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© 20 10 N at ur e A m er ic a, In c. A ll rig ht s re se rv ed . Nature GeNetics  ADVANCE ONLINE PUBLICATION � l e t t e r s Endometriosis is a common gynecological disease associated  with pelvic pain and subfertility. We conducted a genome-wide  association study (GWAS) in 3,�94 individuals with surgically  confirmed endometriosis (cases) and 7,060 controls from  Australia and the UK. Polygenic predictive modeling showed  significantly increased genetic loading among �,364 cases with  moderate to severe endometriosis. The strongest association  signal was on 7p�5.2 (rs�2700667) for ‘all’ endometriosis   (P = 2.6 × �0–7, odds ratio (OR) = �.22, 95% CI �.�3–�.32)  and for moderate to severe disease (P = �.5 × �0−9, OR =  �.38, 95% CI �.24–�.53). We replicated rs�2700667 in an  independent cohort from the United States of 2,392 self- reported, surgically confirmed endometriosis cases and 2,27�  controls (P = �.2 × �0−3, OR = �.�7, 95% CI �.06–�.28),  resulting in a genome-wide significant P value of �.4 × �0−9  (OR = �.20, 95% CI �.�3–�.27) for ‘all’ endometriosis in  our combined datasets of 5,586 cases and 9,33� controls.  rs�2700667 is located in an intergenic region upstream of the  plausible candidate genes NFE2L3 and HOXA10. Endometriosis (MIM131200) is a disease affecting 6–10% of women of reproductive age1 with substantial annual health costs2 and health burden for individuals3,4. Common symptoms include chronic pelvic pain, severe dysmenorrhea (painful periods) and subfertility. The causes of endometriosis remain uncertain despite over 50 years of hypothesis-driven research. Disease severity is classified using the revised American Fertility Society (rAFS) system5, assigning affected individuals to one of four stages (stages I–IV, defined as minimal to severe disease) based on lesion size and associated pelvic adhesions. However, it remains unclear whether the disease progresses through these stages, and it has been suggested that small lesions (present in disease stages I and II) represent an epiphenomenon rather than a disease entity6. Endometriosis risk is influenced by genetic factors7–14 and has an estimated heritability of around 51%. We genotyped 3,194 unrelated cases with surgically confirmed endometriosis recruited by the International Endogene Consortium, IEC (QIMR, Australia dataset, n = 2,270; Oxford, UK dataset, n = 924)15, using the Illumina Human670Quad BeadArray (Online Methods). We assessed disease stage from surgical records using the rAFS classification system5,15 and grouped the subjects into two phenotypes: stage A (stage I or II disease or some ovarian disease with a few adhesions; n = 1,686, 52.7%) or stage B (stage III or IV disease; n = 1,364, 42.7%), or unknown (n = 144, 4.6%) (Supplementary Table 1). Illumina Human610Quad control genotypes for QIMR cases were available for 1,870 individuals in an adolescent twin study16,17. For the Oxford cases, we obtained Illumina Human1M-Duo genotypes for 5,190 UK population controls from the Wellcome Trust Case Control Consortium (WTCCC2). Although endometriosis affects only women, the Australian and UK control sets included men to maximize the power of the association detection on the autosomal chromosomes (Online Methods). We detected no significant auto- somal allele frequency differences between the male and female con- trol samples (Supplementary Fig. 1), indicating that the association signals would not be influenced by a differing female to male ratio in the cases and controls. Studies to date have established that endometriosis is heritable but have not addressed the genetic burden for different disease stages. We used the GWAS data to assess genetic loading in cases in two complementary ways. Using a new method18, we estimated the proportion of