Mendelian randomization Identifies RSPO3 in Serum as a Potential Target for Endometriosis

Mendelian randomization Identifies RSPO3 in Serum as a Potential Target for Endometriosis | 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 Mendelian randomization Identifies RSPO3 in Serum as a Potential Target for Endometriosis Weijie Guo, Zhuoling Zhong, Xiuqi Yang, Taoaixin Ou, Dingyi Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4265646/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 Endometriosis lacks effective early intervention and treatment. Our objective is to explore potential protein drug targets in serum for endometriosis and different subtypes of endometriosis, using Mendelian randomization and Bayesian colocalization to provide support for clinical intervention. Design Multi-validated two-sample Mendelian randomization study, combined with Bayesian co-localization analysis to determine drug targets. Setting Summary statistics from published GWAS in European ancestry populations. Population or Sample Instrumental variants for serum proteins of finding cohort were obtained from a study on 3301 people, and instrumental variants for endometriosis and different subtypes of endometriosis of finding cohort were obtained from FinnGen cohort. Data of endometriosis of replicated cohort including 191747 people were obtained from UK biobank, and data of serum proteins of replicated cohort were obtained from a study including 35559 people. Methods Using Mendelian randomization, we explored and discovered a significant causal association between certain serum proteins and endometriosis. This finding was validated using data on endometriosis and serum proteins from a validation cohort. Finally, Bayesian colocalization analysis was applied to identify potential drug targets. Additionally, Mendelian randomization analysis was conducted on different subtypes of endometriosis to identify proteins potentially associated with these subtypes. Main outcome measures Data for the endometriosis discovery cohort were obtained from the FinnGen cohort, and data for the endometriosis validation cohort were obtained from the UK Biobank. Results Results from the MR analysis in the finding cohort indicated ten protein–Endometriosis pairs, including Intercellular adhesion molecule 2, R-spondin-3, Intercellular adhesion molecule 4, Endoglin, OX-2 membrane glycoprotein, Leukemia inhibitory factor receptor, Insulin-like growth factor 1 receptor, Hydroxycarboxylic acid receptor 2, Tryptase gamma, Alpha-(1,3)-fucosyltransferase 9 in the plasma. After validation analysis and Bayesian co-localization analysis, RSPO3 was identified as a potential drug target for endometriosis. Conclusions We conducted Mendelian Randomization analysis on GWAS data from a large population, confirming a causal relationship between serum levels of RSPO3 and endometriosis. This suggests that RSPO3 may influence the onset and progression of endometriosis, providing a protective effect. This finding supports its potential as a preventive and therapeutic approach for endometriosis. Funding The study was supported by funding from the projects of Chengdu Science and Technology Bureau, (Y.Z., Grant No. 2021-YF05-02110-SN), China Postdoctoral Science Foundation (Y.Z., Grant No. 2020M680149, 2020T130087ZX). Mendelian Randomization Endometriosis Serum proteins Bayesian colocalization Causal Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Endometriosis is a common chronic gynecological disorder, affecting over 190 million patients worldwide( 1 ). It is characterized by the presence of endometrial-like tissue outside the uterus. When this tissue implants, it undergoes inflammation, leading to lesions and cysts. Symptoms include menstrual pain, dyspareunia, pelvic pain, and infertility( 2 , 3 ). The pain symptoms are recurrent and progressively worsen. In addition, patients may experience systemic symptoms such as nausea, vomiting, diarrhea, and dizziness, contributing to a high rate of misdiagnosis( 4 ). The gold standard for diagnosing endometriosis is the surgical removal and biopsy of ectopic endometrial tissue through laparotomy or laparoscopy( 5 ). Laparotomy involves a larger incision, whereas laparoscopy still has limitations in terms of potential missed diagnoses, making the diagnosis challenging. On average, it takes 7 to 9 years from the onset of symptoms to diagnosis for 60% of patients( 6 , 7 ). In the United States, the total annual cost for each patient with endometriosis, including both direct and indirect costs, amounts to a staggering $ 27,855. This results in a nationwide annual expenditure of $ 22 billion( 8 ). In the United Kingdom, the annual cost for patients with endometriosis has also reached a substantial £12.5 billion, resulting in significant economic and health burdens( 9 ). The risk of endometriosis is influenced by genetic factors, environmental factors, and epigenetics. Currently, the pathophysiology of endometriosis remains unclear, necessitating further research to facilitate early diagnosis and prevention. Most of the current clinical drugs used to treat endometriosis are hormones, which bring varying degrees of side effects( 10 ). Further research is needed to develop drugs with better efficacy. An abundance of studies has shown that the risk of endometriosis is largely explained by genetic factors, which provides a useful idea for early intervention and post-diagnosis treatment of endometriosis, considering the increasing number of researchers here proposed the idea that "endometriosis can be treated with drugs before histological diagnosis." There are currently a number of large-scale GWAS (Genome-Wide Association Studies) studies on endometriosis, which have discovered many genetic loci related to the risk of endometriosis( 11 , 12 ), but there are fewer genetic factors that can be converted into drug targets. This is due to the considerable heterogeneity of endometriosis, and many genetic loci are challenging to replicate in different studies( 13 ). This suggests that the process of translating candidate genes into therapeutic drugs using GWAS data alone is lengthy and results in uncertainties. The combination of Mendelian Randomization (MR) and Bayesian Colocalization Analysis provides a reliable analytical framework for drug target identification, facilitating a faster and more accurate localization of effect genes and promoting drug translation( 14 ). MR employs gene differences as instrumental variables to determine causal effects between exposure and outcome, reducing bias from confounding factors and reverse causation( 15 ). Sensitivity analysis is used to eliminate the impact of pleiotropy( 16 ). Bayesian Colocalization Analysis is employed to ascertain whether the effects between exposure and outcome share the same genetic variation, a necessary condition for establishing causal associations( 17 ). Previous studies have primarily focused on GWAS data for endometriosis, neglecting the exploration of potential drug targets( 18 , 19 ). In this study, our efforts are directed towards identifying new drug targets for endometriosis through MR and Bayesian Colocalization Analysis, utilizing plasma pQTL (protein quantitative trait loci) data and endometriosis GWAS data. Validation analysis and subtype analysis of endometriosis are conducted to delve deep into potential drug targets and pathogenic mechanisms, offering a comprehensive exploration of endometriosis. Method Study Design The primary analysis utilized protein data from Sun et al. in conjunction with endometriosis data from the ninth round of FinnGen. The design of this study is presented in Fig. 1 . Test outcomes underwent validation through Bonferroni significance testing, and further validation was conducted using endometriosis data from the UK Biobank. Reverse MR analysis and Bayesian co-localization analysis were executed on proteins demonstrating consistency in both the main and validation analyses. Proteins passing the previous analyzes underwent additional validation using Fer et al.'s protein data( 20 ), employing significant genetic variation strategy and identical genetic variation strategy. Subsequently, the analytical framework was applied to data concerning potential target proteins and endometriosis subtypes to deepen our understanding of endometriosis pathogenesis. GWAS summary statistics of Endometriosis The primary analysis was based on the summary data of the GWAS for endometriosis obtained from the ninth round of FinnGen, comprising 15,088 cases and 107,564 female controls of European ancestry( 21 ). In the validation analysis, endometriosis summary data was acquired from the UKBB GWAS imputed V3 through a phenotype description search for "endometriosis"( 22 ). This dataset included 1,496 cases and 192,678 female controls. Endometriosis subtype data were obtained from the ninth round of FinnGen, and relevant information for all studies is shown in Table 1 . Table 1 Information on the different GWAS data in this study. Type Factor Source n_case n_control Exposure Serum protein PMID: 29875488 / 3301 Exposure Serum protein PMID: 34857953 / 35559 Outcome Endometriosis FinnGen 15088 107564 Outcome Endometriosis UK Biobank 2967 191747 Subtype Fallopian tube FinnGen 213 107564 Subtype Intestine FinnGen 436 107564 Subtype Ovary FinnGen 5867 107564 Subtype Pelvic peritoneum FinnGen 5628 107564 Subtype Rectovaginal septum and vagina FinnGen 2456 107564 Subtype Uterus FinnGen 4267 107564 Plasma Protein quantitative trait loci The serum protein Quantitative Trait Loci (pQTL) data were obtained from the study by Sun et al., where the analysis encompassed 3,301 healthy participants and involved the examination of 3,622 serum proteins( 23 ). The relevant pQTL data were integrated into the IEU Project by Zheng et al( 24 ). Qualified pQTL were selected based on the following criteria: ( 1 ) demonstrated a genome-wide significant association (P < 5 × 10⁻⁸), and ( 2 ) exhibited independent association [linkage disequilibrium (LD) clumping r² < 0.001, window size = 10,000 kb]. Ultimately, pQTL related to 1,806 proteins were identified. For the validation analysis, pQTL data were sourced from the study conducted by Ferkingstad et al., comprising measurements of 4,907 plasma proteins in a cohort of 35,559 participants( 20 ). Statistical analysis Mendelian randomization analysis In the primary analysis, we applied Bonferroni correction to address multiple testing concerns, setting a threshold P value of 0.05/1806 (P < 2.77 × 10⁻⁵) to prioritize results for subsequent analysis( 25 ). The initially identified proteins underwent MR for external validation, with a P value threshold of 0.05. We employed the same variant strategy, utilizing the exact SNPs as genetic instruments from the primary analysis, and also employed a significant variant strategy using genome-wide significant SNPs as genetic instruments to confirm the initial findings. Reverse causality detection The comprehensive GWAS data for proteins were available, and employing the same criteria as used for pQTL screening, we obtained 20 instrumental variables for endometriosis from the ninth round of FinnGen's GWAS data. Endometriosis was considered the exposure, and proteins were treated as outcomes in a reverse Mendelian Randomization (MR) analysis. We utilized the IVW, MR-Egger, and weighted median methods to estimate the causal relationship between them. Steiger filtering was also implemented to ensure the directionality of the association between proteins and endometriosis( 26 ). Statistical significance was defined as P < 0.05 for the results. Bayesian Colocalization Analysis To perform Bayesian colocalization analysis using the "coloc" package ( https://github.com/chr1swallace/coloc ), and to assess the probability that two traits share the same causal variant simultaneously, strictly adhering to default parameter settings. Within the Bayesian colocalization analysis framework, posterior probabilities are systematically calculated for five different hypotheses regarding the shared single variant between two traits. This study specifically investigates the posterior probabilities of Hypothesis 3 (PPH3) and Hypothesis 4 (PPH4). PPH3 posits that protein and endometriosis are concurrently associated with different variants in the region, whereas PPH4 suggests that both traits are associated with a shared variant within the same region. The analysis utilizes the coloc.abf algorithm and establishes a criterion wherein a gene is deemed as evidence for colocalization if the gene exhibits a gene-based PPH4 exceeding 80%( 27 ). Statistical Power Estimate The risk of bias intensifies with weak instrumental variables, to evaluate the strength of these instrumental variables, F statistics serve as a reliable measure, which can be expressed as a function of the first-stage R 2 , the sample size (N) and the number of IVs (k), pQTLs with an F-statistic greater than 10 are considered to have sufficient strength( 28 ). $$F=\frac{N-k-1}{k}\times \frac{{R}^{2}}{1-{R}^{2}}$$ Results Selected Instrumental Variables We selected SNPs that meet the screening criteria (P < 5 × 10⁻⁸, r² 10) as instrumental variables representing serum proteins. We then extracted information about these SNPs from the corresponding outcomes for use in Mendelian Randomization (MR) analysis. Detailed information can be found in Supplementary Table 8. Screening the proteome for endometriosis causal proteins At Bonferroni significance (P < 2.77 × 10 − 5), MR analysis revealed ten protein–Endometriosis pairs (Fig. 2 and Supplementary Table 1), including Intercellular adhesion molecule 2 (ICAM-2), R-spondin-3 (RSPO3), Intercellular adhesion molecule 4 (ICAM-4), Endoglin (ENG), OX-2 membrane glycoprotein (OX2), Leukemia inhibitory factor receptor (LIFR), Insulin-like growth factor 1 receptor (IGF1R), Hydroxycarboxylic acid receptor 2 (HCA2), Tryptase gamma (TPSG1), Alpha-( 1 , 3 )-fucosyltransferase 9 (FUT9) in the plasma. Specifically, increased RSPO3(OR = 0.75, 95%CI: 1.15 ~ 0.83, p = 9.22E-10), HCA2(OR = 0.76, 95%CI: 1.25 ~ 0.86, p = 8.50E-06), TPSG1(OR = 0.78, 95%CI: 1.25 ~ 0.87, p = 9.01E-06) and FUT9(OR = 0.78, 95%CI: 1.25 ~ 0.87, p = 9.01E-06) decreased the risk of endometriosis, whereas elevated ICAM-2 (OR = 1.39, 95%CI: 1.25 ~ 1.55, p = 7.15E-10), ICAM-4 (OR = 1.52, 95%CI: 1.15 ~ 1.76, p = 9.29E-09), ENG (OR = 1.45, 95%CI: 1.15 ~ 1.65, p = 9.29E-09), OX2 (OR = 1.59, 95%CI: 1.15 ~ 1.87, p = 9.29E-09), LIFR (OR = 1.34, 95%CI: 1.15 ~ 1.49, p = 9.29E-09) and IGF1R(OR = 1.50, 95%CI: 1.15 ~ 1.73, p = 9.29E-09) increased the risk of endometriosis. No heterogeneity was detected for the proteins analysed in the primary analysis. The Protein-Protein Interaction Networks (PPI) network (Fig. 3 ) was drawn through the String database using corrected positive proteins from serum. Sensitivity analysis for endometriosis causal proteins After using the GWAS data of endometriosis from UK Biobank (UKB) as outcome data for validation, the MR analysis results for six proteins remained positive (Fig. 4 and supplementary table 15). RSPO3 of the six proteins was identified as potential drug targets for endometriosis. First, bidirectional MR analysis did not reveal any causal effect of endometriosis on the level of six identified proteins further ensured directionality (Table 2 and Supplementary Table 16). Second, Bayesian co-localization strongly suggested that RSPO3 (coloc.abf-PPH4 = 0.974), shared the same variant with endometriosis (Table 3 ). Colocalization results of five additional proteins showed that they do not share the same genetic variant with endometriosis. All Bayesian co-localization results are collected in Supplementary Table 17–22. Table 2 Results of the IVW model from reverse MR analysis. Outcome Method nsnp Beta Se pval Intercellular adhesion molecule 2 IVW 27 4.92E-02 9.80E-02 6.15E-01 R-spondin-3 IVW 27 -1.04E-01 1.21E-01 3.90E-01 Intercellular adhesion molecule 4 IVW 27 -9.71E-03 8.73E-02 9.11E-01 Endoglin IVW 27 -9.82E-03 8.60E-02 9.09E-01 OX-2 membrane glycoprotein IVW 27 -8.69E-02 7.41E-02 2.41E-01 Leukemia inhibitory factor receptor IVW 27 3.95E-02 1.12E-01 7.24E-01 Insulin-like growth factor 1 receptor IVW 27 -3.68E-02 9.43E-02 6.96E-01 Hydroxycarboxylic acid receptor 2 IVW 27 5.62E-02 6.26E-02 3.69E-01 Tryptase gamma IVW 27 3.51E-02 5.29E-02 5.06E-01 Alpha-( 1 , 3 )-fucosyltransferase 9 IVW 27 1.86E-02 5.29E-02 7.25E-01 Table 3 Results of tests of different hypotheses for Bayesian colocalization. Exposure PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf Intercellular adhesion molecule 2 1.10E-03 4.99E-01 7.71E-04 3.48E-01 1.52E-01 R-spondin-3 3.04E-28 6.14E-07 1.69E-23 3.32E-02 9.67E-01 Intercellular adhesion molecule 4 1.14E-148 6.26E-01 5.99E-149 3.29E-01 4.41E-02 Endoglin 3.60E-01 2.35E-01 2.32E-01 1.51E-01 2.11E-02 OX-2 membrane glycoprotein 3.40E-01 2.54E-01 2.19E-01 1.64E-01 2.20E-02 Leukemia inhibitory factor receptor 3.40E-01 2.54E-01 2.19E-01 1.64E-01 2.20E-02 External validation of potential drug targets for endometriosis Using the same mutation strategy and significant mutation strategy, extract the same pQTLs from the research data of Fer et al. as those in the research data of Sun et al. for MR analysis, or use the same screening criteria to extract pQTLs for MR analysis to replicate the main findings. The validation results of both strategies showed that RSPO3 has a significant protective effect against endometriosis (Table 4 and Supplementary Table 23). Table 4 Results from MR analysis of rspo3 from Fer et al. study on endometriosis. Strategy Method nsnp Beta Se pval Significant mutation MR Egger 4 -3.65E-01 1.76E-01 1.74E-01 Significant mutation Weighted median 4 -2.95E-01 5.49E-02 7.61E-08 Significant mutation Inverse variance weighted 4 -2.95E-01 7.92E-02 1.95E-04 Same mutation Wald ratio 1 -3.66E-01 5.72E-02 1.46E-10 Association between proteome and endometriosis subtypes Endometriosis exhibits diverse sub-phenotypes, with the location and growth patterns of ectopic endometriotic lesions influencing categorization. These sub-phenotypes, encompassing the intestine, ovary, pelvic peritoneum, uterus, fallopian tube, and rectovaginal regions, were identified in the FinnGen cohort. The cohort's GWAS summary statistics for different endometriosis sub-phenotypes, with patient numbers varying from 116 for fallopian tube endometriosis to 3231 for ovarian endometriosis, were available. Given the potential impact of sub-phenotypes on symptoms and infertility risk, some patients experienced more than one type due to overlapping manifestations. To delve deeper into the causal effects of genetically predicted plasma protein levels on diverse endometriosis sub-phenotypes, we conducted Mendelian randomization analyses on different subtypes. The MR analysis results, following correction, indicate that there is no significant association between serum proteins and three subtypes of endometriosis: endometriosis of the intestine, endometriosis of the fallopian tube, and endometriosis of the rectovaginal regions (Fig. 5 and Supplementary Table 2–7, 9–14). For endometriosis of the ovary, VSIG2 and TBX22 are identified as risk factors, while PTH receptor is noted as a protective factor. In the case of endometriosis of the pelvic peritoneum, ICAM-4 is identified as a risk factor, and RSPO3 is recognized as a protective factor for endometriosis of the uterus. Discussion To our knowledge, this is the first study to utilize two-sample MR and Bayesian colocalization to explore causal proteins in endometriosis. Finally, we identified a protein as a potential drug target in endometriosis, circulating RSPO3, which is protective against endometriosis. UK Biobank also confirmed that RSPO3 is associated with endometriosis using MR analysis, further demonstrating the robustness of the potential drug targets identified in this study. The cause of endometriosis is complex. It may be caused by the interaction of many genetic and environmental factors. The clustering of endometriosis cases within families and the susceptibility among relatives have revealed that genetic variation plays an important role in the pathogenesis( 29 – 31 ). Therefore, a large number of screening studies for endometriosis risk genes have been conducted( 32 ), and there are a number of GWAS for endometriosis( 11 , 33 – 35 ). Although a certain number of risk genes have been discovered at a genome-wide significant level, these genes are closely related to protein WNT signal transduction, cell migration, angiogenesis, inflammation metabolism and other life activities, and also show significant correlation with