Pediatric and Adult Obesity Concern Female Health: A Mendelian Randomization Study

Pediatric and Adult Obesity Concern Female Health: A Mendelian Randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pediatric and Adult Obesity Concern Female Health: A Mendelian Randomization Study yishang yan, Zihao Qu, Ping-ping Lv, He-Feng Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-390994/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Sep, 2021 Read the published version in Endocrine → Version 1 posted 4 You are reading this latest preprint version Abstract Purpose: Adulthood and childhood obesity are associated with female reproductive endocrinology and gynecological cancers. However, the causality of such association is yet to be identified. Independent of inverse bias and confounding, mendelian randomization is like a random control trial where genetic groups are settled during meiosis, which could be effective to examine the causality. Methods : We carried out several Mendelian randomization trials based on combined genetic scores of 75 adult-associated and 15 child-associated BMI single nucleotide polymorphisms (SNPs), diseases databases of several gynecological cancers and reproductive diseases from UK Biobank with 194,153 participants, using traditional inverse-variance weighted (IVW) method, Weighted Median (WM) approach, MR-Egger regression and updated MR-PRESSO analysis. Results: Elevated adult-associated BMI (effect:1.003;95%CI:1.001,1.004) and child-associated BMI (effect:1.003;95%CI:1.001,1.004) genetic scores were related to higher risk of PCOS incidence by using traditional IVW method. Random IVW method showed a negative causal association between the child-associated BMI and subsequent endometriosis attack(effect:0.995;95%CI:0.991,0.999). Conclusions: Consistent with observational consequences, our findings suggested that childhood and adulthood obesity may play roles in the development of PCOS, and children obesity can elevate the possibility of PCOS but decrease the incidence of endometriosis in later life. More researches need to be conducted for further validation and potential mechanisms. Endocrinology & Metabolism Mendelian Randomization age-related obesity gynecological cancer endometriosis，polycystic ovarian syndrome Figures Figure 1 Figure 2 Introduction Obesity is characterized as body mass index (BMI) ≥ 30kg/m 2 in adults and BMI-for-age percentile ≥ 95% in children. Trends in obesity and severe obesity prevalence in US have come up to approximately 17% in in American girls(Silvestris et al., 2018) and reached 39.6% in 2015–2016 among women(Hales et al., 2018). Obesity has been a worldwide prevalent phenomenon linking to prevalence risk of female infertility and gynecological cancers(Park et al., 2014) which perplex female physiologically and psychologically. Endometriosis and polycystic ovarian syndrome (PCOS) have become two major diseases type that result in female infertility. However, both of them are etiologically unknown yet. Endometriosis is mainly characterized as dysmenorrhea, pelvic pain and infertility. A research showed an inverse relation between current BMI and endometriosis was observed when cases were concurrently infertile(Missmer et al., 2004). An ongoing prospective case-control cohort study(Shah et al., 2013) uncovered that women’s current BMI and BMI at age 18 were statistically associated with the rate of endometriosis inversely, which is stronger among infertile women, and the most robust association was with BMI at age 18. It suggests us that there may be an ‘early window of exposure’ during which lower BMI at adolescence leaves an impact on endometriosis preceding the diagnosis of the disease. PCOS is characterized as hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology, which most attacks reproductive women with 8% morbidity. Observational studies has discovered that obesity, even childhood obesity(Koivuaho et al., 2019a), has a positive association with PCOS as well as other female reproductive system tumors in adulthood. According to a systemic review and meta-analysis, obese patients take a larger proposition than nonobese ones in PCOS(Lim et al., 2012). On the other hand, patients with greater central adiposity positively were associated with more severe metabolic phenotype such as insulin resistant diabetes mellitus(Ollila et al., 2017). PCOS takes place early at puberty. A longitude population-based study concluded that adolescent obesity predicted ovulatory dysfunction in later life(Laitinen et al., 2003). So, it’s necessary to find the causality between adult or child obesity and infertility so that we can take measures to prevent the damage of overweight to health. Cervical cancers, endometrial carcinomas and ovarian cancers are listed as the top three popular cancers in female reproductive system malignancy, some of them can be really aggressive and invasive. Researches and reviewers have also shown association between obesity and overweight, often BMI as proxy, and female gynecological malignant tumors(Markowska et al., 2017, Modesitt and van Nagell, 2005). Nevertheless, whether the link between them is causal or not remains unknown. Mendelian randomization(Emdin et al., 2017)(MR) analysis uses genetic variants usually single nucleotide polymorphisms(SNPs) as instrumental variables substituting a risk factor to calculate its influence value statistically on outcomes of diseases or others in a non-experimental setting, which can determine the observational association between exposures and outcomes is consistent with causality. It seems like a natural and lifetime randomized control trials (RCT) in large population where randomization of genotypes happens during meiosis and fertilization. Different defined SNPs information stratifies the specific population into subsets like matched group and treated group in a RCT. Compared to conventional artificial design of a study, MR is rarely influenced by confounding element, reverse causation and bias. Here, we use body mass index (BMI) as obese measures, MR analysis to detect whether adult or child obesity is responsible for morbidity of both PCOS or gynecological cancers. Materials And Methods MR process is established robustly only when following conditions are met. First, the SNPs working as instrumental variables must represent for BMI. Second, the SNPs must be independent to confounding factors. Third, the SNPs only affects the outcome through the only pathway of BMI except any other potential horizontal factors(Didelez and Sheehan, 2007). With the inaccessibility to all information within one sample, we use two-sample MR methods(Burgess et al., 2015) to obtain genetic statistics of exposures and outcomes from two different published studies which has been proved efficient and useful. Moreover, we use MR-Egger regression(Bowden et al., 2015), weighted median approaches(Bowden et al., 2016) and MR-PRESSO(Verbanck et al., 2018)(Pleiotropy RESidual Sum and Outlier) method to protect results from error of pleiotropy and too many invalid IVs, which ensures the second and third assumption stand. SNP selection and Linkage Disequilibrium Assessment We take advantage of statistics of a recent genetic study of BMI published in Nature(Locke et al., 2015)[dataset1]*. 97 BMI-associated loci identified in 339,224 individuals, whose common variation accounts for more than 20% of BMI variation. Then we selected 77 SNPs among them of 322,154 European descent. 15 childhood BMI-associated SNPs were extracted from a mendelian randomization analysis from Diabetes Care(Geng et al., 2018)[dataset2]*. All the SNPs selected were of great significance (p<5*10 -8 ). We further processed linkage disequilibrium (LD) assessment by using a suite of web-based applications named LDlink search system (https://ldlink.nci.nih.gov/) to interrogate selected IVs are not associated with each other. If any correlation coefficient of LD assessment r 2 higher than 0.1, then the one with higher p value was excluded such as rs3888190 and rs11583200 (Supplementary Table 1), so there were total 75 BMI-associated SNPs (Table 1) and 15 childhood BMI-associated ones left (Table 2), including rs12016871 was merged into rs9581854 in 2005. Genetic data of different outcomes refer to different databases in the UK Biobank (UKBB), who recruited over than 500,000 aged 27-73 years old from the United Kingdom from 2006 to 2010 bringing the total number of participants up to 194153. The association information of the selected BMI-related SNPs with selected diseases outcomes is provided as listed in Supplementary Table 2 and 3. We carried out no experiments involving animals or human subjects directly, but rather got use of and did analytical researches on open databases. The ethical approval has been received by UKBB form the NHS National Research Ethics Service North West. Statistical Analysis By utilizing data sets containing β-coefficient, standard error p value and effect allele of BMI-assoicated and outcomes-associated SNPs respectively, we in turn conducted the traditional inverse-variance weighted(Burgess et al., 2013)(IVW) method, weighted median methods, MR-Egger regression and MR-PRESSO for robustness of the results. No error of measurement and that all the SNPs selected are valid are with acquiescence when processing random IVW, whose fundamental principles was weighted linear regression. Since results of IVW method can be biased if instrument SNPs shows horizontal pleiotropy, acting through another unaccounted casual pathway, we further conducted sensitivity analysis such as Weighted Median methods, MR-Egger regression and MR-PRESSO. MR-Egger regression was done to make sure that no horizontal pleiotropy exists among BMI-associated SNPs. However, neither IVW or MR-Egger regression can foreclose the possibility of invalid SNPs except weighted median (WM) approaches were done, which indicates that at least half of the SNPs selected were valid instrumental variables. MR-PRESSO is an updated analysis that can evaluate whether a specific individual SNP is driving the difference in residual sum of squares (RSS). After being pre-screened for LD, SNP model was incorporated into three stages to examine the extent of horizontal pleiotropy, that is MR-PRESSO global test, MR-PRESSO outlier test and MR-PRESSO distortion test. Here we conducted MR-PRESSO method as verification for pleiotropy. Effect estimate and 95%CI was converted by exponential transformation. All the analysis was conducted in RStudio version 3.6.1 and regarded two-tailed P<0.05 as significance. Results Causal associations between adult-BMI and diseases By means of IVW approach, WM method and MR-Egger analysis, we calculated the estimated causal effect of adulthood BMI on diseases especially for women as listed (Figure 1; Table 3). The positive causal association was found between PCOS and adult-BMI, which was confirmed by both IVW (Odd ratio [OR]= 1.003;95%CI: 1.001-1.004; P=6.83*10 -5 ) and WM methods (OR= 1.003;95%CI: 1.000-1.005; P=2.80*10 -2 ). The negative causality between cervical cancer and adult-BMI was only obtained through WM method (OR= 0.996;95%CI: 0.992-1.000; P=4.17*10 -2 ), although the estimate of IVW approach almost got the significance (OR= 0.998;95%CI: 0.995-1.000; P=6.57*10 -2 ). No significant causal associations were demonstrated between adult-BMI and endometrial cancer, ovarian cancer or endometriosis. The results of MR-Egger regression uncovered that all SNPs were plotted around origin, indicating no directional pleiotropy existed here under any conditions. Causal associations between child-BMI and diseases Traditional IVW approach determined the positive causal effect of child-BMI on PCOS (OR= 1.003;95%CI: 1.001-1.004; P=9.14*10 -4 ) and negative causal association between child-BMI and endometriosis (OR= 0.995;95%CI: 0.991-0.999; P=1.25*10 -2 ). Weighted median method did not obtain the same conclusion. However, WM method implied that negative causal associations between child-BMI and cervical cancer (OR= 0.996;95%CI: 0.993-1.000; P=0.053) or endometrial cancer (OR= 0.997;95%CI: 0.994-1.000; P=0.08). MR-Egger regression concluded that the consequences were not influenced by pleiotropy (Figure 2; Table 4). We then conducted recently updated MR-PRESSO global test to exhibit whether there existed horizontal pleiotropy in selected SNPs. As expected, no outlier SNP in adult-BMI (Supplementary Table 4) or child-BMI SNPs category (Supplementary Table 5) was found in all diseases according to the analytic results except rs11727676 and rs12566985 in adult-BMI when associated with endometriosis. From the perspective of MR-PRESSO conclusion, significance was shown still in the negative association between children BMI and endometriosis as well as in the positive association between PCOS and BMI at adulthood or childhood as IVW has displayed. Discussions In this study, we found pieces of robust evidence about causal association between child and adult BMI and the risk of infertility and cervical cancers. In details, the higher genetic score of adult and childhood BMI was associated with increased of PCOS. Consistent with our conclusions, observational studies and meta-analyses have uncovered the same outcome that women with PCOS had higher risk of central obesity(Lim et al., 2012) and the prevalence of PCOS was increased among overweight and obese women(Alvarez-Blasco et al., 2006), earlier adiposity rebound and severe BMI rise in childhood predicted later development of PCOS(Koivuaho et al., 2019b). The negative causality between child BMI and endometriosis through IVW method was convincing because observational studies has come up with same conclusion that there was an inverse relation between body size at 5,10,20 age and the disease(Vitonis et al., 2010). The prospective case-control aiming at more than 100,000 women from the United States for over 20 years exhibited the inverse tendency to develop endometriosis was more significantly obvious in 18-year-old BMI than current BMI, persisted noteworthy in both infertile women and those without concurrent infertile. An genome-wide enrichment analysis demonstrated a significant enrichment of common variants overlapping both endometriosis and waist-to-hip ratio adjusted for BMI, representing for fat distribution(Rahmioglu et al., 2015). There is another case-control analysis in Australia revealed that women who self-reported overweight at age 10 had an increasing risk of endometriosis(Nagle et al., 2009). However, mother-daughter pair reports came up an opposite association that underweight at 16 years old positively related to endometriosis, which consisted with ours. Nevertheless, we didn’t acquire same positive findings about BMI associated with endometrial cancers, ovarian cancers using the method as other MR analysis and observation did. A MR analysis published in 2016 which uses 4 subsets of cases and controls from EC datasets of Australian and European ancestry(Painter et al., 2016). The final IVW result was combined using random effects meta-analysis after stratified into quartiles and calculated separately. So different races and computing method may contribute to the divergence in our results. While using the same adult BMI-SNPs without abandoning 2 loci in strong linkage disequilibrium and different outcome database, Gao, C., et al.