48.35 SNX18 8 8q12.1 AxSpA rs113969793 4.4 8.95 LYN, BPNT2, NSMAF, SDCBP xxxx 8q12.1 BD rs7463453 2.41 44.76 LINC01606 9 9q22.1 AxSpA rs78872968 4.7 3.87 CDK20, SPIN1 xxxx 9q22.1 BD rs10868677 4.06 35.52 CDK20 10 11p15.4 AxSpA rs11041325 4.72 7.47 SYT9, STK33, NLRP10 SYT9 11p15.4 BD rs10840089 3.73 62.17 STK33, TRIMM66 11 11p14.3-p14.2 AxSpA rs1389409 3.4 19.81 ANO3 xxxx 11p14.3-p14.2 BD rs903154 3.93 14.29 xxxx 12 11q21 AxSpA rs1271188 5.34 9.26 PIWIL4, FUT4, MRE11 GPR83, MRE11 11q21 BD rs542284 2.95 39.33 CCDC82 13 13q14.11 AxSpA rs7995585 4.27 20.4 LACC1 xxxx 13q14.11 BD rs3764147 5.13 65.29 LACC1 14 15q26.1 AxSpA rs12591882 4.7 4.74 CHD2, RGMA xxxx 15q26.1 BD rs2199724 4.59 16.69 xxxx 15 17p12 AxSpA rs59126981 4.74 10.71 CDRT7, TEKT3, PMP22 xxxx 17p12 BD rs8081195 3.83 20.89 xxxx OASIS identified 15 loci that overlapped between AxSpA and BD GWAS datasets, however, the most significant SNPs (Max SNP) at these loci had P >1x10 -8 (Max -log(P)). Mean OLI for the two GWAS was 21.4. Hence, OLI > 20 was considered important. GTEx Portal was used to identify the eQTLs for the Max SNP at each locus. Candidate genes were postulated based on location and biological function. Results HLA-I was the only locus that was highly significant ( P < 1x10 − 8 ) in both GWAS datasets (Fig. S1 ). The SNP rs1013210 ( P = 7.94x10 − 8 ), located in the intergenic region near ADAM28 on 8p21.2, associated with AxSpA and rs74992754 ( P = 6.07x 10 − 8 ), located at 6p21.33 near the psoriasis susceptibility candidate genes in the MHC-1 region, associated with BD. Interestingly, rs1013210 at 8p21.2 also has the highest OLI (58.26) in the AxSpA dataset. The locus with the highest overall OLI of 81.26 is 3p22.3. The most significant SNP at this locus is rs2291897 ( P = 1.82x10 − 5 ), which is an intronic variant in the gene FBXL2 . OASIS identified 234 independent modestly significant non-HLA loci in the two GWAS datasets. As shown in Table 1 , 15 loci overlap between the AxSpA and BD GWAS datasets. The locus 1p31.3, containing the gene IL23R , is most significantly associated in both datasets. However, the most significant variant at this locus, rs3753368, is an eQTL ( P = 1.32x10 − 4 expression in subcutaneous tissue) for a nearby gene GADD45A . Further, GEO expression analysis shows that IL23R is suppressed in both AxSpA and BD, though the significance was only for AxSpA ( P = 6.86x10 − 3 , log 2 Fold = -2.01) (Table 2 ). The second most significant MHC-I-opathy locus is 13q14.11 with its most significant SNP, rs3764147, being an eQTL ( P = 1.47x10 − 22 in the tibial artery) for LACC1 . GEO expression analysis confirms LACC1 as an important candidate gene as it is significantly down regulated in AxSpA ( P = 1.45x10 − 4 , log 2 Fold = -1.47) (Table 2 ). Table 2 GEO Expression Analysis AxSpA BD Gene P log 2 Fold P log 2 Fold LACC1 1.45E-04 -1.47 3.05E-01 -0.03 IL23R 6.86E-03 -2.01 6.81E-02 -0.06 SNX18 2.21E-01 -0.31 8.90E-02 -0.11 FGF2 6.88E-01 -0.28 4.60E-02 -0.06 CXCR6 8.22E-01 -0.08 4.33E-05 -0.31 LIMD1 2.55E-01 0.31 7.82E-01 0.02 FUT4 5.24E-01 0.17 2.85E-01 0.07 CCR9 9.40E-01 0.04 2.57E-01 0.06 GEO datasets GSE181364 for AxSpA and GSE209567 for BD, were analyzed using GEO2R online function and the genes with altered expression were matched with OASIS candidate genes. Five genes were found to be downregulated and three upregulated. The grey highlighted genes had significant expression change. These included IL23R and LACC1 for AxSpA and CXCR6 and FGF2 for BD. The novel MHC-I-opathy locus identified in this analysis is 3p21.31. The most significant SNP at this locus, rs12639224, is an eQTL ( P = 9.9x10 − 8 in the tibial artery) for CXCR6 . GEO expression analysis shows that CXCR6 is down regulated in BD ( P = 4.33x10 − 5 , log 2 Fold = -0.31) (Table 2 ). Other novel loci (Table 1 ) could not be verified in the GEO dataset analysis (Table 2 ) and will require further functional studies to confirm their significance. These may mediate MHC-I-opathy by means other than gene expression. Protein network analysis is performed using STRING to understand the MHC-I-opathy pathways involved. The comprehensive list of 48 genes (Table 1 ) is searched for experimental interaction and co-expression. There are some significant but limited interactions at low confidence (0.15). Interestingly, addition of text-mining and database information led to the emergence of a large network ( P = 7.16x10 − 6 ). The network had a total of 42 nodes involving 60 connections. Mean node degree was 2.86. Even at medium confidence (0.4) this network showed CXCR6 as part of the interactome of IL23R and LACC1 ( P = 0.00243) (Fig. 1 ). Figure 1 . STRING protein network analysis of 48 unique genes, derived from Table 1 , was performed. A) Limited network emerged for co-expression and experimental interactions only even when a low confidence network (CN) of 0.15 was selected. B) Addition of databases and text-mining to the former led to a deeper network (CN = 0.40; PPI enrichment P -value = 2.4x10 − 3 ). This showed that CXCR6 interacts with both IL23R and LACC1 . Discussion Using a cluster-based association approach this meta-analysis identified several overlapping loci that could be key to understanding the immunopathogenesis of MHC-I-opathies (Kuiper et al., 2023 ). As expected, the highest association signal for both AxSpA and BD, was for the MHC class 1 locus on chromosome 6 (Fig. S1 ). This study also confirmed IL23R and LACC1 as MHC-1-opathy genes (Duan et al., 2012 ; Remmers et al., 2010 ). Both these genes were identified in highly significant OASIS loci in both GWAS datasets. Moreover, their