genetic variations in other diseases, but their interpretation of disease risk remains extremely limited( 5 , 10 , 36 , 37 ). The occurrence of endometriosis is also highly influenced by epigenetics. Some genes expressed during the secretory phase of the menstrual cycle have been observed to be highly methylated in patients with endometriosis( 38 ). Studies have shown that the methylation of these genes appears to promote progesterone resistance in patients, which is almost certainly a risk factor for endometriosis( 39 ). MicroRNA can also produce epigenetic effects, such as promoting mitosis at the lesion site and promoting the proliferation and migration of ectopic endometrium tissue cells( 40 ). However, these findings are highly heterogeneous in different studies( 41 , 42 ). Endometriosis is a chronic inflammatory disease for which there are generally three main treatments, including medication, surgery and assisted reproductive technology( 43 ). Drug treatments include hormonal treatments such as progestins, gonadotropin-releasing hormone analogues, contraceptive pills, etc.; non-hormonal treatments such as analgesics or non-steroidal anti-inflammatory drugs, but these treatments have certain effects in the long-term treatment process. Certain limitations. For example, hormone therapy will have a contraceptive effect on women, while analgesics and non-steroidal anti-inflammatory drugs are similar to prescribing medications and cannot produce a curative effect( 1 , 44 , 45 ). Some patients also choose surgical removal of endometriosis, but this cannot treat the root cause of the disease and has a high recurrence rate. Studies have shown that many inadequate or unnecessary surgeries are performed for endometriosis, which not only brings a greater financial burden to patients but also puts patients at a higher risk of surgical complications( 46 , 47 ). Assisted reproductive technology plays a role in infertility due to endometriosis, but there is no evidence that it has any benefit in the treatment of endometriosis, and there is even evidence that these surgeries and Endometriosis itself can adversely affect ovarian reserve( 48 , 49 ). Therefore, the treatment of endometriosis urgently requires clearer treatment targets and a long-term perspective to formulate treatment strategies to help patients. To identify potential therapeutic targets for endometriosis, our research employs the robust framework of Mendelian randomization. This approach allows for the exploration of causal relationships between serum proteins and the risk of developing endometriosis. Integrating data from multiple endometriosis and serum protein databases, we conduct a comprehensive analysis to test our hypotheses. A pivotal aspect of our methodology involves colocalization analysis, which has yielded significant insights. Specifically, we identified shared genetic variants between RSPO3, a member of the R-spondin family involved in modulating WNT signaling, and endometriosis( 50 ). Our results suggest that RSPO3 may exert a protective effect against the development of endometriosis. Certainly, enriching the content about the functions of RSPO3 in the body will provide a more comprehensive understanding of its potential role in endometriosis and other physiological processes. As a crucial modulator in the WNT signaling pathway, fundamental to numerous biological processes, RSPO3 plays a vital role in cell proliferation, differentiation, and embryonic development( 51 – 53 ). This pathway is pivotal in maintaining the balance between stem cell renewal and differentiation, essential for tissue homeostasis and regeneration( 54 , 55 ). In the context of endometrial tissue, this balance is critical, as disruptions can contribute to the aberrant growth patterns observed in endometriosis. By modulating angiogenesis, RSPO3 could potentially impact the viability and proliferation of endometriosis lesions( 53 ). This is particularly relevant in endometriosis, where the establishment and maintenance of ectopic endometrial lesions depend on the development of a sufficient blood supply( 56 ). Consequently, RSPO3's interaction with immune system components may have significant implications for the inflammatory processes underpinning endometriosis pathology( 57 ). Endometriosis is characterized by a chronic inflammatory environment, and modulating this environment is crucial for disease management( 58 ). Understanding RSPO3's potential role in modulating fibrotic pathways could be pivotal in elucidating the progression and severity of endometriosis( 59 , 60 ). Fibrosis, the process of excessive connective tissue formation, is often observed in chronic diseases, including endometriosis( 61 ). The involvement of RSPO3 in these pathways underscores its potential as a promising therapeutic target in endometriosis, offering avenues for both treatment and a deeper understanding of the disease’s pathophysiology. This study, while providing significant insights into endometriosis, acknowledges certain limitations that need to be addressed in future research. Firstly, the protective factor RSPO3, identified at the overall level of endometriosis, could not be consistently replicated in the analysis of specific subtypes. This discrepancy may be attributed to the limited number of samples available for subtype analysis. It is anticipated that future studies with larger subtype-specific sample sizes will allow for a more thorough exploration of this aspect. Secondly, while the association of endometriosis with certain factors was successfully replicated at the protein level, offering a new dimension for identifying potential drug targets, the study fell short in elucidating the numerous loci identified through genome-wide association studies (GWAS). This gap highlights the complexity of endometriosis and suggests that a more extensive investigation is required to fully understand the genetic underpinnings identified by GWAS. Lastly, it is important to note that endometriosis is a condition specific to female reproductive health. Although the outcome data for this study were derived from female-specific GWAS datasets, the plasma protein data, which formed the basis of exposure analysis, were not gender-specific. This could potentially influence the study's findings and their applicability. Furthermore, both the exposure and outcome data were exclusively obtained from European populations. This raises questions about the generalizability of the study's conclusions to other ethnic groups. Consequently, future research should endeavor to include a more diverse population to ensure the findings are universally applicable and relevant. Abbreviations CI Confidence interval GWAS Genome-wide association study IVW Inverse variance weighting LD Linkage disequilibrium MR Mendelian randomization OR Odds ratio RCTs Randomized controlled trials SNP Single nucleotide polymorphism ICAM-2 Intercellular adhesion molecule 2 () RSPO3 R-spondin-3 ICAM-4 Intercellular adhesion molecule 4 ENG Endoglin OX2 OX-2 membrane glycoprotein LIFR Leukemia inhibitory factor receptor IGF1R Insulin-like growth factor 1 receptor HCA2 Hydroxycarboxylic acid receptor 2 TPSG1 Tryptase gamma FUT9 Alpha-(1,3)-fucosyltransferase 9 Declarations Ethics approval and consent to participate The analyses were based on publicly available data that have been approved by relevant review boards. Consent for publication Not applicable. Availability of data and materials All GWAS data used in the article are publicly available. The GWAS data of serum proteins come from the study of Sun et al. and the study of Fer et al. The data of endometriosis come from the ninth round of data released by the FinnGen cohort and the V3 version of the UK Biobank. Competing interests The authors declare that they have no competing interests. Authors' contributions YZ, YX, and WG conceived and designed the study. YZ supervised the study and data analysis. WG and ZZ performed the data analysis with help from XY, TO, DZ, YL. YZ, YX, WG, ZZ and XY wrote the manuscript. All authors revised and approved the final manuscript. Acknowledgements We would like to thank the FinnGen cohort, UK biobank, Sun et al., and Fer et al. haring the GWAS data. We would like to thank the anonymous reviewers for their constructive comments. We would like to thank to Xu Zhang of Youhe AI and Medicine. References Horne AW, Missmer SA. Pathophysiology, diagnosis, and management of endometriosis. BMJ (Clinical research ed). 2022;379:e070750. Fedele L, Berlanda N, Corsi C, et al. 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Significant evidence of one or more susceptibility loci for endometriosis with near-Mendelian inheritance on chromosome 7p13-15. Human reproduction (Oxford, England). 2007;22(3):717-28. Méar L, Herr M, Fauconnier A, et al. Polymorphisms and endometriosis: a systematic review and meta-analyses. Human Reproduction Update. 2019;26(1):73-103. Nyholt DR, Low SK, Anderson CA, et al. Genome-wide association meta-analysis identifies new endometriosis risk loci. Nature genetics. 2012;44(12):1355-9. Painter JN, Anderson CA, Nyholt DR, et al. Genome-wide association study identifies a locus at 7p15.2 associated with endometriosis. Nature genetics. 2011;43(1):51-4. Uno S, Zembutsu H, Hirasawa A, et al. A genome-wide association study identifies genetic variants in the CDKN2BAS locus associated with endometriosis in Japanese. Nature genetics. 2010;42(8):707-10. Rahmioglu N, Macgregor S, Drong AW, et al. Genome-wide enrichment analysis between endometriosis and obesity-related traits reveals novel susceptibility loci. Human molecular genetics. 2015;24(4):1185-99. Reis FM, Petraglia F, Taylor RN. Endometriosis: hormone regulation and clinical consequences of chemotaxis and apoptosis. Hum Reprod Update. 2013;19(4):406-18. Marquardt RM, Tran DN, Lessey BA, et al. Epigenetic Dysregulation in Endometriosis: Implications for Pathophysiology and Therapeutics. Endocrine reviews. 2023;44(6):1074-95. Dyson MT, Roqueiro D, Monsivais D, et al. Genome-wide DNA methylation analysis predicts an epigenetic switch for GATA factor expression in endometriosis. PLoS Genet. 2014;10(3):e1004158. Burney RO, Talbi S, Hamilton AE, et al. Gene expression analysis of endometrium reveals progesterone resistance and candidate susceptibility genes in women with endometriosis. Endocrinology. 