(Gao et al., 2016) concluded that 1 standard deviation in genetically predicted adult BMI was associated with 35% increase risk in overall ovarian cancer. However, after excluding overlap loci, the significance disappeared. They also didn’t find strong association between genetically predicted child BMI with ovarian cancer risk. Another MR study ended up with a consequence that BMI as instrumental invariables as obesity, increased risk of non-high grade serous ovarian cancer of European ancestry, but had nothing to do with high grade serous ovarian cancer(Dixon et al., 2016). This reminds us that subtypes of ovarian cancer may react to obesity differently and contribute together to nonsense of overall OC to obese. The inverse causal relationship between cervical cancers and adult BMI need to be confirmed because the p value of IVW reached 6.57*10 − 2 while WM concluded significantly. A retrospective cohort study of patients of cervical cancer(Frumovitz et al., 2014) were classified as underweight (BMI < 18.5kg/m 2 ), normal weight (BMI 18.5-24.9kg/m 2 ), overweight (BMI 25-29.9kg/m 2 ), obese (BMI 30-34.9 kg/m 2 ) and morbidly obese (BMI ≥ 35.0kg/m 2 ). After controlling for prognostic factors, only morbidly obese remained an independent risk for mortality of CC. So, the classification left us a hint that the extent of obesity or staging of disease could obscure the real causal association of risk factors and results. To the best of our knowledge, it is the first mendelian randomization analysis of the causality of BMI and PCOS or endometriosis. Also, never ever had articles published the association between childhood BMI and common gynecological cancers. With the appearance of genome wide associated study, we can take advantage of information sharing to further MR research on causality of exposures and diseases. Previously, researchers get used to carrying out retrospective studies or prospective studies. Compared to the two classical research methods, strengths of our MR analysis can be listed as follow: 1) less vulnerable to reverse causation or confounding bias from economic and educational levels or lifestyle like smoking or alcoholism; 2) insusceptible of experimental such as acting time and degree of disposal; 3) the sample capacity can be big enough as soon as getting access to database; 4) our results show besides adult obesity, severe child obese can already be predictable to endometriosis and PCOS in later life, so it’s vital for us to prevent the trend of obesity in childhood and take measures to keep in balanced shape whenever weight gains, and if obese has taken place, there’s a need for to lose weight; 5) MR can directly explains the causality between exposure and outcomes not only simple correlation. So, drug use and side-effect of weight gain need to be notice for future health. Of course, there is room for improvement: 1) BMI is not a perfect index for obesity, as we can see, waist to hip ratio as well as subcutaneous fat thickness is often chosen to be proxies of obesity. Fat distribution rather than BMI can also be an independent risk of diseases; 2) different races can have changes in instrumental variables and genetic score of outcome; Here in our MR analysis, results only applied to European; 3) there may be some overlapped loci in adult and child BMI associated SNPs, there’s a possibility that genetic variants influence both of them; 4) we can’t count out the probability of population stratification and other potential confounding, severity levels of outcomes and stages of diseases should be underlined and distinguished if possible, and our final association was not obvious. Conclusions Generally speaking, genetic predispositions to elevated adult and child BMI was related to higher risk of PCOS, increased child BMI was linked to lower risk of endometriosis. No strong evidence had been found of association between adult and child BMI and female gynecological cancers. These results offered us a causal relationship between BMI and PCOS or endometriosis, implying that no matter adult or child obese can leave an adverse impact on PCOS. But too thin and underweight may increase the risk of endometriosis. However, further replications need to be carried out with larger scale, prospective and observational studies about female cancers of reproductive system as well as illuminating the mechanism of how adult and child obese working on PCOS and endometriosis, as PCOS is now still a disease of unknown etiology. Declarations Acknowledgements All the cited projects and datasets were approved by the relevant ethics committees in corresponding published articles. We did not apply required statements of ethics committee approval or checklist referenced at the Equator Network, because our study does not belong to traditional human and animal researches or observational studies. We did mendelian randomization analysis on published data and open databases, thus drew a conclusion on the causal association between exposures and outcomes. We were sincerely appreciated the authors of studies mentioned above for their kindness and generosity with their open dataset so that we can download their reference data. Funding This work was supported by the National Natural Science Foundation of China (82088102) and National Natural Science Foundation of China (81661128010). Conflicts of interest The authors declared no conflicts of interest. Availability of data and material: Besides the selected SNPs derived from published articles [dataset 1, 2], we also got access to the open database website searching for genetic statistics of diseases outcomes. The data underlying this article are available in UKBB listed as followed. https://www.dropbox.com/s/uirk8rmdhmyana7/20001_1041.gwas.imputed_v3.female.tsv.bgz?dl=0 for cervical cancer. https://www.dropbox.com/s/a6hxa2xh6ke726y/20001_1040.gwas.imputed_v3.female.tsv.bgz?dl=0 for endometrial cancer. https://www.dropbox.com/s/dkgr3ejcxza9f7o/20001_1039.gwas.imputed_v3.female.tsv.bgz?dl=0 for ovarian cancer. https://www.dropbox.com/s/4g7f8jpq3dkn6l5/20002_1402.gwas.imputed_v3.female.tsv.bgz?dl=0 for endometriosis. https://www.dropbox.com/s/hvpig2a6n4fpsy2/20002_1350.gwas.imputed_v3.female.tsv.bgz?dl=0 for PCOS. [dataset1]: Locke, Adam E et al. “Genetic studies of body mass index yield new insights for obesity biology.” Nature vol. 518,7538 (2015): 197-206. doi:10.1038/nature14177 [dataset2]: Geng, Tingting et al. “Childhood BMI and Adult Type 2 Diabetes, Coronary Artery Diseases, Chronic Kidney Disease, and Cardiometabolic Traits: A Mendelian Randomization Analysis.” Diabetes care vol. 41,5 (2018): 1089-1096. doi:10.2337/dc17-2141 Code availability: RStudio version 3.6.1 Authors’ contributions: Yi-Shang Yan collected data and wrote the manuscript. Zihao Qu analyzed and revised the data. Pingping Lv and He-feng Huang designed the article. Ethics approval and consent to participate and publication: Ethical approval for this study and written informed consent from the participants of the study were not required in accordance with local legislation and national guidelines. References ALVAREZ-BLASCO, F., BOTELLA-CARRETERO, J. I., SAN MILLáN, J. L. & ESCOBAR-MORREALE, H. F. 2006. Prevalence and characteristics of the polycystic ovary syndrome in overweight and obese women. Archives of internal medicine, 166 , 2081-2086. BOWDEN, J., DAVEY SMITH, G. & BURGESS, S. J. I. J. O. E. 2015. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. 44 , 512-525. BOWDEN, J., DAVEY SMITH, G., HAYCOCK, P. C. & BURGESS, S. J. G. E. 2016. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. 40 , 304-314. BURGESS, S., BUTTERWORTH, A. & THOMPSON, S. G. J. G. E. 2013. Mendelian randomization analysis with multiple genetic variants using summarized data. 37 , 658-665. BURGESS, S., SCOTT, R. A., TIMPSON, N. J., SMITH, G. D., THOMPSON, S. G. & EPIDEMIOLOGY, E.-I. C. J. E. J. O. 2015. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. 30 , 543-552. DIDELEZ, V. & SHEEHAN, N. J. S. M. I. M. R. 2007. Mendelian randomization as an instrumental variable approach to causal inference. 16 , 309-330. DIXON, S. C., NAGLE, C. M., THRIFT, A. P., PHAROAH, P. D., PEARCE, C. L., ZHENG, W., PAINTER, J. N., GROUP, A., STUDY, A. C., CHENEVIX-TRENCH, G. & FASCHING, P. A. J. I. J. O. E. 2016. Adult body mass index and risk of ovarian cancer by subtype: a Mendelian randomization study. 45 , 884-895. EMDIN, C. A., KHERA, A. V. & KATHIRESAN, S. 2017. Mendelian Randomization. JAMA, 318 , 1925-1926. FRUMOVITZ, M., JHINGRAN, A., SOLIMAN, P. T., KLOPP, A. H., SCHMELER, K., EIFEL, P. J. J. O. & GYNECOLOGY 2014. Morbid obesity as an independent risk factor for disease-specific mortality in women with cervical cancer. 124 , 1098. GAO, C., PATEL, C. J., MICHAILIDOU, K., PETERS, U., GONG, J., SCHILDKRAUT, J., SCHUMACHER, F. R., ZHENG, W., BOFFETTA, P. & STUCKER, I. J. I. J. O. E. 2016. Mendelian randomization study of adiposity-related traits and risk of breast, ovarian, prostate, lung and colorectal cancer. 45 , 896-908. GENG, T., SMITH, C. E., LI, C. & HUANG, T. J. D. C. 2018. Childhood BMI and adult type 2 diabetes, coronary artery diseases, chronic kidney disease, and cardiometabolic traits: a mendelian randomization analysis. 41 , 1089-1096. HALES, C. M., FRYAR, C. D., CARROLL, M. D., FREEDMAN, D. S. & OGDEN, C. L. J. J. 2018. Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007-2008 to 2015-2016. 319 , 1723-1725. KOIVUAHO, E., LARU, J., OJANIEMI, M., PUUKKA, K., KETTUNEN, J., TAPANAINEN, J., FRANKS, S., JäRVELIN, M.-R., MORIN-PAPUNEN, L. & SEBERT, S. J. I. J. O. O. 2019a. Age at adiposity rebound in childhood is associated with PCOS diagnosis and obesity in adulthood—longitudinal analysis of BMI data from birth to age 46 in cases of PCOS. 43 , 1370-1379. KOIVUAHO, E., LARU, J., OJANIEMI, M., PUUKKA, K., KETTUNEN, J., TAPANAINEN, J. S., FRANKS, S., JäRVELIN, M. R., MORIN-PAPUNEN, L., SEBERT, S. & PILTONEN, T. T. 2019b. Age at adiposity rebound in childhood is associated with PCOS diagnosis and obesity in adulthood-longitudinal analysis of BMI data from birth to age 46 in cases of PCOS. International journal of obesity (2005), 43 , 1370-1379. LAITINEN, J., TAPONEN, S., MARTIKAINEN, H., POUTA, A., MILLWOOD, I., HARTIKAINEN, A. L., RUOKONEN, A., SOVIO, U., MCCARTHY, M. I., FRANKS, S. & JäRVELIN, M. R. 2003. Body size from birth to adulthood as a predictor of self-reported polycystic ovary syndrome symptoms. International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity, 27 , 710-715. LIM, S. S., DAVIES, M. J., NORMAN, R. J. & MORAN, L. J. 2012. Overweight, obesity and central obesity in women with polycystic ovary syndrome: a systematic review and meta-analysis. Human reproduction update, 18 , 618-637. LOCKE, A. E., KAHALI, B., BERNDT, S. I., JUSTICE, A. E., PERS, T. H., DAY, F. R., POWELL, C., VEDANTAM, S., BUCHKOVICH, M. L. & YANG, J. J. N. 2015. Genetic studies of body mass index yield new insights for obesity biology. 518 , 197-206. MARKOWSKA, A., STANISŁAWIAK-RUDOWICZ, J. & JASZCZYŃSKA-NOWINKA, K. 2017. The role of overweight and obesity in selected gynecological malignancies. European journal of gynaecological oncology, 38 , 335-341. MISSMER, S. A., HANKINSON, S. E., SPIEGELMAN, D., BARBIERI, R. L., MARSHALL, L. M. & HUNTER, D. J. J. A. J. O. E. 2004. Incidence of laparoscopically confirmed endometriosis by demographic, anthropometric, and lifestyle factors. 160 , 784-796. MODESITT, S. C. & VAN NAGELL, J. R. 2005. The impact of obesity on the incidence and treatment of gynecologic cancers: a review. Obstetrical & gynecological survey, 60 , 683-692. NAGLE, C. M., BELL, T., PURDIE, D. M., TRELOAR, S., OLSEN, C. M., GROVER, S. & GREEN, A. C. J. H. R. 2009. Relative weight at ages 10 and 16 years and risk of endometriosis: a case–control analysis. 24 , 1501-1506. OLLILA, M. M., WEST, S., KEINäNEN-KIUKAANNIEMI, S., JOKELAINEN, J., AUVINEN, J., PUUKKA, K., RUOKONEN, A., JäRVELIN, M. R., TAPANAINEN, J. S., FRANKS, S., PILTONEN, T. T. & MORIN-PAPUNEN, L. C. 2017. Overweight and obese but not normal weight women with PCOS are at increased risk of Type 2 diabetes mellitus-a prospective, population-based cohort study. Human reproduction (Oxford, England), 32 , 423-431. PAINTER, J. N., O'MARA, T. A., MARQUART, L., WEBB, P. M., ATTIA, J., MEDLAND, S. E., CHENG, T., DENNIS, J., HOLLIDAY, E. G., MCEVOY, M. J. C. E. & BIOMARKERS, P. 2016. Genetic risk score Mendelian randomization shows that obesity measured as body mass index, but not waist: hip ratio, is causal for endometrial cancer. 25 , 1503-1510. PARK, J., MORLEY, T. S., KIM, M., CLEGG, D. J. & SCHERER, P. E. J. N. R. E. 2014. Obesity and cancer—mechanisms underlying tumour progression and recurrence. 10 , 455. RAHMIOGLU, N., MACGREGOR, S., DRONG, A. W., HEDMAN, Å. K., HARRIS, H. R., RANDALL, J. C., PROKOPENKO, I., INTERNATIONAL ENDOGENE CONSORTIUM , T. G. C., NYHOLT, D. R. & MORRIS, A. P. J. H. M. G. 2015. Genome-wide enrichment analysis between endometriosis and obesity-related traits reveals novel susceptibility loci. 24 , 1185-1199. SHAH, D. K., CORREIA, K. F., VITONIS, A. F. & MISSMER, S. A. J. H. R. 2013. Body size and endometriosis: results from 20 years of follow-up within the Nurses' Health Study II prospective cohort. 