Endometriosis and Epithelial Ovarian Cancer: A Two-Sample Mendelian Randomization analysis

Endometriosis and Epithelial Ovarian Cancer: A Two-Sample Mendelian Randomization analysis | 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 Short Report Endometriosis and Epithelial Ovarian Cancer: A Two-Sample Mendelian Randomization analysis Li Wang, Xuri Li, Yan Wang, Guofeng Li, Shuzhen Dai, Mengying Cao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2379913/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 Background Endometriosis is a common disease and was considered a chronic, debilitating disease affecting an estimated 1790 million women worldwide. Observational studies have shown a link between endometriosis and ovarian cancer. Therefore, we sought to use Two-sample Mendelian randomization (MR) using summary statistics from genome-wide association study (GWAS) of endometriosis and epithelia ovarian cancer to infer causal effects with genetic markers as a proxy for epithelial ovarian cancer. Results The analysis indicated a significant association between them. For histotype-specific analyses, there was strong evidence for an association of endometriosis with risk of endometrioid carcinoma, clear cell carcinoma and low malignant potential tumors. Conclusions These findings provide a theoretical basis for further research to increase the potential therapeutic benefit of endometriosis life management to prevent the onset and progression of ovarian cancer. Endometriosis Epithelial Ovarian Cancer Mendelian Randomization Figures Figure 1 Figure 2 Figure 3 Background Endometriosis is a common disease and was considered a chronic, debilitating disease 1 , 2 affecting 5–10% of women of reproductive age globally 3 . The association between endometriosis and cancer has been studied for a number of years. Significant data have accumulated to support the thought that some gynecologic cancers originate from endometriosis over the last few decades 4 . Ovarian cancer which is the most lethal gynaecological malignancy 5 and second most common cause of gynecologic cancer death in women around the world 6 often associated with endometriosis, especially the relatively uncommon carcinoma of the ovary clear cell carcinoma 4 . Risk factors for epithelial ovarian cancer include the family history of this cancer, number of lifetime ovulations, benign gynaecological conditions including endometriosis 7 , and potentially use of talcum powder 8 . Indeed, endometriosis has been reported to be associated with a higher risk of several cancer types in population research 9 .Sampson first published a report in 1927 alluding to malignancy associated with endometriosis, wherein he described specific criteria for endometriosis-associated ovarian cancers 10 . The meta-analyses of relationships between endometriosis and ovarian cancer have been published these years. The meta-analysis including 24 observational studies to evaluated the association between endometriosis and ovarian cancer calculated summary relative risk 1.93 (SRR = 1.93, 95% CI 1.68–2.22) of ovarian cancer among women with endometriosis compared with those without 11 , another study based on 21case-control or cohort studies published in 1990-2012 12 , estimated the summary relative risk (SRR) of the endometriosis on ovarian cancer is 1.27 (95% CI = 1.21–1.32), and of 1.80 (95% CI = 1.28–2.53) based on five studies including women with endometriosis only. Wang et al. 13 reported the odds ratio (OR) of 1.42 (95% CI = 1.28–1.57) based on 12 case-control studies. Quantifying ovarian cancer risk in women with endometriosis is crucial for several reasons, but these observations pose management challenges to clinicians who care for women with endometriosis and might have important public health implications 14 for women in terms of cancer screening and prevention, and for clinicians in terms of the long-term management of women with endometriosis 15 . Although endometriosis seems to be closely related to epithelia ovarian cancer, it is not clear whether there is a causal relationship between the two. Mendelian randomization (MR) may help to clarify this relationship 16 . MR is an analytical research method provides evidence about putative causal relations between modifiable risk factors and disease 17 using genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data 16,18−20 . MR is less likely to be affected by confounding or reverse causation than conventional observational studies, so that statistical power is usually much higher in two sample studies 17 . These advantages come with two additional assumptions: the two samples are assumed to represent the same underlying population, and overlap in participants between the two samples can cause bias towards the risk factor-outcome association 21 , 22 . Given the unclear causal relevance of previously observational associations of endometriosis in the etiology of epithelial ovarian cancer, in this study, we performed two-sample MR of published GWAS data to evaluate the association of between endometriosis and epithelial ovarian cancer, epithelial ovarian cancer histotypes, and low malignant potential tumors. Results There was strong evidence for an association of endometriosis persistent with risk of epithelial ovarian cancer (OR per year: 1.23, 95% CI 1.11–1.36; P = 5.44E-05) in IVW models. The association of the 14-SNP instrument with endometriosis was also evaluated using the MR-Egger, weighted median and weighted mode techniques. MR-Egger regression (OR 1.72, 95% CI 1.11–2.66), weighted median (OR 1.26, 95% CI 1.13–1.41) and a weighted mode (1.29, 95% CI 1.10–1.52), directionally consistent results were observed, suggesting that the findings are relatively unaffected by violation of MR assumptions, while the intercept = 0.99 (OR 0.96–1.01, P = 0.34) indicating no statistical significance, which means that genetic pleiority did not driving the MR results (Table 1 ). In order to obtain the MR estimates using each of the SNPs singly, we performs the analysis multiple times for each exposure-outcome combination - each time using a different single SNP to perform the analysis, and the association of the 14-SNP instrument with endometriosis was also evaluated as in seen in Table 2 . Scatterplots (Fig. 1 ) demonstrate that SNPs with greater effect on endometriosis have a greater effect on the risk of ovarian cancer, lines are drawn for each method used different color, the slope of the line corresponding to the estimated causal effect. It is possible to perform a leave-one-out analysis, where the MR is performed again but leaving out each SNP in turn, to identify most of the associated signals were not driven by a single genetic marker were showed in the leave-one-out analysis (Fig. 2 ). We use the forest plot to compare the MR estimates using the different MR methods against the single SNP tests, it shows the endometriosis as estimated using each of the SNPs on their own, and comparing against the causal effect as estimated using the methods that use all the SNPs. (Fig. 3 ) Table 1 IVW and sensitivity analysis estimates for the association of Endometriosis with risk of Ovarian Cancer Exposure Outcome Method OR 95% CI P-value Endometriosis Ovarian Cancer IVW 1.23 1.11–1.36 5.44E-05 MR-Egger regression 1.72 1.11–2.66 0.032 MR-Egger intercept 0.99 0.96–1.01 0.34 Weighted median 1.26 1.13–1.41 3.42E-05 Weighted mode 1.29 1.10–1.52 0.01 Table 2 Association analysis of the SNPs instrument with Endometriosis with the diagnosis of Ovarian Cancer Exposure Outcome SNPs OR 95% CI P -value Endometriosis Ovarian Cancer rs10167914 1.182 0.90–1.55 0.23 rs10757272 1.24 0.80–1.94 0.34 rs11674184 1.43 1.12–1.83 0.004 rs12037376 1.46 1.15–1.86 0.002 rs12700667 1.26 0.92–1.73 0.15 rs1448792 0.68 0.48–0.96 0.03 rs1537377 1.25 0.88–1.96 0.21 rs17803970 1.14 0.82–1.57 0.44 rs1903068 1.36 1.04–1.78 0.02 rs1971256 1.26 0.90–1.77 0.17 rs2206949 1.35 0.99–1.83 0.055 rs6546324 0.90 0.62–1.29 0.57 rs71575922 1.14 0.81–1.61 0.45 rs74485684 1.30 0.92–1.83 0.14 For histotype-specific analyses, there was suggestive evidence for an association of endometriosis with risk of endometrioid carcinoma (OR 1.49, 95%CI 1.24–1.81, P = 3.18E-05), clear cell carcinoma (OR 2.56, 95%CI 1.75–3.73, P = 1.09E-06) and low malignant potential tumors (OR 1.28, 95% CI 1.08–1.53, P = 0.005) in IVW models showed in Table 3 , which was consistent in sensitivity analyses examining horizontal pleiotropy. Furthermore, we performed the analysis multiple times for each exposure-outcome combination - each time using a different single SNP to perform the analysis, 14 SNPs were analyzed as instrument with endometriosis and the three histotype-specific outcomes as described above (Table 4 ). Table 3 IVW and sensitivity analysis estimates for the association of Endometriosis with risk of invasive epithelial ovarian cancer histotypes and low malignant potential tumors Outcome Ovarian Cancer IVW OR (95% CI) P - value MR-Egger regression OR (95% CI) P -value MR-Egger intercept OR (95% CI) P -value Weighted median OR (95%C) P - value Weighted mode OR (95% CI) P -value HGSC 1.10(0.99–1.23) 0.09 1.68(1.06–2.67) 0.05 0.99 (0.98–1.02) 0.42 1.11(0.96–1.27) 0.15 1.13(0.87–1.46) 0.36 LGSC 1.07(0.79–1.44) 0.66 0.90(0.23–3.53) 0.88 1.01 (0.98–1.04) 0.17 1.06(0.70–1.60) 