2007;148(8):3814-26. Burney RO, Hamilton AE, Aghajanova L, et al. MicroRNA expression profiling of eutopic secretory endometrium in women with versus without endometriosis. Molecular human reproduction. 2009;15(10):625-31. Saare M, Rekker K, Laisk-Podar T, et al. Challenges in endometriosis miRNA studies - From tissue heterogeneity to disease specific miRNAs. Biochimica et biophysica acta Molecular basis of disease. 2017;1863(9):2282-92. Chapron C, Marcellin L, Borghese B, et al. Rethinking mechanisms, diagnosis and management of endometriosis. Nature Reviews Endocrinology. 2019;15(11):666-82. Brown J, Crawford TJ, Allen C, et al. Nonsteroidal anti-inflammatory drugs for pain in women with endometriosis. The Cochrane database of systematic reviews. 2017;1(1):Cd004753. Crosignani P, Olive D, Bergqvist A, et al. Advances in the management of endometriosis: an update for clinicians. Hum Reprod Update. 2006;12(2):179-89. Vercellini P, Crosignani PG, Abbiati A, et al. The effect of surgery for symptomatic endometriosis: the other side of the story. Hum Reprod Update. 2009;15(2):177-88. Sibiude J, Santulli P, Marcellin L, et al. Association of history of surgery for endometriosis with severity of deeply infiltrating endometriosis. Obstetrics and gynecology. 2014;124(4):709-17. de Ziegler D, Borghese B, Chapron C. Endometriosis and infertility: pathophysiology and management. Lancet (London, England). 2010;376(9742):730-8. D'Hooghe TM, Denys B, Spiessens C, et al. Is the endometriosis recurrence rate increased after ovarian hyperstimulation? Fertility and sterility. 2006;86(2):283-90. Ter Steege EJ, Bakker ERM. The role of R-spondin proteins in cancer biology. Oncogene. 2021;40(47):6469-78. ter Steege EJ, Doornbos LW, Haughton PD, et al. R-spondin-3 promotes proliferation and invasion of breast cancer cells independently of Wnt signaling. Cancer Letters. 2023;568:216301. Shan TD, Yue H, Sun XG, et al. Rspo3 regulates the abnormal differentiation of small intestinal epithelial cells in diabetic state. Stem cell research & therapy. 2021;12(1):330. Kazanskaya O, Ohkawara B, Heroult M, et al. The Wnt signaling regulator R-spondin 3 promotes angioblast and vascular development. Development (Cambridge, England). 2008;135(22):3655-64. Nusse R, Clevers H. Wnt/β-Catenin Signaling, Disease, and Emerging Therapeutic Modalities. Cell. 2017;169(6):985-99. Katoh M, Katoh M. WNT signaling pathway and stem cell signaling network. Clinical cancer research : an official journal of the American Association for Cancer Research. 2007;13(14):4042-5. Asante A, Taylor RN. Endometriosis: the role of neuroangiogenesis. Annual review of physiology. 2011;73:163-82. Zhou B, Magana L, Hong Z, et al. The angiocrine Rspondin3 instructs interstitial macrophage transition via metabolic-epigenetic reprogramming and resolves inflammatory injury. Nature immunology. 2020;21(11):1430-43. Taylor HS, Kotlyar AM, Flores VA. Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet (London, England). 2021;397(10276):839-52. Yang L, Yue W, Zhang H, et al. The role of roof plate-specific spondins in liver homeostasis and disease. Liver Research. 2022;6(3):139-45. Zhang M, Haughey M, Wang N-Y, et al. Targeting the Wnt signaling pathway through R-spondin 3 identifies an anti-fibrosis treatment strategy for multiple organs. PLOS ONE. 2020;15(3):e0229445. Vigano P, Candiani M, Monno A, et al. Time to redefine endometriosis including its pro-fibrotic nature. Human reproduction (Oxford, England). 2018;33(3):347-52. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable17.xlsx SupplementaryTable1523.xlsx SupplementaryTable814.xlsx 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. 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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-4265646","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291319504,"identity":"9a54fc9c-23e5-4de9-9598-52f11cd4de62","order_by":0,"name":"Weijie Guo","email":"","orcid":"","institution":"Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Taipa, Macao SAR, 999078","correspondingAuthor":false,"prefix":"","firstName":"Weijie","middleName":"","lastName":"Guo","suffix":""},{"id":291319505,"identity":"e6b9d2e5-c434-4b6f-a3a4-48e532c01815","order_by":1,"name":"Zhuoling Zhong","email":"","orcid":"","institution":"State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, Taipa, Macao SAR, 999078","correspondingAuthor":false,"prefix":"","firstName":"Zhuoling","middleName":"","lastName":"Zhong","suffix":""},{"id":291319506,"identity":"7e98f74b-227d-49b8-833f-21173d13f3c7","order_by":2,"name":"Xiuqi Yang","email":"","orcid":"","institution":"West China School of Medicine, Sichuan University, Chengdu, Sichuan 610065","correspondingAuthor":false,"prefix":"","firstName":"Xiuqi","middleName":"","lastName":"Yang","suffix":""},{"id":291319507,"identity":"cefffce6-32fd-451d-a55f-787054aeba65","order_by":3,"name":"Taoaixin Ou","email":"","orcid":"","institution":"West China School of Medicine, Sichuan University, Chengdu, Sichuan 610065","correspondingAuthor":false,"prefix":"","firstName":"Taoaixin","middleName":"","lastName":"Ou","suffix":""},{"id":291319508,"identity":"c9316083-3f08-4186-ac22-29ed4578676b","order_by":4,"name":"Dingyi Zhang","email":"","orcid":"","institution":"West China School of Medicine, Sichuan University, Chengdu, Sichuan 610065","correspondingAuthor":false,"prefix":"","firstName":"Dingyi","middleName":"","lastName":"Zhang","suffix":""},{"id":291319509,"identity":"393c6005-eaf5-4862-89e0-4b213f837c74","order_by":5,"name":"Yanxu Liu","email":"","orcid":"","institution":"West China School of Medicine, Sichuan University, Chengdu, Sichuan 610065","correspondingAuthor":false,"prefix":"","firstName":"Yanxu","middleName":"","lastName":"Liu","suffix":""},{"id":291319510,"identity":"95c511e0-52dc-4f4e-b0c8-a84993a566c0","order_by":6,"name":"Yaoyao Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACfvnHBx984JHg4WdvIFKLZENasuEMGRs5yZ4DRGoxaMhRk+awSTM2uJFArBaGMwzSDDmHExtuPt54g6HGJpqgFnPG3gPGBWcOJzbOTiu2YDiWlttASItlM19C8syew4nN0jlmEowNhwlrMTjGY3CY99/hxDbJM8RqOcNj2MzDk2YMDGcitUjOYEtmnMFjIyfBA/RLAjF+4ZdgPv4DFJX2xw9vvPGhxoawFhRHSiSQohyihVQdo2AUjIJRMDIAAFdOQB6r7FSaAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Obstetrics and Gynecology of West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610065","correspondingAuthor":true,"prefix":"","firstName":"Yaoyao","middleName":"","lastName":"Zhang","suffix":""},{"id":291319511,"identity":"8a63c5ab-4319-4f06-b8a3-181b63c669d4","order_by":7,"name":"Yang Xiong","email":"","orcid":"","institution":"Department of Urology, West China Hospital, Sichuan University, Chengdu 610041","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Xiong","suffix":""}],"badges":[],"createdAt":"2024-04-14 15:59:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4265646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4265646/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54908342,"identity":"92854335-b3b7-4566-a5e4-d1d010dadaf8","added_by":"auto","created_at":"2024-04-18 12:19:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3996365,"visible":true,"origin":"","legend":"\u003cp\u003eThe description covered the entire study design, data analysis workflow, and quality control standards.\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4265646/v1/0eb8e7ec05abf6a6b02217b2.jpeg"},{"id":54908339,"identity":"41d3d0a2-c9cd-4f30-b1ac-b19852c8d345","added_by":"auto","created_at":"2024-04-18 12:19:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":890428,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot summarizing MR analysis results between serum proteins and endometriosis in the finding cohort.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4265646/v1/3ff3e0bbbef9937b3d23a723.jpg"},{"id":54909099,"identity":"f393b429-7644-47d0-8e52-46a76a22d964","added_by":"auto","created_at":"2024-04-18 12:27:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1374137,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction network map of proteins potentially causally related to endometriosis in the finding cohort.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4265646/v1/60ce8b3cde3b745b0c6686cc.jpg"},{"id":54908340,"identity":"5c96be8a-f802-4b97-85af-bacc7d408c9f","added_by":"auto","created_at":"2024-04-18 12:19:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1944837,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of MR analysis results for proteins potentially causally associated with endometriosis in both the finding and validation cohorts for endometriosis.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4265646/v1/78ee31adbddd1cd5eef0a31a.jpg"},{"id":54908344,"identity":"0bf86bfc-7ec4-4893-b878-5f6508177c3c","added_by":"auto","created_at":"2024-04-18 12:19:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4163300,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot summarizing MR analysis results between serum proteins and different subtypes of endometriosis in the finding cohort.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4265646/v1/6e185eb69ed6a3f6b00db036.jpg"},{"id":55264682,"identity":"c5e3e717-0546-42bd-a4e8-3d30db1226e1","added_by":"auto","created_at":"2024-04-25 01:46:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1044366,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4265646/v1/9bc5be1b-2ac7-45c2-9ceb-48228c5a52ed.pdf"},{"id":54908347,"identity":"51f16c82-061a-4457-8aff-845ed76ca17e","added_by":"auto","created_at":"2024-04-18 12:19:32","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39795560,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable17.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4265646/v1/e1ab0d8647c457b3b40f620a.xlsx"},{"id":54908345,"identity":"5fb4db6b-0bdf-47df-b0b7-969c010e6b6d","added_by":"auto","created_at":"2024-04-18 12:19:31","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12467443,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1523.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4265646/v1/b66294103be53b112b14aa6b.xlsx"},{"id":54908343,"identity":"b82db7b6-03e2-44b8-a1dc-39a99cae64ee","added_by":"auto","created_at":"2024-04-18 12:19:30","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3041006,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable814.