variation in case-control status that can be explained Genome-wide association study identifies a locus at 7p15.2 associated with endometriosis Jodie N Painter1,13, Carl A Anderson2,3,13, Dale R Nyholt4,13, Stuart Macgregor5, Jianghai Lin6, Sang Hong Lee5, Ann Lambert6, Zhen Z Zhao1, Fenella Roseman6, Qun Guo7, Scott D Gordon8, Leanne Wallace1, Anjali K Henders1, Peter M Visscher5, Peter Kraft9,10, Nicholas G Martin8, Andrew P Morris2, Susan A Treloar1,11,14, Stephen H Kennedy6,14, Stacey A Missmer7,9,12,14, Grant W Montgomery1,14 & Krina T Zondervan2,6,14 1Molecular Epidemiology, Queensland Institute of Medical Research, Herston, Queensland, Australia. 2Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. 3Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK. 4Neurogenetics Laboratory, Queensland Institute of Medical Research, Herston, Queensland, Australia. 5Queensland Statistical Genetics, Queensland Institute of Medical Research, Herston, Queensland, Australia. 6Nuffield Department of Obstetrics and Gynaecology, University of Oxford, John Radcliffe Hospital, Oxford, UK. 7Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA. 8Genetic Epidemiology, Queensland Institute of Medical Research, Herston, Queensland, Australia. 9Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. 10Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. 11Centre for Military and Veterans’ Health, The University of Queensland, Mayne Medical School, Queensland, Australia. 12Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA. 13These authors contributed equally to this work. 14These authors jointly directed this work. Correspondence should be addressed to K.T.Z. (krina.zondervan@well.ox.ac.uk) or J.N.P. (jodie.painter@qimr.edu.au). Received 14 May; accepted 17 November; published online 12 December 2010; doi:10.1038/ng.731 lenovo 高亮 lenovo 矩形 © 20 10 N at ur e A m er ic a, In c. A ll rig ht s re se rv ed . 2  ADVANCE ONLINE PUBLICATION Nature GeNetics l e t t e r s by considering all SNPs simultaneously through inference of distant relatedness from marker data and comparing it to case-control status (Online Methods). The proportion of variation in case-control status explained by the GWAS data was highly significant for both ‘all’ and stage B endometriosis (Table 1 and Supplementary Table 2). The estimate for stage B endometriosis (0.34, s.e. = 0.04) was signi- ficantly higher than that for stage A endometriosis (0.15, s.e. = 0.04; Table 1). We also assessed the genetic loading of the different stages using a prediction approach (Online Methods)19 in which we used the Oxford data as a discovery set to identify increasingly large SNP sets ranked on their significance of association (‘allele specific scores’) and used these scores to predict disease status in target samples from QIMR. The discovery and target sets were then reversed (Supplementary Fig. 2). Oxford ‘all’ endometriosis predicted endometriosis in the QIMR sample, with the smallest P value (P = 8.4 × 10−6) obtained for a score set including ~75% of the SNPs (Fig. 1). This result was highly significant, although the proportion of variance explained was small (maximum Nagelkerke r2 of 0.007; 0.7% of the variance). For stage B cases, the proportion of variance explained by most score sets was higher; for example, the score set including the ~20% most associated SNPs (P = 3.5 × 10−7) explained 1.3% of the variance, consistent with a greater (polygenic) genetic loading for stage B disease. We performed two genome-wide association analyses stratified by dataset (QIMR and Oxford) using (i) 3,194 ‘all’ endometriosis cases and (ii) 1,364 stage B cases, given their substantially greater genetic loading (Online Methods). For ‘all’ endometriosis, we observed the strongest signal for rs12700667 in an intergenic region on chromo- some 7p15.2 (P = 2.6 × 10−7, OR = 1.22, 95% CI 1.13–1.32; Table 2). As predicted from our quantitative genetic analyses, we observed stronger signals of association across the genome for stage B disease compared to ‘all’ endometriosis (Supplementary Fig. 3). The 7p15.2 signal for stage B endometriosis was considerably stronger, producing P = 1.5 × 10−9, OR = 1.38, 95% CI 1.24–1.53 (Table 2) for rs12700667 and P = 6.0 × 10−8, OR = 1.34, 95% CI 1.21–1.49 for the nearby SNP rs7798431 (r2 = 0.87). A second strong association was found for rs1250248 (2q35) within FN1 (P = 3.2 × 10−8) (Supplementary Table 3). Results for the SNPs rs12700667, rs7798431 and rs1250248 remained genome- wide significant after adjustment for multiple testing in the two non-independent genome- wide association analyses using permutation (Online Methods). Only one of the permuted genome-wide association analyses produced an independent P value less than that observed for rs12700667 (P = 0.001). The SNPs rs12700667 and rs7798431 lie in a narrow region of strong LD (r2 > 0.8) that extends approximately 48 kb. Following imputation using 1000 Genomes Project and HapMap data (Fig. 2 and Supplementary Note) conditioning on the effect of rs12700667 in logistic regression analysis showed no other independent associations with ‘all’ or stage B endometriosis in the region. In addition to the three genome-wide significant SNPs, we geno- typed 70 SNPs that produced nominal evidence of association with ‘all’ (P < 1.0 × 10−4) or stage B endometriosis (P <1.0 × 10−4 in stage B and P <1.0 × 10−3 in ‘all’ endometriosis analyses; Online Methods) in an independent IEC dataset comprising 2,392 self-reported surgically confirmed cases from the Nurses’ Health Study II (NHSII) and 2,271 controls from GWAS of breast cancer20 and kidney function from table 1 estimates of proportion of variation due to common genetic variants for ‘all’ endometriosis and stage A or B disease using genome-wide sNP data from cases and controlsa Phenotypes Cases Controls Proportion of variation (s.e.) P All endometriosis 3,154 6,981 0.27 (0.04) 4.4 × 10−16 Stage B 1,347 6,981 0.34 (0.04) 4.4 × 10−16 Stage A 1,666 6,981 0.15 (0.04) 2.6 × 10−4 aProportion of variation and associated P values for the likelihood ratio test were estimated using a linear mixed model incorporating 203,826 SNPs from the GWA panel after additional QC. Case and control numbers are slightly lower than for the GWA analyses due to the stricter QC measures (Online Methods). Stage A and stage B estimates of the variance explained are significantly different from each other (P = 1.8 × 10−3, using a two sample t-test which is conservative since the control samples are the same). Results were verified by prediction of individual genetic risk using QIMR and Oxford as alternate “discovery” and “target” datasets (supplementary table 2). 0 0.01 0.01 0.1 0.2 0.3 0.4 0.5 0.750.050.05 0.1 0.2 0.3 0.4 0.5 0.75 Proportion of top SNPs included in prediction Proportion of top SNPs included in prediction 0.005 0.010 0.015 P ro po rt io n of v ar ia nc e ex pl ai ne d (R 2 ) 0.020 a bP values for each R2 appear above each bar P values for each R2 appear above each bar 0. 03 11 5. 93 × 1 0– 5 0. 00 01 48 8. 4 × 1 0– 5 1. 58 × 1 0– 5 1. 48 × 1 0– 5 1. 2 × 1 0– 5 8. 39 × 1 0– 6 6. 55 × 1 0– 5 4. 39 × 1 0– 6 3. 51 × 1 0– 7 7. 86 × 1 0– 7 6. 06 × 1 0– 7 1. 41 × 1 0– 6 3. 2 × 1 0– 6 0. 