0.77 1.00(0.54–1.87) 1.00 Mucinous 1.29(0.98–1.68) 0.07 1.48(0.41–5.33) 0.56 1.01 (0.97–1.05) 0.47 1.07(0.75–1.52) 0.71 0.93(0.49–1.76) 0.82 Endometrioid * 1.49(1.24–1.81) 3.18E-05 1.28(0.52–3.16) 0.60 0.99 (0.98–1.03) 0.60 1.48(1.16–1.90) 0.002 1.46(1.02–20.9) 0.06 Clear cell * 2.56(1.75–3.73) 1.09E-06 7.69(1.41–41.78) 0.054 0.99 (0.96–1.01) 0.43 2.94(2.03–4.26) 1.32E-08 3.00(1.79–5.04) 0.001 LMP * 1.28(1.08–1.53) 0.005 2.37(1.06–5.30) 0.057 1.12 (0.96–1.23) 0.58 1.27(1.01–1.61) 0.04 1.33(0.92–1.94) 0.15 Causal estimates are scaled to represent the association of endometriosis persistent exposure. IVW = Inverse-variance weighted, HGSC = High grade serous carcinoma, LGSC = Low grade serous carcinoma, LMP = Low malignant potential tumours. * Suggestive evidence for an association of endometriosis with risk of diagnosis. Table 4 Association analysis of the single SNPs instrument with Endometriosis with the histotypes of Ovarian Cancer SNPs Endometrioid Carcinoma Clear cell Carcinoma Low malignant potential tumour Associated Gene/ cytoband OR 95% CI P -value OR 95% CI P -value OR 95% CI P -value rs10167914 1.30 0.72–2.34 0.39 4.51 2.02–10.06 0.00 1.48 0.83–2.64 0.19 interleukin (IL)-1A/2q13 rs10757272 1.92 0.73–5.01 0.18 2.83 0.74–10.85 0.13 0.65 0.25–1.68 0.38 CDKN2BAS /9p21.3 rs11674184 1.51 0.89–2.57 0.13 3.08 1.46–6.47 0.003 1.26 0.75–2.13 0.38 GREB1 /2p25.1 rs12037376 1.47 0.87–2.46 0.15 2.20 1.09–4.46 0.02 1.89 1.11–3.14 0.01 WNT4/1p36.12 rs12700667 1.53 0.77–3.05 0.23 3.51 1.32–9.33 0.01 1.24 0.63–2.44 0.53 inter-genic region upstream of plausible candidate genes NFE2L3 and HOXA 10./7p15.2 rs1448792 0.82 0.39–1.72 0.59 0.32 0.11–0.91 0.033 0.92 0.44–1.92 0.83 LINC01239 RNA/ 9p21.3 rs1537377 1.45 0.69–3.05 0.33 1.65 0.58–4.66 0.35 1.27 0.61–2.65 0.52 CDKN2B-AS1 /9p21.3 rs17803970 1.02 0.51–2.06 0.96 5.08 1.78–14.50 0.002 0.97 0.49–1.94 0.93 SYNE1/6q25.1 rs1903068 1.52 0.85–2.70 0.16 4.09 1.81–9.27 0.001 1.58 0.90–2.79 0.11 KDR/4q12 rs1971256 3.26 1.60–6.63 0.001 3.01 1.11–8.15 0.03 0.76 0.37–1.57 0.46 CCDC170 / 6q25.1 rs2206949 1.95 1.01–3.75 0.05 2.64 1.06–6.55 0.04 1.69 0.89–3.22 0.11 ESR1 / 6q25.1 rs6546324 1.18 0.53–2.60 0.68 0.72 0.24–2.21 0.57 1.35 0.62–2.94 0.45 ETAA1/2p14 rs71575922 0.88 0.42–1.86 0.75 6.01 2.21–16.33 0.00 1.14 0.56–2.39 0.71 SYNE1/6q25.1 rs74485684 2.93 1.38–6.23 0.005 1.76 0.61–5.02 0.29 1.30 0.62–2.69 0.49 FSHB/11p14.1 IVW 1.50 1.24–1.81 3.18E-05 2.56 1.75–3.73 1.09E-06 1.28 1.08–1.53 0.005 MR-Egger 1.28 0.52–3.15 0.60 7.69 1.41–41.78 0.05 2.37 1.06–5.30 0.06 Discussion This study used Two-sample MR analyses to assess the relationship between endometriosis and epithelial ovarian cancer involving data from large-scale GWAS of the OCAC, and the causal association between them, with increased endometriosis persistent increasing the risk of ovarian cancer were observed. We also report detail results of association for the epithelial ovarian cancer histotypes, even the low malignant potential tumors. Endometriosis affect about 10% of the female population and not only can it significantly impact adversely on quality of life and result in infertility, but data are accumulating that malignant transformation is an important consideration 23 . Ovarian cancer which is the second most common cause of gynecologic cancer death in women around the world 6 was considered as the most important associated cancer, primarily endometrioid and clear cell carcinoma 23 . The meta-analysis including 24 observational studies to evaluated the association between endometriosis and ovarian cancer calculated summary relative risk 1.93 (SRR = 1.93, 95% CI 1.68–2.22) of ovarian cancer among women with endometriosis compared with those without 11 . Another study based on 21case-control or cohort studies published in 1990-2012 12 , estimated the summary relative risk (SRR) of the endometriosis on ovarian cancer is 1.27 (95% CI = 1.21–1.32), and of 1.80 (95% CI = 1.28–2.53) based on five studies including women with endometriosis only. Wang et al. 13 reported the odds ratio (OR) of 1.42 (95% CI = 1.28–1.57) based on 12 case-control studies. However, there was strong evidence of publication bias towards an overestimation of the association 11 . An association of genetic liability to endometriosis with invasive epithelial ovarian cancer corroborates findings from conventional analyses that women with this condition are at elevated risk of subsequent disease 13 , 24 . One MR analysis 25 provided strong evidence for an association of genetic liability to endometriosis with invasive epithelial ovarian cancer using 10 SNPs as instrument (OR per 50% higher odds liability to endometriosis: 1.10, 95% CI 1.06–1.15; P = 6.94 × 10 − 7 ). However, in our Two-sample MR analysis, we calculated the consistent results that is women with endometriosis has 23% (OR = 1.23 95% CI 1.11–1.36; P = 5.44E-05) greater risk of epithelial ovarian cancer using 14 SNPs as instruments, which was consistent in different models and across sensitivity analyses examining horizontal pleiotropy. As MR studies can provide reliable evidence on the effect of modifiable endometriosis as risk factor for ovarian cancer as outcome and can overcome some limitations of traditional observational epidemiology 17 . We could definite the endometriosis as the causal or etiology of epithelial ovarian cancer but not only a risk factor. The associations we observed between endometriosis and epithelial ovarian cancer risk by histotype using Two-sample MR, lend support to the previous observational study results. In histotype-specific analyses, there was suggestive evidence for an association of endometriosis with risk of endometrioid carcinoma (OR 1.49, 95%CI 1.24–1.81, P = 3.18E-05), clear cell carcinoma (OR 2.56, 95%CI 1.75–3.73, P = 1.09E-06) and low malignant potential tumors (OR 1.28, 95% CI 1.08–1.53, P = 0.005), which was consistent in sensitivity analyses examining horizontal pleiotropy. Although endometriosis is reported to be present in 25–58% of clear cell carcinoma cases and be considered as a risk factor for its developing 10,26−29 , most of them concerned the association between clear cell carcinoma and endometriosis 30 , 31 . which is risk of clear cell carcinoma was found significantly elevated in the patients with endometrioma of the ovary (RR/relative risk = 12.4) 30 . Few studies have produced risk estimates endometriosis and histotype of ovarian cancer and Saavalainen L 32 found that endometrioma was positively associated with the clear cell (SIR = 10.1), endometrioid (SIR = 4.7) and serous (SIR = 1.62) histotypes. A pooled analysis of 13 case-control studies including 7911 women with ovarian cancer and 13,226 controls showed that self-reported endometriosis was associated with an increased risk of ovarian clear cell carcinoma (OR = 3.05, 95%CI 2.43–3.84), endometrioid carcinoma (OR = 2.04, 95%CI 1.67–2.48), and low grade serous carcinoma (OR = 2.11, 95%CI 1.39–3.20)] 33 . reinforcing that the higher risk of ovarian cancer in women with endometriosis is restricted to the clear cell, endometrioid and low malignant ovarian cancer histotypes, and future studies should focus on these three histotypes rather than on ovarian cancer overall. Furthermore, we found different SNPs associated with different histotype, also suggests that subclinical manifestations of or pathways leading to oncogenesis, indicating important avenues for future mechanistic work. While we detected little association between endometriosis and serous or mucinous cancer, for HGSC (OR = 1.10 95%CI 0.99–1.23, P = 0.09), LGSC (OR = 1.07 95%CI 0.79–1.44, P = 0.66) and Mucinous (OR = 1.29 95% CI 0.98–1.68, P = 0.07).However, ovarian clear cell carcinoma is a different entity from the other endometriosis-associated ovarian carcinomas with a distinct gene expression profile 34 . Although observational and MR estimates examining associations between endometriosis and risk of epithelial ovarian cancer are qualitatively similar, it is important to emphasize that observational effect estimates for disease states examined cannot be compared quantitatively to the MR estimates in our study. In settings for which the instrumental variable assumptions are well justified (assessed as described above and using biological knowledge), the findings could help prioritise clinical trials or drug development and inform clinical or public health decision making 22 , 35 , 36 . Additionally, further work to understand the possible mechanisms through which factors that appear to influence epithelial ovarian cancer in endometriosis promote oncogenesis could help to increase the scope for prevention opportunities across the life course. The mechanisms underlying the differential associations observed between endometriosis and the risk of specific ovarian cancer types need to be explored. The health of women with endometriosis can be affected by care decisions that might result from the potential misinterpretation of the link between endometriosis and ovarian cancer 37 . It is necessary to emerge fundamental discoveries of omic driven pathways that associated with endometriosis-specific cancer pathophysiologic patterns to elucidate the pathways of women with endometriosis appear to be at higher risk of some types of epithelial ovarian cancer. There are several limitations in