expression was significantly altered in GEO datasets. Interestingly, the variant with the highest significance at the LACC1 locus was also an eQTL determining LACC1 expression in arterial tissue. LACC1 is involved in inflammasome activation and loss of function mutations have been found in BD and SpA (Lahiri, Hedl, Yan, & Abraham, 2017 ; Wakil et al., 2015 ). The novel candidate gene that this study identified was CXCR6 at the 3p21.31 locus. The most significant variant at 3p21.31, is an eQTL for CXCR6 and the gene expression is down regulated in BD (Table 2 ). CXCR6 codes for a G-protein coupled receptor expressed in several T-cell subsets, regulating their migration to tissues. A biological study showed that CXCR6 + CD8 + T-cells were actively recruited to psoriasis affected skin in response to the chemokine CXCL16, which is the ligand for CXCR6 (Günther, Carballido-Perrig, Kaesler, Carballido, & Biedermann, 2012 ). It also showed that TNF-α inhibition, that is part of the therapeutics in AxSpA and BD, reduced the CXCR6 mediated T-cell recruitment. Importantly, CXCR6 formed part of the IL23R and LACC1 interactome, providing insight into MHC-I-opathy pathogenesis (Fig. 1 ). Another important candidate gene that this study identified is FBXL2 at the 3p22.3 locus. The most significant SNP by OLI, in both GWAS combined is rs2291897, which is an intronic variant of FBXL2 . However, this association was with BD only. Like LACC1, FBXL2 is involved in inflammasome functioning. The NLRP3 inflammasome is involved in the pathogenesis of a broad range of inflammatory diseases and its activation leads to the release of proinflammatory cytokines IL-1β and IL-18 (Chen et al., 2023 ). FBXL2 endogenously inhibits NLRP3 by inducing its ubiquitin-mediated proteasomal degradation, thus functioning as an anti-inflammatory protein (Han et al., 2015 ). FBXL2 is involved in TNF-receptor degradation as well (Han et al., 2015 ). Interestingly, mutations in autoinflammatory disease genes have been found in BD patients (Amoura et al., 2005 ; Koné-Paut, Sanchez, Le Quellec, Manna, & Touitou, 2007 ; Touitou et al., 2000 ). GWAS for AxSpA and BD from Turkiÿe only were analyzed to minimize population stratification effects (Li et al., 2019 ; Remmers et al., 2010 ). Likely due to this population homogeneity the 5q15 locus, harboring the ERAP1 and ERAP2 genes was not significant in this study. These genes were also not found in the original Turkish cohorts (Li et al., 2019 ; Remmers et al., 2010 ), nor in a subsequent Chinese study (Su et al., 2018 ). However ERAP1/2 are associated in European cohorts signifying population based effect for these genes (Kuiper et al., 2023 ). However, on chromosome 5, OASIS identified 5p15 and 5q11 as common AxSpA and BD loci (Table 1 ), which may be explored further with the remaining loci in Table 1 , using biological studies to determine their significance. In summary, this study identified several novel MHC-I-opathy candidate genes / loci that provide a basis for further investigation into this elusive group of disorders. Limitations of this study include a need for multiple GWAS datasets not only for AxSpA and BD, but also other MHC-I-opathies. The identification of IL23R , LACC1 , CXCR6 and FBXL2 as MHC-I-opathy susceptibility genes will provide a deeper insight into disease pathogenesis. Several candidate loci (Table 1 ) and genes such as ADAM28 need additional data for verification. Deep sequencing and functional studies, including in vitro and in vivo strategies, would help to confirm the pathogenic relevance of these genes in MHC-I-opathies. Declarations Conflict of interest: None Competing interests: None Funding: None Author Contribution M.S - designed the study, analyzed the data, wrote the manuscript Acknowledgements: This study was presented as Oral Presentation at the British Society for Rheumatology 2024 Meeting in Liverpool, UK. Web Resources: dbGAP: www.ncbi.nlm.nih.gov/gap/ GWAS Catalog: www.ebi.ac.uk/gwas/downloads/summary-statistics GEO: https://www.ncbi.nlm.nih.gov/gds/ GTEx Portal: https://gtexportal.org/home/ References Amoura Z, Dodé C, Hue S, Caillat-Zucman S, Bahram S, Delpech M, Piette JC (2005) Association of the R92Q TNFRSF1A mutation and extracranial deep vein thrombosis in patients with Behçet's disease. Arthritis Rheum 52(2):608–611. 10.1002/art.20873 Chen Y, Ye X, Escames G, Lei W, Zhang X, Li M, Yang Y (2023) The NLRP3 inflammasome: contributions to inflammation-related diseases. 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HLA-I was the only highly significant (P<10 -8 ) MHC-I-opathy locus OASIS graphs for the A) AxSpA and B) BD GWAS datasets, showing SNPs with P <1x10 -8 as -log P-values (P-val). Only one locus, the HLA-I, showed such high degree of significance. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5959154","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":416898740,"identity":"bee926ea-d7b7-4be7-9b4b-a31edbc6241d","order_by":0,"name":"Mohammad Saeed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYNACNhseZC4zMVrSSNdyGIWLX4tu/+GHDz6UnZfRnXbG7MGHmsP5/Ay8hw3waTG7kWZsOOPcbR6z2znmhjOOHbac2cCXnIBfC4OZNG8bWAuQ0XDYwOAAj/EBvFrOH//++2/bOYQWe4JaDuSYMTO2HUCyhYHHmIDDcoole84lA7WklUnOOJZuIHGYLxm/988f3/jhR5mdvdnt5G0SH2qsDfjbew9L4NOCBTDzEFaDDsjQMgpGwSgYBcMaAABam0ciReLG+AAAAABJRU5ErkJggg==","orcid":"","institution":"ImmunoCure - Center for Inflammatory Diseases","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Saeed","suffix":""}],"badges":[],"createdAt":"2025-02-04 15:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5959154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5959154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76688092,"identity":"acb3dbfd-722f-4e68-ad71-d36b3ab96eac","added_by":"auto","created_at":"2025-02-19 16:31:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMHC-I-opathy network analysis to detect functional pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTRING protein network analysis of 48 unique genes, derived from Table 1, was performed. A) Limited network emerged for co-expression and experimental interactions only even when a low confidence network (CN) of 0.15 was selected. B) Addition of databases and text-mining to the former led to a deeper network (CN=0.40; PPI enrichment \u003cem\u003eP\u003c/em\u003e-value = 2.4x10\u003csup\u003e-3\u003c/sup\u003e). This showed that \u003cem\u003eCXCR6\u003c/em\u003e interacts with both \u003cem\u003eIL23R\u003c/em\u003e and \u003cem\u003eLACC1\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"MHCIOpathyFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5959154/v1/262613b673ad1c964a75ad5a.jpg"},{"id":76689194,"identity":"91e6afe1-210e-45ff-8bfd-09b176a0c798","added_by":"auto","created_at":"2025-02-19 16:39:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":761258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5959154/v1/da24603d-f111-4c5e-acc3-2b4d1116a584.pdf"},{"id":76688097,"identity":"63cddad7-09d1-4cb0-a47a-94a8edf8cd24","added_by":"auto","created_at":"2025-02-19 16:31:38","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":80168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S1. HLA-I was the only highly significant (P\u0026lt;10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-8\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e) MHC-I-opathy locus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOASIS graphs for the A) AxSpA and B) BD GWAS datasets, showing SNPs with \u003cem\u003eP\u003c/em\u003e\u0026lt;1x10\u003csup\u003e-8 \u003c/sup\u003eas -log P-values (P-val). Only one locus, the HLA-I, showed such high degree of significance.\u003c/p\u003e","description":"","filename":"MHCIOpathyFigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5959154/v1/925dba670e7cfb3ab5f2343d.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"GWAS meta-analysis of Axial spondyloarthritis and Behçet's disease identifies CXCR6 as a novel MHC-I-opathy gene","fulltext":[{"header":"Key Message","content":"\u003cul\u003e\n \u003cli\u003eMHC-I-opathy immunopathogenesis is incompletely understood\u0026nbsp;and challenged by risk genes of modest effect.\u003c/li\u003e\n \u003cli\u003eOASIS, a clustering algorithm, can help identify\u0026nbsp;genes of modest significance for\u0026nbsp;MHC-I-opathy,\u0026nbsp;rapidly and cost-effectively using publically available GWAS datasets.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;This meta-analysis identified 15 loci common to both AxSpA and BD.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Major candidate genes for\u0026nbsp;MHC-I-opathy\u0026nbsp;identified here are \u003cem\u003eIL23R\u003c/em\u003e, \u003cem\u003eLACC1\u003c/em\u003e and \u003cem\u003eCXCR6\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003e\u0026nbsp;FBXL2\u0026nbsp;\u003c/em\u003eassociated strongly with BD by OASIS analysis\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe \u0026rsquo;MHC-I-opathy\u0026rsquo; describes a family of inflammatory disorders with a strong genetic link to the major histocompatibility complex class I antigen presentation pathway. Classical MHC-I-opathies include Axial spondyloarthritis (AxSpA), Beh\u0026ccedil;et\u0026rsquo;s disease (BD), psoriasis, and birdshot uveitis (McGonagle, Aydin, G\u0026uuml;l, Mahr, \u0026amp; Direskeneli, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). AxSpA is strongly associated with HLA-B27 while BD with HLA-B51. MHC-1-opathies represent an intermediate between aberrant innate and adaptive immunity and therefore lie on the spectrum of autoinflammatory and autoimmune disorders. They have overlapping clinical manifestations. BD and SpA, both have skin, eye, joint and gastrointestinal system involvement, however axial features are rare in BD while neurological and vascular features are absent in SpA (McGonagle et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). MHC-1-opathies are characterized by enthesitis, i.e. inflammation at sites of contact with the environment (oral mucosa, gut and skin) and physical stress, including in the eye, valves and vessel walls.\u003c/p\u003e \u003cp\u003eThere is substantial immunopathogenetic link between SpA and BD. Though the MHC-I genetic associations were discovered about 50-years ago the pathogenesis of these disorders is not fully understood (Kuiper et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In order to unravel the immunogenetic pathways and identify common genes for MHC-I-opathies, a cluster-based meta-analysis of AxSpA and BD genome-wide association studies (GWAS) was conducted. Both selected GWAS were from Turki\u0026yuml;e to minimize population stratification effects (Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Remmers et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). They consisted of genotypes of 4,321 subjects. OASIS (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eO\u003c/span\u003ebjective \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003essimilation of \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eS\u003c/span\u003eNPs \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eI\u003c/span\u003enteracting in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eS\u003c/span\u003eynchrony) is