{"paper_id":"98bac49c-dd57-4acb-95ec-591376d98909","body_text":"The Assoc iat ion b etwe en  En domet riosis  and  Im munolo g ical diseases  \nNina Shigesi 1 , Holly R. Harris 2,3 , Hai Fang 4,5 , Anne Ndungu 4 †, Matthew R. Lincoln 6 , The \nInternational Endometriosis Genome Consortium 7 , The 23andMe Research Team 8 , Chris \nCotsapas 9 , Julian Knight 4 , Stacey A. Missmer 10,11 , Andrew P. Morris 12 , Christian M. Becker 2 , Nilufer \nRahmioglu 1,4*# , Krina T. Zondervan 1,4*# .  \n* Jointly directed the work. \n†Deceased. \n1  Oxford Endometriosis CaRe Centre, Nuffield Department of Women’s and Reproductive Health, \nJohn Radcliffe Hospital, University of Oxford, Oxford, UK. \n2 Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, USA. \n3 Department of Epidemiology, Schoo l of Public Health, University of Washington, Seattle, WA  \n4 Centre for Human Genetics, University of Oxford, Oxford, UK. \n5 Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research \nCenter for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University \nSchool of Medicine, Shanghai 200025, China. \n6 Institute of Medical Sciences, University of Toronto, Canada. \n7  The International Endometriosis Genome Consortium.  \n8  23andMe, Inc. Sunnyvale, CA, USA. \n9 Center for Neurocognition and Behavior / Center for Neurodevelopment and Plasticity, Wu Tsai \nInstitute, Yale University, USA. \n10 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. \n11 Department of Obstetrics & Gynecology, Universit y of Michigan, Ann Arbor, MI, USA. \n12 Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The \nUniversity of Manchester, Manchester, UK. \n \n# Corresponding authors: nilufer.rahmioglu@wrh.ox.ac.uk  / krina.zondervan@wrh.ox.ac.uk  \n  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\nAbstr ac t  \n \nThe evidence for a greater prevalence of immunological-diseases among endometriosis patients \nhas varied in robustness and been subject to selection bias. We investigated the phenotypic and \ngenetic association between endometriosis and 31 immunological-diseases in the UK Biobank \n(8,223 endometriosis, 64,620 immunological-disease cases). In cross-sectional and retrospective \ncohort analyses, endometriosis patients were at significantly increased (30-80%) risk of classical-\nautoimmune (rheumatoid arthritis, multiple sclerosis, coeliac disease), autoinflammatory \n(osteoarthritis) and mixed-pattern (psoriasis) diseases. Osteoarthritis (genetic-correlation \n(rg)=0.28, P=3.25x10 -15 ), rheumatoid arthritis (rg=0.27, P=1.54x10 -5 ) and multiple sclerosis \n(rg=0.09, P=4.00x10 -3 ) were significantly genetically correlated with endometriosis. Mendelian \nrandomisation analysis suggested a causal association between endometriosis and rheumatoid \narthritis (OR=1.16, 95%CI=1.02-1.33). Expression QTL analyses highlighted effector genes enriched \nfor seven pathways across all four conditions, with three genetic loci shared between \nendometriosis and osteoarthritis and one with rheumatoid arthritis. Although the increased risk of \nimmunological-diseases among endometriosis patients is modest, their shared genetic basis \nopens-up opportunities for new treatments.   \n \n \n \n \n  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nIntr oductio n \nEndometriosis is a chronic inflammatory condition that features the presence of endometrial-like \ntissue in locations outside the uterus, mainly in the pelvic cavity\n1 . The most  commonly accepted \nexplanation for the origin of the majority of these endometrial deposits is r etrograde \nmenstruation, when menstrual blood containing endometrial cells travels up the fallopian tubes \ninto the pelvic cavity\n2 . However, this phenomenon is experienced by the majority of menstruating \nindividuals 3 , leaving the question why only in some endometrial cells are able to adhere to \nperitoneal surfaces, thrive, and proliferate 4 . Proliferation of the endometrial implants requires \noestrogen, which is provided both systemically but also from localised production of aromatase 1  \nand expression of oestrogen receptor beta 5 , inhibition of TNF-alpha induced apoptosis, increased \ninterl eukin-1beta which enhances cellular adhesion and proliferation, and localised inflammation 6 . \nEndometriotic implants secrete various cytokines, chemokines and prostaglandins, eliciting an \ninflammatory response that attracts macrophages, monocytes, neutrophils, T cells and \neosinophils. Impairment of the innate, and possibly adaptive, immune system in removing ectopic \nendometrial cells from the peritoneal cavity appears to play a role in endometriosis\n1,7 . In \nparticular, altered function of natural killer cells and macrophages has been implicated, but it is \nunclear if these aberrations play a role in causation or are part of pathophysiology\n1,8 .  \n \nGiven the link with aberrant immune response, many clinical and population studies have \ninvestigated the association between endometriosis and auto-immune diseases, even postulating \nthat endometriosis itself may be an auto-immune disorder\n9-11  because of the dysfunction in \nnatural immunity. While auto-antibodies are not typically involved in the pathogenesis of \nendometriosis\n12  and thus it is not classified as an auto-immune condition 13 , a systematic review of \npublished clinical and population studies suggested an increased risk of several autoimmune \nconditions (systemic lupus erythematosus, Sjögren’s syndrome, rheumatoid arthritis, autoimmune \nthyroid disorder, coeliac disease, multiple sclerosis, inflammatory bowel disease, and Addison’s \ndisease) among females with endometriosis. However, most of the studies were limited by small \nsample sizes, selection biases, and lack of adjustment for confounding factors\n14 .  \n \nHere we aim to investigate the association, and any shared biological basis, between \nendometriosis and 31 immunological disorders grouped into classical auto-immune, auto-\ninflammatory and mixed-pattern conditions\n15 . We explore the association between endometriosis \nand immunological conditions in one of the largest available data sources, the UK Biobank data; \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nconduct the largest possible genome-wide association study (GWAS) meta-analyses of conditions \nexhibiting significant phenotypic associations with endometriosis; use these datasets to \ninvestigate genetic correlations, potential causal pathways, and shared genetic risk variants \nbetween endometriosis and immunological diseases; and use endometrium and blood expression \nQTL data to identify genes dysregulated by shared disease-associated variants. \n \nResults \nPhen ot ypic as soc iat ion bet we e n end omet riosis  and  i m mune condit ions  \nThe phenotypic association between endometriosis and immunological conditions was \ninvestigated in the UK Biobank (UKBB) using both cross-sectional and retrospective cohort study \ndesigns, with the latter assuming endometriosis as a diagnostic risk factor preceding an \nimmunological disease diagnosis (see Methods). For the cross-sectional analysis, 8,223 \nendometriosis cases vs. 265,181 female controls without known endometriosis were included; and \n64,620 immunological disease cases vs. 208,784 female controls without known immunological \ndiseases.  Supplementary Table 1 shows factors that were determined as potential confounders or \nmediators in the association analyses between endometriosis and immunological diseases. Adding \nfactors significantly associated with both endometriosis and immunological diseases in a logistic \nregression model with endometriosis as exposure and any immunological disease as the outcome \n(see Methods), none were found to be confounders or mediators that significantly affected the \neffect size of association (>5% change). However, genetically determined ancestry and age at \nrecruitment were included a-priori as potential confounders. \n \nIn both the cross-sectional and retrospective cohort analyses (Table 1), females with \nendometriosis vs. those without had a significantly increased risk for all immunological diseases \ncombined (OR: 1.32 (1.25-1.39); HR: 1.32 (1.20-1.45)), classic autoimmune diseases (OR: 1.24 \n(1.13-1.36); HR: 1.41 (1.15-1.74)), aut oinflammatory diseases (OR: 1.33 (1.26-1.41); HR: 1.29 (1.17-\n1.43)), and mixed-pattern diseases (OR: 1.23 (1.10-1.52); HR: 1.88 (1.25-2.81)).  \n \nImmunological diseases significantly associated with endometriosis in both cross-sectional and \ncohort analyses were: rheumatoid arthritis (OR:1.22 (1.04-1.41), P = 0.011; HR: 1.57 (1.18-2.10), P \n= 0.002); coeliac disease  (OR: 1.35 (1.06-1.70), P = 0.011; HR: 1.99 (1.30-3.07), P = 0.002); and \nosteoarthritis (OR: 1.35 (1.27-1.43), P < 0.001; HR: 1.31 (1.19-1.44), P < 0.001).  In addition, in the \n‘gold standard’ cohort analyses, p soriasis (HR: 1.67 (1.05-2.65), P = 0.030) was significantly \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nassociated with endometriosis. One immunological condition significantly associated with \nendometriosis in cross-sectional analysis, systemic lupus erythematosus (OR:1.62 (1.14-2.24), \nP=0.005), myasthenia gravis ( OR: 2.55 (1.30-4.49), P=0.003) and gout (OR:1.66 (1.18-2.26), \nP=0.002), could not be tested in a cohort study design due to insufficient case numbers. Overall, \nfemales with endometriosis compared to females without known endometriosis exhibited a 14% \nincreased risk for at least having one immunological disease (OR = 1.14 (1.08-1.21)), a 21% \nincreased risk for at least having two immunological diseases (OR = 1.21 (1.05-1.39)), and a 30% \nincreased risk for having at least three immunological diseases (OR = 1.30 (0.92-1.78)) at any point \nin their lifetime (P < 0.001) (Supplementary Table 2).  \n \nWhen stratifying by menopausal status, gynaecological surgery (hysterectomy/oophorectomy), or \nhormone replacement therapy (HRT) use, effect sizes for the association between endometriosis \nand overall immunological disease risk remained largely unchanged (Supplementary Table 3). \n \nG en etic c or relatio n between e n dom etr iosis  and imm unological dis ease s  \nFor a total of eight immunological diseases associated with endometriosis either in cross-sectional \nor cohort analyses (ankylosing spondylitis, coeliac disease, inflammatory bowel disease, multiple \nsclerosis, osteoarthritis, psoriasis, rheumatoid arthritis, and systemic lupus erythematosus), we \nconducted female-only and sex-combined European ancestry GWAS analyses in UKBB (Table 2, \nSee Methods). To achieve the greatest power to detect variants associated with each disease, sex-\ncombined UKBB GWAS results were meta-analysed with existing GWAS summary statistics based \non the largest sample sizes where available (Table 2), using the inverse vari ance weighted fixed-\neffects method as implemented in METAL (Supplementary Figures 1-8). \n \nUtilising the GWAS meta-analysis summary results for the immunological diseases and the largest \npublished endometriosis GWAS meta-analysis result s (excluding UKBB GWAS results to achieve \nsample independence)\n16 , we applied linkage disequilibrium (LD)-score regression (LDSC) \nanalysis 17,18  to estimate the genetic correlation (rg) between endometriosis and the eight \nimmunological diseases. Osteoarthritis (rg=0.29, p=3.25x10 -15 ), rheumatoid arthritis (rg=0.26, \np=1.54x10 -5 ) and multiple sclerosis (r g=0.09, p=4.00x10 -3 ) showed significant (p<6.25x10 -3 , see \nMethods) positive genetic correlations (rg) with endometriosis, suggesting a shared genetic \ncomponent (Table 3).  \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nM en delian rando mis at ion (MR) anal yse s   \nMR analyses using genetic instrumental variables (IVs) were conducted to further investigate a \npotential causal relationship between endometriosis (exposure) and the increased risk of \nosteoarthritis, rheumatoid arthritis, and multiple sclerosis (outcomes) observed in the phenotypic \nassociation and genetic correlation analyses. Table 4 shows the results from the main MR-IVW \nmodel, with weighted median MR and MR-Egger regression provided as sensitivity analyses to test \nthe robustness of the results. Analyses were conducted utilising 39 genome-wide significant \n(p<5x10\n-8 ) endometriosis associated LD-independent autosomal variants as IVs. Summary statistics \nfor the IVs were extracted from UKBB female-only and meta-analysed sex-combined GWAS results \nto represent the outcomes of the three immunological diseases (see Methods).  \n \nFor endometriosis vs. rheumatoid arthritis, utilising the 39 IVs illustrated a suggestive causal \nrelationship between endometriosis vs. rheumatoid arthritis in females-only (OR [95%CI] = 1.16 \n[1.02-1.33], p-value=0.028). For endometriosis vs. osteoarthritis, and vs. multiple sclerosis, the MR \nanalysis did not identify a significant causal relationship in either sex-combined or female-only \nanalyses (Table 4).  \n \nM u lti-tr ait  analysis  of endom et riosis  and  im munological diseases: oste oar thrit is , r heumat oid \nart hr it i s and mu ltiple sc ler os is \nTo leverage the genetic sharing of association signals between endometriosis and osteoarthritis, \nrheumatoid arthritis, and multiple sclerosis for the discovery of additional endometriosis risk \nvariants, we conducted a multi-trait analysis of GWAS (MTAG). MTAG capit alises on the genetic \ncorrelation between diseases to boost statistical power for detecting associations in genome-wide \nanalyses\n19 . The MTAG analysis was conducted for all four diseases simultaneously and identified \n42 genome-wide significant lead SNPs for endometriosis (Supplementary Table 4), 6 of which were \nnot reported previously 16  (Supplementary Figure 9a-f). These 6 genetic variants are eQTLs for \nvarious genes across multiple tissues including MS RA  and PO N 2 protecting and repairing cells from \noxidative stress in blood 20,21 , BLK and ZAP70 encoding enzymes belong to Tyrosine kinase family \nwith roles in cell proliferation and differentiation in particular B-cell and T-cell development and \nadhesion 22,23 , ATRAID , S LC35F6, TMEM214  and XKR6 involved in apoptosis-related pathways 24,25  \nand, TRPS1  encoding a transcription factor that represses GATA-regulated genes involved in \nprogesterone resistance and endometriosis progression in the pelvis 26  (Supplementary Table 5). \nThe MTAG analysis for osteoarthritis yielded 27 significant variants (Supplementary Table 6), for \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nrheumatoid arthritis yielded 28 significant variants (Supplementary Table 7) and for multiple \nsclerosis, it identified 64 genome-wide significant variants (Supplementary Table 8).  \n \nFunctional ann otatio n of ide nt ified g enome- wi d e s ignificant  var iants and path way  a nalysis \nWe mapped genome-wide significant asso ciations from each MTAG analysis for endometriosis, \nosteoarthritis, rheumatoid arthritis, and multiple sclerosis to the genes whose expression they are \nassociated with, using GTEx V8 (54 tissues) and eQTLGen (31,684 blood datasets). We identified \n439 genes regulated by 42 genome-wide significant endometriosis associated variants; 379 genes \nby 27 genome-wide significant osteoarthritis associated variants (Supplementary Table 6); 490 \ngenes by 28 genome-wide significant rheumatoid arthritis variants (Supplementary Table 7); and \n1,113 genes by 64 genome-wide significant multiple sclerosis associated variants (Supplementary \nTable 8). Of the 439 genes regulated by endometriosis risk variants, 192 were also regulated by a \ngenome-wide significant risk variant of one or more of the other immune diseases (Figure 1).  \n \nPathway analysis (see Methods) based on the identified genes per disease identified numerous \ncanonical pathways enriched with these genes (Supplementary Table 9-12). Among the top \nenriched pathways for endometriosis was ‘ s ignalli ng by receptor t y r os ine kinases’,  a major class of \ncell surface proteins involved in signal transduction which triggers many downstream signalling \npathways including NF k B , MA PK  and AKT. These pathways are activated in endometriosis and \nhave been suggested to harbour potential targets for non-hormonal therapeutics\n27 .  \n \nInvestigating the overlap of enriched genetically driven pathways between endometriosis, \nosteoarthritis, multiple sclerosis, and rheumatoid arthritis, we discovered that 45 out of the 79 \nenriched pathways for endometriosis were also enriched in the other immune conditions (Figure \n2). In total 7 enriched pathways were shared across all four conditions, including ‘ signalli ng by \nreceptor  t yrosi ne  kin as e s ’, ‘innate im mune s y stem’ , ‘adaptive immune syst em’ , ‘ extr ac ellular \nmatr ix  or ganis at ion ’ , ‘leuk ocyte tr ans- endot helia l migration’ , ‘l ipi d me t abo lis m’, and  ‘a rachidonic  \naci d  metabo lis m’  (Supplementary Figure 10).  Within these overlapping enriched pathways, there \nare genes shared between conditions and also genes specific to each condition contributing to the \npathway. For example, of the 21 genes enriched from endometriosis in ‘ signa l l ing by reception \ntyrosine kinas e ’, 8 are shared with osteoarthritis, including N CF4 , LAMB2, RHOA, MS T1, MST1R, \nMA PKAPK3, D O CK3 , and P TK2 B, and 3 are shared with multiple sclerosis including IT GB3 , PRK CA , \nand MM P9 (Supplementary Figure 10a). Another enriched pathway across the 4 conditions is \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\n‘ arachidonic ac i d met abo lis m’ ; of the 5 endometriosis genes enriched in this pathway, 4 are \nshared with the other 3 immune conditions, namely, D P EP3 , G P X1, D PE P2 , and PON2 ( Figure 2i) .  \n \nIdent ification of shar ed ge netic v a r iants betw een  e ndom et riosis  and im mune dis eas e s  \nA total of 12 osteoarthritis, rheumatoid arthritis and multiple sclerosis gen ome-wide significant \nlead SNPs were mapped within 1Mb of endometriosis genome-wide significant lead SNPs, with \nfour of them tagging the same signal (r\n2 >0.5) (Table 5, Supplementary Figure 9a-f). Three of these \nwere shared with osteoarthritis ( BMPR2/2q33.1, BSN /3p21.31, and ML L T10 /10p12.31), and one \nwith both osteoarthritis and rheumatoid arthritis ( XKR6/8p23.1). MTAG association results of the \n12 genome-wide significant endometriosis SNPs for osteoarthritis, rheumatoid arthritis and \nmultiple sclerosis are provided in Supplementary Table 13. \n \nAt the BMPR 2/ 2q33.1  locus, the lead SNPs rs72928925 for endometriosis and rs72928605 for \nosteoarthritis are both eQTLs for  BMP R2  in blood and oesophagus muscularis (Supplementary \nTable 14). BMP R2 encodes a member of the BMP receptor family of transmembrane \nserine/threonine kinases. The ligands of this receptor are members of the TGF-beta superfamily. \nThe TGF-beta signalling pathway, involved in diverse cellular processes including cell proliferation, \ndifferentiation, apoptosis, and migration invasion, was also one of the pathways enriched with 10 \neQTL genes regulated by endometriosis, osteoarthritis and multiple sclerosis associated variants \n(Figure 2, Supplementary Tables 9-12).  \n \nAt the BSN /3p21.31 locus, the lead SNP rs6774202 associated with endometriosis and rs6809879 \nwith osteoarthritis are both eQTLs for a diverse set of overlapping genes (Table 5) that are part of \npathways enriched between endometriosis and the other three immune conditions \n(Supplementary Tables 9-12). In particular, RH O A  is part of the ‘ leukocy t e trans-endot hel i al \nmigrat io n’ pathway that was enriched across all four conditions.  \n \nA thir d sh ar ed locus i s XKR6 / 8p 23.1 , where the lead endometriosis SNP rs12542037 is in strong LD \nwith the lead genome-wide significant osteoarthritis and rheum atoid arthritis SNPs (Table 5). This \nlocus is involved in the regulation of multiple genes, namely BLK, CT S B , and MTM P9 ,  which play \nroles in innate and adaptive immune system pathways\n28 . In addition, FD F T1 , regulated by the \ncorrelated genetic risk variants, encodes for squalene synthase that is involved in cholesterol \nbiosynthesis. The ‘ lipid metabol ism pathway’  is also enriched with genes r egulated by genetic \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nvariants in each of the investigated 4 conditions (Supplementary Tables 9-12, Figure 2 and \nSupplementary Figure 10f).  \n \nThe fourth locus previously implicated and described for endometriosis and osteoarthritis is \nMLLT10 /10p12.31, which harbours genes such as MLLT10  associated with pain perception and \nmaintenance in multiple tissues\n16 . \n \n \nD iscu s sion  \n \nOur study of UKBB data reveals a significant increase in the risk of auto-immune and auto-\ninflammatory diseases among endometriosis patients, particularly in rheumatoid arthritis (HR: \n1.57 (1.18-2.10), P = 0.002), coeliac disease (HR: 1.99 (1.30-3.07), P = 0.002), osteoarthritis (HR: \n1.31 (1.19-1.44), P < 0.001), and psoriasis (HR: 1.67 (1.05-2.65), P = 0.030). Given the age at \nrecruitment into UKBB (40-69 years, in 2006-10) and changes in awareness of endometriosis over \ntime, the proportion of diagnosed females in UKBB (8,223 cases in 273,404 females = 0.03%) is \nrelatively low compared to population prevalence estimates (up to 10%\n1 ) . This would have \nresulted in the undiagnosed females driving effect sizes for associations towards the null 29,30 .  \nNevertheless, our results are consistent with evidence from previous case/control and cohort \nstudies which showed significant association between endometri osis rheumatoid arthritis (RR:1.46 \n(0.70-3.03), coeliac disease (RR:1.39( 1.14-1.70)), multiple sclerosis (OR:7.1 (4.4-11.3)) 14  and \npsoriasis (RR:1.75 (1.10-2.78)) 31 . For systemic lupus erythematosus, cross-sectional evidence \nshowed significant evidence (OR: 1.62 (1.14-2.24)) but we didn’t have enough cases to carry out \nthe cohort study analysis. However, there is previous longitudinal evidence showing increased risk \nof systemic lupus erythematosus (HR:2.03 (1.17-3.51))\n32 . Moreov er, endometriosis patients \ncompared to females without endometriosis were at increased risk of suffering from multiple \nimmunological diseases which was most pronounced for autoinflammatory diseases \n(Supplementary table 2). This trend was observed in an early adulthood cohort study which we \nnow expand to a broader age range. \nOur genetic correlation analysis suggests that genetic factors contribute to the association \nbetween endometriosis and the increased risk of rheumatoid arthritis (rg=0.27, P=1.54x10\n-5 ), \nosteoarthritis (rg=0.28, P=3.25x10 -15 ), and to a lesser extent, multiple sclerosis (rg=0.09, \nP=4.00x10 -3 ). The significant genetic correlation between endometriosis and an immunological \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\ncondition can be attributed to multiple mechanism s as investigated through MR analyses: (1) \nendometriosis causally leads to the subsequent development of the immunological condition; (2) \nendometriosis and the immunological condition share a common genetically driven cause; or (3) \nendometriosis and the immunological condition share multiple common causes, and the direction \nof effect between them can be complex\n33 . Genetic correlation analyses are more powerful when \nthe genetic architecture between conditions is polygenic involving many causal SNPs of small \neffect to l everage their aggregated effects, which is the case for endometriosis and the \nimmunological conditions we studied here.  \n \nThe MR analyses yielded no robust evidence of causal relationships between endometriosis and \ngenetically correlated immunological conditions, except for a suggestive causal effect of \nendometriosis on rheumatoid arthritis in females-only (OR=1.16, 95% CI=1.02-1.33, p=0.028). MR \nanalysis provides insights into whether the association between two complex conditions is causal \nby utilising genetic variants associated with the exposure (endometriosis). However, it is assumed \nthat the genetic variants utilised as IVs have strong predictive power of the exposure, and the \nrecommendation is to limit these to genome-wide significant (GWS) associated variants. However, \neven GWS variants as instruments often modestly predict the exposure, which can limit the power \nof MR  an alysis \n34 . The 39 endometriosis associated variants included as IVs in our MR analyses \nexplain <2% of heritable variation (5% of stage III/IV disease 16 ) which has implications for the \ninterpretation of non-significant MR results. Our MR instruments would have been weighted \ntowards risk for stage III/IV disease, in particular ovarian endometrioma\n16 . Previous studies \nassociating risk of auto-immune and inflammatory conditions with endometriosis included \npredominantly stage I/II cases\n32,35 , although some of this evidence was based on adolescents who \nmay have been genetically predisposed to develop stage III/IV disease later in li fe 35 . Another \nlimitation we encounter is a lack of available female-specific immunological disease GWAS meta-\nanalysis results, which is surprising given that many immunological conditions exhibit higher \nprevalence in females. We conducted female-specific GWAS analyses in the UK Biobank, however, \nsample sizes were limited compared to sex-combined GWAS meta-analysis for these conditions in \nthe literature.  \n \nThe suggestive causal effect of endometriosis on rheumatoid arthritis is intriguing and warrants \nfurther exploration in the future, with IVs that explain a greater proportion of the genetic \nvariability for endometriosis. This will require larger endometriosis GWAS to uncover m ore genetic \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nvariants contributing to the polygenic component of endometriosis. A large-scale female-specific \nrheumatoid arthritis GWAS meta-analysis should also result in a more relevant dataset to explore \nits potential causal basis with endometriosis. While power limitations hamper the interpretation \nof causal relationships between endometriosis and osteoarthritis, rheumatoid arthritis, or multiple \nsclerosis, results of the genetic correlation analyses highlight a shared genetic basis. \nUnderstanding the basis of genetic sharing regardless of causality is important, as shared \nbiological mechanisms of pathogenesis and pathophysiology could open new avenues for \ntreatment development.  \n \nThe MTAG analyses, leveraging genet ic correlations between endometriosis, osteoarthritis, \nrheumatoid arthritis and multiple sclerosis, identified 42 genome-wide significant loci for \nendometriosis, 6 of which were not identified before: AB H D 1 / 2p23.3, TMEM131/ 2q11.2, \nXRCC4/ 5q14.2, PP P1R9 A/ 7q21.3, XK R 6 / 8p23.1 and TR PS1/8p23.3. These variants were eQTLs for \nvarious genes involved in protecting and repairing cells from oxidative stress, in B-cell and T-cell \ndevelopment, apoptosis-related pathways and regulation of progesterone resistance. The MTAG-\nderived 42 GWS lead endometriosis-associated variants were mapped to 439 genes, 27 GWS lead \nosteoarthritis SNPs to 379 genes, 28 GWS lead rheumatoid arthritis SNPs to 490 genes and 64 \nGWS lead multiple sclerosis variants t o 1113 genes. When we considered the overlap between \nthese genes, 43.7% of endometriosis eQTL genes were shared with at least one of the three \nimmunological conditions; the majori ty with osteoarthritis (33%) followed by multiple sclerosis \n(11.6%) and rheumatoid arthritis (7%). Pathway analysis revealed that 50.6% of pathways enriched \nin gene lists for endometriosis were also enriched in multiple sclerosis, 26.6% in osteoarthritis and \n16.5% in rheumatoid arthritis. Seven pathways were enriched across endometriosis and all three \nimmune conditions, including large, general immune regulatory pathways ‘ i nn ate immune system’  \n(1100 genes) and ‘adaptive immune sy stem’  (807 genes). A more specific shared pathway was \n‘ si gn al ling by receptor  tyrosine k inase s ’. Receptor tyrosine kinases are a large family of cell-surface \nreceptors that involved wide-variety inter and intra-cellular signalling.  