00 48 2 0 0.005 0.010 0.015 0.020 ‘All’ endometriosis Stage B endometriosis Figure 1 Allele-specific score prediction for endometriosis, using the Oxford population as the discovery dataset and the QIMR population as the target dataset. Results for ‘all’ endometriosis are shown in a, and results for stage B endometriosis are shown in b. The variance explained in the target dataset on the basis of allele-specific scores derived in the discovery dataset for eight significance thresholds (P < 0.01, P < 0.05, P < 0.1, P < 0.2, P < 0.3, P < 0.4, P < 0.5 and P < 0.75, plotted left to right in each study). The y axis indicates Nagelkerke’s pseudo R2 representing the proportion of variance explained. The number above each bar is the P value for the target dataset analysis. This figure shows that the results were not driven by a few highly associated regions, indicating a substantial number of common variants underlying disease. table 2 GWAs, replication and meta-analysis results for rs12700667 Analysis Number of cases/controls Risk allele (A) frequency in controls P OR (95% CIs) Heterogeneity test P value 1. GWA – all endometriosis QIMR 2,270/1,870 0.73 1.5 × 10−5 1.25 (1.13–1.38) – Oxford 924/5,190 0.74 3.9 × 10−3 1.19 (1.06–1.34) – Combined 3,194/7,060 0.74 2.6 × 10−7 1.22 (1.13–1.32) 0.56 2. GWA – stage B QIMR 910/1,870 0.73 8.3 × 10−7 1.40 (1.22–1.60) – Oxford 454/5,190 0.74 4.2 × 10−4 1.35 (1.14–1.60) – Combined 1,364/7,060 0.74 1.5 × 10−9 1.38 (1.24–1.53) 0.75 3. Replication NHSII – all endometriosisa 2,392/2,271 0.73 1.2 × 10−3 1.17 (1.06–1.28) – 4. Meta-analysis All endometriosis (1 + 3) 5,586/9,331 0.74 1.4 × 10−9 1.20 (1.13–1.27) 0.64 aStage was unknown for cases in the NHSII replication cohort, though it was estimated to include ~40% stage B cases21. © 20 10 N at ur e A m er ic a, In c. A ll rig ht s re se rv ed . Nature GeNetics  ADVANCE ONLINE PUBLICATION 3 l e t t e r s the Nurses’ Health Study (NHS) I and II. Stage information was not available for NHSII cases, but the proportion likely to have stage B disease has been estimated at approximately 40% (ref. 21), similar to that observed in the QIMR case set (Supplementary Table 1). Association with ‘all’ endometriosis for the two SNPs on 7p15.2 was replicated in the US dataset, with P = 1.2 × 10−3, OR = 1.17, 95% CI 1.06–1.28 for rs12700667 and P = 1.6 × 10−3, OR = 1.17, 95% CI 1.06–1.28 for rs7798431 (Supplementary Table 3). There was no evidence (nominal P ≤ 0.05) for replication of rs12540248 (FN1) or association with the remaining 70 SNPs (Supplementary Table 3). Analysis of all 5,586 cases and 9,331 controls from the combined QIMR, Oxford and NHS cohorts further confirmed association between ‘all’ endometriosis and 7p15.2, producing P = 1.4 × 10−9, OR = 1.20, 95% CI 1.13–1.27 for rs12700667 and P = 1.1 × 10−7, OR = 1.18, 95% CI 1.11–1.25 for rs7798431 (Table 2). Although effect sizes from discovery datasets may be inflated22, the similarity of ORs for ‘all’ endometriosis in our discovery (GWAS) and replication data- sets (Table 2) suggests this type of bias has not played a major role. Assuming the estimated OR of 1.20 and allele frequency of 0.74 for the rs12700667 A allele, a multiplicative risk model and a population prevalence of 8% (refs. 10,21,23), the estimated percentage of ‘all’ endometriosis variance explained by rs12700667 was 0.36, or 0.69% of the estimated 51% heritability of endometriosis9. The associated SNPs are located in a ~924-kb intergenic region containing at least one noncoding RNA (AK057379), predicted tran- scripts and regulatory elements, and a miRNA (hsa-mir-148a) ~88 kb upstream of rs12700667. The closest gene, NFE2L3, which is highly expressed in placenta, is located ~331 kb downstream of rs12700667. Two endometriosis candidate genes, HOXA10 and HOXA11 (refs. 24,25), encoding members of the homeobox A family of tran- scription factors that play a role in uterine development, lie ~1.35 Mb downstream of this SNP. Among reported candidate gene associations for endometrio- sis14, the only gene with P < 10−3 for SNPs in the GWAS data was PGR on chromosome 11 (Supplementary Table 3), but the result for the SNP in this gene was not significant in the replication stage. A recent genome-wide association scan in Japanese women