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4265646/v1/4fe03ce8445b91435aca764e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mendelian randomization Identifies RSPO3 in Serum as a Potential Target for Endometriosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis is a common chronic gynecological disorder, affecting over 190\u0026nbsp;million patients worldwide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is characterized by the presence of endometrial-like tissue outside the uterus. When this tissue implants, it undergoes inflammation, leading to lesions and cysts. Symptoms include menstrual pain, dyspareunia, pelvic pain, and infertility(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The pain symptoms are recurrent and progressively worsen. In addition, patients may experience systemic symptoms such as nausea, vomiting, diarrhea, and dizziness, contributing to a high rate of misdiagnosis(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The gold standard for diagnosing endometriosis is the surgical removal and biopsy of ectopic endometrial tissue through laparotomy or laparoscopy(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Laparotomy involves a larger incision, whereas laparoscopy still has limitations in terms of potential missed diagnoses, making the diagnosis challenging. On average, it takes 7 to 9 years from the onset of symptoms to diagnosis for 60% of patients(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In the United States, the total annual cost for each patient with endometriosis, including both direct and indirect costs, amounts to a staggering \u003cspan\u003e$\u003c/span\u003e27,855. This results in a nationwide annual expenditure of \u003cspan\u003e$\u003c/span\u003e22\u0026nbsp;billion(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In the United Kingdom, the annual cost for patients with endometriosis has also reached a substantial \u0026pound;12.5\u0026nbsp;billion, resulting in significant economic and health burdens(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The risk of endometriosis is influenced by genetic factors, environmental factors, and epigenetics. Currently, the pathophysiology of endometriosis remains unclear, necessitating further research to facilitate early diagnosis and prevention.\u003c/p\u003e \u003cp\u003eMost of the current clinical drugs used to treat endometriosis are hormones, which bring varying degrees of side effects(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Further research is needed to develop drugs with better efficacy. An abundance of studies has shown that the risk of endometriosis is largely explained by genetic factors, which provides a useful idea for early intervention and post-diagnosis treatment of endometriosis, considering the increasing number of researchers here proposed the idea that \"endometriosis can be treated with drugs before histological diagnosis.\" There are currently a number of large-scale GWAS (Genome-Wide Association Studies) studies on endometriosis, which have discovered many genetic loci related to the risk of endometriosis(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), but there are fewer genetic factors that can be converted into drug targets. This is due to the considerable heterogeneity of endometriosis, and many genetic loci are challenging to replicate in different studies(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This suggests that the process of translating candidate genes into therapeutic drugs using GWAS data alone is lengthy and results in uncertainties.\u003c/p\u003e \u003cp\u003eThe combination of Mendelian Randomization (MR) and Bayesian Colocalization Analysis provides a reliable analytical framework for drug target identification, facilitating a faster and more accurate localization of effect genes and promoting drug translation(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). MR employs gene differences as instrumental variables to determine causal effects between exposure and outcome, reducing bias from confounding factors and reverse causation(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Sensitivity analysis is used to eliminate the impact of pleiotropy(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Bayesian Colocalization Analysis is employed to ascertain whether the effects between exposure and outcome share the same genetic variation, a necessary condition for establishing causal associations(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Previous studies have primarily focused on GWAS data for endometriosis, neglecting the exploration of potential drug targets(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In this study, our efforts are directed towards identifying new drug targets for endometriosis through MR and Bayesian Colocalization Analysis, utilizing plasma pQTL (protein quantitative trait loci) data and endometriosis GWAS data. Validation analysis and subtype analysis of endometriosis are conducted to delve deep into potential drug targets and pathogenic mechanisms, offering a comprehensive exploration of endometriosis.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThe primary analysis utilized protein data from Sun et al. in conjunction with endometriosis data from the ninth round of FinnGen. The design of this study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Test outcomes underwent validation through Bonferroni significance testing, and further validation was conducted using endometriosis data from the UK Biobank. Reverse MR analysis and Bayesian co-localization analysis were executed on proteins demonstrating consistency in both the main and validation analyses. Proteins passing the previous analyzes underwent additional validation using Fer et al.'s protein data(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), employing significant genetic variation strategy and identical genetic variation strategy. Subsequently, the analytical framework was applied to data concerning potential target proteins and endometriosis subtypes to deepen our understanding of endometriosis pathogenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGWAS summary statistics of Endometriosis\u003c/h2\u003e \u003cp\u003eThe primary analysis was based on the summary data of the GWAS for endometriosis obtained from the ninth round of FinnGen, comprising 15,088 cases and 107,564 female controls of European ancestry(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In the validation analysis, endometriosis summary data was acquired from the UKBB GWAS imputed V3 through a phenotype description search for \"endometriosis\"(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This dataset included 1,496 cases and 192,678 female controls. Endometriosis subtype data were obtained from the ninth round of FinnGen, and relevant information for all studies is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eInformation on the different GWAS data in this study.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en_case\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en_control\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerum protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMID: 29875488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerum protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMID: 34857953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK Biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e191747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFallopian tube\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntestine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePelvic peritoneum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRectovaginal septum and vagina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUterus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePlasma Protein quantitative trait loci\u003c/h2\u003e \u003cp\u003eThe serum protein Quantitative Trait Loci (pQTL) data were obtained from the study by Sun et al., where the analysis encompassed 3,301 healthy participants and involved the examination of 3,622 serum proteins(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The relevant pQTL data were integrated into the IEU Project by Zheng et al(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Qualified pQTL were selected based on the following criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) demonstrated a genome-wide significant association (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸), and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) exhibited independent association [linkage disequilibrium (LD) clumping r\u0026sup2; \u0026lt; 0.001, window size\u0026thinsp;=\u0026thinsp;10,000 kb]. Ultimately, pQTL related to 1,806 proteins were identified. For the validation analysis, pQTL data were sourced from the study conducted by Ferkingstad et al., comprising measurements of 4,907 plasma proteins in a cohort of 35,559 participants(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eMendelian randomization analysis\u003c/h2\u003e \u003cp\u003eIn the primary analysis, we applied Bonferroni correction to address multiple testing concerns, setting a threshold P value of 0.05/1806 (P\u0026thinsp;\u0026lt;\u0026thinsp;2.77 \u0026times; 10⁻⁵) to prioritize results for subsequent analysis(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The initially identified proteins underwent MR for external validation, with a P value threshold of 0.05. We employed the same variant strategy, utilizing the exact SNPs as genetic instruments from the primary analysis, and also employed a significant variant strategy using genome-wide significant SNPs as genetic instruments to confirm the initial findings.