our research. Our research also has some limitations. First, the Two-sample MR analysis used summarized data from GWAS catalog and it was not possible to detect the nonlinear relationship between exposure factors and disease outcomes. Second, the Two-sample analysis cannot explain the pathogenesis of epithelia ovarian cancer, so it is necessary to further explore the pathogenic mechanism of endometriosis on epithelial ovarian cancer, especially for the clear cell, endometrioid and low malignant histotypes. Finally, these findings need to be carried out in the context of other evidence with specific associations, but the MR method we used in this study can provide strong support for clarifying the direction of causality. In this way, we believe that this study can contribute to clinical treatment for endometriosis and prevention efforts of epithelial ovarian cancer to improve public health. Conclusion In conclusion, our findings provide strong evidence for a causal relationship between endometriosis and increased epithelial ovarian cancer risk, with the most robust association observed for the clear cell, endometrioid and low malignant histotypes. These findings provide a theoretical basis for further research to increase the potential therapeutic benefit of endometriosis life management to prevent the onset and progression of ovarian cancer. Future studies are needed to understand the mechanisms underlying this association. Methods Study design Two-sample MR analysis 38 was used to estimate the casual relationship between Endometriosis and the risk of Ovarian Cancer. This Two-sample MR study is an extension based on MR that is a form of instrumental variable analysis that uses genetic variants to proxy for environmental exposures 39 and the effects of the genetic instrument on the exposure and on the outcome in the Two-sample MR are obtained from separate genome-wide association studies (GWAS). Identification of SNPs associated with Endometrioses For Endometriosis, we used the GWAS (discovery and replication meta-analysis, including 17,045 Endometriosis cases and 191,596 controls.) 40 which identified and extract information for single nucleotide polymorphisms (SNPs) that were associated with Endometriosis at the genome-wide significance level (P < 5×10 8 ). We identified 19 SNPs associated with Endometriosis from GWAS publication, and 14 were included in our instrument (rs10167914; rs10757272; rs11674184; rs12037376; rs12700667; rs1448792; rs1537377; rs17803970; rs1903068; rs1971256; rs2206949; rs6546324; rs71575922; rs74485684). GWAS of Ovarian Cancer To assess whether Endometriosis is associated with ovarian cancer, we used data from a GWAS of epithelial ovarian cancer (EOC) using DNA samples from OncoArray Consortium consisted of 25,509 women with epithelial ovarian cancer and 40,941 controls of European ancestry that passed QC 41 , 42 . This database comprised 63 genotyping project/case-control sets representing participants recruited from 14 countries. The analyses included 66,450 samples from 7 genotyping projects: 40,941 controls, 22,406 invasive cases including 1,012 low-grade serous, 13,037 high-grade serous, 2,810 endometrioid, 1,366 clear cell, 1,417 mucinous and other 2,764 EOC. Analyses were also performed for 3,103 borderline cases including 1,954 serous borderline and 1,149 mucinous borderline tumors. Genotypes for OCAC samples were preferentially selected from the different projects in the following order: OncoArray, Mayo GWAS, Collaborative Oncological Gene-environment Study (COGS), and other GWAS. SNP quality control (QC) was carried out according to standard QC guidelines 41 and datas were obtained either by direct genotyping using an Illumina Custom Infinium array (OncoArray) considering of approximately 530,000 SNPs or by imputation with reference to the 1000 Genomes reference panel phase 3 version 5 43 . Ethical approval from relevant research ethics committees was granted for each of the original GWAS studies and details can be found in the respective publications. Statistical analyses Genetic instruments for exposures used in an MR framework allows for unbiased causal effects of risk factors on disease outcomes to be estimated is based on three key assumptions: (i) genetic instrument is robustly associated with the risk factor of interest; (ii) the instrument must not be associated with any confounding factor(s) of the association between the exposure and outcome; and (iii) there must be no no effects of the genetic variants on the outcome, that do not go via the risk factor (i.e. no horizontal pleiotropy) 44 . SNPs were pruned for linkage disequilibrium at R 2 < 0.001 at a clumping distance of 10,000 kilobases from the lead SNP at P < 5 × 10 − 8 with reference to the 1000 Genomes Project ( https://www.internationalgenome.org ) when obtaining effect estimates from relevant GWASs. We conducted Two-sample MR analyses using an inverse variance weighted (IVW) to estimate the association between endometriosis and ovarian cancer. IVW is a weighted linear regression model, which aggregates and minimizes the sum of the variances of two or more random variables, and each random variable is inversely proportional to its variance 45 . 14 SNPs were used to construct the genetic instrument for endometriosis. The primary MR analysis was conducted using the inverse variance weighted (IVW) method, wherein the SNP to outcome estimate is regressed on the SNP to exposure estimate, all genetic variations are valid instrumental variables 44 . Fixed effects IVW were used to give that we did not detect instrument variable assumption violations (neither heterogeneity nor pleiotropy were observed). MR-Egger regression 44 , weighted median estimation 46 , and weighted mode estimation 47 , each of which makes different assumptions about the underlying nature of horizontal pleiotropy were also built. Additionally, leave-one-out permutation analyses were performed to examine whether any results were driven by individual influential SNPs in IVW models 25 .The proportion of risk factor and outcome variance could explained by SNPs used as instruments in this method, to help establish whether SNPs associated with both risk factors and outcomes, further to primarily represent (1) a direct association of a SNP with a risk factor, which then influences an outcome, or (2) a direct association of a SNP with an outcome, which then influences the level of a risk factor 25 . ORs [95% confidence intervals (CI)] were estimated in all invasive ovarian cancers, borderline disease and by histotype (serous borderline, mucinous borderline, low-grade serous, high-grade serous, mucinous invasive, clear cell and endometrioid) samples. MR Egger regression to assess bias from directional pleiotropy 41 , and using a weighted median estimator that can provide a consistent estimate of the effect when ≤ 50% of the information comes from invalid instrumental variables 46 . All analyses were conducted in R studio and R (version 3.6.3) with the packages (‘TwoSampleMR’ 18 , 48 and MendilianRandomization). Declarations The authors declare no potential conflicts of interest. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable Availability of data and materials The data was from previous published study as listed in the references when mentioned in this main text. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Natural Science Foundation of Shandong Province (ZR2020MH139). Authors' contributions Each author made substantial contributions to the design of the work; Shuzhen Dai conceived and designed this study; Li Wang, Xuri Li, Yan Wang, Guofeng Li and Songtao Ren analyzed data, Li Wang and Songtao Ren wrote the first draft and substantively revised it.; Shuzhen Dai, Xuri Li and Mengying Cao contributed to the manuscript writing. All authors have agreed with the manuscript’s results and conclusions and have approved the submitted version, confirmed that they meet, ICMJE criteria for authorship. Corresponding authors Li Wang and Songtao Ren Acknowledgements We would like to thank Rui Wang- Sattler and Jialing Huang in Research Unit of Molecular Epidemiology, Helmholtz Zentrum München for supporting our analysis. References Giudice LC. Clinical practice. Endometriosis. N Engl J Med 2010 , 362 , 2389-98. Macer ML,Taylor HS. Endometriosis and infertility: a review of the pathogenesis and treatment of endometriosis-associated infertility. 