Previous studies have \nsu ggested the involvement of kinase signalling pathways and potential non-hormonal treatment \ntargets therein for endometriosis\n27 , which our analyses support. Another shared pathway, \n‘ extracellular matr ix  or g anisatio n’, included MMP9, PRKCA and ITGB3 . MMP9 encodes for a \nmetalloproteinase that has a purported role in the progression of invasion in endometriosis as \nwell as angiogenesis and fibrosis\n36 , has involvement in a variety of inflammatory autoimmune \ndiseases, and has been suggested to be a therapeutic target for autoimmune conditions 37,38 . \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nPRKCA  is involved in immune cell trafficking and ITG B 3  is coding for integrin beta3 expression of \nwhich is associated with autoimmune conditions including multiple sclerosis 39 . \nIntriguingly,‘ ar ac hidonic aci d metabo l ism’  was another shared pathway, including four genes \nshared between endometriosis and osteoarthritis, rheumatoid arthritis and multiple sclerosis \n(D PEP 3, G P X1, D PE P2  and PON2). Arachidonic acid is an essential fatty acid ingested through diet. \nArachidonic acid derived prostaglandins contribute to inflammation through their role as \nintercellular pro-inflammatory mediators, and promote excitability of the peripheral \nsomatosensory system contributing to pain exacerbation 40 . \n \nAmong the GWAS loci and effectors genes we identified through MTAG and eQTL analyses, three \nwere shared between endometriosis and osteoarthritis ( BMPR2/2q33.1, BS N /3p21.31, and \nMLLT10 /10p12.31), and one, XKR6 /8p23.1, between endometriosis and rheumatoid arthritis. \nMLLT10  has been associated with pain perception and maintenance across multiple tissues and \nhas been previously described 16 . BMPR2 encodes a member of BMP receptor family of \ntransmembrane serine/threonine kinases, acting as receptor for the TGF-beta superfamily. SNP s at \nthe BSN/ 3p21.3 1  locus are eQTLs for various genes in cluding RH O A ,  part of the ‘ leukoc yte t rans-\nendothel ial mi grat io n’ pathway that is enriched across all four conditions. Leukocytes migrate \nfrom blood into tissues as part of inflammation and immune surveillance. During this process \nleukocytes bind to cell adhesion molecules and migrate across the vascular endothelium. Another \ninteresting eQTL gene asso ciated with variants at BS N/3p21.31 is HY A L 3 ,  which is involved in the \nhyaluronan/hyaluronic acid metabolism and glycosaminoglycan degradation pathways. Hyaluronic \nacid is a naturally occurring glycosaminoglycan most abundant in the extracellular matrix involved \nin various physiological processes including wound healing, tissue regeneration and joint \nlubrication. It is also used for relief of joint pain, wound healing and various other applications, \nand has been shown to reduce production of proinflammatory mediators, reduce sensitivity \nassociated with osteoarthritis pain\n41,42 . Recent in-vivo and in-vitro studies suggest that hyaluronic \nacid may have the ability to reduce endometriosis lesion size in mice but that it may also promote \ninflammation when administered acutely 43 , suggesting further research into mechanism of action \nand therapeutic potential in endometriosis is needed.  \n \nXKR6/8p23 .1, shared between endometriosis and rheumatoid arthritis, was asso ciated with the \nregulation of multiple genes including BLK, CTSB, and MTM P9 - which play roles in innate and \nadaptive immune system pathways\n28  – and FDFT1 , involved in cholesterol biosynthesis. \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nCholesterol is a precursor of steroid hormones and essential part of plasma membranes. It is also \nenriched in lipid rafts which play an important part in many cellular processes including signal \ntransduction pathways, membrane trafficking, cytoskeletal organisation, apoptosis, cell adhesions \nand migration\n44 . In the context of inflammatory conditions, lipid metabolism has been suggested \nto harbour targets for reducing inflammation without the undesirable side-effects of anti-\ninflammatory therapies 45 . \n \nAs mentioned, our analyses were limited by the lack of large-scale female-specific GWAS meta-\nanalyses for immune conditions, particularly those exhibiting higher prevalence in females. It is \nwell established that sex-specific genetic signatures are present for conditions showing variability \nby sex\n46 , and female-specific GWAS r esults for immunological conditions may offer increased \ngenetic correlations with endometriosis and opportunities for discovery and shared genetic \nsignals. Future genetic comorbidity analyses should also explore results for different \nendometriosis subtypes. Recent GWAS analyses have suggested that ovarian endometriosis has a \ndifferent genetic basis to peritoneal disease\n16 , but the sample sizes for which summary statistics \nwere generated did not allow for sufficiently powered inclusion in the present analyses. Similarly, \nfuture analyses should explore signals for different subtypes of immunological diseases, such as \nosteoarthritis\n47 , once larger GWAS datasets become available.  Lastly, genetic analyses were \nlimited to European ancestry individuals and larger GWAS across more diverse ancestry groups are \nneeded. \n \nIn conclusion, our results show that females with endometriosis are at a modestly (30-40%) \nincreased risk of both auto-immune and auto-inflammatory conditions, and that comorbidity with \nosteoarthritis, rheumatoid arthritis, and to a more limited ex tent multiple sclerosis, is biologically \nunderpinned. In terms of clinical relevance, we suggest awareness among treating physicians of \nthis increased risk of comorbidity, in order to spot early symptoms of immunological conditions \namong females with endometriosis, and vice versa. While current clinical action is limited to \nincreased vigilance, the results offer a wide range of novel avenues and targets for exploring \nmechanism s and potential cross-condition treatment development or repurposing.  \n \n \nMe t h o d s  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nPhen ot ypic an alysis s t ud y pop ulatio n and disea s e a sc er tainme nt  \nThe UK Biobank (UKBB) is comprised of 500K individuals aged 40-69 at time of recruitment (2006-\n2010) from across the UK. The biobank was approved by the North West M ulti-Centre Research \nEthics Committee (MREC). In the UKBB, information was collected from participants during \nrecruitment using questionnaires on socioeconomic status, behavioural, family history and \nmedical history. Participants were also followed up for cause-specific morbidity and mortality \nthrough linkage to disease registries, death registries, hospital admission records and primary care \ndata. In addition, a range of biological samples including blood, urine and saliva were collected \nfrom the participants. A more detailed description of the UKBB can be found in the UK Biobank \nprotocol\n48 . \nGiven that endometriosis is a gynaecological condition affecting those assigned female at birth, \nonly the individual’s assigned female at birth (N=273,404) were included in the phenotypic \nassociation analysis with the immunological conditions. From this point onwards we will refer to \nthose assigned female at birth as females in this manuscript. Endometriosis was identified based \non self-reported data from questionnaires and/or hospital records (ICD10/9: N801-809 and 617.1-\n9). A total 31 immunological conditions were identified from self-reported data and/or hospital \nrecords (ICD 10/9) that were classified into three groups (94) as following: (1) autoinflammatory \nconditions: Acne (ICD10/9: L70* and 7060, 7061), acute respiratory distress syndrome (ICD10/9: \nJ80, P220, 769, 769.9), erythema nodosum (ICD10/9: L52, 6952) , giant cell/Takayasu arteritis \n(ICD10/9: M314, M315, M 316, 4465, 4467), gout/pseudogout (ICD10/9: M10*, M11*, 274, 2740, \n2741, 2748, 2749, 712, 7120, 7121, 7122, 7123, 7128, 71280, 71281, 71282, 71283, 71284, 71285, \n71286, 71287, 71288, 71289, 7129, 71290, 71291, 71292, 71293, 71294, 71295, 71296, 71297, \n71298, 71299), total inflammatory bowel disease (ICD10/9: K50, K500, K501, K508, K509, K51, \nK510, K511, K512, K513, K514, K515, K518, K519, 555, 5550, 5551, 5552, 5559, 556, 5560, 5569),  \nCrohn’s disease (ICD10/9: K50, K500, K501, K508, K509, 555, 5550, 5551, 5552, 5559) Ulcerative \ncolitis (ICD10/9: K51, K510, K511, K512, K513, K514, K515, K518, K519, 556, 5560, 5569), \nosteoarthritis (ICD10/9: M15, M150, M1500, M151, M152, M153, M154, M158, M159, M1599, \nM16, M160, M161, M162, M163, M164, M165, M166, M167, M169, M17, M170, M171, M172, \nM173, M174, M175, M179, M18, M180, M181, M182, M183, M184, M185, M189, M19, M190, \nM1900, M1911, M1912, M1913, M1914, M1915, M1916, M1917, M1918, M1919, M192, M1920, \nM1921, M1922, M1923, M1924, M1925, M1926, M1927, M1928, M1929, M198, M1980, M1981, \nM1982, M1983, M1984, M1985, M1986, M1987, M1988, M1989, M199, M19990, M 1991, M1992, \nM1993, M1994, M1995, M1996, M1997, M1998, M1999, 715, 7150, 7151, 71510, 71511, 71512, \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\n71513, 71514, 71515, 71516, 71517, 71518, 71519, 7152, 71520, 71521, 71522, 71523, 71524, \n71525, 71526, 71527, 71528, 71529, 7153, 71530, 71531, 71532, 71533, 71534, 71535, 71536, \n71537, 71538, 71539, 7158, 7159), sarcoidosis (ICD10/9: D860, D861, D862, D863, D868, D869, \n135, 1359), (2) classical autoimmune conditions: Addison’s disease (ICD 10/9: E271, E272, 25540, \n25542), autoimmune gastritis(ICD10/9: D510, 2810), autoimmune thyroid disease (ICD10/9: E050, \nE063, 2420, 2452), Graves’ disease (ICD10/9: E050, 2420), Hashimoto’s disease (ICD10/9: E063, \n2452), coeliac disease (ICD10/9: K900, 5790), dermatomyositis/polymyositis (ICD10/9: M33*, \nM360, 7103, 7104), multiple sclerosis (ICD10/9: G35, 340), myasthenia gravis (ICD10/9: G70, G700, \nG701, G702, G708, G709, 358, 3580, 35800, 35801, 35809, 3581, 3582, 3588, 3589), \npemphigus/pemphigoid (ICD10/9: H133, L10, L100, L101, L102, L103, L104, L105, L108, L109, L12, \nL120, L121, L122, L123, L128, L129, 6944, 6945, 6946), primary biliary cirrhosis (ICD10/9: K743, \n5716), rheumatoid arthritis (ICD10/9: M05*, M06*, 714, 7140, 71400-71409, 7141, 71410-71419, \n7142, 71420-71429), Sjögren's syndrome (ICD10/9: M350, 7102), systemic lupus erythematosus \n(ICD10/9: M32*, L90*, 6954, 7100), systemic sclerosis (ICD10/9: M34*, 5172, 7101), type 1 \ndiabetes (ICD10/9: E10*, 25001, 25011, 25021, 25091), vitiligo (ICD10/9: L80, 70901), (3) \nCombination of autoinflammatory and autoimmune condition categories: Ankylosing spondylitis \n(ICD10/9: M081*, M45*, 7200), Behcet’s syndrome (ICD10/9: M352, 1361, 7112), reactive arthritis \n(ICD10/9: M023*, 0993, 7111), psoriasis/psoriatic arthritis/psoriatic arthropathies (ICD10/9: L40*, \nM070*, M073*, 6961, 6960). A common control set was defined as females without \nendometriosis diagnosis excluding anyone with diagnoses of any of the 31 immunological \nconditions.  \nPotential confounding or mediating factors were determined including age of recruitment, \ngenetically determined ancestry, menopause status, age at menarche, parity, body size, BMI and \nfat distribution\n49 , alcohol consumption, smoking, infertility and disease such as ovarian cancer 50  \nand cardiovascular disease 51 , which h ave been illustrated to be associated with endometriosis and \nsome immunological conditions. Age at recruitment (which represents potential age-related \ncohort effects) and ancestry were considered a-priori variables to be included in the models. Many \nof the other factors were assessed only at baseline recruitment into UKBB, which for most females \nwould have followed rather than coincided with, or preceded, an endometriosis diagnosis, and \ntherefore the potential for confounding vs. mediation effects could not be accurately assessed. \nNevertheless, to assess their potential impact on the associations, factors associated both with \nendometriosis and immunological disease were include in a logistic regression model with \nendometriosis as exposure and immunological disease as outcome. None of these factors either \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nshowed >5% change in effect (potential confounders) or removal of effects (mediators), and \ntherefore only a-priori variables age at recruitment and genetically determined ancestry were \nincluded in the models. \n \nPhen ot ypic as soc iat ion analysis \nPhenotypic association analysis between endometriosis and immune conditions was conducted \nutilising two different analysis methods: (1) a cross- sectional analysis to test for a simple \nassociation between risk of an immunological disease diagnosis with a diagnosis of endometriosis \nat any point in time, including all fem ales in the UKBB; (2) a ‘gold standard’ cohort study design to \nincorporate temporality between diagnoses, where entry time was defined as the recruitment \ndate into UKBB. Cross-sectional analysis was conducted for 26 immunological disease that had at \nleast 100 female cases in UK Biobank. A total of 5 immune conditions were excluded from analysis \ndue to number of cases <100: Reactive arthritis (N=4), Behcet’s syndrome (N=27), acute \nrespiratory distress syndrome (N=79), erythema nodosum associated disease (N=79), \npemphigus/pemphigoid (N=95). Cohort analysis was conducted for 9 immunological diseases with \na minimum of 1,500 female cases to allow enough number of immunological disease cases after \nexcluding prevalent immunological diseases diagnosed before cohort entry time or before the \nendometriosis diagnosis. The majority of females had immunological diseases diagnosed after \nendometriosis (66.8%, 1,275 out of 1,909 females with both diagnoses, had an immunological \ndisease diagnosis after their endometriosis diagnosis). Therefore, endometriosis was treated as \nthe exposure and immunological disease as the outcome in the cohort analyses. This also fits with \nthe observation that many individuals with endometriosis have symptom onset in their teens or \ntwenties, often many years before their ultimate diagnosis\n52 . Females with an endometriosis \ndiagnosis at the time of recruitment were classified as exposed, whereas those who had not had \nan endometriosis diagnosis at the time of recruitment were classified as unexposed. Those \nindividuals who received an endometriosis diagnosis during follow-up, prior to any immunological \ndisease diagnosis, contributed person-time to the unexposed group until the occurrence of \nendometriosis diagnosis, if any, and subsequently to the exposed group after diagnosis. For each \nimmunological disease, females who had the respective immunological disease diagnosed before \nendometriosis or those who had the respective immunological disease diagnosed before cohort \nentry time or had immunological disease diagnosis time missing were excluded from cohort \nanalysis (Table 1).  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nIn the cross-sectional study analysis, the prevalence of each specific and categorized \nimmunological diseases in females with and without a history of endometriosis diagnosis was \ninvestigated using logistic regression models with odds ratios (ORs) as risk measure. Cross-\nsectional study analysis for each specific and categorized immunological diseases was conducted \nwith adjustment of age and genetically determined ancestry. In the cohort study, the risk of \nincident immunological diseases in females with and without endometriosis history was \ninvestigated using Cox proportional hazards regression models with calculated hazard ratios (HRs). \nThe proportional hazards assumption was tested by function of “cox.zph” in the “survival” R \nlibrary. In the cohort analysis, time to event was formulated from entry to the cohort until the end \nof follow-up time. The follow-up time (rather than age) is used as the underlying time variable, \nsince the date of assessment is described in more detail with information on the exact date and \nmonths participants attended the assessment centre (to be used as the index date) in the UK \nBiobank. The end of follow-up time is the date of incident immunological diseases, death, loss to \nfollow-up or end of follow-up (end date of follow-up is the date of last download of the dataset, \nwhich is 8\nth  Jan. 2019), whichever occurred first. Cohort analysis for each specific and categorized \nimmunological diseases was conduct ed with adjustment of age (categorical: <50, 50-60, >=60) and \ngenetically determined ancestry (categorical: white, non-white). All risk estimates were reported \nwith 95% confidence intervals (CIs) and two-sided P-values. Person- years and mean follow-up time \nfor each cohort analysis were calculated. All analyses were carried out using R software. \n \nG en om e-wide as s o c iat ion s t ud y (G WA S) and met a-analys is for  immune c ondit ions  \nOnly genetically determined European ancestry individuals were included in the analysis. GWAS \nwas conducted using UKBB data for females-only and sex-combined study population for 8 \nimmune conditions: inflammatory bowel disease (N = 2,869 females; N = 5,751 sex-combined), \nosteoarthritis (N = 39,866 females; N = 68,878 sex-combined), ankylosing spondylitis (N = 547 \nfemales, N = 1,493 sex-combined), psoriasis (N = 3,036 females, N = 6,591 sex-combined), coeliac \ndisease (N = 1,706 females, N = 2,640 sex-combined), multiple sclerosis ( N = 1,314 females, N = \n1,883 sex-combined), rheumatoid arthritis (N = 4,662 females, N = 7,153 sex-combined) and \nsystemic lupus erythematosus (N = 545 females, N = 673 sex-combined). Controls were defined as \na common control set without any diagnosis of immunological diseases or endometriosis within \nUKBB. The linear mixed model (LMM) implemented in BOLT\n53   was utilised for GWAS analysis to \ntake into account relatedness in the data and to increase power of analysis by a linear mixed \neffects model. GWAS results were adjusted for a binary variable denoting the genotyping chip (the \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nUKBB Axiom array or the UK BiLEVE array), for SNPs with minimum MAF filter of < 1% and included \nSNPs with ≤ 60% missingness.  \nFurthermore, published largest European ancestry GWAS results on these 8 immunological \nconditions were identified through literature\n47,54-60  and downloaded for meta-analysis with UKBB \nGWAS results. Before meta-analysis, GWAS study-level QC was performed and markers absent in \nthe 1000G reference panel, large missing values (≥ 60%) or lack beta/odds ratio estimates were \nexcluded. GWAS meta-analysis for each immunological disease was carried out using METAL \nsoftware using an inverse variance weighted fixed effect meta-analysis method 61 . GWAS meta-\nanalysis results were filtered and excluded those with MAF < 1%, high heterogeneity (HetISquare > \n90), present < 50% effective sample size (Neff; Neff = 4NCases*NControls/( NCases + NControls). \nAll genetic analyses were done on genome reference of homo sapiens (human) genome assembly \nGRCh37 (hg19). Genetic information on chromosome X was excluded. MHC region of \nChr6:24000000-35000000 was excluded as it has a dense clustering of imm une-relevant genes \nwith extreme polymorphism and very strong long-range linkage disequilibrium, which complicates \nthe determination of the exact genes and alleles that are responsible for signals of disease \nassociation in the region\n62 .  \n \nG en etic c or relat i o n analys is  \nIn genetic correlation analysis, for endometriosis, the GWAS meta-analysis results from the \nInternational Endometriosis Genome Consortium (IEGC) were used including 52,350 cases and \n504,157 controls from 20 GWAS studies excluding UKBB to prevent overlapping study \npopulation\n16 . Then genetic correlation analysis was conducted between endometriosis and \nimmunological diseases GWAS meta-analysis results via linkage disequilibrium score regression \n(LDSC) analysis\n17,18 . The LD-score was calculated using software available at \n(http://github.com/bulik/ldsc), which was based on the 1000 Genomes Eur opean population and \nestimated within 1-cM windows, the significan ce threshold was set as p-value=0.00625 to account \nfor multiple testing of eight immunological diseases.  \n \nM en delian rando mis at ion analysi s  \nThe potential causal relationship between endometriosis, as exposure, and those genetically \ncorrelated immunological disease, as outcome, were investigated by two-sample Mendelian \nrandomisation (MR) using the TwoSampleMR software\n63 . As instrumental variables (IVs) we \nutilised the 39 established genome-wide significant LD-independent lead autosomal SNP s \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nassociated with endometriosis to assess whether endometriosis causally affects those genetic \ncorrelation immune conditions namely, osteoarthritis, rheumatoid arthritis or multiple sclerosis. \nInverse variance weighted MR (MR-IVW) was applied as the initial method to detect causal \neffect\n64 . As sensitivity analysis, other two-sample MR methods including weighted median MR 65   \nand MR-Egger regression 66  were impl emented in case the assumption of valid IVs was violated. \nWeighted median MR was shown to have lower Type 1 error rates than the inverse-variance \nweighted method in a simulation analysis\n65 . MR Egger provides a sensitivity analysis to detect \nevidence of heterogeneity and pleiotropy of IVs 66 . To detect IVs with effect of heterogeneity and \npleiotropy, MR PRESSO was applied to identify outliers 67 . Also, scatter plots 68   were generated to \npresent the SNP-outcome association estimates versus the SNP-exposure associations in \ninvestigating the causal relationship using the MR models, including IVW, weighted median MR \nand MR-Egger regression\n67 . \nFurthermore, the strength of the 39 IVs used in this analysis was evaluated by calculating R-\nsquared statistics using the “add_rsq()” function in the TwoSampleMR software and the total R-\nsquared statistics for all 39 IVs is 0.298%. F statistics were calculated for all 39 IVs (a sum of Z \nstatistics for each SNP squared) as 1656.30. Although the F statistics is relatively large for the 39 \nIVs, given a low R\n2  statistics for the 39 IVs used in the MR analysis, the set of IVs used for the MR \nanalysis in this study is limited in power to assess if endometriosis is causal to certain \nimmunological diseases. \n \nM u lti-tr ait  analysis  of G WA S (MTA G)  \nThe input files for MTAG are the GWAS meta-analysis summary results files which were pre-\nprocessed by filtering out: 1) SNPs with MAF =< 1%, or with a MAF differences >=20% among \ndatasets; 2) restricting all analyses to a common set of SNPs present among dataset s; 3) multiple \nSNPs that were mapped to an identical chromosomal position among datasets; 4) SNPs with \nconflicting alleles among datasets. Z scores (log(OR/SE)) were computed for all SNPs. After variant \nfiltering, a total of 3,873,419 common SNPs between endometriosis, osteoarthritis, rheumatoid \narthritis, and multiple sclerosis were included in the MTAG analysis. MTAG is a generalization of \nthe standard inverse variance weighted meta-analysis framework. Here endometriosis, \nosteoarthritis, rheumatoid arthritis and multiple sclerosis pre-processed GWAS summary statistics \nwere included in a single MTAG analysis. Within MTAG, bi-variate LD score regression is employed \nto account for possibly unknown sample overlap between GWAS results of different traits. In the \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nresults, MTAG outputs trait-specific effects estimated for each SNP, and the resulting p-value can \nbe interpreted and used like those in single-trait GWAS 19 . \n \nIdent ification of shar ed ge netic v a r iants and th eir functio nal annotat ion and pat hwa y \nenrichme nts  \nFor each disease, genome-wide significant lead SNPs were identified based on (1) achieving a \ngenome-wide significant P-value (P<5×10 −8 ), (2) being 500kb distant from each other and (3) being \nindependent (r 2 <0.1). Then, the genome-wide significant lead SNPs associated with respective \ndiseases that sit within 1Mb were identified and LD between them was checked. If the LD between \nlead SNPs of respective disease was r\n2 >=0.5, they were considered shared loci between those \ndiseases. The identified shared lead S NPs were looked-up in (1) Genotype-Tissue Expression \n(GTEx) portal to identify whether they are eQTLs for genes across 49 human tissues from 838 \ndonors with 15,201 samples 69 , and (2) eQTLGen to identify blood eQTLs from 31,684 individuals 70 . \nPathway enrichment analysis was conducted in FUMA based on MTAG results for endometriosis, \nosteoarthritis, rheumatoid arthritis, and multiple sclerosis where pathways were limited to \ncanonical pathways 71 .  \n \nD at a a va ilab il it y  \nThe GWAS meta-analyses for immunological conditions made use of data from the UK Biobank \n(Application Number 9637) and publicly available GWAS summary statistics for immunological \nconditions [expand on sources]. GWAS data for endometriosis was based on the latest analyses of \nInternational Endogene Consortium: summary statistics excluding 23andMe data is available from \nEBI GWAS Catalog Stud y Accession GCST90205183; endometriosis GWAS summary statistics from \n23andMe, Inc. were made available under a data use agreement that protects participant privacy. \nPlease contact dataset-request@23andme. com or visit research.23andMe.com/collaborate for \nmore information and to apply to access the data.  \n \nAut hor Co ntribution s  \nCar r ied out  d ata analys is: N.S., N.R. \nD ra ft e d t h e  m a nu s c r i p t:  N.R., K.T.Z. \nSuper v is ed th e p hen otyp ic  dat a a n al y sis: K.T.Z., C.B., N.R., S.A.M., H.R.H., J.K., H.F. \nSuper v is ed th e gen etic d ata analy s is: K.T.Z., C.B., N.R., A.P.M., C.C., J.K., H.F.  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nAll authors contributed and discussed the results and commented on the final version of the \nmanuscript. \n \nAcknowledg eme nts  \nWe thank all the UK Biobank and 23andMe participants. Part of this research was conducted using \nthe UK Biobank Resource under Application Number 9637. N.R. was supported by a grant from \nWellbeing of Women UK (RG2031) and the EU Horizon 2020 funded project FEMaLe (101017562). \nA.P.M. was supported in part by Versus Arthritis (grant 21754). H.F. was supported by National \nNatural Science Foundation of China (grant 32170663). N.R., S.A.M and K.T.Z. were supported in \npart by a grant from CDMRP DoD PRMRP (W81XWH-20-PRMRP-IIRA). S.A.M. and K.T.Z. gratefully \nacknowledge funding provided by the Nezhat Family Foundation on behalf of Worldwide \nEndoMarch to their research programmes.  \n \nComp eting Inte rest s \nK.T.Z. and C.M.B. reported grants in three years prior, outside the submitted work, from Bayer AG, \nAbbVie Inc, Volition Rx, MDNA Life Sciences, PrecisionLife Ltd, Roche Diagnostics Inc. S.A.M. \nreports grants in the three years prior, outside this submitted work, from AbbVie Inc. N.R. is a \nconsultant for Endogene.bio. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\n \n \n \nFigure 1. Overlap o f  g en es a s soc iat ed w it h GW AS le ad S NPs in e Q TL  ana lys e s  fo r  e ndometri o s i s  (42 \ngenom e- wid e sig ni fican t l ead S N Ps ar e e QTL s  for 43 9 gen e s), o s teo arth r i t i s (27 g enome- w ide signi fic ant \nlead  SN Ps  a r e  e Q T Ls for 379 g ene s), mult iple scl er o s i s (64 g enom e- wi d e signi fic an t lea d SN Ps  a re  e QTLs for \n1,113  genes ) and rhe uma toid ar thri ti s  (2 8  genome - w ide signi f i cant l e ad SN Ps ar e e QTL s  f or 490  gene s) in \nvarious ti s s ue s in G T Ex.\n \n \n \n \nFigure 2. Overlap o f  pathways enriched with eQTL genes that are regulated by GWAS lead SNPs \nassociated with endometriosis, osteoarthritis, rheumatoid arthritis and multiple sclerosis. \n \n \n \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\n \n \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nTable 1. Immunological diseas e ri sks among female s with vs. wi thout endomet ri osi s in U K Biobank utili sing two dif ferent study designs : C ross - sectional st udy \ndesign (N=26 immunological dis ease with >100 femal e cas es) and cohort s tudy design (N=9 immunol ogical di s ease with >1500 female  c ases)  \nImm u nol ogic al D is ease  \nC ross-s ection a l stud y  des ign a  C oho rt  stud y  d esign b  \nT o tal f ema le s  End o cas e s  \n(8,2 2 3 )  \nFe m al e  c o n t r ol s \n( 2 65 , 1 8 1)   OR ( 95 %  C I) P-va l ue Tot a l  Endo  \nca se s  \nFemale  \ncontro ls  \nFollow-\nup  tim e, \nYe a r s  \nH R ( 95%C I )  P -v alue  P er son \nye a rs \nOvera l l  646 2 0  ( 23.6 4 % ) 2 0 64 (2 5.1 0 %)  62 556 ( 23. 