an association clustering algorithm that takes into account all SNPs at a locus, and the OASIS Locus Index (OLI) invokes a weighting procedure to select significant loci (Saeed, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saeed, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). OLI merges two aspects of the linkage disequilibrium (LD) phenomenon, strength of association and the number of surrounding significant SNPs, into a single statistic (Saeed, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By binning variants in loci, OASIS (Saeed, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reduces multiple-testing burden and provides an alternative to increasing sample size for GWAS to detect modest associations (Saeed, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Saeed et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOASIS Algorithm\u003c/h2\u003e \u003cp\u003eOASIS is a binning method that functions in a manner akin to gene- or pathway-based tests (Christoforou et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Neale \u0026amp; Sham, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The algorithm has been previously described in detail (Saeed, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saeed et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Briefly, publically available GWAS summary statistics are used to identify overlapping loci (2Mbp). The first variant with a P\u0026thinsp;\u0026le;\u0026thinsp;0.05 is considered the start of a new locus and all SNPs with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05, located within 200kbp of this initial SNP, are counted to define the OASIS\u003csub\u003escore\u003c/sub\u003e. The lowest \u003cem\u003eP\u003c/em\u003e-value at this locus (-log.P\u003csub\u003emax\u003c/sub\u003e) is noted. Expected number of significant SNPs (SNP\u003csub\u003eExpected\u003c/sub\u003e) is calculated as 5% of the number of genotyped SNPs at the locus. OLI is defined as -log.P\u003csub\u003emax\u003c/sub\u003e x (OASIS\u003csub\u003escore\u003c/sub\u003e / SNP\u003csub\u003eExpected\u003c/sub\u003e) (Saeed, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDatasets\u003c/h3\u003e\n\u003cp\u003eGWAS datasets were downloaded from the publically available dbGAP and the GWAS Catalog repositories. Two large GWAS from Turki\u0026yuml;e were subjected to OASIS meta-analysis to identify common non-HLA loci. The AxSpA dataset had 921 cases and 907 controls (Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) whereas, the BD GWAS had 1215 cases and 1278 healthy controls (Remmers et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMHC-I-opathy pathway analysis was performed using protein interactions with STRING (Szklarczyk et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). All candidate genes identified by OASIS that were common between AxSpA and BD GWAS were evaluated (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Gene relationships that were based only on experimentation or co-expression were included. In order to allow maximum interactions to surface, low confidence (0.15) network was initially selected. Most significant SNPs in AxSpA and BD common loci were tested using GTEx Portal to identify eQTLs (expression quantitative trait loci) (\"The GTEx Consortium atlas of genetic regulatory effects across human tissues,\" 2020). Expression of candidate genes at MHC-I-opathy loci were evaluated using Gene Expression Omnibus (GEO) datasets, GSE181364 for AxSpA and GSE209567 for BD (Edgar, Domrachev, \u0026amp; Lash, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverlapping AxSpA and BD loci of modest significance (P\u0026thinsp;\u0026gt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGWAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax SNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax -log(P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCandidate Genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eeQTL GTEx\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1p31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers3753368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIL23R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGADD45A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1p31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers924080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2q11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers79915040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAFF3, LONRF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAFF3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2q11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers1519662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePDCL3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3p21.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers75057315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCCR9, CXCR6, LIMD1, SACM1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3p21.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers12639224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCXCR6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3q22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers55999829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCLDN18, ARMC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3q22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers2622694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA4GNT, DBR1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4q28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers546994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFGF2, SPRY1, NUDT6, IL21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4q27-4q28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers1519238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLINC01091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5p15.