28 , 1783-1792. SILVESTRIS, E., DE PERGOLA, G., ROSANIA, R., LOVERRO, G. J. R. B. & ENDOCRINOLOGY 2018. Obesity as disruptor of the female fertility. 16 , 22. VERBANCK, M., CHEN, C.-Y., NEALE, B. & DO, R. J. N. G. 2018. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. 50 , 693-698. VITONIS, A. F., BAER, H. J., HANKINSON, S. E., LAUFER, M. R. & MISSMER, S. A. J. H. R. 2010. A prospective study of body size during childhood and early adulthood and the incidence of endometriosis. 25 , 1325-1334. Tables Table 1 Characteristics of adult BMI-associated SNPs from GWAS Gene SNP Chromosome: Position EA Association with exposure β(SE) P value AGBL4 rs657452 1:49589847 A 0.023(0.003) 5.48 × 10 -13 CADM1 rs12286929 11:115022404 G 0.022(0.003) 1.31 × 10 -12 TCF7L2 rs7903146 10:114758349 C 0.023(0.003) 1.31 × 10 -12 STXBP6 rs10132280 14:25928179 C 0.023(0.003) 1.14 × 10 -11 HIF1AN rs17094222 10:102395440 C 0.025(0.004) 5.94 × 10 -11 ERBB4 rs7599312 2:213413231 G 0.022(0.003) 1.17 × 10 -10 FHIT rs2365389 3:61236462 C 0.02(0.003) 1.63 × 10 -10 NAV1 rs2820292 1:201784287 C 0.02(0.003) 1.83 × 10 -10 PRKD1 rs12885454 14:29736838 C 0.021(0.003) 1.94 × 10 -10 RASA2 rs16851483 3:141275436 T 0.048(0.008) 3.55 × 10 -10 HIP1 rs1167827 7:75163169 G 0.02(0.003) 6.33 × 10 -10 NLRC3 rs758747 16:3627358 T 0.023(0.004) 7.47× 10 -10 TLR4 rs1928295 9:120378483 T 0.019(0.003) 7.91× 10 -10 KAT8 rs9925964 16:31129895 A 0.019(0.003) 8.11× 10 -10 KCNK3 rs11126666 2:26928811 A 0.021(0.003) 1.33 × 10 -9 SBK1 rs2650492 16:28333411 A 0.021(0.004) 1.92 × 10 -9 RARB rs6804842 3:25106437 G 0.019(0.003) 2.48 × 10 -9 CCDC171 rs4740619 9:15634326 T 0.018(0.003) 4.56 × 10 -9 PRKN rs13191362 6:163033350 A 0.028(0.005) 7.34 × 10 -9 DMXL2 rs3736485 15:51748610 A 0.018(0.003) 7.41 × 10 -9 SCARB2 rs17001654 4:77129568 G 0.031(0.005) 7.76 × 10 -9 NT5CA rs11191560 10:104869038 C 0.031(0.005) 8.45 × 10 -9 UBE2E3 rs1528435 2:181550962 T 0.018(0.003) 1.20 × 10 -8 RABEP1 rs1000940 17:5283252 G 0.019(0.003) 1.28 × 10 -8 rs2033529 6:40348653 G 0.019(0.003) 1.39 × 10 -8 FOXO3 rs9400239 6:108977663 C 0.019(0.003) 1.61 × 10 -8 LMX1B rs10733682 9:129460914 A 0.017(0.003) 1.83 × 10 -8 EHBP1 rs11688816 2:63053048 G 0.017(0.003) 1.89 × 10 -8 CLIP1 rs11057405 12:122781897 G 0.031(0.006) 2.02 × 10 -8 HHIP rs11727676 4:145659064 T 0.036(0.006) 2.55 × 10 -8 GBE1 rs3849570 3:81792112 A 0.019(0.003) 2.60 × 10 -8 FRRS1L rs6477694 9:111932342 C 0.017(0.003) 2.67 × 10 -8 GRID1 rs7899106 10:87410904 G 0.04(0.007) 2.96 × 10 -8 HSD17B12 rs2176598 11:43864278 T 0.02(0.004) 2.97 × 10 -8 PMS2L11 rs2245368 7:76608143 C 0.032(0.006) 3.19 × 10 -8 PGPEP1 rs17724992 19:18454825 A 0.019(0.004) 3.42 × 10 -8 GRP rs7243357 18:56883319 T 0.022(0.004) 3.86 × 10 -8 RP11-120I21.3 rs2033732 8:85079709 C 0.019(0.004) 4.89 × 10 -8 FTO rs1558902 16:53803574 A 0.082(0.003) 7.51 × 10 -153 MC4R rs6567160 18:57829135 C 0.056(0.004) 3.53 × 10 -53 TMEM18 rs13021737 2:632348 G 0.06(0.004) 1.11 × 10 -50 NMU rs10938397 4:45182527 G 0.04(0.003) 3.21 × 10 -38 SEC16B rs543874 1:177889480 G 0.048(0.004) 2.62 × 10 -35 TFAP2B rs2207139 6:50845490 G 0.045(0.004) 4.13 × 10 -29 BDAF rs11030104 11:27684517 A 0.041(0.004) 5.56× 10 -28 NEGR1 rs3101336 1:72751185 C 0.033(0.003) 2.66× 10 -26 RP11-70F11.7 rs7138803 12:50247468 A 0.032(0.003) 8.15× 10 -24 ADCY3 rs10182181 2:25150296 G 0.031(0.003) 8.78× 10 -24 E7V5 rs1516725 3:185824004 C 0.045(0.005) 1.89× 10 -22 AC134300.1 rs12446632 16:19935389 G 0.04(0.005) 1.48× 10 -18 QPCTL rs2287019 19:46202172 C 0.036(0.004) 4.59× 10 -18 MAP2K5 rs16951275 15:68077168 T 0.031(0.004) 1.91× 10 -17 MTCH2 rs3817334 11:47650993 T 0.026(0.003) 5.15× 10 -17 POC5 rs2112347 5:75015242 T 0.026(0.003) 6.19× 10 -17 FPGT-TNNI3K rs12566985 1:75002193 G 0.024(0.003) 3.28× 10 -15 ZC3H4 rs3810291 19:47569003 A 0.028(0.004) 4.81× 10 -15 NRXN3 rs7141420 14:79899454 T 0.024(0.003) 1.23× 10 -14 CADM2 rs13078960 3:85807590 G 0.03(0.004) 1.74× 10 -14 LINGO2 rs10968576 9:28414339 G 0.025(0.003) 6.61× 10 -14 GNAI3 rs17024393 1:110154688 C 0.066(0.009) 7.03× 10 -14 OLFM4 rs12429545 13:54102206 A 0.033(0.005) 1.09× 10 -12 SLC39A8 rs13107325 4:103188709 T 0.048(0.007) 1.83× 10 -12 EEF1A1P11 rs11165643 1:96924097 T 0.022(0.003) 2.07× 10 -12 snoU13 rs17405819 8:76806584 T 0.022(0.003) 2.07× 10 -11 LINC01122 rs1016287 2:59305625 T 0.023(0.003) 2.25× 10 -11 TRIM66 rs4256980 11:8673939 G 0.021(0.003) 2.90× 10 -11 DNAJB4 rs12401738 1:78446761 A 0.021(0.003) 1.15× 10 -10 ILRUN rs205262 6:34563164 G 0.022(0.004) 1.75× 10 -10 MTIF3 rs12016871 13:28017782 T 0.03(0.005) 2.29× 10 -10 RPTOR rs12940622 17:78615571 G 0.018(0.003) 2.49× 10 -9 PRKD1 rs11847697 14:30515112 T 0.049(0.008) 3.99× 10 -9 TOMM40 rs2075650 19:45395619 A 0.026(0.005) 1.25× 10 -8 LRP1B rs2121279 2:143043285 T 0.025(0.004) 2.31× 10 -8 KCTD15 rs29941 19:34309532 G 0.018(0.003) 2.41× 10 -8 NPC1 rs1808579 18:21104888 C 0.017(0.003) 4.17× 10 -8 Gene = nearest gene to the SNP; EA = effect allele; β = per allele effect on the exposure; SE = standard error; P value = p-value for the genetic association. Table 2 Characteristics of child BMI-associated SNPs from GWAS Gene SNP Chromosome: Position EA Association with exposure β(SE) P value GNPDA2 rs13130484 4:45175691 T 0.067(0.007) 1.58 × 10 -23 ADCY3 rs11676272 2:25141538 G 0.068(0.007) 7.12 × 10 -23 TMEM18 rs4854349 2:647861 C 0.09(0.009) 5.41 × 10 -22 SEC16B rs543874 1:177889480 G 0.077(0.009) 2.20 × 10 -19 FAIM2 rs7132908 12:50263148 A 0.066(0.008) 1.57 × 10 -18 FTO rs1421085 16:53800954 C 0.059(0.007) 4.53 × 10 -16 OLFM4 rs12429545 13:54102206 A 0.076(0.01) 2.08 × 10 -14 TFAP2B rs987237 6:50803050 G 0.062(0.009) 1.80 × 10 -12 TNNI3K rs12041852 1:75003500 G 0.046(0.007) 2.28 × 10 -10 MC4R rs6567160 18:57829135 C 0.05(0.008) 1.21 × 10 -9 ELP3 rs13253111 8:28061974 A 0.042(0.007) 4.89 × 10 -9 RAB27B rs8092503 18:52479487 G 0.045(0.008) 8.17 × 10 -9 LMX1B rs3829849 9:129390800 T 0.041(0.007) 8.81 × 10 -9 ADAM23 rs13387838 2:207281447 A 0.139(0.025) 2.84 × 10 -8 GPR61 rs7550711 1:110082886 T 0.105(0.019) 4.52 × 10 -8 Gene = nearest gene to the SNP; EA = effect allele; β = per allele effect on the exposure; SE = standard error; P value = p-value for the genetic association. Table 3 Inverse-variance weighted method, weighted median approach, MR-Egger analysis for genetic associations between 75 adult BMI and indicated diseases Disease traits Analysis method effect Standard Error 95%CI P value Cervical Cancer IVW 0.998 1.28*10 -3 (0.995,1.000) 6.57*10 -2 WM* 0.996 2.08*10 -3 (0.992,1.000) 4.17*10 -2 MR-Egger 1.000 9.12*10 -5 (0.9999,1.0002) 0.51 Endometrial cancer IVW 1.000 1.01*10 -3 (0.998,1.002) 0.99 WM 0.998 1.65*10 -3 (0.994,1.001) 0.15 MR-Egger 1.000 7.28*10 -5 (0.9999,1.0002) 0.35 Ovarian Cancer IVW 1.000 8.34*10 -4 (0.999,1.002) 0.66 WM 1.001 1.39*10 -3 (0.998,1.003) 0.66 MR-Egger 1.000 5.98*10 -5 (0.9999,1.0001) 0.71 Endometriosis IVW 0.999 2.12*10 -3 (0.995,1.003) 0.56 WM 0.999 3.07*10 -3 (0.993,1.005) 0.67 MR-Egger 1.000 1.50*10 -4 (0.9999-1.0005) 0.19 PCOS IVW*** 1.003 7.40*10 -4 (1.001,1.004) 6.83*10 -5 WM* 1.003 1.16*10 -3 (1.000,1.005) 2.80*10 -2 MR-Egger 1.000 5.28*10 -5 (0.9999,1.0001) 0.39 CI = confidence interval; P value = p-value of the causal estimate. Table 4 Inverse-variance weighted method, weighted median approach, MR-Egger analysis for genetic associations between 15 child BMI and indicated diseases Disease traits Analysis method effect Standard Error 95%CI P value Cervical Cancer IVW 0.998 1.43*10 -3 (0.995,1.001) 0.12 WM 0.996 1.93*10 -3 (0.993,1.000) 0.053 MR-Egger 1.000 3.50*10 -4 (0.9996,1.0009) 0.49 Endometrial Cancer IVW 0.998 1.30*10 -3 (0.996,1.001) 0.24 WM 0.997 1.54*10 -3 (0.994,1.000) 0.08 MR-Egger 1.000 3.22*10 -4 (0.9994,1.0006) 0.97 Ovarian Cancer IVW 1.000 8.94*10 -4 (0.998,1.002) 0.96 WM 1.000 1.24*10 -3 (0.998,1.003) 0.83 MR-Egger 1.000 2.14*10 -4 (0.9996,1.0005) 0.82 Endometriosis IVW* 0.995 1.99*10 -3 (0.991,0.999) 1.25*10 -2 WM 0.996 2.64*10 -3 (0.991,1.001) 0.11 MR-Egger 1.000 4.94*10 -4 (0.9990,1.0009) 0.89 PCOS IVW*** 1.003 8.01*10 -4 (1.001,1.004) 9.14*10 -4 WM 1.002 1.06*10 -3 (1.000,1.004) 0.12 MR-Egger 1.000 1.79*10 -4 (1.0000,1.0007) 8.37*10 -2 CI = confidence interval; P value = p-value of the causal estimate. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 20 Sep, 2021 Read the published version in Endocrine → Version 1 posted Reviews received at journal 17 Apr, 2021 Reviewers invited by journal 16 Apr, 2021 Editor assigned by journal 04 Apr, 2021 First submitted to journal 02 Apr, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-390994","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":21913712,"identity":"e3e8badc-9e2e-4934-bccb-777cb62180ab","order_by":0,"name":"yishang yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie3RMUvDQBTA8XdcyVTS9YWC8SMkBFoFxa/yjoJTEaeuBgqZ/AD1O3S41e3kwC6pWU9wqAhOFQJZHDL0jIPbtW6C9x+OO3g/bngAPt9fjUDZkyvAr5c6nAT0C/I91k9+7q6S1foRN+1LPB4sm/q0gKPQEGuuXaS8ukRRvKf3iw+ZRAVkkSE+XLiImo5Q5JpJs5ZkiZCGAt53kWo7Qmr1hTTlRllys58Y+wsFWsjqluWWULKPRGabnYhCT6QJMsAnTO/K1/nQRcJqmj5/tvpcVvqtwdlZHK4mD42LHCvodQsEJODYLZPlDgAQ58Dr7jZQwGrnrM/n8/3XdkJPVyIhy14cAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0531-6317","institution":"Zhejiang University School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"yishang","middleName":"","lastName":"yan","suffix":""},{"id":21913713,"identity":"5c57dbba-4deb-4fb4-97f8-866626b694f6","order_by":1,"name":"Zihao Qu","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Qu","suffix":""},{"id":21913714,"identity":"357787ad-9f5b-4c28-a357-450655d0cfe6","order_by":2,"name":"Ping-ping Lv","email":"","orcid":"","institution":"Zhejiang Academy of Medical Sciences: Zhejiang University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping-ping","middleName":"","lastName":"Lv","suffix":""},{"id":21913715,"identity":"2e8e9a72-d4ff-4217-a90d-904cc3531f6b","order_by":3,"name":"He-Feng Huang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"He-Feng","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2021-04-04 01:28:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-390994/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-390994/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12020-021-02867-y","type":"published","date":"2021-09-20T13:42:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":8169980,"identity":"f0840c8e-71e5-4ce8-bc9e-139f91720335","added_by":"auto","created_at":"2021-04-19 14:31:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15812,"visible":true,"origin":"","legend":"Causal effects of adult BMI on the risk of indicated diseases. The estimated effects, 95% confidence intervals and p-values of associations were contained. Effect = the combined causal effect; CI = confidence interval; P value = p-value of the causal estimate.","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-390994/v1/84d46921eec585e3e9f7055e.png"},{"id":8170401,"identity":"2d9136bb-93f1-4386-865c-05b64cff3334","added_by":"auto","created_at":"2021-04-19 14:34:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15409,"visible":true,"origin":"","legend":"Causal effects of child BMI on the risk of indicated diseases. The estimated effects, 95% confidence intervals and p-values of associations were contained. Effect = the combined causal effect; CI = confidence interval; P value = p-value of the causal estimate.","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-390994/v1/105a12d95d3e60cdd898df5a.png"},{"id":17936972,"identity":"3e05f139-4578-4994-8f3c-3c8da35ab4f2","added_by":"auto","created_at":"2022-02-04 13:42:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":552045,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-390994/v1/46195a66-6bf7-4c78-9cd2-7fefeb1af255.pdf"},{"id":8170402,"identity":"a200f134-f25d-49fc-92f6-c7c69532fd57","added_by":"auto","created_at":"2021-04-19 14:34:26","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":51106,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-390994/v1/7295de827c699b8965fdfca0.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePediatric and Adult Obesity Concern Female Health: A Mendelian Randomization Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eObesity is characterized as body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e in adults and BMI-for-age percentile\u0026thinsp;\u0026ge;\u0026thinsp;95% in children. Trends in obesity and severe obesity prevalence in US have come up to approximately 17% in in American girls(Silvestris et al., 2018) and reached 39.6% in 2015\u0026ndash;2016 among women(Hales et al., 2018). Obesity has been a worldwide prevalent phenomenon linking to prevalence risk of female infertility and gynecological cancers(Park et al., 2014) which perplex female physiologically and psychologically. Endometriosis and polycystic ovarian syndrome (PCOS) have become two major diseases type that result in female infertility. However, both of them are etiologically unknown yet. Endometriosis is mainly characterized as dysmenorrhea, pelvic pain and infertility. A research showed an inverse relation between current BMI and endometriosis was observed when cases were concurrently infertile(Missmer et al., 2004). An ongoing prospective case-control cohort study(Shah et al., 2013) uncovered that women\u0026rsquo;s current BMI and BMI at age 18 were statistically associated with the rate of endometriosis inversely, which is stronger among infertile women, and the most robust association was with BMI at age 18. It suggests us that there may be an \u0026lsquo;early window of exposure\u0026rsquo; during which lower BMI at adolescence leaves an impact on endometriosis preceding the diagnosis of the disease. PCOS is characterized as hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology, which most attacks reproductive women with 8% morbidity. Observational studies has discovered that obesity, even childhood obesity(Koivuaho et al., 2019a), has a positive association with PCOS as well as other female reproductive system tumors in adulthood. According to a systemic review and meta-analysis, obese patients take a larger proposition than nonobese ones in PCOS(Lim et al., 2012). On the other hand, patients with greater central adiposity positively were associated with more severe metabolic phenotype such as insulin resistant diabetes mellitus(Ollila et al., 2017). PCOS takes place early at puberty. A longitude population-based study concluded that adolescent obesity predicted ovulatory dysfunction in later life(Laitinen et al., 2003). So, it\u0026rsquo;s necessary to find the causality between adult or child obesity and infertility so that we can take measures to prevent the damage of overweight to health.