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Imaging in gynecological disease (14): clinical and ultrasound characteristics of ovarian clear cell carcinoma. Ultrasound Obstet Gynecol 2018 , 52 , 792-800. Park JY,Kim DY,Suh DS,Kim JH,Kim YM,Kim YT et al. Significance of Ovarian Endometriosis on the Prognosis of Ovarian Clear Cell Carcinoma. Int J Gynecol Cancer 2018 , 28 , 11-18. Bai H,Cao D,Yuan F,Sha G,Yang J,Chen J et al. Prognostic value of endometriosis in patients with stage I ovarian clear cell carcinoma: Experiences at three academic institutions. Gynecol Oncol 2016 , 143 , 526-31. Jang JYA,Yanaihara N,Pujade-Lauraine E,Mikami Y,Oda K,Bookman M et al. Update on rare epithelial ovarian cancers: based on the Rare Ovarian Tumors Young Investigator Conference. J Gynecol Oncol 2017 , 28 , e54. Kobayashi H,Sumimoto K,Moniwa N,Imai M,Takakura K,Kuromaki T et al. Risk of developing ovarian cancer among women with ovarian endometrioma: a cohort study in Shizuoka, Japan. Int J Gynecol Cancer 2007 , 17 , 37-43. Yamamoto S,Tsuda H,Takano M,Hase K,Tamai S,Matsubara O. Clear-cell adenofibroma can be a clonal precursor for clear-cell adenocarcinoma of the ovary: a possible alternative ovarian clear-cell carcinogenic pathway. J Pathol 2008 , 216 , 103-10. Saavalainen L,Lassus H,But A,Tiitinen A,Härkki P,Gissler M et al. Risk of Gynecologic Cancer According to the Type of Endometriosis. Obstet Gynecol 2018 , 131 , 1095-102. Pearce CL,Templeman C,Rossing MA,Lee A,Near AM,Webb PM et al. Association between endometriosis and risk of histological subtypes of ovarian cancer: a pooled analysis of case-control studies. Lancet Oncol 2012 , 13 , 385-94. Zhao T,Shao Y,Liu Y,Wang X,Guan L,Lu Y. Endometriosis does not confer improved prognosis in ovarian clear cell carcinoma: a retrospective study at a single institute. J Ovarian Res 2018 , 11 , 53. Walker VM,Davey Smith G,Davies NM,Martin RM. Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities. Int J Epidemiol 2017 , 46 , 2078-89. Fordyce CB,Roe MT,Ahmad T,Libby P,Borer JS,Hiatt WR et al. Cardiovascular drug development: is it dead or just hibernating? J Am Coll Cardiol 2015 , 65 , 1567-82. Anglesio MS,Papadopoulos N,Ayhan A,Nazeran TM,Noë M,Horlings HM et al. Cancer-Associated Mutations in Endometriosis without Cancer. N Engl J Med 2017 , 376 , 1835-48. Lawlor DA. Commentary: Two-sample Mendelian randomization: opportunities and challenges. Int J Epidemiol 2016 , 45 , 908-15. Davey Smith G,Paternoster L,Relton C. When Will Mendelian Randomization Become Relevant for Clinical Practice and Public Health? Jama 2017 , 317 , 589-91. Sapkota Y,Steinthorsdottir V,Morris AP,Fassbender A,Rahmioglu N,De Vivo I et al. Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism. Nat Commun 2017 , 8 , 15539. Amos CI,Dennis J,Wang Z,Byun J,Schumacher FR,Gayther SA et al. The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers. Cancer Epidemiol Biomarkers Prev 2017 , 26 , 126-35. Phelan CM,Kuchenbaecker KB,Tyrer JP,Kar SP,Lawrenson K,Winham SJ et al. Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat Genet 2017 , 49 , 680-91. Auton A,Brooks LD,Durbin RM,Garrison EP,Kang HM,Korbel JO et al. A global reference for human genetic variation. Nature 2015 , 526 , 68-74. Bowden J,Davey Smith G,Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015 , 44 , 512-25. Burgess S,Butterworth A,Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013 , 37 , 658-65. Bowden J,Davey Smith G,Haycock PC,Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 2016 , 40 , 304-14. Hartwig FP,Davey Smith G,Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 2017 , 46 , 1985-98. Hemani G,Zheng J,Elsworth B,Wade KH,Haberland V,Baird D et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 2018 , 7 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-2379913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":160229649,"identity":"03301a45-acf0-49e4-b023-02df86b0dc10","order_by":0,"name":"Li Wang","email":"","orcid":"","institution":"Liaocheng University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Wang","suffix":""},{"id":160229651,"identity":"3a1b9f83-e7c6-48b4-aac5-6be7841b5ef5","order_by":1,"name":"Xuri Li","email":"","orcid":"","institution":"Qingdao Hospital of Traditional Chinese Medicine, Qingdao Hiser hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuri","middleName":"","lastName":"Li","suffix":""},{"id":160229653,"identity":"e81f1dca-ac83-4453-a631-b45d10021ce1","order_by":2,"name":"Yan Wang","email":"","orcid":"","institution":"Fourth People's Hospital of Liaocheng","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wang","suffix":""},{"id":160229654,"identity":"5160d31a-6d85-4120-bedb-bd190446feac","order_by":3,"name":"Guofeng Li","email":"","orcid":"","institution":"Fourth People's Hospital of Liaocheng","correspondingAuthor":false,"prefix":"","firstName":"Guofeng","middleName":"","lastName":"Li","suffix":""},{"id":160229655,"identity":"81b55d08-d098-477f-a2cc-82f2bf05c784","order_by":4,"name":"Shuzhen Dai","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Shuzhen","middleName":"","lastName":"Dai","suffix":""},{"id":160229656,"identity":"dac74ea0-980b-4ebb-a6dc-9af03e66ae25","order_by":5,"name":"Mengying Cao","email":"","orcid":"","institution":"Liaocheng University","correspondingAuthor":false,"prefix":"","firstName":"Mengying","middleName":"","lastName":"Cao","suffix":""},{"id":160229657,"identity":"88c44c96-ded2-4f48-9642-34a1ec0c7efc","order_by":6,"name":"Songtao Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnklEQVRIiWNgGAWjYLCCBxUScvwkqGdmYEg4Y2Es2UCSlsS2isQNRGsx7z9/TCJxngTjBgbmh49uEKNF5kYym0TiNglmcwY2Y+McYrRISDCz3QBqYbNs4GGTJk4L/2GgljkSPAYHiNbCkAzU0iAhQYIWiWTzHwnHJAwkm4n2C//BxwYfaurq+9mbHz4mSgsCMJOmfBSMglEwCkYBPgAASkUppNF7/ekAAAAASUVORK5CYII=","orcid":"","institution":"Liaocheng People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Songtao","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2022-12-15 03:44:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2379913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2379913/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":30440069,"identity":"7b4a3db1-50c5-4380-8025-e7c84a0868d4","added_by":"auto","created_at":"2022-12-16 16:41:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plot of single SNP with Endometriosis as the exposure and Ovarian Cancer as the outcome.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2379913/v1/0dcc09619fc1fa82fb3e737e.png"},{"id":30440071,"identity":"e97683f9-40a2-4507-9076-25e4573c1bef","added_by":"auto","created_at":"2022-12-16 16:41:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47154,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLeave-one-out analysis of single SNP with Endometriosis as the exposure and Ovarian Cancer as the outcome.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2379913/v1/dc831e560de245ff04dbf282.png"},{"id":30440070,"identity":"e4affd90-e658-4596-873f-9ca52c99f875","added_by":"auto","created_at":"2022-12-16 16:41:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot analysis of single SNP with Endometriosis as the exposure and Ovarian Cancer as the outcome.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2379913/v1/af15d0797d9b3d586a183bbd.png"},{"id":30594924,"identity":"3a663078-fef5-4ba8-8c70-b0e96a8aa3dd","added_by":"auto","created_at":"2022-12-21 00:59:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":672351,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2379913/v1/aab3d48d-c3f5-4fe6-9c7e-73fa14861141.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Endometriosis and Epithelial Ovarian Cancer: A Two-Sample Mendelian Randomization analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eEndometriosis is a common disease and was considered a chronic, debilitating disease\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e affecting 5\u0026ndash;10% of women of reproductive age globally\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The association between endometriosis and cancer has been studied for a number of years. Significant data have accumulated to support the thought that some gynecologic cancers originate from endometriosis over the last few decades\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Ovarian cancer which is the most lethal gynaecological malignancy\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and second most common cause of gynecologic cancer death in women around the world\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e often associated with endometriosis, especially the relatively uncommon carcinoma of the ovary clear cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Risk factors for epithelial ovarian cancer include the family history of this cancer, number of lifetime ovulations, benign gynaecological conditions including endometriosis\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and potentially use of talcum powder\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Indeed, endometriosis has been reported to be associated with a higher risk of several cancer types in population research\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.Sampson first published a report in 1927 alluding to malignancy associated with endometriosis, wherein he described specific criteria for endometriosis-associated ovarian cancers\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The meta-analyses of relationships between endometriosis and ovarian cancer have been published these years. The meta-analysis including 24 observational studies to evaluated the association between endometriosis and ovarian cancer calculated summary relative risk 1.93 (SRR\u0026thinsp;=\u0026thinsp;1.93, 95% CI 1.68\u0026ndash;2.22) of ovarian cancer among women with endometriosis compared with those without\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, another study based on 21case-control or cohort studies published in 1990-2012\u003csup\u003e12\u003c/sup\u003e, estimated the summary relative risk (SRR) of the endometriosis on ovarian cancer is 1.27 (95% CI\u0026thinsp;=\u0026thinsp;1.21\u0026ndash;1.32), and of 1.80 (95% CI\u0026thinsp;=\u0026thinsp;1.28\u0026ndash;2.53) based on five studies including women with endometriosis only. Wang et al.