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReverse causality detection\u003c/h2\u003e \u003cp\u003eThe comprehensive GWAS data for proteins were available, and employing the same criteria as used for pQTL screening, we obtained 20 instrumental variables for endometriosis from the ninth round of FinnGen's GWAS data. Endometriosis was considered the exposure, and proteins were treated as outcomes in a reverse Mendelian Randomization (MR) analysis. We utilized the IVW, MR-Egger, and weighted median methods to estimate the causal relationship between them. Steiger filtering was also implemented to ensure the directionality of the association between proteins and endometriosis(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBayesian Colocalization Analysis\u003c/h2\u003e \u003cp\u003eTo perform Bayesian colocalization analysis using the \"coloc\" package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/chr1swallace/coloc\u003c/span\u003e\u003cspan address=\"https://github.com/chr1swallace/coloc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and to assess the probability that two traits share the same causal variant simultaneously, strictly adhering to default parameter settings. Within the Bayesian colocalization analysis framework, posterior probabilities are systematically calculated for five different hypotheses regarding the shared single variant between two traits. This study specifically investigates the posterior probabilities of Hypothesis 3 (PPH3) and Hypothesis 4 (PPH4). PPH3 posits that protein and endometriosis are concurrently associated with different variants in the region, whereas PPH4 suggests that both traits are associated with a shared variant within the same region. The analysis utilizes the coloc.abf algorithm and establishes a criterion wherein a gene is deemed as evidence for colocalization if the gene exhibits a gene-based PPH4 exceeding 80%(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Power Estimate\u003c/h2\u003e \u003cp\u003eThe risk of bias intensifies with weak instrumental variables, to evaluate the strength of these instrumental variables, F statistics serve as a reliable measure, which can be expressed as a function of the first-stage R\u003csup\u003e2\u003c/sup\u003e, the sample size (N) and the number of IVs (k), pQTLs with an F-statistic greater than 10 are considered to have sufficient strength(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$F=\\frac{N-k-1}{k}\\times \\frac{{R}^{2}}{1-{R}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSelected Instrumental Variables\u003c/h2\u003e \u003cp\u003eWe selected SNPs that meet the screening criteria (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸, r\u0026sup2; \u0026lt; 0.001, window size\u0026thinsp;=\u0026thinsp;10,000 kb, F\u003csub\u003estat\u003c/sub\u003e \u0026gt; 10) as instrumental variables representing serum proteins. We then extracted information about these SNPs from the corresponding outcomes for use in Mendelian Randomization (MR) analysis. Detailed information can be found in Supplementary Table\u0026nbsp;8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eScreening the proteome for endometriosis causal proteins\u003c/h2\u003e \u003cp\u003eAt Bonferroni significance (P\u0026thinsp;\u0026lt;\u0026thinsp;2.77 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;5), MR analysis revealed ten protein\u0026ndash;Endometriosis pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;1), including Intercellular adhesion molecule 2 (ICAM-2), R-spondin-3 (RSPO3), Intercellular adhesion molecule 4 (ICAM-4), Endoglin (ENG), OX-2 membrane glycoprotein (OX2), Leukemia inhibitory factor receptor (LIFR), Insulin-like growth factor 1 receptor (IGF1R), Hydroxycarboxylic acid receptor 2 (HCA2), Tryptase gamma (TPSG1), Alpha-(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)-fucosyltransferase 9 (FUT9) in the plasma. Specifically, increased RSPO3(OR\u0026thinsp;=\u0026thinsp;0.75, 95%CI: 1.15\u0026thinsp;~\u0026thinsp;0.83, p\u0026thinsp;=\u0026thinsp;9.22E-10), HCA2(OR\u0026thinsp;=\u0026thinsp;0.76, 95%CI: 1.25\u0026thinsp;~\u0026thinsp;0.86, p\u0026thinsp;=\u0026thinsp;8.50E-06), TPSG1(OR\u0026thinsp;=\u0026thinsp;0.78, 95%CI: 1.25\u0026thinsp;~\u0026thinsp;0.87, p\u0026thinsp;=\u0026thinsp;9.01E-06) and FUT9(OR\u0026thinsp;=\u0026thinsp;0.78, 95%CI: 1.25\u0026thinsp;~\u0026thinsp;0.87, p\u0026thinsp;=\u0026thinsp;9.01E-06) decreased the risk of endometriosis, whereas elevated ICAM-2 (OR\u0026thinsp;=\u0026thinsp;1.39, 95%CI: 1.25\u0026thinsp;~\u0026thinsp;1.55, p\u0026thinsp;=\u0026thinsp;7.15E-10), ICAM-4 (OR\u0026thinsp;=\u0026thinsp;1.52, 95%CI: 1.15\u0026thinsp;~\u0026thinsp;1.76, p\u0026thinsp;=\u0026thinsp;9.29E-09), ENG (OR\u0026thinsp;=\u0026thinsp;1.45, 95%CI: 1.15\u0026thinsp;~\u0026thinsp;1.65, p\u0026thinsp;=\u0026thinsp;9.29E-09), OX2 (OR\u0026thinsp;=\u0026thinsp;1.59, 95%CI: 1.15\u0026thinsp;~\u0026thinsp;1.87, p\u0026thinsp;=\u0026thinsp;9.29E-09), LIFR (OR\u0026thinsp;=\u0026thinsp;1.34, 95%CI: 1.15\u0026thinsp;~\u0026thinsp;1.49, p\u0026thinsp;=\u0026thinsp;9.29E-09) and IGF1R(OR\u0026thinsp;=\u0026thinsp;1.50, 95%CI: 1.15\u0026thinsp;~\u0026thinsp;1.73, p\u0026thinsp;=\u0026thinsp;9.29E-09) increased the risk of endometriosis. No heterogeneity was detected for the proteins analysed in the primary analysis. The Protein-Protein Interaction Networks (PPI) network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was drawn through the String database using corrected positive proteins from serum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis for endometriosis causal proteins\u003c/h2\u003e \u003cp\u003eAfter using the GWAS data of endometriosis from UK Biobank (UKB) as outcome data for validation, the MR analysis results for six proteins remained positive (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and supplementary table 15). RSPO3 of the six proteins was identified as potential drug targets for endometriosis. First, bidirectional MR analysis did not reveal any causal effect of endometriosis on the level of six identified proteins further ensured directionality (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;16). Second, Bayesian co-localization strongly suggested that RSPO3 (coloc.abf-PPH4\u0026thinsp;=\u0026thinsp;0.974), shared the same variant with endometriosis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Colocalization results of five additional proteins showed that they do not share the same genetic variant with endometriosis. All Bayesian co-localization results are collected in Supplementary Table\u0026nbsp;17\u0026ndash;22.\u003c/p\u003e \u003cp\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\u003eResults of the IVW model from reverse MR analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ensnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercellular adhesion molecule 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.92E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.80E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.15E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-spondin-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.04E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.90E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercellular adhesion molecule 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.71E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.73E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.11E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoglin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.82E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.60E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.09E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOX-2 membrane glycoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.69E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.41E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.41E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukemia inhibitory factor receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.95E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.24E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin-like growth factor 1 receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.68E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.43E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.96E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxycarboxylic acid receptor 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.62E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.26E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.69E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTryptase gamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.51E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.29E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.06E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha-(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)-fucosyltransferase 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.29E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.25E-01\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of tests of different hypotheses for Bayesian colocalization.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP.H0.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePP.H1.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePP.H2.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP.H3.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePP.H4.abf\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercellular adhesion molecule 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.99E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.71E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.48E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-spondin-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.04E-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.14E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69E-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.32E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.67E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercellular adhesion molecule 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14E-148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.26E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.99E-149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.29E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.41E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoglin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.60E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.32E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.11E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOX-2 membrane glycoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.40E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.64E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.20E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukemia inhibitory factor receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.40E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.64E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.20E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eExternal validation of potential drug targets for endometriosis\u003c/h2\u003e \u003cp\u003eUsing the same mutation strategy and significant mutation strategy, extract the same pQTLs from the research data of Fer et al. as those in the research data of Sun et al. for MR analysis, or use the same screening criteria to extract pQTLs for MR analysis to replicate the main findings. The validation results of both strategies showed that RSPO3 has a significant protective effect against endometriosis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Table\u0026nbsp;23).