\u003c/p\u003e \u003cp\u003eCervical cancers, endometrial carcinomas and ovarian cancers are listed as the top three popular cancers in female reproductive system malignancy, some of them can be really aggressive and invasive. Researches and reviewers have also shown association between obesity and overweight, often BMI as proxy, and female gynecological malignant tumors(Markowska et al., 2017, Modesitt and van Nagell, 2005). Nevertheless, whether the link between them is causal or not remains unknown.\u003c/p\u003e \u003cp\u003eMendelian randomization(Emdin et al., 2017)(MR) analysis uses genetic variants usually single nucleotide polymorphisms(SNPs) as instrumental variables substituting a risk factor to calculate its influence value statistically on outcomes of diseases or others in a non-experimental setting, which can determine the observational association between exposures and outcomes is consistent with causality. It seems like a natural and lifetime randomized control trials (RCT) in large population where randomization of genotypes happens during meiosis and fertilization. Different defined SNPs information stratifies the specific population into subsets like matched group and treated group in a RCT. Compared to conventional artificial design of a study, MR is rarely influenced by confounding element, reverse causation and bias. Here, we use body mass index (BMI) as obese measures, MR analysis to detect whether adult or child obesity is responsible for morbidity of both PCOS or gynecological cancers.\u003c/p\u003e "},{"header":"Materials And Methods","content":"\u003cp\u003eMR process is established robustly only when following conditions are met. First, the SNPs working as instrumental variables must represent for BMI. Second, the SNPs must be independent to confounding factors. Third, the SNPs only affects the outcome through the only pathway of BMI except any other potential horizontal factors(Didelez and Sheehan, 2007). With the inaccessibility to all information within one sample, we use two-sample MR methods(Burgess et al., 2015) to obtain genetic statistics of exposures and outcomes from two different published studies which has been proved efficient and useful. Moreover, we use MR-Egger regression(Bowden et al., 2015), weighted median approaches(Bowden et al., 2016) and MR-PRESSO(Verbanck et al., 2018)(Pleiotropy RESidual Sum and Outlier) method to protect results from error of pleiotropy and too many invalid IVs, which ensures the second and third assumption stand.\u003c/p\u003e\n\u003ch2\u003eSNP selection and Linkage Disequilibrium Assessment\u003c/h2\u003e\n\u003cp\u003eWe take advantage of statistics of a recent genetic study of BMI published in Nature(Locke et al., 2015)[dataset1]*. 97 BMI-associated loci identified in 339,224 individuals, whose common variation accounts for more than 20% of BMI variation. Then we selected 77 SNPs among them of 322,154 European descent. 15 childhood BMI-associated SNPs were extracted from a mendelian randomization analysis from Diabetes Care(Geng et al., 2018)[dataset2]*. All the SNPs selected were of great significance (p\u0026lt;5*10\u003csup\u003e-8\u003c/sup\u003e). We further processed linkage disequilibrium (LD) assessment by using a suite of web-based applications named LDlink search system (https://ldlink.nci.nih.gov/) to interrogate selected IVs are not associated with each other. If any correlation coefficient of LD assessment r\u003csup\u003e2\u003c/sup\u003e higher than 0.1, then the one with higher p value was excluded such as rs3888190 and rs11583200 (Supplementary Table 1), so there were total 75 BMI-associated SNPs (Table 1) and 15 childhood BMI-associated ones left (Table 2), including rs12016871 was merged into\u0026nbsp;rs9581854 in 2005. Genetic data of different outcomes refer to different databases in the UK Biobank (UKBB), who recruited over than 500,000 aged 27-73 years old from the United Kingdom from 2006 to 2010 bringing the total number of participants up to 194153. The association information of the selected BMI-related SNPs with selected diseases outcomes is provided as listed in Supplementary Table 2 and 3. We carried out no experiments involving animals or human subjects directly, but rather got use of and did analytical researches on open databases. The ethical approval has been received by UKBB form the NHS National Research Ethics Service North West.\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eBy utilizing data sets containing \u0026beta;-coefficient, standard error p value and effect allele of BMI-assoicated and outcomes-associated SNPs respectively, we in turn conducted the traditional inverse-variance weighted(Burgess et al., 2013)(IVW) method, weighted median methods, MR-Egger regression and MR-PRESSO for robustness of the results. No error of measurement and that all the SNPs selected are valid are with acquiescence when processing random IVW, whose fundamental principles was weighted linear regression. Since results of IVW method can be biased if instrument SNPs shows horizontal pleiotropy, acting through another unaccounted casual pathway, we further conducted sensitivity analysis such as Weighted Median methods, MR-Egger regression and MR-PRESSO. MR-Egger regression was done to make sure that no horizontal pleiotropy exists among BMI-associated SNPs. However, neither IVW or MR-Egger regression can foreclose the possibility of invalid SNPs except weighted median (WM) approaches were done, which indicates that at least half of the SNPs selected were valid instrumental variables. MR-PRESSO is an updated analysis that can evaluate whether a specific individual SNP is driving the difference in residual sum of squares (RSS). After being pre-screened for LD, SNP model was incorporated into three stages to examine the extent of horizontal pleiotropy, that is MR-PRESSO global test, MR-PRESSO outlier test and MR-PRESSO distortion test. Here we conducted MR-PRESSO method as verification for pleiotropy. Effect estimate and 95%CI was converted by exponential transformation. All the analysis was conducted in RStudio version 3.6.1 and regarded two-tailed P\u0026lt;0.05 as significance.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eCausal associations between adult-BMI and diseases\u003c/h2\u003e\n\u003cp\u003eBy means of IVW approach, WM method and MR-Egger analysis, we calculated the estimated causal effect of adulthood BMI on diseases especially for women as listed (Figure 1; Table 3). The positive causal association was found between PCOS and adult-BMI, which was confirmed by both IVW (Odd ratio [OR]= 1.003;95%CI: 1.001-1.004; P=6.83*10\u003csup\u003e-5\u003c/sup\u003e) and WM methods (OR= 1.003;95%CI: 1.000-1.005; P=2.80*10\u003csup\u003e-2\u003c/sup\u003e). The negative causality between cervical cancer and adult-BMI was only obtained through WM method (OR= 0.996;95%CI: 0.992-1.000; P=4.17*10\u003csup\u003e-2\u003c/sup\u003e), although the estimate of IVW approach almost got the significance (OR= 0.998;95%CI: 0.995-1.000; P=6.57*10\u003csup\u003e-2\u003c/sup\u003e). No significant causal associations were demonstrated between adult-BMI and endometrial cancer, ovarian cancer or endometriosis. The results of MR-Egger regression uncovered that all SNPs were plotted around origin, indicating no directional pleiotropy existed here under any conditions.\u003c/p\u003e\n\u003ch2\u003eCausal associations between child-BMI and diseases\u003c/h2\u003e\n\u003cp\u003eTraditional IVW approach determined the positive causal effect of child-BMI on PCOS (OR= 1.003;95%CI: 1.001-1.004; P=9.14*10\u003csup\u003e-4\u003c/sup\u003e) and negative causal association between child-BMI and endometriosis (OR= 0.995;95%CI: 0.991-0.999; P=1.25*10\u003csup\u003e-2\u003c/sup\u003e). Weighted median method did not obtain the same conclusion. However, WM method implied that negative causal associations between child-BMI and cervical cancer (OR= 0.996;95%CI: 0.993-1.000; P=0.053) or endometrial cancer (OR= 0.997;95%CI: 0.994-1.000; P=0.08). MR-Egger regression concluded that the consequences were not influenced by pleiotropy (Figure 2; Table 4).\u003c/p\u003e\n\u003cp\u003eWe then conducted recently updated MR-PRESSO global test to exhibit whether there existed horizontal pleiotropy in selected SNPs. As expected, no outlier SNP in adult-BMI (Supplementary Table 4) or child-BMI SNPs category (Supplementary Table 5) was found in all diseases according to the analytic results except rs11727676 and rs12566985 in adult-BMI when associated with endometriosis. From the perspective of MR-PRESSO conclusion, significance was shown still in the negative association between children BMI and endometriosis as well as in the positive association between PCOS and BMI at adulthood or childhood as IVW has displayed.\u003c/p\u003e"},{"header":"Discussions","content":" \u003cp\u003eIn this study, we found pieces of robust evidence about causal association between child and adult BMI and the risk of infertility and cervical cancers. In details, the higher genetic score of adult and childhood BMI was associated with increased of PCOS. Consistent with our conclusions, observational studies and meta-analyses have uncovered the same outcome that women with PCOS had higher risk of central obesity(Lim et al., 2012) and the prevalence of PCOS was increased among overweight and obese women(Alvarez-Blasco et al., 2006), earlier adiposity rebound and severe BMI rise in childhood predicted later development of PCOS(Koivuaho et al., 2019b). The negative causality between child BMI and endometriosis through IVW method was convincing because observational studies has come up with same conclusion that there was an inverse relation between body size at 5,10,20 age and the disease(Vitonis et al., 2010). The prospective case-control aiming at more than 100,000 women from the United States for over 20 years exhibited the inverse tendency to develop endometriosis was more significantly obvious in 18-year-old BMI than current BMI, persisted noteworthy in both infertile women and those without concurrent infertile. An genome-wide enrichment analysis demonstrated a significant enrichment of common variants overlapping both endometriosis and waist-to-hip ratio adjusted for BMI, representing for fat distribution(Rahmioglu et al., 2015). There is another case-control analysis in Australia revealed that women who self-reported overweight at age 10 had an increasing risk of endometriosis(Nagle et al., 2009). However, mother-daughter pair reports came up an opposite association that underweight at 16 years old positively related to endometriosis, which consisted with ours. Nevertheless, we didn\u0026rsquo;t acquire same positive findings about BMI associated with endometrial cancers, ovarian cancers using the method as other MR analysis and observation did. A MR analysis published in 2016 which uses 4 subsets of cases and controls from EC datasets of Australian and European ancestry(Painter et al., 2016). The final IVW result was combined using random effects meta-analysis after stratified into quartiles and calculated separately. So different races and computing method may contribute to the divergence in our results. While using the same adult BMI-SNPs without abandoning 2 loci in strong linkage disequilibrium and different outcome database, Gao, C., et al.(Gao et al., 2016) concluded that 1 standard deviation in genetically predicted adult BMI was associated with 35% increase risk in overall ovarian cancer. However, after excluding overlap loci, the significance disappeared. They also didn\u0026rsquo;t find strong association between genetically predicted child BMI with ovarian cancer risk. Another MR study ended up with a consequence that BMI as instrumental invariables as obesity, increased risk of non-high grade serous ovarian cancer of European ancestry, but had nothing to do with high grade serous ovarian cancer(Dixon et al., 2016). This reminds us that subtypes of ovarian cancer may react to obesity differently and contribute together to nonsense of overall OC to obese. The inverse causal relationship between cervical cancers and adult BMI need to be confirmed because the p value of IVW reached 6.57*10\u0026thinsp;\u0026minus;\u0026thinsp;2 while WM concluded significantly. A retrospective cohort study of patients of cervical cancer(Frumovitz et al., 2014) were classified as underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5kg/m\u003csup\u003e2\u003c/sup\u003e), normal weight (BMI 18.5-24.9kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (BMI 25-29.9kg/m\u003csup\u003e2\u003c/sup\u003e), obese (BMI 30-34.9 kg/m\u003csup\u003e2\u003c/sup\u003e) and morbidly obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;35.0kg/m\u003csup\u003e2\u003c/sup\u003e). After controlling for prognostic factors, only morbidly obese remained an independent risk for mortality of CC. So, the classification left us a hint that the extent of obesity or staging of disease could obscure the real causal association of risk factors and results.