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e reported the odds ratio (OR) of 1.42 (95% CI\u0026thinsp;=\u0026thinsp;1.28\u0026ndash;1.57) based on 12 case-control studies.\u003c/p\u003e \u003cp\u003eQuantifying ovarian cancer risk in women with endometriosis is crucial for several reasons, but these observations pose management challenges to clinicians who care for women with endometriosis and might have important public health implications\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e for women in terms of cancer screening and prevention, and for clinicians in terms of the long-term management of women with endometriosis\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough endometriosis seems to be closely related to epithelia ovarian cancer, it is not clear whether there is a causal relationship between the two. Mendelian randomization (MR) may help to clarify this relationship\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. MR is an analytical research method provides\u0026ensp;evidence\u0026ensp;about\u0026ensp;putative\u0026ensp;causal\u0026ensp;relations\u0026ensp;between\u0026ensp;modifiable\u0026ensp;risk\u0026ensp;factors\u0026ensp;and\u0026ensp;disease\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e using genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data\u003csup\u003e16,18\u0026minus;20\u003c/sup\u003e. MR\u0026ensp;is\u0026ensp;less\u0026ensp;likely\u0026ensp;to\u0026ensp;be\u0026ensp;affected\u0026ensp;by\u0026ensp;confounding\u0026ensp;or\u0026ensp;reverse\u0026ensp;causation\u0026ensp;than\u0026ensp;conventional\u0026ensp;observational\u0026ensp;studies, so that statistical power is usually much higher in two sample studies\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. \u0026ensp; These advantages come with two additional assumptions: the two samples are assumed to represent the same underlying population, and overlap in participants between the two samples can cause bias towards the risk factor-outcome association\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the unclear causal relevance of previously observational associations of endometriosis in the etiology of epithelial ovarian cancer, in this study, we performed two-sample MR of published GWAS data to evaluate the association of between endometriosis and epithelial ovarian cancer, epithelial ovarian cancer histotypes, and low malignant potential tumors.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThere was strong evidence for an association of endometriosis persistent with risk of epithelial ovarian cancer (OR per year: 1.23, 95% CI 1.11\u0026ndash;1.36; P\u0026thinsp;=\u0026thinsp;5.44E-05) in IVW models. The association of the 14-SNP instrument with endometriosis was also evaluated using the MR-Egger, weighted median and weighted mode techniques. MR-Egger regression (OR 1.72, 95% CI 1.11\u0026ndash;2.66), weighted median (OR 1.26, 95% CI 1.13\u0026ndash;1.41) and a weighted mode (1.29, 95% CI 1.10\u0026ndash;1.52), directionally consistent results were observed, suggesting that the findings are relatively unaffected by violation of MR assumptions, while the intercept\u0026thinsp;=\u0026thinsp;0.99 (OR 0.96\u0026ndash;1.01, P\u0026thinsp;=\u0026thinsp;0.34) indicating no statistical significance, which means that genetic pleiority did not driving the MR results (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In order to obtain the MR estimates using each of the SNPs singly, we performs the analysis multiple times for each exposure-outcome combination - each time using a different single SNP to perform the analysis, and the association of the 14-SNP instrument with endometriosis was also evaluated as in seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Scatterplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) demonstrate that SNPs with greater effect on endometriosis have a greater effect on the risk of ovarian cancer, lines are drawn for each method used different color, the slope of the line corresponding to the estimated causal effect. It is possible to perform a leave-one-out analysis, where the MR is performed again but leaving out each SNP in turn, to identify most of the associated signals were not driven by a single genetic marker were showed in the leave-one-out analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We use the forest plot to compare the MR estimates using the different MR methods against the single SNP tests, it shows the endometriosis as estimated using each of the SNPs on their own, and comparing against the causal effect as estimated using the methods that use all the SNPs. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\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\u003eIVW and sensitivity analysis estimates for the association of Endometriosis with risk of Ovarian Cancer\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eEndometriosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eOvarian Cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eIVW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.11\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.44E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMR-Egger regression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.11\u0026ndash;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMR-Egger intercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWeighted median\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.13\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.42E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWeighted mode\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.10\u0026ndash;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\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=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation analysis of the SNPs instrument with Endometriosis with the diagnosis of Ovarian Cancer\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \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\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003e\u003cb\u003eEndometriosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003e\u003cb\u003eOvarian Cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10167914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10757272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80\u0026ndash;1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11674184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12\u0026ndash;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers12037376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15\u0026ndash;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers12700667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92\u0026ndash;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1448792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1537377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u0026ndash;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers17803970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1903068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04\u0026ndash;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1971256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u0026ndash;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2206949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u0026ndash;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6546324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers71575922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u0026ndash;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers74485684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92\u0026ndash;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\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 \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor histotype-specific analyses, there was suggestive evidence for an association of endometriosis with risk of endometrioid carcinoma (OR 1.49, 95%CI 1.24\u0026ndash;1.81, P\u0026thinsp;=\u0026thinsp;3.18E-05), clear cell carcinoma (OR 2.56, 95%CI 1.75\u0026ndash;3.73, P\u0026thinsp;=\u0026thinsp;1.09E-06) and low malignant potential tumors (OR 1.28, 95% CI 1.08\u0026ndash;1.53, P\u0026thinsp;=\u0026thinsp;0.005) in IVW models showed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which was consistent in sensitivity analyses examining horizontal pleiotropy. Furthermore, we performed the analysis multiple times for each exposure-outcome combination - each time using a different single SNP to perform the analysis, 14 SNPs were analyzed as instrument with endometriosis and the three histotype-specific outcomes as described above (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eIVW and sensitivity analysis estimates for the association of Endometriosis with risk of invasive epithelial ovarian cancer histotypes and low malignant potential tumors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome Ovarian Cancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR-Egger regression\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMR-Egger intercept\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003cp\u003eOR (95%C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10(0.99\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.68(1.06\u0026ndash;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.11(0.96\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.13(0.87\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07(0.79\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90(0.23\u0026ndash;3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.98\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.06(0.70\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.00(0.54\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucinous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29(0.98\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.48(0.41\u0026ndash;5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.97\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.07(0.75\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.93(0.49\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrioid\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.49(1.24\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.18E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28(0.52\u0026ndash;3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.48(1.16\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.46(1.02\u0026ndash;20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear cell\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.56(1.75\u0026ndash;3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.69(1.41\u0026ndash;41.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.94(2.03\u0026ndash;4.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.32E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.00(1.79\u0026ndash;5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMP\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.28(1.08\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.37(1.06\u0026ndash;5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.12 (0.96\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.27(1.01\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.33(0.92\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eCausal estimates are scaled to represent the association of endometriosis persistent exposure. IVW\u0026thinsp;=\u0026thinsp;Inverse-variance weighted, HGSC\u0026thinsp;=\u0026thinsp;High grade serous carcinoma, LGSC\u0026thinsp;=\u0026thinsp;Low grade serous carcinoma, LMP\u0026thinsp;=\u0026thinsp;Low malignant potential tumours.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e* Suggestive evidence for an association of endometriosis with risk of diagnosis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eAssociation analysis of the single SNPs instrument with Endometriosis with the histotypes of Ovarian Cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eEndometrioid Carcinoma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eClear cell Carcinoma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eLow malignant potential tumour\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAssociated Gene/ cytoband\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10167914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026ndash;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.02\u0026ndash;10.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.83\u0026ndash;2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003einterleukin (IL)-1A/2q13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10757272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026ndash;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u0026ndash;10.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.25\u0026ndash;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCDKN2BAS /9p21.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11674184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.46\u0026ndash;6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.75\u0026ndash;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eGREB1 /2p25.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers12037376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026ndash;2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.09\u0026ndash;4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.11\u0026ndash;3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eWNT4/1p36.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers12700667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32\u0026ndash;9.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.63\u0026ndash;2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003einter-genic region upstream of plausible candidate genes NFE2L3 and HOXA 10./7p15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1448792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39\u0026ndash;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u0026ndash;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.44\u0026ndash;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLINC01239 RNA/ 9p21.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1537377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026ndash;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u0026ndash;4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.61\u0026ndash;2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCDKN2B-AS1 /9p21.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers17803970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026ndash;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.78\u0026ndash;14.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.49\u0026ndash;1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSYNE1/6q25.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1903068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026ndash;2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.81\u0026ndash;9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.90\u0026ndash;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eKDR/4q12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1971256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.60\u0026ndash;6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.11\u0026ndash;8.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.37\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCCDC170 / 6q25.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2206949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.06\u0026ndash;6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.89\u0026ndash;3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eESR1 / 6q25.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6546324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u0026ndash;2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u0026ndash;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.62\u0026ndash;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eETAA1/2p14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers71575922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026ndash;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.21\u0026ndash;16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.56\u0026ndash;2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSYNE1/6q25.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers74485684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.38\u0026ndash;6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u0026ndash;5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.62\u0026ndash;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFSHB/11p14.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u0026ndash;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.18E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.75\u0026ndash;3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.08\u0026ndash;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026ndash;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.41\u0026ndash;41.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.06\u0026ndash;5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study used Two-sample MR analyses to assess the relationship between endometriosis and epithelial ovarian cancer involving data from large-scale GWAS of the OCAC, and the causal association between them, with increased endometriosis persistent increasing the risk of ovarian cancer were observed. We also report detail results of association for the epithelial ovarian cancer histotypes, even the low malignant potential tumors.\u003c/p\u003e \u003cp\u003eEndometriosis affect about 10% of the female population and not only can it significantly impact adversely on quality of life and result in infertility, but data are accumulating that malignant transformation is an important consideration\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Ovarian cancer which is the second most common cause of gynecologic cancer death in women around the world\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e was considered as the most important associated cancer, primarily endometrioid and clear cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe meta-analysis including 24 observational studies to evaluated the association between endometriosis and ovarian cancer calculated summary relative risk 1.93 (SRR\u0026thinsp;=\u0026thinsp;1.93, 95% CI 1.68\u0026ndash;2.22) of ovarian cancer among women with endometriosis compared with those without\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Another study based on 21case-control or cohort studies published in 1990-2012\u003csup\u003e12\u003c/sup\u003e, estimated the summary relative risk (SRR) of the endometriosis on ovarian cancer is 1.27 (95% CI\u0026thinsp;=\u0026thinsp;1.21\u0026ndash;1.32), and of 1.80 (95% CI\u0026thinsp;=\u0026thinsp;1.28\u0026ndash;2.53) based on five studies including women with endometriosis only. Wang et al.