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults from MR analysis of rspo3 from Fer et al. study on endometriosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eStrategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ensnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSignificant mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.65E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.76E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.74E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSignificant mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.95E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.49E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.61E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSignificant mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.95E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.92E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.95E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSame mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eWald ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.66E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.72E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.46E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between proteome and endometriosis subtypes\u003c/h2\u003e \u003cp\u003eEndometriosis exhibits diverse sub-phenotypes, with the location and growth patterns of ectopic endometriotic lesions influencing categorization. These sub-phenotypes, encompassing the intestine, ovary, pelvic peritoneum, uterus, fallopian tube, and rectovaginal regions, were identified in the FinnGen cohort. The cohort's GWAS summary statistics for different endometriosis sub-phenotypes, with patient numbers varying from 116 for fallopian tube endometriosis to 3231 for ovarian endometriosis, were available. Given the potential impact of sub-phenotypes on symptoms and infertility risk, some patients experienced more than one type due to overlapping manifestations. To delve deeper into the causal effects of genetically predicted plasma protein levels on diverse endometriosis sub-phenotypes, we conducted Mendelian randomization analyses on different subtypes.\u003c/p\u003e \u003cp\u003eThe MR analysis results, following correction, indicate that there is no significant association between serum proteins and three subtypes of endometriosis: endometriosis of the intestine, endometriosis of the fallopian tube, and endometriosis of the rectovaginal regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Supplementary Table\u0026nbsp;2\u0026ndash;7, 9\u0026ndash;14). For endometriosis of the ovary, VSIG2 and TBX22 are identified as risk factors, while PTH receptor is noted as a protective factor. In the case of endometriosis of the pelvic peritoneum, ICAM-4 is identified as a risk factor, and RSPO3 is recognized as a protective factor for endometriosis of the uterus.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to utilize two-sample MR and Bayesian colocalization to explore causal proteins in endometriosis. Finally, we identified a protein as a potential drug target in endometriosis, circulating RSPO3, which is protective against endometriosis. UK Biobank also confirmed that RSPO3 is associated with endometriosis using MR analysis, further demonstrating the robustness of the potential drug targets identified in this study.\u003c/p\u003e \u003cp\u003eThe cause of endometriosis is complex. It may be caused by the interaction of many genetic and environmental factors. The clustering of endometriosis cases within families and the susceptibility among relatives have revealed that genetic variation plays an important role in the pathogenesis(\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Therefore, a large number of screening studies for endometriosis risk genes have been conducted(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), and there are a number of GWAS for endometriosis(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Although a certain number of risk genes have been discovered at a genome-wide significant level, these genes are closely related to protein WNT signal transduction, cell migration, angiogenesis, inflammation metabolism and other life activities, and also show significant correlation with genetic variations in other diseases, but their interpretation of disease risk remains extremely limited(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The occurrence of endometriosis is also highly influenced by epigenetics. Some genes expressed during the secretory phase of the menstrual cycle have been observed to be highly methylated in patients with endometriosis(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Studies have shown that the methylation of these genes appears to promote progesterone resistance in patients, which is almost certainly a risk factor for endometriosis(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). MicroRNA can also produce epigenetic effects, such as promoting mitosis at the lesion site and promoting the proliferation and migration of ectopic endometrium tissue cells(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). However, these findings are highly heterogeneous in different studies(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEndometriosis is a chronic inflammatory disease for which there are generally three main treatments, including medication, surgery and assisted reproductive technology(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Drug treatments include hormonal treatments such as progestins, gonadotropin-releasing hormone analogues, contraceptive pills, etc.; non-hormonal treatments such as analgesics or non-steroidal anti-inflammatory drugs, but these treatments have certain effects in the long-term treatment process. Certain limitations. For example, hormone therapy will have a contraceptive effect on women, while analgesics and non-steroidal anti-inflammatory drugs are similar to prescribing medications and cannot produce a curative effect(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Some patients also choose surgical removal of endometriosis, but this cannot treat the root cause of the disease and has a high recurrence rate. Studies have shown that many inadequate or unnecessary surgeries are performed for endometriosis, which not only brings a greater financial burden to patients but also puts patients at a higher risk of surgical complications(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Assisted reproductive technology plays a role in infertility due to endometriosis, but there is no evidence that it has any benefit in the treatment of endometriosis, and there is even evidence that these surgeries and Endometriosis itself can adversely affect ovarian reserve(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Therefore, the treatment of endometriosis urgently requires clearer treatment targets and a long-term perspective to formulate treatment strategies to help patients.\u003c/p\u003e \u003cp\u003eTo identify potential therapeutic targets for endometriosis, our research employs the robust framework of Mendelian randomization. This approach allows for the exploration of causal relationships between serum proteins and the risk of developing endometriosis. Integrating data from multiple endometriosis and serum protein databases, we conduct a comprehensive analysis to test our hypotheses. A pivotal aspect of our methodology involves colocalization analysis, which has yielded significant insights. Specifically, we identified shared genetic variants between RSPO3, a member of the R-spondin family involved in modulating WNT signaling, and endometriosis(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Our results suggest that RSPO3 may exert a protective effect against the development of endometriosis. Certainly, enriching the content about the functions of RSPO3 in the body will provide a more comprehensive understanding of its potential role in endometriosis and other physiological processes. As a crucial modulator in the WNT signaling pathway, fundamental to numerous biological processes, RSPO3 plays a vital role in cell proliferation, differentiation, and embryonic development(\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). This pathway is pivotal in maintaining the balance between stem cell renewal and differentiation, essential for tissue homeostasis and regeneration(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). In the context of endometrial tissue, this balance is critical, as disruptions can contribute to the aberrant growth patterns observed in endometriosis. By modulating angiogenesis, RSPO3 could potentially impact the viability and proliferation of endometriosis lesions(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). This is particularly relevant in endometriosis, where the establishment and maintenance of ectopic endometrial lesions depend on the development of a sufficient blood supply(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Consequently, RSPO3's interaction with immune system components may have significant implications for the inflammatory processes underpinning endometriosis pathology(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Endometriosis is characterized by a chronic inflammatory environment, and modulating this environment is crucial for disease management(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Understanding RSPO3's potential role in modulating fibrotic pathways could be pivotal in elucidating the progression and severity of endometriosis(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Fibrosis, the process of excessive connective tissue formation, is often observed in chronic diseases, including endometriosis(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). The involvement of RSPO3 in these pathways underscores its potential as a promising therapeutic target in endometriosis, offering avenues for both treatment and a deeper understanding of the disease\u0026rsquo;s pathophysiology.