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, it is the first mendelian randomization analysis of the causality of BMI and PCOS or endometriosis. Also, never ever had articles published the association between childhood BMI and common gynecological cancers. With the appearance of genome wide associated study, we can take advantage of information sharing to further MR research on causality of exposures and diseases. Previously, researchers get used to carrying out retrospective studies or prospective studies. Compared to the two classical research methods, strengths of our MR analysis can be listed as follow: 1) less vulnerable to reverse causation or confounding bias from economic and educational levels or lifestyle like smoking or alcoholism; 2) insusceptible of experimental such as acting time and degree of disposal; 3) the sample capacity can be big enough as soon as getting access to database; 4) our results show besides adult obesity, severe child obese can already be predictable to endometriosis and PCOS in later life, so it\u0026rsquo;s vital for us to prevent the trend of obesity in childhood and take measures to keep in balanced shape whenever weight gains, and if obese has taken place, there\u0026rsquo;s a need for to lose weight; 5) MR can directly explains the causality between exposure and outcomes not only simple correlation. So, drug use and side-effect of weight gain need to be notice for future health. Of course, there is room for improvement: 1) BMI is not a perfect index for obesity, as we can see, waist to hip ratio as well as subcutaneous fat thickness is often chosen to be proxies of obesity. Fat distribution rather than BMI can also be an independent risk of diseases; 2) different races can have changes in instrumental variables and genetic score of outcome; Here in our MR analysis, results only applied to European; 3) there may be some overlapped loci in adult and child BMI associated SNPs, there\u0026rsquo;s a possibility that genetic variants influence both of them; 4) we can\u0026rsquo;t count out the probability of population stratification and other potential confounding, severity levels of outcomes and stages of diseases should be underlined and distinguished if possible, and our final association was not obvious.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eGenerally speaking, genetic predispositions to elevated adult and child BMI was related to higher risk of PCOS, increased child BMI was linked to lower risk of endometriosis. No strong evidence had been found of association between adult and child BMI and female gynecological cancers. These results offered us a causal relationship between BMI and PCOS or endometriosis, implying that no matter adult or child obese can leave an adverse impact on PCOS. But too thin and underweight may increase the risk of endometriosis. However, further replications need to be carried out with larger scale, prospective and observational studies about female cancers of reproductive system as well as illuminating the mechanism of how adult and child obese working on PCOS and endometriosis, as PCOS is now still a disease of unknown etiology.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eAll the cited projects and datasets were approved by the relevant ethics committees in corresponding published articles. We did not apply required statements of ethics committee approval or checklist referenced at the Equator Network, because our study does not belong to traditional human and animal researches or observational studies. We did mendelian randomization analysis on published data and open databases, thus drew a conclusion on the causal association between exposures and outcomes. We were sincerely appreciated the authors of studies mentioned above for their kindness and generosity with their open dataset so that we can download their reference data.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82088102) and National Natural Science Foundation of China (81661128010).\u003c/p\u003e\n\u003ch2\u003eConflicts of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declared no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material:\u003c/h2\u003e\n\u003cp\u003eBesides the selected SNPs derived from published articles [dataset 1, 2], we also got access to the open database website searching for genetic statistics of diseases outcomes. The data underlying this article are available in UKBB listed as followed.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.dropbox.com/s/uirk8rmdhmyana7/20001_1041.gwas.imputed_v3.female.tsv.bgz?dl=0\"\u003ehttps://www.dropbox.com/s/uirk8rmdhmyana7/20001_1041.gwas.imputed_v3.female.tsv.bgz?dl=0\u003c/a\u003e for cervical cancer.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.dropbox.com/s/a6hxa2xh6ke726y/20001_1040.gwas.imputed_v3.female.tsv.bgz?dl=0\"\u003ehttps://www.dropbox.com/s/a6hxa2xh6ke726y/20001_1040.gwas.imputed_v3.female.tsv.bgz?dl=0\u003c/a\u003e for endometrial cancer.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.dropbox.com/s/dkgr3ejcxza9f7o/20001_1039.gwas.imputed_v3.female.tsv.bgz?dl=0\"\u003ehttps://www.dropbox.com/s/dkgr3ejcxza9f7o/20001_1039.gwas.imputed_v3.female.tsv.bgz?dl=0\u003c/a\u003e for ovarian cancer.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.dropbox.com/s/4g7f8jpq3dkn6l5/20002_1402.gwas.imputed_v3.female.tsv.bgz?dl=0\"\u003ehttps://www.dropbox.com/s/4g7f8jpq3dkn6l5/20002_1402.gwas.imputed_v3.female.tsv.bgz?dl=0\u003c/a\u003e for endometriosis.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.dropbox.com/s/hvpig2a6n4fpsy2/20002_1350.gwas.imputed_v3.female.tsv.bgz?dl=0\"\u003ehttps://www.dropbox.com/s/hvpig2a6n4fpsy2/20002_1350.gwas.imputed_v3.female.tsv.bgz?dl=0\u003c/a\u003e\u0026nbsp; for PCOS.\u003c/p\u003e\n\u003cp\u003e[dataset1]: Locke, Adam E et al. \u0026ldquo;Genetic studies of body mass index yield new insights for obesity biology.\u0026rdquo;\u0026nbsp;\u003cem\u003eNature\u003c/em\u003e\u0026nbsp;vol. 518,7538 (2015): 197-206. doi:10.1038/nature14177\u003c/p\u003e\n\u003cp\u003e[dataset2]: Geng, Tingting et al. \u0026ldquo;Childhood BMI and Adult Type 2 Diabetes, Coronary Artery Diseases, Chronic Kidney Disease, and Cardiometabolic Traits: A Mendelian Randomization Analysis.\u0026rdquo;\u0026nbsp;\u003cem\u003eDiabetes care\u003c/em\u003e\u0026nbsp;vol. 41,5 (2018): 1089-1096. doi:10.2337/dc17-2141\u003c/p\u003e\n\u003ch2\u003eCode availability:\u003c/h2\u003e\n\u003cp\u003eRStudio version 3.6.1\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions:\u003c/h2\u003e\n\u003cp\u003eYi-Shang Yan collected data and wrote the manuscript. Zihao Qu analyzed and revised the data. Pingping Lv and He-feng Huang designed the article.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate and publication:\u003c/h2\u003e\n\u003cp\u003eEthical approval for this study and written informed consent from the participants of the study were not required in accordance with local legislation and national guidelines.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eALVAREZ-BLASCO, F., BOTELLA-CARRETERO, J. I., SAN MILL\u0026aacute;N, J. L. \u0026amp; ESCOBAR-MORREALE, H. F. 2006. Prevalence and characteristics of the polycystic ovary syndrome in overweight and obese women. \u003cem\u003eArchives of internal medicine,\u003c/em\u003e 166\u003cstrong\u003e,\u003c/strong\u003e 2081-2086.\u003c/li\u003e\n\u003cli\u003eBOWDEN, J., DAVEY SMITH, G. \u0026amp; BURGESS, S. J. I. J. O. E. 2015. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. 44\u003cstrong\u003e,\u003c/strong\u003e 512-525.\u003c/li\u003e\n\u003cli\u003eBOWDEN, J., DAVEY SMITH, G., HAYCOCK, P. C. \u0026amp; BURGESS, S. J. G. E. 2016. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. 40\u003cstrong\u003e,\u003c/strong\u003e 304-314.\u003c/li\u003e\n\u003cli\u003eBURGESS, S., BUTTERWORTH, A. \u0026amp; THOMPSON, S. G. J. G. E. 2013. Mendelian randomization analysis with multiple genetic variants using summarized data. 37\u003cstrong\u003e,\u003c/strong\u003e 658-665.\u003c/li\u003e\n\u003cli\u003eBURGESS, S., SCOTT, R. A., TIMPSON, N. J., SMITH, G. D., THOMPSON, S. G. \u0026amp; EPIDEMIOLOGY, E.-I. C. J. E. J. O. 2015. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. 30\u003cstrong\u003e,\u003c/strong\u003e 543-552.\u003c/li\u003e\n\u003cli\u003eDIDELEZ, V. \u0026amp; SHEEHAN, N. J. S. M. I. M. R. 2007. Mendelian randomization as an instrumental variable approach to causal inference. 16\u003cstrong\u003e,\u003c/strong\u003e 309-330.\u003c/li\u003e\n\u003cli\u003eDIXON, S. C., NAGLE, C. M., THRIFT, A. P., PHAROAH, P. D., PEARCE, C. L., ZHENG, W., PAINTER, J. N., GROUP, A., STUDY, A. C., CHENEVIX-TRENCH, G. \u0026amp; FASCHING, P. A. J. I. J. O. E. 2016. Adult body mass index and risk of ovarian cancer by subtype: a Mendelian randomization study. 45\u003cstrong\u003e,\u003c/strong\u003e 884-895.\u003c/li\u003e\n\u003cli\u003eEMDIN, C. A., KHERA, A. V. \u0026amp; KATHIRESAN, S. 2017. Mendelian Randomization. \u003cem\u003eJAMA,\u003c/em\u003e 318\u003cstrong\u003e,\u003c/strong\u003e 1925-1926.\u003c/li\u003e\n\u003cli\u003eFRUMOVITZ, M., JHINGRAN, A., SOLIMAN, P. T., KLOPP, A. H., SCHMELER, K., EIFEL, P. J. J. O. \u0026amp; GYNECOLOGY 2014. Morbid obesity as an independent risk factor for disease-specific mortality in women with cervical cancer. 124\u003cstrong\u003e,\u003c/strong\u003e 1098.\u003c/li\u003e\n\u003cli\u003eGAO, C., PATEL, C. J., MICHAILIDOU, K., PETERS, U., GONG, J., SCHILDKRAUT, J., SCHUMACHER, F. R., ZHENG, W., BOFFETTA, P. \u0026amp; STUCKER, I. J. I. J. O. E. 2016. Mendelian randomization study of adiposity-related traits and risk of breast, ovarian, prostate, lung and colorectal cancer. 45\u003cstrong\u003e,\u003c/strong\u003e 896-908.\u003c/li\u003e\n\u003cli\u003eGENG, T., SMITH, C. E., LI, C. \u0026amp; HUANG, T. J. D. C. 2018. Childhood BMI and adult type 2 diabetes, coronary artery diseases, chronic kidney disease, and cardiometabolic traits: a mendelian randomization analysis. 41\u003cstrong\u003e,\u003c/strong\u003e 1089-1096.\u003c/li\u003e\n\u003cli\u003eHALES, C. M., FRYAR, C. D., CARROLL, M. D., FREEDMAN, D. S. \u0026amp; OGDEN, C. L. J. J. 2018. Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007-2008 to 2015-2016. 319\u003cstrong\u003e,\u003c/strong\u003e 1723-1725.\u003c/li\u003e\n\u003cli\u003eKOIVUAHO, E., LARU, J., OJANIEMI, M., PUUKKA, K., KETTUNEN, J., TAPANAINEN, J., FRANKS, S., J\u0026auml;RVELIN, M.-R., MORIN-PAPUNEN, L. \u0026amp; SEBERT, S. J. I. J. O. O. 2019a. Age at adiposity rebound in childhood is associated with PCOS diagnosis and obesity in adulthood\u0026mdash;longitudinal analysis of BMI data from birth to age 46 in cases of PCOS. 43\u003cstrong\u003e,\u003c/strong\u003e 1370-1379.\u003c/li\u003e\n\u003cli\u003eKOIVUAHO, E., LARU, J., OJANIEMI, M., PUUKKA, K., KETTUNEN, J., TAPANAINEN, J. S., FRANKS, S., J\u0026auml;RVELIN, M. R., MORIN-PAPUNEN, L., SEBERT, S. \u0026amp; PILTONEN, T. T. 2019b. Age at adiposity rebound in childhood is associated with PCOS diagnosis and obesity in adulthood-longitudinal analysis of BMI data from birth to age 46 in cases of PCOS. \u003cem\u003eInternational journal of obesity (2005),\u003c/em\u003e 43\u003cstrong\u003e,\u003c/strong\u003e 1370-1379.\u003c/li\u003e\n\u003cli\u003eLAITINEN, J., TAPONEN, S., MARTIKAINEN, H., POUTA, A., MILLWOOD, I., HARTIKAINEN, A. L., RUOKONEN, A., SOVIO, U., MCCARTHY, M. I., FRANKS, S. \u0026amp; J\u0026auml;RVELIN, M. R. 2003. Body size from birth to adulthood as a predictor of self-reported polycystic ovary syndrome symptoms. \u003cem\u003eInternational journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity,\u003c/em\u003e 27\u003cstrong\u003e,\u003c/strong\u003e 710-715.\u003c/li\u003e\n\u003cli\u003eLIM, S. S., DAVIES, M. J., NORMAN, R. J. \u0026amp; MORAN, L. J. 2012. Overweight, obesity and central obesity in women with polycystic ovary syndrome: a systematic review and meta-analysis. \u003cem\u003eHuman reproduction update,\u003c/em\u003e 18\u003cstrong\u003e,\u003c/strong\u003e 618-637.\u003c/li\u003e\n\u003cli\u003eLOCKE, A. E., KAHALI, B., BERNDT, S. I., JUSTICE, A. E., PERS, T. H., DAY, F. R., POWELL, C., VEDANTAM, S., BUCHKOVICH, M. L. \u0026amp; YANG, J. J. N. 2015. Genetic studies of body mass index yield new insights for obesity biology. 518\u003cstrong\u003e,\u003c/strong\u003e 197-206.\u003c/li\u003e\n\u003cli\u003eMARKOWSKA, A., STANISŁAWIAK-RUDOWICZ, J. \u0026amp; JASZCZYŃSKA-NOWINKA, K. 2017. The role of overweight and obesity in selected gynecological malignancies. \u003cem\u003eEuropean journal of gynaecological oncology,\u003c/em\u003e 38\u003cstrong\u003e,\u003c/strong\u003e 335-341.\u003c/li\u003e\n\u003cli\u003eMISSMER, S. A., HANKINSON, S. E., SPIEGELMAN, D., BARBIERI, R. L., MARSHALL, L. M. \u0026amp; HUNTER, D. J. J. A. J. O. E. 2004. Incidence of laparoscopically confirmed endometriosis by demographic, anthropometric, and lifestyle factors. 160\u003cstrong\u003e,\u003c/strong\u003e 784-796.\u003c/li\u003e\n\u003cli\u003eMODESITT, S. C. \u0026amp; VAN NAGELL, J. R. 2005. The impact of obesity on the incidence and treatment of gynecologic cancers: a review. \u003cem\u003eObstetrical \u0026amp; gynecological survey,\u003c/em\u003e 60\u003cstrong\u003e,\u003c/strong\u003e 683-692.\u003c/li\u003e\n\u003cli\u003eNAGLE, C. M., BELL, T., PURDIE, D. M., TRELOAR, S., OLSEN, C. M., GROVER, S. \u0026amp; GREEN, A. C. J. H. R. 2009. Relative weight at ages 10 and 16 years and risk of endometriosis: a case\u0026ndash;control analysis. 24\u003cstrong\u003e,\u003c/strong\u003e 1501-1506.\u003c/li\u003e\n\u003cli\u003eOLLILA, M. M., WEST, S., KEIN\u0026auml;NEN-KIUKAANNIEMI, S., JOKELAINEN, J., AUVINEN, J., PUUKKA, K., RUOKONEN, A., J\u0026auml;RVELIN, M. R., TAPANAINEN, J. S., FRANKS, S., PILTONEN, T. T. \u0026amp; MORIN-PAPUNEN, L. C. 2017. Overweight and obese but not normal weight women with PCOS are at increased risk of Type 2 diabetes mellitus-a prospective, population-based cohort study. \u003cem\u003eHuman reproduction (Oxford, England),\u003c/em\u003e 32\u003cstrong\u003e,\u003c/strong\u003e 423-431.\u003c/li\u003e\n\u003cli\u003ePAINTER, J. N., O'MARA, T. A., MARQUART, L., WEBB, P. M., ATTIA, J., MEDLAND, S. E., CHENG, T., DENNIS, J., HOLLIDAY, E. G., MCEVOY, M. J. C. E. \u0026amp; BIOMARKERS, P. 2016. Genetic risk score Mendelian randomization shows that obesity measured as body mass index, but not waist: hip ratio, is causal for endometrial cancer. 25\u003cstrong\u003e,\u003c/strong\u003e 1503-1510.\u003c/li\u003e\n\u003cli\u003ePARK, J., MORLEY, T. S., KIM, M., CLEGG, D. J. \u0026amp; SCHERER, P. E. J. N. R. E. 2014. Obesity and cancer\u0026mdash;mechanisms underlying tumour progression and recurrence. 10\u003cstrong\u003e,\u003c/strong\u003e 455.\u003c/li\u003e\n\u003cli\u003eRAHMIOGLU, N., MACGREGOR, S., DRONG, A. W., HEDMAN, \u0026Aring;. K., HARRIS, H. R., RANDALL, J. C., PROKOPENKO, I., INTERNATIONAL ENDOGENE CONSORTIUM , T. G. C., NYHOLT, D. R. \u0026amp; MORRIS, A. P. J. H. M. G. 2015. Genome-wide enrichment analysis between endometriosis and obesity-related traits reveals novel susceptibility loci. 24\u003cstrong\u003e,\u003c/strong\u003e 1185-1199.\u003c/li\u003e\n\u003cli\u003eSHAH, D. K., CORREIA, K. F., VITONIS, A. F. \u0026amp; MISSMER, S. A. J. H. R. 2013. Body size and endometriosis: results from 20 years of follow-up within the Nurses' Health Study II prospective cohort. 