reported significant association of endometriosis with rs10965235 (P = 5.8 × 10−12, OR = 1.44), located on chromosome 9p21, and possible asso- ciations with rs13271465 on 8p22 and rs16826658 on 1p36 (ref. 26). The Japanese GWAS did not report our 7p15.2 signal among their 100 top SNPs followed up for replication, but with 1,423 cases and 1,318 controls, they would have had only 13% power to detect the effect of rs12700667 with P ≤ 1.8 × 10-4 (Online Methods). We found no evidence for association with rs10965235 (which is monomorphic in individuals of European descent, reflecting the different genetic (ancestral) backgrounds between the studies) or any other SNP in LD (r2 > 0.5 in the HapMap Japanese JPT population) in the QIMR and Oxford data (Supplementary Table 4). We also found no evidence of association with 8p22. We did find evidence for replication of rs7521902 on 1p36, which is close to WNT4, for both ‘all’ endometriosis (P = 9.0 × 10−5, OR = 1.16, 95% CI 1.08–1.25) and stage B cases (P = 7.5 × 10−6, OR = 1.25, 95% CI 1.13–1.38), with the stronger signal in stage B providing additional empirical evidence for the benefit in examining stage B cases. Importantly, a meta-analysis of the QIMR and Oxford ‘all’ endometriosis OR with the reported Japanese OR of 1.25 (95% CI 1.12–1.39) for rs7521902 produced a genome-wide significant P value of 4.2 × 10−8 (OR = 1.19, 95% CI 1.12–1.27). The frequency of the rs7521902 risk allele (A) was 0.57 and 0.51 in the Japanese GWAS cases and controls, respectively, and 0.26 and 0.24 in our combined GWAS cases and controls, respectively. WNT4 is important for develop- ment of the female reproductive tract27, ovarian follicle development and steroidogenesis28,29, making it a plausible biological candidate. We have identified a new locus on chromosome 7p15.2 that is sig- nificantly associated with risk of endometriosis in women of European ancestry, and we confirm a previously reported suggestive association for SNPs close to the WNT4 locus. Our analyses also demonstrate a higher genetic loading for moderate to severe (stage B) endome- triosis, and consistent with these results, we observed the strongest association signals with stage B disease. Our predictive modeling demonstrates that there are additional common variants contribut- ing to risk for this disease and that future larger studies enriched for laparoscopically-confirmed moderate to severe cases will be better powered to identify risk loci and aberrant pathways contributing to the development of endometriosis. URLs. ECR Browser, http://ecrbrowser.dcode.org/; SNPTESTv2, http://www.stats.ox.ac.uk/~marchini/software/gwas/snptest.html; 1000 Genomes Project, http://www.1000genomes.org/; HapMap, http://hapmap.ncbi.nlm.nih.gov/. METhOdS Methods and any associated references are available in the online version of the paper at http://www.nature.com/naturegenetics/. Note: Supplementary information is available on the Nature Genetics website. ACKNoWLeDGMeNTS We acknowledge with appreciation all the women who participated in the QIMR, OXEGENE and NHS studies. We thank Endometriosis Associations for supporting the study recruitment. We also thank the many hospital directors and staff, gynecologists, general practitioners and pathology services in Australia, the UK and the United States who provided assistance with confirmation of diagnoses. We thank S. Nicolaides and the Queensland Medical Laboratory for pro bono collection and delivery of blood samples and other pathology services for assistance with blood collection. 10 a b 100 80 R ecom bination rate (cM /M b) 60 40 20 0 100 80 R ecom bination rate (cM /M b) 60 40 20 0 rs12700667 rs12700667 0.8 0.6 0.4 0.2 r2 0.8 0.6 0.4 0.2 r2 Plotted SNPs Plotted SNPs ‘All’ endometriosis Stage B endometriosis –l og 10 P 8 6 4 2 0 25.0 25.5 26.526.0 Position o