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers13354547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTENT4A, SRD5A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLOC105374645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5p15.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers563624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUBE2QL1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5p15.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers6864374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5q11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers286008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eESM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDDX4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5q11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers10940434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSNX18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8q12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers113969793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLYN, BPNT2, NSMAF, SDCBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8q12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers7463453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLINC01606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9q22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers78872968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCDK20, SPIN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9q22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers10868677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCDK20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11p15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers11041325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSYT9, STK33, NLRP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSYT9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11p15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers10840089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSTK33, TRIMM66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11p14.3-p14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers1389409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eANO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11p14.3-p14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers903154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11q21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers1271188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePIWIL4, FUT4, MRE11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGPR83, MRE11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11q21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers542284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCCDC82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13q14.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers7995585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLACC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13q14.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers3764147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLACC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15q26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers12591882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCHD2, RGMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15q26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers2199724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17p12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers59126981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCDRT7, TEKT3, PMP22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17p12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers8081195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003exxxx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eOASIS identified 15 loci that overlapped between AxSpA and BD GWAS datasets, however, the most significant SNPs (Max SNP) at these loci had \u003cem\u003eP\u003c/em\u003e\u0026gt;1x10\u003csup\u003e-8\u003c/sup\u003e (Max -log(P)). Mean OLI for the two GWAS was 21.4. Hence, OLI \u0026gt; 20 was considered important. GTEx Portal was used to identify the eQTLs for the Max SNP at each locus. Candidate genes were postulated based on location and biological function.\u0026nbsp;\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eHLA-I was the only locus that was highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1x10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) in both GWAS datasets (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The SNP rs1013210 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.94x10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), located in the intergenic region near \u003cem\u003eADAM28\u003c/em\u003e on 8p21.2, associated with AxSpA and rs74992754 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.07x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), located at 6p21.33 near the psoriasis susceptibility candidate genes in the MHC-1 region, associated with BD. Interestingly, rs1013210 at 8p21.2 also has the highest OLI (58.26) in the AxSpA dataset. The locus with the highest overall OLI of 81.26 is 3p22.3. The most significant SNP at this locus is rs2291897 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.82x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), which is an intronic variant in the gene \u003cem\u003eFBXL2\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eOASIS identified 234 independent modestly significant non-HLA loci in the two GWAS datasets. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 15 loci overlap between the AxSpA and BD GWAS datasets. The locus 1p31.3, containing the gene \u003cem\u003eIL23R\u003c/em\u003e, is most significantly associated in both datasets. However, the most significant variant at this locus, rs3753368, is an eQTL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.32x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e expression in subcutaneous tissue) for a nearby gene \u003cem\u003eGADD45A\u003c/em\u003e. Further, GEO expression analysis shows that \u003cem\u003eIL23R\u003c/em\u003e is suppressed in both AxSpA and BD, though the significance was only for AxSpA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.86x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, log\u003csub\u003e2\u003c/sub\u003e Fold = -2.01) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The second most significant MHC-I-opathy locus is 13q14.11 with its most significant SNP, rs3764147, being an eQTL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.47x10\u003csup\u003e\u0026minus;\u0026thinsp;22\u003c/sup\u003e in the tibial artery) for \u003cem\u003eLACC1\u003c/em\u003e. GEO expression analysis confirms \u003cem\u003eLACC1\u003c/em\u003e as an important candidate gene as it is significantly down regulated in AxSpA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.45x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, log\u003csub\u003e2\u003c/sub\u003e Fold = -1.47) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGEO Expression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAxSpA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elog\u003csub\u003e2\u003c/sub\u003eFold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elog\u003csub\u003e2\u003c/sub\u003eFold\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLACC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.05E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL23R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.86E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.81E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNX18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.90E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFGF2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.88E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.60E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCXCR6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.22E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.33E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLIMD1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.55E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.82E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFUT4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.24E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.85E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCR9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.40E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eGEO datasets GSE181364 for AxSpA and GSE209567 for BD, were analyzed using GEO2R online function and the genes with altered expression were matched with OASIS candidate genes. Five genes were found to be downregulated and three upregulated. The grey highlighted genes had significant expression change. These included \u003cem\u003eIL23R\u003c/em\u003e and \u003cem\u003eLACC1\u0026nbsp;\u003c/em\u003efor AxSpA and \u003cem\u003eCXCR6\u003c/em\u003e and \u003cem\u003eFGF2\u0026nbsp;\u003c/em\u003efor BD.\u003c/p\u003e \u003cp\u003eThe novel MHC-I-opathy locus identified in this analysis is 3p21.31. The most significant SNP at this locus, rs12639224, is an eQTL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.9x10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e in the tibial artery) for \u003cem\u003eCXCR6\u003c/em\u003e. GEO expression analysis shows that \u003cem\u003eCXCR6\u003c/em\u003e is down regulated in BD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.33x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, log\u003csub\u003e2\u003c/sub\u003e Fold = -0.31) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Other novel loci (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) could not be verified in the GEO dataset analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and will require further functional studies to confirm their significance. These may mediate MHC-I-opathy by means other than gene expression.