28\u003cstrong\u003e,\u003c/strong\u003e 1783-1792.\u003c/li\u003e\n\u003cli\u003eSILVESTRIS, E., DE PERGOLA, G., ROSANIA, R., LOVERRO, G. J. R. B. \u0026amp; ENDOCRINOLOGY 2018. Obesity as disruptor of the female fertility. 16\u003cstrong\u003e,\u003c/strong\u003e 22.\u003c/li\u003e\n\u003cli\u003eVERBANCK, M., CHEN, C.-Y., NEALE, B. \u0026amp; DO, R. J. N. G. 2018. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. 50\u003cstrong\u003e,\u003c/strong\u003e 693-698.\u003c/li\u003e\n\u003cli\u003eVITONIS, A. F., BAER, H. J., HANKINSON, S. E., LAUFER, M. R. \u0026amp; MISSMER, S. A. J. H. R. 2010. A prospective study of body size during childhood and early adulthood and the incidence of endometriosis. 25\u003cstrong\u003e,\u003c/strong\u003e 1325-1334.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eCharacteristics of adult BMI-associated SNPs from GWAS\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGene\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSNP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChromosome: Position\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"44\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"3\" width=\"208\"\u003e\n\u003cp\u003eAssociation with exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"111\"\u003e\n\u003cp\u003e\u0026beta;(SE)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"97\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eAGBL4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers657452\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e1:49589847\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.023(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e5.48 \u0026times; 10\u003csup\u003e-13\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eCADM1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers12286929\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e11:115022404\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.022(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.31 \u0026times; 10\u003csup\u003e-12\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eTCF7L2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers7903146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e10:114758349\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.023(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.31 \u0026times; 10\u003csup\u003e-12\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eSTXBP6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers10132280\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e14:25928179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.023(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.14 \u0026times; 10\u003csup\u003e-11\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eHIF1AN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers17094222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e10:102395440\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.025(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e5.94 \u0026times; 10\u003csup\u003e-11\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eERBB4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers7599312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e2:213413231\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.022(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.17 \u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eFHIT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2365389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e3:61236462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.02(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.63 \u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eNAV1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2820292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e1:201784287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.02(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.83 \u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ePRKD1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers12885454\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e14:29736838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.021(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.94 \u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eRASA2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers16851483\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e3:141275436\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.048(0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e3.55 \u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eHIP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers1167827\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e7:75163169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.02(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e6.33 \u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eNLRC3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers758747\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e16:3627358\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.023(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e7.47\u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eTLR4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers1928295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e9:120378483\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.019(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e7.91\u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eKAT8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers9925964\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e16:31129895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.019(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e8.11\u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eKCNK3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers11126666\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e2:26928811\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.021(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.33 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eSBK1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2650492\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e16:28333411\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.021(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.92 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eRARB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers6804842\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e3:25106437\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.019(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.48 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eCCDC171\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers4740619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e9:15634326\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.018(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e4.56 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ePRKN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers13191362\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e6:163033350\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.028(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e7.34 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eDMXL2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers3736485\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e15:51748610\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.018(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e7.41 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eSCARB2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers17001654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e4:77129568\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.031(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e7.76 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eNT5CA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers11191560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e10:104869038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.031(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e8.45 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eUBE2E3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers1528435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e2:181550962\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.018(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.20 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eRABEP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers1000940\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e17:5283252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.019(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.28 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2033529\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e6:40348653\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.019(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.39 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eFOXO3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers9400239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e6:108977663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.019(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.61 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eLMX1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers10733682\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e9:129460914\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.017(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.83 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eEHBP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers11688816\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e2:63053048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.017(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.89 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eCLIP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers11057405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e12:122781897\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.031(0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.02 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eHHIP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers11727676\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e4:145659064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.036(0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.55 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eGBE1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers3849570\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e3:81792112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.019(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.60 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eFRRS1L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers6477694\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e9:111932342\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.017(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.67 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eGRID1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers7899106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e10:87410904\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.04(0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.96 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eHSD17B12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2176598\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e11:43864278\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.02(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.97 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ePMS2L11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2245368\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e7:76608143\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.032(0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e3.19 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ePGPEP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers17724992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e19:18454825\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.019(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e3.42 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eGRP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers7243357\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e18:56883319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.022(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e3.86 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eRP11-120I21.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2033732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e8:85079709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.019(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e4.89 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eFTO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers1558902\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e16:53803574\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"111\"\u003e\n\u003cp\u003e0.082(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"97\"\u003e\n\u003cp\u003e7.51 \u0026times; 10\u003csup\u003e-153\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eMC4R\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers6567160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e18:57829135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.056(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e3.53 \u0026times; 10\u003csup\u003e-53\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eTMEM18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers13021737\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e2:632348\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.06(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.11 \u0026times; 10\u003csup\u003e-50\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eNMU\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers10938397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e4:45182527\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.04(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e3.21 \u0026times; 10\u003csup\u003e-38\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eSEC16B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers543874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e1:177889480\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.048(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.62 \u0026times; 10\u003csup\u003e-35\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eTFAP2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2207139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e6:50845490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.045(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e4.13 \u0026times; 10\u003csup\u003e-29\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eBDAF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers11030104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e11:27684517\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.041(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e5.56\u0026times; 10\u003csup\u003e-28\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eNEGR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers3101336\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e1:72751185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.033(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.66\u0026times; 10\u003csup\u003e-26\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eRP11-70F11.