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e reported the odds ratio (OR) of 1.42 (95% CI\u0026thinsp;=\u0026thinsp;1.28\u0026ndash;1.57) based on 12 case-control studies. However, there was strong evidence of publication bias towards an overestimation of the association\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. An association of genetic liability to endometriosis with invasive epithelial ovarian cancer corroborates findings from conventional analyses that women with this condition are at elevated risk of subsequent disease\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. One MR analysis\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e provided strong evidence for an association of genetic liability to endometriosis with invasive epithelial ovarian cancer using 10 SNPs as instrument (OR per 50% higher odds liability to endometriosis: 1.10, 95% CI 1.06\u0026ndash;1.15; P\u0026thinsp;=\u0026thinsp;6.94 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e). However, in our Two-sample MR analysis, we calculated the consistent results that is women with endometriosis has 23% (OR\u0026thinsp;=\u0026thinsp;1.23 95% CI 1.11\u0026ndash;1.36; P\u0026thinsp;=\u0026thinsp;5.44E-05) greater risk of epithelial ovarian cancer using 14 SNPs as instruments, which was consistent in different models and across sensitivity analyses examining horizontal pleiotropy. As MR studies can provide reliable evidence on the effect of modifiable endometriosis as risk factor for ovarian cancer as outcome and can overcome some limitations of traditional observational epidemiology\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. We could definite the endometriosis as the causal or etiology of epithelial ovarian cancer but not only a risk factor.\u003c/p\u003e \u003cp\u003eThe associations we observed between endometriosis and epithelial ovarian cancer risk by histotype using Two-sample MR, lend support to the previous observational study results. In histotype-specific analyses, there was suggestive evidence for an association of endometriosis with risk of endometrioid carcinoma (OR 1.49, 95%CI 1.24\u0026ndash;1.81, P\u0026thinsp;=\u0026thinsp;3.18E-05), clear cell carcinoma (OR 2.56, 95%CI 1.75\u0026ndash;3.73, P\u0026thinsp;=\u0026thinsp;1.09E-06) and low malignant potential tumors (OR 1.28, 95% CI 1.08\u0026ndash;1.53, P\u0026thinsp;=\u0026thinsp;0.005), which was consistent in sensitivity analyses examining horizontal pleiotropy. Although endometriosis is reported to be present in 25\u0026ndash;58% of clear cell carcinoma cases and be considered as a risk factor for its developing\u003csup\u003e10,26\u0026minus;29\u003c/sup\u003e, most of them concerned the association between clear cell carcinoma and endometriosis\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. which is risk of clear cell carcinoma was found significantly elevated in the patients with endometrioma of the ovary (RR/relative risk\u0026thinsp;=\u0026thinsp;12.4)\u003csup\u003e30\u003c/sup\u003e. Few studies have produced risk estimates endometriosis and histotype of ovarian cancer and Saavalainen L\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e found that endometrioma was positively associated with the clear cell (SIR\u0026thinsp;=\u0026thinsp;10.1), endometrioid (SIR\u0026thinsp;=\u0026thinsp;4.7) and serous (SIR\u0026thinsp;=\u0026thinsp;1.62) histotypes. A pooled analysis of 13 case-control studies including 7911 women with ovarian cancer and 13,226 controls showed that self-reported endometriosis was associated with an increased risk of ovarian clear cell carcinoma (OR\u0026thinsp;=\u0026thinsp;3.05, 95%CI 2.43\u0026ndash;3.84), endometrioid carcinoma (OR\u0026thinsp;=\u0026thinsp;2.04, 95%CI 1.67\u0026ndash;2.48), and low grade serous carcinoma (OR\u0026thinsp;=\u0026thinsp;2.11, 95%CI 1.39\u0026ndash;3.20)]\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. reinforcing that the higher risk of ovarian cancer in women with endometriosis is restricted to the clear cell, endometrioid and low malignant ovarian cancer histotypes, and future studies should focus on these three histotypes rather than on ovarian cancer overall. Furthermore, we found different SNPs associated with different histotype, also suggests that subclinical manifestations of or pathways leading to oncogenesis, indicating important avenues for future mechanistic work. While we detected little association between endometriosis and serous or mucinous cancer, for HGSC (OR\u0026thinsp;=\u0026thinsp;1.10 95%CI 0.99\u0026ndash;1.23, P\u0026thinsp;=\u0026thinsp;0.09), LGSC (OR\u0026thinsp;=\u0026thinsp;1.07 95%CI 0.79\u0026ndash;1.44, P\u0026thinsp;=\u0026thinsp;0.66) and Mucinous (OR\u0026thinsp;=\u0026thinsp;1.29 95% CI 0.98\u0026ndash;1.68, P\u0026thinsp;=\u0026thinsp;0.07).However, ovarian clear cell carcinoma is a different entity from the other endometriosis-associated ovarian carcinomas with a distinct gene expression profile\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough observational and MR estimates examining associations between endometriosis and risk of epithelial ovarian cancer are qualitatively similar, it is important to emphasize that observational effect estimates for disease states examined cannot be compared quantitatively to the MR estimates in our study. In settings for which the instrumental variable assumptions are well justified (assessed as described above and using biological knowledge), the findings could help prioritise clinical trials or drug development and inform clinical or public health decision making\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, further work to understand the possible mechanisms through which factors that appear to influence epithelial ovarian cancer in endometriosis promote oncogenesis could help to increase the scope for prevention opportunities across the life course. The mechanisms underlying the differential associations observed between endometriosis and the risk of specific ovarian cancer types need to be explored. The health of women with endometriosis can be affected by care decisions that might result from the potential misinterpretation of the link between endometriosis and ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. It is necessary to emerge fundamental discoveries of omic driven pathways that associated with endometriosis-specific cancer pathophysiologic patterns to elucidate the pathways of women with endometriosis appear to be at higher risk of some types of epithelial ovarian cancer.\u003c/p\u003e \u003cp\u003eThere are several limitations in our research. Our research also has some limitations. First, the Two-sample MR analysis used summarized data from GWAS catalog and it was not possible to detect the nonlinear relationship between exposure factors and disease outcomes. Second, the Two-sample analysis cannot explain the pathogenesis of epithelia ovarian cancer, so it is necessary to further explore the pathogenic mechanism of endometriosis on epithelial ovarian cancer, especially for the clear cell, endometrioid and low malignant histotypes. Finally, these findings need to be carried out in the context of other evidence with specific associations, but the MR method we used in this study can provide strong support for clarifying the direction of causality. In this way, we believe that this study can contribute to clinical treatment for endometriosis and prevention efforts of epithelial ovarian cancer to improve public health.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings provide strong evidence for a causal relationship between endometriosis and increased epithelial ovarian cancer risk, with the most robust association observed for the clear cell, endometrioid and low malignant histotypes. These findings provide a theoretical basis for further research to increase the potential therapeutic benefit of endometriosis life management to prevent the onset and progression of ovarian cancer. Future studies are needed to understand the mechanisms underlying this association.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eTwo-sample MR analysis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e was used to estimate the casual relationship between Endometriosis and the risk of Ovarian Cancer. This Two-sample MR study is an extension based on MR that is a form of instrumental variable analysis that uses genetic variants to proxy for environmental exposures\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and the effects of the genetic instrument on the exposure and on the outcome in the Two-sample MR are obtained from separate genome-wide association studies (GWAS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of SNPs associated with Endometrioses\u003c/h2\u003e \u003cp\u003eFor Endometriosis, we used the GWAS (discovery and replication meta-analysis, including 17,045 Endometriosis cases and 191,596 controls.)