59%)  1.3 2  (1 .25-1. 39)  <0.001 *  14 , 248 4 7 2 13 , 776 9 . 43  1 . 31 (1. 1 9- 1 . 4 4 )  < 0 .0 0 1* 5 , 75 0 . 9 0 \nCl a ssic  autoimmune \ndi s eas e 147 6 4  ( 5 .40%)  505  ( 6. 14%)  142 5 9  (5 .38%)  1.2 4  (1 .13-1. 36)  <0.001 *  2 ,6 27  9 8  2 ,5 29  9 . 78  1 . 42 (1. 1 6- 1 . 7 5 )  < 0 .0 0 1* 6 ,98 2 . 9 9  \nS yst e mic l u p u s  \ne r ythem atosus 7 8 0 (0.2 9 %)  36 ( 0 . 4 4%)  7 4 4 (0 . 2 8 % ) 1.62  ( 1.14-2. 2 4)  0 . 005* --  --  --  --  --  - -  --   \nSjo g ren’ s  sy n drome  8 0 3 (0.2 9 %)  23 ( 0 . 2 8%)  7 8 0 (0 . 2 9 % ) 1.07  ( 0.68-1. 5 9)  0. 7 6  --  --  --  --  --  - -  --  \nSys t emic sc l e ro s i s  2 5 5 (0.0 9 %)  10 ( 0 . 1 2%)  2 4 5 (0 . 0 9 % ) 1.60  ( 0.79-2. 8 6)  0. 1 5  --  --  --  --  --  - -  --  \nM u ltip le sclerosis  1 5 71 ( 0 . 57%)  61 ( 0 . 7 4%)  151 0  ( 0 . 57%)  1.23  ( 0.93-1. 5 8)  0. 1 3  1 4 9 9  1 4 0 9 . 8 3  1 . 84 ( 0 . 9 3- 3 . 61 )  0 . 08 7 , 309.1 6 \nR h e u m ato id art hri t is 5 8 18 ( 2 . 13%)  187  (2 .27%)  563 1  ( 2 . 12%)  1.22  ( 1.04-1. 4 1)  0 . 011* 1 , 2 6 2  4 8  1 , 214  9 . 8 1  1 . 57 ( 1 . 1 8- 2 . 10 )  0. 0 0 2 *  7 , 207.0 5 \nCoeli a c  d ise as e   2 0 23 ( 0 . 74%)  76 ( 0 . 9 2%)  194 7  ( 0 . 73%)  1.35  ( 1.06-1. 7 0)  0 . 011* 3 9 1 2 2  3 6 9 9 . 8 2  1 . 99 ( 1 . 3 0- 3 . 07 )  0. 0 0 2 *  7 , 299.3 7 \nV i ti li g o 1 74  ( 0 . 06 % ) 6  (0 .07 % ) 1 68  ( 0. 06 %)   1 . 2 9 ( 0. 5 1 - 2 . 6 8 ) 0 .5 4 - -  - -  - -  - -  - -  - -  - -  \nPrimary  bi l ia ry cirrhosis 2 6 3 (0.1 0 %)  8  ( 0 . 1 0 %)  2 5 5 (0 . 1 0 % )  1.17  ( 0 .53-2.21)  0. 6 7  --  --  --  --  --  - -  --  \nAddi s on' s  disea s e  1 8 0 (0.0 7 %)  6  ( 0 . 0 7 %)  1 7 4 (0 . 0 7 % ) 1.21  ( 0.47-2. 5 0)  0. 6 5  --  --  --  --  --  - -  --  \nTy p e  1  d ia be tes  1 6 03 ( 0 . 59%)  57 ( 0 . 6 9%)  154 6  ( 0 . 58%)  1.26  ( 0.95-1. 6 4)  0. 0 9   5 11  17  4 9 4  9. 8 2   1. 3 8  ( 0. 8 5-2. 2 5)    0 . 1 9 7 0 74.59   \nM ya stheni a  grav i s  1 7 3 (0.0 6 %)  11 ( 0 . 1 3%)  1 6 2 (0 . 0 6 % ) 2.55  ( 1.30-4. 4 9)  0 . 003* --  --  --  --  --  - -  --  \nAut o imm une ga stritis 1 4 66 ( 0 . 54%)  48 ( 0 . 5 8%)  141 8  ( 0 . 53%)  1. 2 0  ( 0.88-1. 5 8)  0. 2 2            \nDerma tomyositi s ,  \nP olym yo s i t is 1 1 5 (0.0 4 %)  3  ( 0 . 0 4 %)  1 1 2 (0 . 0 4 % )  0.97  ( 0 .24-2.59)  0. 9 6  --  --  --  --  --  - -  --   \nGra ve s' d ise as e 5 4 5 (0.2 0 %)  23 ( 0 . 2 8%)  5 2 2 (0 . 2 0 % ) 1.28  ( 0.80-1. 9 3)  0. 2 7  --  --  --  --  --  - -  --  \nHas h imo to' s d i se as e 2 4 4 (0.0 9 %)  6  ( 0 . 0 7 %)  2 3 8 (0 . 0 9 % ) 0.89  ( 0.35-1. 8 4)  0. 7 9  --  --  --  --  --  - -  --  \nAu t oi m m u n e  thyroid \ndi se ase  7 8 8 (0.2 9 %)  29 ( 0 . 3 5%)  7 5 9 (0 . 2 9 % )  1.17  ( 0 .79-1.69)  0. 4 2  --  --  --  --  --  - -  --   \nAu toin fla mmatory \ndi s eas e 520 2 7  ( 19.0 3 % ) 1 6 38 (1 9.9 2 %)  50 389 ( 19. 00%)  1.3 3  (1 .26-1. 41)  <0.001 *  13 , 710 4 4 3 13 , 267 9 . 46  1 . 28 (1. 1 6- 1 . 4 1 )  < 0 .0 0 1* 6 ,08 0 . 8 1  \nCro h n' s di se ase 1 3 82 ( 0 . 51%)  48 ( 0 . 5 8%)  133 4  ( 0 . 50%)  1.18  ( 0.87-1. 5 8)  0. 2 7  --  --  --  --  --  - -  --  \nUlce rativ e  co l itis  2 3 65 ( 0 . 87%)  82 ( 1 . 0 0%)  228 3  ( 0 . 86%)  1.24  ( 0.98-1. 5 5)  0. 0 6  4 3 0  13  4 1 7  9 . 8 2 1. 0 4  ( 0. 5 8-1. 8 4)   0. 9 0   7,0 52. 21   \nInf la mma t o ry  b owel \ndi se ase  3 5 27 ( 1 . 29%)  129  (1 .57%)  339 8  ( 1 . 2 8 %)  1.28  ( 1.06-1. 5 3)  0 . 009* 3 1 6 1 2  3 0 4 9 . 8 2  1 . 40 ( 0 . 7 9- 2 . 50 )  0 . 25 4 , 247.0 5  \nOste o a rth ritis 476 4 2  ( 1 7 . 4 3 % ) 1 4 93 (1 8 . 16 %)  4 6 149 ( 1 7 .40%)  1.35  ( 1.27-1. 4 3)  <0.0 0 1 * 13, 3 7 1 4 1 6 12, 9 5 5 9 . 4 8  1 . 31 ( 1 . 1 9- 1 . 44 )  <0. 001* 6 , 196.1 3 \nGia nt  c ell, Ta ka yas u  ce l l 3 6 6 (0.1 3 %)  10 ( 0 . 1 2%)  3 5 6 (0 . 1 3 % ) 1.35  ( 0.67-2. 4 0)  0. 3 5  --  --  --  --  --  - -  --  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\narte ri tis  \nSa rco id o sis 7 4 1 (0.2 7 %)  26 ( 0 . 3 2%)  7 1 5 (0 . 2 7 % ) 1.39  ( 0.91-2. 0 1)  0. 1 0  --  --  --  --  --  - -  --  \nAc ne , a c ne  fo r m  \nas s o c i a t ed  d i s ea s e  2 8 7 (0.1 1 %)  8  ( 0 . 1 0 %)  2 7 9 (0 . 1 1 % ) 0.78  ( 0.35-1. 4 8)  0. 5 0  --  --  --  --  --  - -  --   \nG o ut ,  ps e ud o g o ut ,  \ncrystal arthr o pa thy  1 1 17 ( 0 . 41%)  41 ( 0 . 5 0%)  107 6  ( 0 . 41%)  1.66  ( 1.18-2. 2 6)  0 . 002* --  --  --  --  --  - -  --   \nM ix ed -Pattern  d is e a se  4 3 79 (1. 60%)  156  ( 1. 90%)  422 3  (1. 59 %)  1.2 3  (1 .10-1. 52)  0 . 0 0 2* 5 2 7 2 7  5 0 0 9 . 82  1 . 81 (1. 2 1- 2 . 7 1 )  0. 004 *  723 7 .37 3 \nAn k ylo s i ng s pon d y l it is 6 7 6 (0.2 5 %)  28 ( 0 . 3 4%)  6 4 8 (0 . 2 4 % ) 1.59  ( 1.06-2. 3 0)  0 . 019* 9 6  4  9 2  9 . 8 3  1 . 66 ( 0 . 6 1- 4 . 52 )  0 . 32 7 , 332.2 2 \nPsori a sis 3 6 97 ( 1 . 36%)  121  (1 .47%)  357 6  ( 1 . 35%)  1.18  ( 0.97-1. 4 1)  0. 0 8  4 2 7 1 9  4 0 8 9 . 8 2  1 . 67 ( 1 . 0 5- 2 . 65 )  0. 0 3 0 * 7 , 254.4 0 \nC o nfou n ders  inc luded  i n  t he ana ly se s are  age at rec ru itme n t a n d  g enetica ll y  dete rmin e d a n c estry.  \n-- Insuf fic ient  num ber of c ase s to  ge n e rat e  m ea n ingfu l  risk estima tes.  \na  C ross-s e ctio n al  d e sign i n c lu ded 8, 223 en d ome tri o sis ca ses a n d  fe ma l e  co ntr ol s.  \nb  C o hort d e sign:  F or ea ch  imm un o l ogica l co ndi tio n ,  fem ales who ha d the re sp e ctiv e  imm un ologica l di se ase d iag n ose d  befo r e end ome trio sis o r th o se  who h a d t he resp ec ti v e immun olo g ica l disea s e  \nd ia g no se d  before cohort entry t i m e or  ha d  i m m u nologica l  disea s e di a g no sis time m i ss i ng we re e x clu ded f rom  c oh o rt a na lysis. Her e  a re the num ber of exclud e d i ndivi duals  fo r ea ch  d isea s e and  \nd ise as e  cate gory: Ov era ll  imm unol ogical  dise as e  N= 5 0,67 6 7 , Cl a ssica l a ut oi m m u n e  d isea s e N=12, 5 1 1 ,  m ult iple sc l e ro sis N=1,8 1 5, r h e u m ato i d  art hrit i s N=4,94 6 ,  coel iac di se ase  N =2, 0 2 6 ,  ty p e  1  \nd ia b e tes N=1,4 8 6 ,  Aut oinfl a mm a tory d ise as e  N= 3 8, 661, ulce rative  c o lit i s N= 2 ,32 6 ,  I n fla m mat ory b owe l d isea s e N=11 5, 4 0 4, o ste oart hri t is N=34,5 68, mixe d -patt ern i mm un ologica l di se as e  N=42 4 0 ,  \nAn k ylo sing s p o n d y l it is N = 9 7 6 ,  p s o rias i s N= 3 , 662. \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nTable 2. N u mber of case s  and contr ol s  of  UKBB based female only and s ex- combined  GWAS for \nimmunological c o nditions a n d s umm ary of their  me ta-analysi s with the lar g est pub l icl y available \nEurop e an  ancestr y GW A S  r e s ult s .  \nIm m un o l o g ic al \ndi s e as es  \nUK B B GWAS (Cases : C ontr o ls)  \nP u blis hed s e x -co m b in e d \nGWA S  (C a s e s  : Co ntrols )  \nFina l  sex-c om bine d  \nG W A S m et a -an a l y s i s  \n(C ase s  : Controls )  \nFemal e -o nly  S ex - c ombi ned  \nAnky l osin g \nspon dy l i tis  \n5 47 : 162,4 03 1,4 93 : 3 1 9, 532 N /A 1 ,49 3 :  319,5 32  \nCo e lia c  di seas e  1 ,70 6 :  162,4 03  2,6 40 : 3 1 9, 532 4 ,53 3 : 10, 7 50 72  7 ,17 3 :  330,2 82  \nInfl a m mato ry  \nbo w e l d is eas e \n2 ,86 9 :  162,4 03  5,7 51 : 3 1 9, 532 25 ,04 2 :  3 4 ,91 5 54  3 0,7 93 : 3 5 4,4 47 \nM ultiple \ns c l e r o si s \n1 ,31 4 :  162,4 03  1,8 83 : 3 1 9, 532 14 ,49 8 :  2 4 ,09 1 56  1 6,3 81 : 3 4 3,6 23 \nOs teoa rt hritis  39 ,86 6 : 1 6 2,4 03 68, 878  : 319 ,53 2  77 ,05 2 : 3 78 ,16 9 73  77 ,05 2 :  3 7 8,1 69 *  \nP sorias i s  3 ,03 6 :  162,4 03  6,5 91 : 3 1 9, 532 15 ,96 7 :  2 8 ,19 4 59  2 2,5 58 : 3 4 7,7 26 \nRheuma toi d \na r th ritis  \n4 ,66 2 :  162,4 03  7,1 53 : 3 1 9, 532 14 ,36 1 :  4 3 ,92 3 57  2 1,5 14 : 3 6 3,4 55 \nS y s t e m ic  L up u s  \neryt he m a tos u s  \n5 45 : 162,4 03 673  : 3 1 9 ,53 2  5, 874  : 3 2 8 ,59 8 74  6 ,54 7 :  648,1 30  \n*  Pub l i she d G W AS  m et a - a na l y s is  in c lu ded  UK BB  h en c e ,  s u mm a ry  st at i st i c s f ro m  T a c hm z ido u l  e t  a l . 73  w e re use d i n th e \nan a l ys e s . \n \nTable 3. G en e t ic  cor relations from  LDSC a n al y si s  bet w een  en dometr i o s is and immunol ogical \ndis ease. Mult iple-testing correction  f or number  o f diseas e included in the analy s is i s  applied \n(0.05/8=6.2 5x10 -3 ) to det e rmine signific ant  c or relations . UKBB end o met r io si s G W AS  re sult s we re \nex clude d from e ndome trio si s met a- anal y s i s  to a void ov e rlap i n LDS C a naly si s wi t h i mmunolog ical  \nc ondition s f or whic h we  have  analy s e d t his  da ta s e t . H 2: He r it abi lity, S E: S tand a r d  err or , RG: Gen etic  \nco r r e lat i o n.  \n \n UK BB fem ale -o n ly GW AS U KBB  se x - com b i ned  G WAS  \nSe x - com bi n e d  GW AS  me t a -\nan a l ys is  \nI m muno l o g ic al \ndise a s e s  \nH 2  ( S E ) RG  ( S E) P -v al u e  H2 ( SE) RG  ( S E) P-v al u e  H2 ( SE) RG  ( SE ) P-v a lu e  \nA nky l o si ng s pon d y l itis  \n0 .006 \n(0 .003)  \n0 .308 \n(0 .124)  \n0.0 1 3 \n0.0 0 3 \n(0.001)  \n0 .300 \n(0 .124)  \n0. 