\u003c/p\u003e \u003cp\u003eProtein network analysis is performed using STRING to understand the MHC-I-opathy pathways involved. The comprehensive list of 48 genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is searched for experimental interaction and co-expression. There are some significant but limited interactions at low confidence (0.15). Interestingly, addition of text-mining and database information led to the emergence of a large network (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.16x10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). The network had a total of 42 nodes involving 60 connections. Mean node degree was 2.86. Even at medium confidence (0.4) this network showed \u003cem\u003eCXCR6\u003c/em\u003e as part of the interactome of \u003cem\u003eIL23R\u003c/em\u003e and \u003cem\u003eLACC1\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00243) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. STRING protein network analysis of 48 unique genes, derived from Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, was performed. A) Limited network emerged for co-expression and experimental interactions only even when a low confidence network (CN) of 0.15 was selected. B) Addition of databases and text-mining to the former led to a deeper network (CN\u0026thinsp;=\u0026thinsp;0.40; PPI enrichment \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;2.4x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). This showed that \u003cem\u003eCXCR6\u003c/em\u003e interacts with both \u003cem\u003eIL23R\u003c/em\u003e and \u003cem\u003eLACC1\u003c/em\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing a cluster-based association approach this meta-analysis identified several overlapping loci that could be key to understanding the immunopathogenesis of MHC-I-opathies (Kuiper et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As expected, the highest association signal for both AxSpA and BD, was for the MHC class 1 locus on chromosome 6 (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This study also confirmed \u003cem\u003eIL23R\u003c/em\u003e and \u003cem\u003eLACC1\u003c/em\u003e as MHC-1-opathy genes (Duan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Remmers et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Both these genes were identified in highly significant OASIS loci in both GWAS datasets. Moreover, their expression was significantly altered in GEO datasets. Interestingly, the variant with the highest significance at the \u003cem\u003eLACC1\u003c/em\u003e locus was also an eQTL determining \u003cem\u003eLACC1\u003c/em\u003e expression in arterial tissue. \u003cem\u003eLACC1\u003c/em\u003e is involved in inflammasome activation and loss of function mutations have been found in BD and SpA (Lahiri, Hedl, Yan, \u0026amp; Abraham, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wakil et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe novel candidate gene that this study identified was \u003cem\u003eCXCR6\u003c/em\u003e at the 3p21.31 locus. The most significant variant at 3p21.31, is an eQTL for \u003cem\u003eCXCR6\u003c/em\u003e and the gene expression is down regulated in BD (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eCXCR6\u003c/em\u003e codes for a G-protein coupled receptor expressed in several T-cell subsets, regulating their migration to tissues. A biological study showed that CXCR6\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T-cells were actively recruited to psoriasis affected skin in response to the chemokine CXCL16, which is the ligand for CXCR6 (G\u0026uuml;nther, Carballido-Perrig, Kaesler, Carballido, \u0026amp; Biedermann, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It also showed that TNF-α inhibition, that is part of the therapeutics in AxSpA and BD, reduced the CXCR6 mediated T-cell recruitment. Importantly, \u003cem\u003eCXCR6\u003c/em\u003e formed part of the \u003cem\u003eIL23R\u003c/em\u003e and \u003cem\u003eLACC1\u003c/em\u003e interactome, providing insight into MHC-I-opathy pathogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother important candidate gene that this study identified is \u003cem\u003eFBXL2\u003c/em\u003e at the 3p22.3 locus. The most significant SNP by OLI, in both GWAS combined is rs2291897, which is an intronic variant of \u003cem\u003eFBXL2\u003c/em\u003e. However, this association was with BD only. Like LACC1, FBXL2 is involved in inflammasome functioning. The NLRP3 inflammasome is involved in the pathogenesis of a broad range of inflammatory diseases and its activation leads to the release of proinflammatory cytokines IL-1β and IL-18 (Chen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). FBXL2 endogenously inhibits NLRP3 by inducing its ubiquitin-mediated proteasomal degradation, thus functioning as an anti-inflammatory protein (Han et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). FBXL2 is involved in TNF-receptor degradation as well (Han et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Interestingly, mutations in autoinflammatory disease genes have been found in BD patients (Amoura et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kon\u0026eacute;-Paut, Sanchez, Le Quellec, Manna, \u0026amp; Touitou, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Touitou et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGWAS for AxSpA and BD from Turki\u0026yuml;e only were analyzed to minimize population stratification effects (Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Remmers et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Likely due to this population homogeneity the 5q15 locus, harboring the \u003cem\u003eERAP1\u003c/em\u003e and \u003cem\u003eERAP2\u003c/em\u003e genes was not significant in this study. These genes were also not found in the original Turkish cohorts (Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Remmers et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), nor in a subsequent Chinese study (Su et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However \u003cem\u003eERAP1/2\u003c/em\u003e are associated in European cohorts signifying population based effect for these genes (Kuiper et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, on chromosome 5, OASIS identified 5p15 and 5q11 as common AxSpA and BD loci (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which may be explored further with the remaining loci in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, using biological studies to determine their significance.