\u003c/p\u003e \u003cp\u003eThis study, while providing significant insights into endometriosis, acknowledges certain limitations that need to be addressed in future research. Firstly, the protective factor RSPO3, identified at the overall level of endometriosis, could not be consistently replicated in the analysis of specific subtypes. This discrepancy may be attributed to the limited number of samples available for subtype analysis. It is anticipated that future studies with larger subtype-specific sample sizes will allow for a more thorough exploration of this aspect. Secondly, while the association of endometriosis with certain factors was successfully replicated at the protein level, offering a new dimension for identifying potential drug targets, the study fell short in elucidating the numerous loci identified through genome-wide association studies (GWAS). This gap highlights the complexity of endometriosis and suggests that a more extensive investigation is required to fully understand the genetic underpinnings identified by GWAS. Lastly, it is important to note that endometriosis is a condition specific to female reproductive health. Although the outcome data for this study were derived from female-specific GWAS datasets, the plasma protein data, which formed the basis of exposure analysis, were not gender-specific. This could potentially influence the study's findings and their applicability. Furthermore, both the exposure and outcome data were exclusively obtained from European populations. This raises questions about the generalizability of the study's conclusions to other ethnic groups. Consequently, future research should endeavor to include a more diverse population to ensure the findings are universally applicable and relevant.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-wide association study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse variance weighting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCTs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized controlled trials\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICAM-2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntercellular adhesion molecule 2 ()\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRSPO3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eR-spondin-3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICAM-4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntercellular adhesion molecule 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eENG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEndoglin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOX2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOX-2 membrane glycoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLIFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeukemia inhibitory factor receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIGF1R\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsulin-like growth factor 1 receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCA2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHydroxycarboxylic acid receptor 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTPSG1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTryptase gamma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFUT9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlpha-(1,3)-fucosyltransferase 9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe analyses were based on publicly available data that have been approved by relevant review boards.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll GWAS data used in the article are publicly available. The GWAS data of serum proteins come from the study of Sun et al. and the study of Fer et al. The data of endometriosis come from the ninth round of data released by the FinnGen cohort and the V3 version of the UK Biobank.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eYZ, YX, and WG conceived and designed the study. YZ supervised the study and data analysis. WG and ZZ performed the data analysis with help from XY, TO, DZ, YL. YZ, YX, WG, ZZ and XY wrote the manuscript. All authors revised and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank the FinnGen cohort, UK biobank, Sun et al., and Fer et al. haring the GWAS data. We would like to thank the anonymous reviewers for their constructive comments. We would like to thank to Xu Zhang of Youhe AI and Medicine.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHorne AW, Missmer SA. Pathophysiology, diagnosis, and management of endometriosis. BMJ (Clinical research ed). 2022;379:e070750.\u003c/li\u003e\n\u003cli\u003eFedele L, Berlanda N, Corsi C, et al. Ileocecal endometriosis: clinical and pathogenetic implications of an underdiagnosed condition. Fertility and sterility. 2014;101(3):750-3.\u003c/li\u003e\n\u003cli\u003ePrescott J, Farland LV, Tobias DK, et al. A prospective cohort study of endometriosis and subsequent risk of infertility. Human reproduction (Oxford, England). 2016;31(7):1475-82.\u003c/li\u003e\n\u003cli\u003eSaunders PTK, Horne AW. Endometriosis: Etiology, pathobiology, and therapeutic prospects. Cell. 2021;184(11):2807-24.\u003c/li\u003e\n\u003cli\u003eZondervan KT, Becker CM, Koga K, et al. Endometriosis. 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Human reproduction (Oxford, England). 2018;33(3):347-52.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Mendelian Randomization, Endometriosis, Serum proteins, Bayesian colocalization, Causal Analysis","lastPublishedDoi":"10.21203/rs.3.rs-4265646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4265646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective\u003c/p\u003e\n\u003cp\u003eEndometriosis lacks effective early intervention and treatment. Our objective is to explore potential protein drug targets in serum for endometriosis and different subtypes of endometriosis, using Mendelian randomization and Bayesian colocalization to provide support for clinical intervention.\u003c/p\u003e\n\u003cp\u003eDesign\u003c/p\u003e\n\u003cp\u003eMulti-validated two-sample Mendelian randomization study, combined with Bayesian co-localization analysis to determine drug targets.\u003c/p\u003e\n\u003cp\u003eSetting\u003c/p\u003e\n\u003cp\u003eSummary statistics from published GWAS in European ancestry populations.\u003c/p\u003e\n\u003cp\u003ePopulation or Sample\u003c/p\u003e\n\u003cp\u003eInstrumental variants for serum proteins of finding cohort were obtained from a study on 3301 people, and instrumental variants for endometriosis and different subtypes of endometriosis of finding cohort were obtained from FinnGen cohort. Data of \u0026nbsp;endometriosis of replicated cohort including 191747 people were obtained from UK biobank, and data of serum proteins of replicated cohort were obtained from a study including 35559 people.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eUsing Mendelian randomization, we explored and discovered a significant causal association between certain serum proteins and endometriosis. This finding was validated using data on endometriosis and serum proteins from a validation cohort. Finally, Bayesian colocalization analysis was applied to identify potential drug targets. Additionally, Mendelian randomization analysis was conducted on different subtypes of endometriosis to identify proteins potentially associated with these subtypes.\u003c/p\u003e\n\u003cp\u003eMain outcome measures\u003c/p\u003e\n\u003cp\u003eData for the endometriosis discovery cohort were obtained from the FinnGen cohort, and data for the endometriosis validation cohort were obtained from the UK Biobank.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eResults from the MR analysis in the finding cohort indicated ten protein–Endometriosis pairs, including Intercellular adhesion molecule 2, R-spondin-3, Intercellular adhesion molecule 4, Endoglin, OX-2 membrane glycoprotein, Leukemia inhibitory factor receptor, Insulin-like growth factor 1 receptor, Hydroxycarboxylic acid receptor 2, Tryptase gamma, Alpha-(1,3)-fucosyltransferase 9 in the plasma. After validation analysis and Bayesian co-localization analysis, RSPO3 was identified as a potential drug target for endometriosis.\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eWe conducted Mendelian Randomization analysis on GWAS data from a large population, confirming a causal relationship between serum levels of RSPO3 and endometriosis. This suggests that RSPO3 may influence the onset and progression of endometriosis, providing a protective effect. This finding supports its potential as a preventive and therapeutic approach for endometriosis.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe study was supported by funding from the projects of Chengdu Science and Technology Bureau, (Y.Z., Grant No. 2021-YF05-02110-SN), China Postdoctoral Science Foundation (Y.Z., Grant No. 2020M680149, 2020T130087ZX).\u003c/p\u003e","manuscriptTitle":"Mendelian randomization Identifies RSPO3 in Serum as a Potential Target for Endometriosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 12:19:25","doi":"10.21203/rs.3.rs-4265646/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":"1e191b37-29e5-4c7c-9859-ce0d5b0bb18c","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-21T15:56:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-18 12:19:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4265646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4265646","identity":"rs-4265646","version":["v1"]},"buildId":"0SHbDDIpRTBOrFPTvp6pu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}