01 6 N/ A  N /A N/A \nC o el i a c  d is e a s e \n0 .013 \n(0 .003)  \n0 .215 \n(0 .083)  \n0.0 1 0 \n0.0 0 9 \n(0.002)  \n0 .154 \n(0 .068)  \n0. 02 4 \n0 . 07 7 \n(0.0 0 5) \n0.0 7 6 \n( 0.054)  \n0. 16 1 9  \nInflam m a tor y b owe l \ndi s e as e  \n0 .014 \n(0 .003)  \n0 .054 \n(0 .085)  \n0.5 2 1 \n0.0 1 3 \n(0.002)  \n0 .007 \n(0 .063)  \n0. 91 0 \n0 . 27 7 \n(0.0 2 8) \n-0. 04 1  \n( 0.035)  \n0. 23 7 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nM u l t ipl e  s cle r o s is  \n0 .004 \n(0 .003)  \n0 .247 \n(0 .139)  \n0.0 7 5 \n0.0 0 3 \n(0.001)  \n0 .273 \n(0 .116)  \n0. 01 8 \n0 . 07 9 \n(0.0 0 5) \n0.0 8 8 \n( 0.031)  \n4. 0 0 x 10 -3  \nO ste oa rt hrit is  \n0 .051 \n(0 .004)  \n0 .322 \n(0 .042)  \n1.7 6 x10 -14  \n0.0 4 2 \n(0.002)  \n0 .278 \n(0 .038)  \n2.4 6 x10 -13  \n0 . 04 6 \n(0.0 0 2) \n0.2 7 8 \n( 0.035)  \n3.2 5 x10 -15  \nPsori a s i s  \n0 .012 \n(0 .003)  \n0 .043 \n(0 .084)  \n0.6 0 5 \n0.0 1 2 \n(0.002)  \n0 .049 \n(0 .061)  \n0. 42 0 \n0 . 23 9 \n(0.0 2 7) \n0.0 6 6 \n( 0.037)  \n0. 07 6 \nRh eu m a to id  a r th r i t is \n0 .013 \n(0 .003)  \n0 .277 \n(0 .079)  \n0.0 0 1 \n0.0 1 1 \n(0.002)  \n0 .284 \n(0 .068)  \n2. 6 5 x 10 -5  \n0 . 06 4 \n(0.0 0 9) \n0.2 6 6 \n( 0.062)  \n1. 5 4 x 10 -5  \nS ys t em i c l upu s \ne r y th em ato su s \n0 .003 \n(0 .003)  \n0 .154 \n(0 .154)  \n0.3 1 6 \n0.0 0 2 \n(0.001)  \n0 .190 \n(0 .165)  \n0. 24 8 \n0 . 33 2 \n(0.0 4 8) \n0.1 2 7 \n( 0.071)  \n0. 07 4 \n \nTable 4. Mend e lian Rando mis at i o n ( MR)  r es u l t s  fo r endome t riosis  vs. o s t eoar thr i tis , r heumatoid \narth rit is and multipl e sc ler os i s.  \nI mm u nol o gic al dis e ase s  SN P s  \nI n v e rs e  va r i a n c e  w e igh te d We i g ht ed  m ed i an  M R  Egg e r MR  E gg e r  ( P -va l u es ) \nO R  ( 95%  C I) P - va lu e  O R  ( 95%  C I) P - v a lu e O R  ( 95 %  C I ) P - va l u e H e t a  P l e i o t r o p y b  \nOs te oa rt h r i t i s  (3 9  IVs ) \nF ema l e-o nly U KB B  39  1.0 4  ( 0 . 9 7-1. 11 )  0 . 282  1 . 05 ( 0 . 9 7-1. 13)  0.2 6 7  0.8 9  ( 0. 72 -1. 07 )  0.2 03 0.0 02  0 . 084  \nFe m ale - o n l y  UK BB  ( ou t lie rs  \nre m o ve d )  \n35  1.0 3  ( 0 . 9 8-1. 09 )  0 . 287  1 . 05 ( 0 . 9 7-1. 13)  0.2 5 0  0.9 5  ( 0. 79 -1. 15 )  0.5 95 0.5 72 0 . 387  \nS ex-c o mb in ed  meta-a n al y s i s  39  1.0 2  ( 0 . 9 6-1. 07 )  0 . 533  1 . 02 ( 0 . 9 7-1. 08)  0.4 0 0  1.0 8  ( 0. 92 -1. 26 )  0.3 53 1.5 6x 1 0 -5  0 . 229  \nS ex-c o mb in ed  meta-a n al y s i s \n( o utl i e r s  r e mo ve d )  36  1.0 1  ( 0 . 9 7-1. 06 )  0 . 495  1 . 03 ( 0 . 9 8-1. 09)  0.2 8 1  1.0 3  ( 0. 90 -1. 18 )  0.6 55 0.1 59 0 . 800  \nRh e uma t o id  Ar th r it is  ( 3 9  IVs ) \nF ema l e-o nly U KB B  39  1.1 6  ( 1 . 0 2-1. 33 )  0 . 028  1 . 15 ( 0 . 9 4-1. 40)  0.1 6 9  1.1 5  ( 0. 78 -1. 70 )  0.4 94 0.7 56 0 . 959  \nS ex-c o mb in ed  meta-a n al y s i s  31  1.0 6  ( 0 . 9 6-1. 17 )  0 . 220  1 . 09 ( 0 . 9 4-1. 26)  0.2 6 1  1.0 8  ( 0. 76 -1. 53 )  0.6 82 0.6 11 0 . 435  \nM ulti ple S cler osi s ( 39 I V s)  \nF ema l e-o nly U KB B  39  1.1 2  ( 0 . 8 7-1. 43 )  0 . 376  1 . 23 ( 0 . 8 7-1. 73)  0.2 4 3  1.7 5  ( 0. 84 -3. 64 )  0.1 43 0.8 15 0 . 211  \nS ex-c o mb in ed  meta-a n al y s i s  39  1.0 6  ( 0 . 9 4-1. 21 )  0 . 343  1 . 03 ( 0 . 8 9-1. 20)  0.6 9 2  1.2 4  ( 0. 84 -1. 84 )  0.2 80 0.0 12*  0 . 145  \nS ex-c o mb in ed  Met a-an al ysi s \n( o utl i e r s  r e mo ve d )  38  1.0 4  ( 0 . 9 3-1. 18 )  0 . 484  1 . 02 ( 0 . 8 8-1. 2)  0.7 5 9  1.2 0  ( 0. 84 -1. 73 )  0.3 25 0.0 63 0 . 202  \na Tes t  for H ete rogeneit y , b  Tes t  f or Pleiotr opy, c O u tlier IVs were ident ified by  M R PRE SS O  \nso f t w a r e 67 (See m etho ds  and Supplemen tary Table 15). \n \n \n \n \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\n \n \nTable 5. G en ome-wide s ignificant lead SNP s  a ssociated with endom etr iosis ( EN D O ) and \nrheum atoid a rt hr itis (RA) , o s teo a rthr i tis ( O A ) or multiple sclerosis  (M S) tha t ar e located  wit h in \n1Mb, with  LD  between them. Chr : Chro mos o me, AE: Effective alle le, Fr q: Effective al lel e \nfreq uenc y, LD: Linka ge dis equilibr i u m, eQ TL: Expre s s ion quant itative tr ai t  l oc i.  \nLocus  Tra its S N P Chr  P ositi on  E A  ( Frq ) OR  ( 95 %  CI) P-v al u e  LD (r 2) eQTLs  r e s ul t s* \nGR E B 1 /  \n2 p25.1 \nE ND O r s 12 0 305 76 1 115 8 172 21 G  ( 0 . 65 ) 1 . 02  ( 1 .01 - 1 .03 ) 1 .94 x10 -1 4  0 .0 02 TS P A N 2  \nRA r s 66 7 967 7 1 114 3 038 08 C  (0 .1 0)  0 . 85  ( 0 .84 - 0 .87 ) 1 .61 x10 -92  A P4B 1, A PBA2, CD 247, C D 5, C D 6 ,  \nCT LA4, D C LR E 1B, FB L N2, F O XP3,  \nGZ MB, I L 10RA,  I L2 R A , M A F ,  ME D1 5 ,  \nP H TF1, R N F2 1 4, RT K N2, SLA MF 1, \nS T8S IA1, STA P1, WLS, ZN F 8 3 1 \nDNM 3/  \n1 q24.3 \nE ND O r s 24 2 198 5 1 172 0 991 36 T  ( 0 .52 ) 0 . 98  ( 0 .97 - 0 .98 ) 1 .94 x10 -1 4  0 .0 001  M E TT L 13 \nOA r s 55 6 981 00 1 174 2 274 33 T  ( 0 .73 ) 1 . 01  ( 1 .01 - 1 .02 ) 7 .97 x10\n-1 0\n C A CY B P ,  DA R S 2, G PR 52 ,  K IAA 0 0 40 ,  \nKLHL 20, M R P S 1 4 ,  P R D X6, \nR A B G A P1L, RC 3H1, S E RPI N C1  \nET A A1/  \n2p 1 4 \nE ND O r s 11 1 261 43 2 678 6 540 8 C  (0 .3 2)  1 . 02  ( 1 .01 - 1 .02 ) 6 .08 x10\n-1 0\n 0 .0 1  None  \nM S r s 12 6 226 70 2 686 4 653 6 C  (0 .4 6)  0 . 96  ( 0 .98 - 0 .99 ) 1 .56 x10 -9  C NR IP1 ,  P LEK , PP P3 R 1 \nBM P R 2/  \n2 q33.1 \nE ND O r s 72 9 289 25 2 203 5 543 24 A  (0 .82 ) 1 . 02  ( 1 .02 - 1 .03 ) 9 .61 x10 -1 0   BM P R2, CA R F,  R A M11 7B, I CA 1 L, \nNB E AL 1  \nOA r s 72 9 286 05 2 203 8 321 20 A  (0 .87 ) 1 . 02  ( 1 .01 - 1 .02 ) 4 .33 x10 -8  0 .5 3  BM P R2, CA R F,  C Y P 2 0A 1, FA M117 B, \nICA 1L, N BEAL 1, NO P58  \nRA r s 30 8 724 3 2 204 7 389 19 G  ( 0 . 45 ) 1 . 03  ( 1 .02 - 1 .04 ) 2 .39 x10\n-9\n 0 .0 01 CT LA4, I CO S  \nBS N /  \n3 p21.31  \nE ND O r s 67 7 420 2 3 496 8 777 9 T  ( 0 .17 ) 1 . 03  ( 1 .01 - 1 .03 ) 9 .70 x10 -12  0 .9 7  A MT, A RI H2, A P EH,  C 3orf62 , \nC A C NA 2 D2 ,  CCDC3 6 ,  C CDC7 1 ,  \nCD HR4, DA G 1, D A L RD3 , FA M 2 12 A , \nGM PP B ,  G P X 1 ,  HE M K1,  HYA L3, \nI M PD H 2 ,  I P 6 K 1,  K LH D C 88 ,  \nM A P K A PK3 , M ON 1 A,  M ST 1R ,  NAT 6 , \nN CKI PS D,  N D U FAF3, N I CN 1 ,  P4 H TM,  \nPR K A R2A , Q RI CH 1, RB M6,  R H OA,  \nR N F1 23 , TC T A , TR AIP ,  U B A7 , WDR6  \nOA r s 68 0 987 9 3 499 3 610 2 G  ( 0 . 18 ) 1 . 02  ( 1 .01 - 1 .02 ) 7 .71 x10 -9  A MT, A PEH,  A RIH 2 ,  BSN , CA CNA 2D 2, \nC A M KV ,  CD HR 4 ,  CTD - 2 33 0K9 .3 ,  \nDO C K 3, FAM 212A,  G M P PB, G PX 1, \nHEMK 1, H Y A L3 ,  I P6 K1, KLH D C8B , \nM A PKAP K 3 ,  MO N1A , MS T1, MS T 1 R ,  \nNAT 6, N I CN 1, P4H T M ,  RASSF1, \nRB M5, RB M 6 ,  RHOA , RN F1 2 3,  \nS E MA3F , T RAI P, U BA7, WD R 6  \nEBF 1/  \n5 q33.3 \nE ND O r s 29 6 448 5 5 157 9 048 39 G  ( 0 . 22 ) 0 . 98  ( 0 .97 - 0 .99 ) 1 .11 x10 -8  0 .0 01 None  \nM S r s 25 4 689 0 5 158 7 599 00 A  (0 .52 ) 1 . 05  ( 1 .04 - 1 .07 ) 7 .71 x10\n-13\n UB LCP1  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted July 9, 2024. ; https://doi.org/10.1101/2024.07.08.24310092doi: medRxiv preprint \n\nPP P 1 R9A /  \n7 q21.3 \nE ND O r s 34 7 510 86 7 945 9 269 1 C  (0 .8 7)  1 . 02  ( 1 .02 - 1 .03 ) 3 .23 x10 -8  0 .0 3  A SB 4 , C A SD 1, PEG 10 ,  P ON 1, P O N 2 ,  \nPP P1R 9 A  \n OA r s 69 6 654 0 7 957 2 796 7 T  ( 0 .63 ) 0 . 99  ( 0 .98 - 0 .99 ) 1 .46 x10 -9  DY N C1I 1 ,  S L C25A1 3 \nXK R6 /  \n8 p23.1 \nE ND O r s 12 5 420 37 8 107 5 849 6 A  (0 .45 ) 1 . 02  ( 1 .01 - 1 .02 ) 7 .02 x10 -11   BL K, C TSB, FAM 167A, F DF T 1,  MSRA,  \nMT M R 9, N EI L2, R P11- 297N 6, R P 1 L 1, \nS LC3 5 G5 , X KR6  \nOA r s 42 4 067 3 8 107 8 761 2 T  ( 0 .45 ) 1 . 01  ( 1 .01 - 1 .02 ) 2 .24 x10 -12  0 .9  BL K, C TS B , FAM 167A,  F DF T1 ,  MSRA,  \nMT M R 9, N EI L2, R P11- 297N 6. 4, \nRP 1L1, S LC3 5 G 5 ,  SO X7,  X KR6 \nRA r s 27 3 634 0 8 113 4 397 3 C  (0 .2 6)  0 . 97  ( 0 .96 - 0 .98 ) 2 .78 x10 -9  0 .3  A RG2,  BL K,  CTS B, F A M16 7 A , F D FT1, \nGGA 2 ,  M T MR 9, N E I L2, RP1 1 -2 97N 6 ,  \nSL C 3 5G 5,  X K R 6  \nMLLT 10/  \n10 p 12 .31 \nE ND O r s 11 0 127 32 10 218 3 010 4 A  (0 .66 ) 0 . 98  ( 0 .97 - 0 .98 ) 3 .84 x10 -1 6  0 .7  C A SC 10 ,  M L LT10 , N EB L  \nOA r s 12 3 573 21 10 217 9 047 6 G  ( 0 . 68 ) 0 . 99  ( 0 .98 - 0 .99 ) 3 .28 x10\n-9\n C A SC 10 ,  M L LY10 , N EB L  \nVEZ T/  \n12 q 22  \nE ND O r s 12 3 201 96 12 956 4 538 5 A  (0 .52 ) 0 . 98  ( 0 .98 - 0 .99 ) 5 .37 x10\n-12  0 .0 03 F G D6, N D UFA 1 2, N R2 C1 ,  V EZ T  \nOA r s 21 7 112 6 12 941 6 722 0 C  (0 .4 9)  0 . 99  ( 0 .98 - 0 .99 ) 2 .88 x10\n-9\n SO CS 2  \nDLE U 1/ \n13 q 14 .2 \nE ND O r s 10 2 886 2 13 510 5 513 4 G  ( 0 . 86 ) 0 . 98  ( 0 .97 - 0 .98 ) 9 .63 x10 -1 0  0 .0 005  None  \nM S r s 95 9 132 5 13 508 1 122 0 T  ( 0 .93 ) 1 . 09  ( 1 .06 - 1 .12 ) 1 .59 x10\n-1 0\n C OR O1C ,  DLE U1 , EB PL ,  KPN A 3 ,  \nPH F 1 1  \nSKA P 1 /  \n17 q 21 .32 \nE ND O r s 67 4 467 70 17 462 3 574 5 A  (0 .41 ) 0 . 98  ( 0 .98 - 0 .99 ) 8 .37 x10 -1 0  0 .0 02 CB X1, CD K 5 RA P 3 ,  HO XB2 ,  H O XB 3, \nHOX B4, L RR C4 6, M R PL1 0 ,  NF E2 L 1,  \nS C R N 2, S KA P1 ,  S N X 1 1  \nM S r s 11 0 797 84 17 457 0 228 0 T  ( 0 .49 ) 0 . 96  ( 0 .94 - 0 .97 ) 1 .97 x10 -1 0  C OPZ2 ,  EFC A B 13 ,  HOXB 2 ,  IT GB 3 ,  \nN PEP PS ,  S CR N2, SKA P 1, T B KB P 1 \n* F ull e Q T L  re s u l ts  fr o m  54 G T E x  t i s s u e s  a nd eQ TLGe n bl o o d  t iss ue  a re  pr ovi d e d i n  Su p pl e ment ary  T able  14. \n \n  \n . 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