\u003c/p\u003e \u003cp\u003eIn summary, this study identified several novel MHC-I-opathy candidate genes / loci that provide a basis for further investigation into this elusive group of disorders. Limitations of this study include a need for multiple GWAS datasets not only for AxSpA and BD, but also other MHC-I-opathies. The identification of \u003cem\u003eIL23R\u003c/em\u003e, \u003cem\u003eLACC1\u003c/em\u003e, \u003cem\u003eCXCR6\u003c/em\u003e and \u003cem\u003eFBXL2\u003c/em\u003e as MHC-I-opathy susceptibility genes will provide a deeper insight into disease pathogenesis. Several candidate loci (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and genes such as \u003cem\u003eADAM28\u003c/em\u003e need additional data for verification. Deep sequencing and functional studies, including \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e strategies, would help to confirm the pathogenic relevance of these genes in MHC-I-opathies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest:\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eM.S - designed the study, analyzed the data, wrote the manuscript\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eThis study was presented as Oral Presentation at the British Society for Rheumatology 2024 Meeting in Liverpool, UK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeb Resources:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003edbGAP: www.ncbi.nlm.nih.gov/gap/\u003c/p\u003e\n\u003cp\u003eGWAS Catalog: www.ebi.ac.uk/gwas/downloads/summary-statistics\u003c/p\u003e\n\u003cp\u003eGEO: https://www.ncbi.nlm.nih.gov/gds/\u003c/p\u003e\n\u003cp\u003eGTEx Portal: https://gtexportal.org/home/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmoura Z, Dod\u0026eacute; C, Hue S, Caillat-Zucman S, Bahram S, Delpech M, Piette JC (2005) Association of the R92Q TNFRSF1A mutation and extracranial deep vein thrombosis in patients with Beh\u0026ccedil;et's disease. 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Arthritis Rheumatol 67(1):288\u0026ndash;295. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/art.38877\u003c/span\u003e\u003cspan address=\"10.1002/art.38877\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MHC-I-opathy, AxSpA, Behçet, GWAS, OASIS","lastPublishedDoi":"10.21203/rs.3.rs-5959154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5959154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eObjective:\u003c/em\u003e Axial spondyloarthritis (AxSpA) and Behçet's disease (BD) have clinical and HLA locus overlap and have been grouped under MHC-I-opathy. This study aimed to identify overlapping loci between AxSpA and BD to help elucidate MHC-I-opathy pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods:\u003c/em\u003e Association clustering methods, such as OASIS, reduce the multiple-testing burden and are more powerful than single variant analysis for identifying modest genetic effects. Two large publically available genome-wide association studies (GWAS) of AxSpA (921 cases, 907 controls) and BD (1215 cases and 1278 healthy controls) from Turkiÿe, were subjected to OASIS meta-analyses to identify common non-HLA loci. Statistics used to identify significant loci included the novel OASIS locus index (OLI). Expression analysis was performed using GEO datasets, GSE181364 for AxSpA and GSE209567 for BD. STRING network analysis was performed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults:\u003c/em\u003e GWAS for both diseases had the highest significance at the HLA-I locus. Of the 234 independent modestly significant non-HLA loci, there were 15 loci common to both AxSpA and BD. These included known MHC-I-opathy loci, 1p31.3 for \u003cem\u003eIL23R\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e= 5.37x10\u003csup\u003e-6\u003c/sup\u003e, OLI=52.7) and 13q14.11 for \u003cem\u003eLACC1\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e=7.41x10\u003csup\u003e-6\u003c/sup\u003e, OLI=65.3). A novel locus identified in this study is 3p21.31 containing \u003cem\u003eCXCR6\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e=2.46x10\u003csup\u003e-5\u003c/sup\u003e, OLI=25.8). The locus 3p22.3 had the highest overall OLI (81.3) and the most significant SNP at this locus (rs2291897; \u003cem\u003eP\u003c/em\u003e=1.82x10\u003csup\u003e-5\u003c/sup\u003e), is an intronic variant in the gene \u003cem\u003eFBXL2\u003c/em\u003e. However, this association was specific for BD only.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion:\u003c/em\u003e Several loci containing pathologically relevant genes for MHC-I-opathy were identified here, using a cluster-based approach in AxSpA and BD GWAS, with \u003cem\u003eCXCR6\u003c/em\u003e being a novel target.\u003c/p\u003e","manuscriptTitle":"GWAS meta-analysis of Axial spondyloarthritis and Behçet's disease identifies CXCR6 as a novel MHC-I-opathy gene","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 16:31:32","doi":"10.21203/rs.3.rs-5959154/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f6de07e-baa1-48ae-b696-191ccb4bf695","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-19T16:31:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-19 16:31:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5959154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5959154","identity":"rs-5959154","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}