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e which identified and extract information for single nucleotide polymorphisms (SNPs) that were associated with Endometriosis at the genome-wide significance level (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e8\u003c/sup\u003e). We identified 19 SNPs associated with Endometriosis from GWAS publication, and 14 were included in our instrument (rs10167914; rs10757272; rs11674184; rs12037376; rs12700667; rs1448792; rs1537377; rs17803970; rs1903068; rs1971256; rs2206949; rs6546324; rs71575922; rs74485684).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGWAS of Ovarian Cancer\u003c/h2\u003e \u003cp\u003eTo assess whether Endometriosis is associated with ovarian cancer, we used data from a GWAS of epithelial ovarian cancer (EOC) using DNA samples from OncoArray Consortium consisted of 25,509 women with epithelial ovarian cancer and 40,941 controls of European ancestry that passed QC\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. This database comprised 63 genotyping project/case-control sets representing participants recruited from 14 countries. The analyses included 66,450 samples from 7 genotyping projects: 40,941 controls, 22,406 invasive cases including 1,012 low-grade serous, 13,037 high-grade serous, 2,810 endometrioid, 1,366 clear cell, 1,417 mucinous and other 2,764 EOC. Analyses were also performed for 3,103 borderline cases including 1,954 serous borderline and 1,149 mucinous borderline tumors. Genotypes for OCAC samples were preferentially selected from the different projects in the following order: OncoArray, Mayo GWAS, Collaborative Oncological Gene-environment Study (COGS), and other GWAS. SNP quality control (QC) was carried out according to standard QC guidelines\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and datas were obtained either by direct genotyping using an Illumina Custom Infinium array (OncoArray) considering of approximately 530,000 SNPs or by imputation with reference to the 1000 Genomes reference panel phase 3 version 5\u003csup\u003e43\u003c/sup\u003e. Ethical approval from relevant research ethics committees was granted for each of the original GWAS studies and details can be found in the respective publications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eGenetic instruments for exposures used in an MR framework allows for unbiased causal effects of risk factors on disease outcomes to be estimated is based on three key assumptions: (i) genetic instrument is robustly associated with the risk factor of interest; (ii) the instrument must not be associated with any confounding factor(s) of the association between the exposure and outcome; and (iii) there must be no no effects of the genetic variants on the outcome, that do not go via the risk factor (i.e. no horizontal pleiotropy)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. SNPs were pruned for linkage disequilibrium at R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 at a clumping distance of 10,000 kilobases from the lead SNP at P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e with reference to the 1000 Genomes Project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.internationalgenome.org\u003c/span\u003e\u003cspan address=\"https://www.internationalgenome.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) when obtaining effect estimates from relevant GWASs.\u003c/p\u003e \u003cp\u003eWe conducted Two-sample MR analyses using an inverse variance weighted (IVW) to estimate the association between endometriosis and ovarian cancer. IVW is a weighted linear regression model, which aggregates and minimizes the sum of the variances of two or more random variables, and each random variable is inversely proportional to its variance\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e14 SNPs were used to construct the genetic instrument for endometriosis. The primary MR analysis was conducted using the inverse variance weighted (IVW) method, wherein the SNP to outcome estimate is regressed on the SNP to exposure estimate, all genetic variations are valid instrumental variables\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Fixed effects IVW were used to give that we did not detect instrument variable assumption violations (neither heterogeneity nor pleiotropy were observed). MR-Egger regression\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, weighted median estimation\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, and weighted mode estimation\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, each of which makes different assumptions about the underlying nature of horizontal pleiotropy were also built. Additionally, leave-one-out permutation analyses were performed to examine whether any results were driven by individual influential SNPs in IVW models\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.The proportion of risk factor and outcome variance could explained by SNPs used as instruments in this method, to help establish whether SNPs associated with both risk factors and outcomes, further to primarily represent (1) a direct association of a SNP with a risk factor, which then influences an outcome, or (2) a direct association of a SNP with an outcome, which then influences the level of a risk factor\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eORs [95% confidence intervals (CI)] were estimated in all invasive ovarian cancers, borderline disease and by histotype (serous borderline, mucinous borderline, low-grade serous, high-grade serous, mucinous invasive, clear cell and endometrioid) samples. MR Egger regression to assess bias from directional pleiotropy\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, and using a weighted median estimator that can provide a consistent estimate of the effect when \u0026le;\u0026thinsp;50% of the information comes from invalid instrumental variables\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll analyses were conducted in R studio and R (version 3.6.3) with the packages (\u0026lsquo;TwoSampleMR\u0026rsquo;\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and MendilianRandomization).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data was from previous published study as listed in the references when mentioned in this main text.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Shandong Province (ZR2020MH139).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEach author made substantial contributions to the design of the work; Shuzhen Dai conceived and designed this study; Li Wang, Xuri Li, Yan Wang, Guofeng Li and Songtao Ren analyzed data, Li Wang and Songtao Ren wrote the first draft and substantively revised it.; Shuzhen Dai, Xuri Li and Mengying Cao contributed to the manuscript writing. All authors have agreed with the manuscript\u0026rsquo;s results and conclusions and have approved the submitted version, confirmed that they meet, ICMJE criteria for authorship.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorresponding authors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLi Wang and Songtao Ren\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Rui Wang- Sattler and Jialing Huang in Research Unit of Molecular Epidemiology, Helmholtz Zentrum M\u0026uuml;nchen for supporting our analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGiudice LC. Clinical practice. Endometriosis. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e2010\u003c/strong\u003e, \u003cem\u003e362\u003c/em\u003e, 2389-98.\u003c/li\u003e\n\u003cli\u003eMacer ML,Taylor HS. 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Observational studies have shown a link between endometriosis and ovarian cancer. Therefore, we sought to use Two-sample Mendelian randomization (MR) using summary statistics from genome-wide association study (GWAS) of endometriosis and epithelia ovarian cancer to infer causal effects with genetic markers as a proxy for epithelial ovarian cancer.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe analysis indicated a significant association between them. For histotype-specific analyses, there was strong evidence for an association of endometriosis with risk of endometrioid carcinoma, clear cell carcinoma and low malignant potential tumors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings provide a theoretical basis for further research to increase the potential therapeutic benefit of endometriosis life management to prevent the onset and progression of ovarian cancer.\u003c/p\u003e","manuscriptTitle":"Endometriosis and Epithelial Ovarian Cancer: A Two-Sample Mendelian Randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-16 16:41:49","doi":"10.21203/rs.3.rs-2379913/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":"9fa9db6a-ac5b-42f6-ba9d-0b041bd6d5fc","owner":[],"postedDate":"December 16th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-12-21T00:59:17+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-16 16:41:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2379913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2379913","identity":"rs-2379913","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}