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers7138803\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e12:50247468\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.032(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e8.15\u0026times; 10\u003csup\u003e-24\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eADCY3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers10182181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e2:25150296\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.031(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e8.78\u0026times; 10\u003csup\u003e-24\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eE7V5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers1516725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e3:185824004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.045(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.89\u0026times; 10\u003csup\u003e-22\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eAC134300.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers12446632\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e16:19935389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.04(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.48\u0026times; 10\u003csup\u003e-18\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eQPCTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2287019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e19:46202172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.036(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e4.59\u0026times; 10\u003csup\u003e-18\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eMAP2K5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers16951275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e15:68077168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.031(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.91\u0026times; 10\u003csup\u003e-17\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eMTCH2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers3817334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e11:47650993\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.026(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e5.15\u0026times; 10\u003csup\u003e-17\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ePOC5\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2112347\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e5:75015242\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.026(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e6.19\u0026times; 10\u003csup\u003e-17\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eFPGT-TNNI3K\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers12566985\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e1:75002193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.024(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e3.28\u0026times; 10\u003csup\u003e-15\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eZC3H4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers3810291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e19:47569003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.028(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e4.81\u0026times; 10\u003csup\u003e-15\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eNRXN3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers7141420\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e14:79899454\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.024(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.23\u0026times; 10\u003csup\u003e-14\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eCADM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers13078960\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e3:85807590\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.03(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.74\u0026times; 10\u003csup\u003e-14\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eLINGO2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers10968576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e9:28414339\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.025(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e6.61\u0026times; 10\u003csup\u003e-14\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eGNAI3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers17024393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e1:110154688\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.066(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e7.03\u0026times; 10\u003csup\u003e-14\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eOLFM4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers12429545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e13:54102206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.033(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.09\u0026times; 10\u003csup\u003e-12\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eSLC39A8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers13107325\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e4:103188709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.048(0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.83\u0026times; 10\u003csup\u003e-12\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eEEF1A1P11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers11165643\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e1:96924097\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.022(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.07\u0026times; 10\u003csup\u003e-12\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003esnoU13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers17405819\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e8:76806584\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.022(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.07\u0026times; 10\u003csup\u003e-11\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eLINC01122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers1016287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e2:59305625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.023(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.25\u0026times; 10\u003csup\u003e-11\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eTRIM66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers4256980\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e11:8673939\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.021(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.90\u0026times; 10\u003csup\u003e-11\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eDNAJB4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers12401738\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e1:78446761\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.021(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.15\u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eILRUN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers205262\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e6:34563164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.022(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.75\u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eMTIF3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers12016871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e13:28017782\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.03(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.29\u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eRPTOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers12940622\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e17:78615571\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.018(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.49\u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ePRKD1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers11847697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e14:30515112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.049(0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e3.99\u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eTOMM40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2075650\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e19:45395619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.026(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.25\u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eLRP1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers2121279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e2:143043285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.025(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.31\u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eKCTD15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers29941\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e19:34309532\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.018(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.41\u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 38px;\"\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003eNPC1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"98\"\u003e\n\u003cp\u003ers1808579\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"109\"\u003e\n\u003cp\u003e18:21104888\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" width=\"104\"\u003e\n\u003cp\u003e0.017(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 38px;\" colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e4.17\u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13.8977px;\"\u003e\n\u003ctd style=\"height: 13.8977px;\" colspan=\"7\" width=\"98\"\u003e\n\u003cp\u003eGene = nearest gene to the SNP; EA = effect allele; \u0026beta; = per allele effect on the exposure; SE = standard error; P value = p-value for the genetic association.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003e Characteristics of child BMI-associated SNPs from GWAS\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGene\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSNP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChromosome: Position\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"44\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"208\"\u003e\n\u003cp\u003eAssociation with exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"109\"\u003e\n\u003cp\u003e\u0026beta;(SE)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eGNPDA2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers13130484\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e4:45175691\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.067(0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.58 \u0026times; 10\u003csup\u003e-23\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eADCY3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers11676272\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e2:25141538\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.068(0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e7.12 \u0026times; 10\u003csup\u003e-23\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eTMEM18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers4854349\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e2:647861\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.09(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e5.41 \u0026times; 10\u003csup\u003e-22\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eSEC16B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers543874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e1:177889480\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.077(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.20 \u0026times; 10\u003csup\u003e-19\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eFAIM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers7132908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e12:50263148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.066(0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.57 \u0026times; 10\u003csup\u003e-18\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eFTO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers1421085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e16:53800954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.059(0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e4.53 \u0026times; 10\u003csup\u003e-16\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eOLFM4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers12429545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e13:54102206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.076(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.08 \u0026times; 10\u003csup\u003e-14\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eTFAP2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers987237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e6:50803050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.062(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.80 \u0026times; 10\u003csup\u003e-12\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eTNNI3K\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers12041852\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e1:75003500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.046(0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.28 \u0026times; 10\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eMC4R\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers6567160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e18:57829135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.05(0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e1.21 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eELP3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers13253111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e8:28061974\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.042(0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e4.89 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eRAB27B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers8092503\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e18:52479487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.045(0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e8.17 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eLMX1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers3829849\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e9:129390800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.041(0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e8.81 \u0026times; 10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eADAM23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers13387838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e2:207281447\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.139(0.025)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e2.84 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003eGPR61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003ers7550711\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e1:110082886\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"44\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.105(0.019)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"104\"\u003e\n\u003cp\u003e4.52 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" width=\"99\"\u003eGene = nearest gene to the SNP; EA = effect allele; \u0026beta; = per allele effect on the exposure; SE = standard error; P value = p-value for the genetic association.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003e Inverse-variance weighted method, weighted median approach, MR-Egger analysis for genetic associations between 75 adult BMI and indicated diseases \u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u003cstrong\u003eDisease traits\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emethod\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e\u003cstrong\u003eeffect\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"112\"\u003e\n\u003cp\u003e\u003cstrong\u003eCervical\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e0.998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.28*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.995,1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e6.57*10\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eWM*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e2.08*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.992,1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003e4.17*10\u003csup\u003e-2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eMR-Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e9.12*10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.9999,1.0002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"112\"\u003e\n\u003cp\u003e\u003cstrong\u003eEndometrial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ecancer\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.01*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.998,1.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eWM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e0.998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.65*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.994,1.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eMR-Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e7.28*10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.9999,1.0002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"112\"\u003e\n\u003cp\u003e\u003cstrong\u003eOvarian\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e8.34*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.999,1.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eWM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.39*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.998,1.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eMR-Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e5.98*10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.9999,1.0001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"112\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEndometriosis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e0.999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e2.12*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.995,1.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eWM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e0.999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e3.07*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.993,1.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eMR-Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.50*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.9999-1.0005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"112\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCOS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eIVW***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e7.40*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(1.001,1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003e6.83*10\u003csup\u003e-5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eWM*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.16*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(1.000,1.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.80*10\u003csup\u003e-2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003eMR-Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e5.28*10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e(0.9999,1.0001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"80\"\u003e\n\u003cp\u003eCI = confidence interval; P value = p-value of the causal estimate.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eInverse-variance weighted method, weighted median approach, MR-Egger analysis for genetic associations between 15 child BMI and indicated diseases\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u003cstrong\u003eDisease traits\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emethod\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e\u003cstrong\u003eeffect\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"99\"\u003e\n\u003cp\u003e\u003cstrong\u003eCervical\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.43*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.995,1.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eWM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.93*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.993,1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.053\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eMR-Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e3.50*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.9996,1.0009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"99\"\u003e\n\u003cp\u003e\u003cstrong\u003eEndometrial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.30*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.996,1.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eWM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.54*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.994,1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eMR-Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e3.22*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.9994,1.0006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"99\"\u003e\n\u003cp\u003e\u003cstrong\u003eOvarian\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eIVW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e8.94*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.998,1.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eWM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.24*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.998,1.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eMR-Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e2.14*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.9996,1.0005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"99\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEndometriosis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eIVW*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.99*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.991,0.999)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.25*10\u003csup\u003e-2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eWM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e2.64*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.991,1.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eMR-Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e4.94*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(0.9990,1.0009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"99\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCOS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eIVW***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e8.01*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(1.001,1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003e9.14*10\u003csup\u003e-4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eWM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.06*10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(1.000,1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eMR-Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e1.79*10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"139\"\u003e\n\u003cp\u003e(1.0000,1.0007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e8.37*10\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"72\"\u003e\n\u003cp\u003eCI = confidence interval; P value = p-value of the causal estimate.\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Mendelian Randomization, age-related obesity, gynecological cancer, endometriosis，polycystic ovarian syndrome","lastPublishedDoi":"10.21203/rs.3.rs-390994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-390994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eAdulthood and childhood obesity are associated with female reproductive endocrinology and gynecological cancers. However, the causality of such association is yet to be identified. Independent of inverse bias and confounding, mendelian randomization is like a random control trial where genetic groups are settled during meiosis, which could be effective to examine the causality. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We carried out several Mendelian randomization trials based on combined genetic scores of 75 adult-associated and 15 child-associated BMI single nucleotide polymorphisms (SNPs), diseases databases of several gynecological cancers and reproductive diseases from UK Biobank with 194,153 participants, using traditional inverse-variance weighted (IVW) method, Weighted Median (WM) approach, MR-Egger regression and updated MR-PRESSO analysis. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eElevated adult-associated BMI (effect:1.003;95%CI:1.001,1.004) and child-associated BMI (effect:1.003;95%CI:1.001,1.004) genetic scores were related to higher risk of PCOS incidence by using traditional IVW method. Random IVW method showed a negative causal association between the child-associated BMI and subsequent endometriosis attack(effect:0.995;95%CI:0.991,0.999).\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Consistent with observational consequences, our findings suggested that childhood and adulthood obesity may play roles in the development of PCOS, and children obesity can elevate the possibility of PCOS but decrease the incidence of endometriosis in later life. More researches need to be conducted for further validation and potential mechanisms.\u003c/p\u003e","manuscriptTitle":"Pediatric and Adult Obesity Concern Female Health: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-19 14:31:24","doi":"10.21203/rs.3.rs-390994/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-04-18T00:00:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-04-17T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-04-05T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Endocrine","date":"2021-04-02T22:38:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"84d731e4-e377-486c-b344-dda782010733","owner":[],"postedDate":"April 19th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":3729751,"name":"Endocrinology \u0026 Metabolism"}],"tags":[],"updatedAt":"2022-02-04T13:42:02+00:00","versionOfRecord":{"articleIdentity":"rs-390994","link":"https://doi.org/10.1007/s12020-021-02867-y","journal":{"identity":"endocrine","isVorOnly":false,"title":"Endocrine"},"publishedOn":"2021-09-20 13:42:02","publishedOnDateReadable":"September 20th, 2021"},"versionCreatedAt":"2021-04-19 14:31:24","video":"","vorDoi":"10.1007/s12020-021-02867-y","vorDoiUrl":"https://doi.org/10.1007/s12020-021-02867-y","workflowStages":[]},"version":"v1","identity":"rs-390994","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-390994","identity":"rs-390994","version":["v1"]},"buildId":"uwybb5PU2iWlRI8EIam5Y","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}