Integration of Population-Level Genotype Data with Functional Annotation Reveals Over-Representation of Long Noncoding RNAs at Ovarian Cancer Susceptibility Loci

Epithelial ovarian cancer (EOC) risk has a significant genetic component that is not fully characterized. Risk is greatly increased by rare inherited mutations in highly penetrant genes like BRCA1 and BRCA2 that segregate in an autosomal dominant manner and confer lifetime risks as high as 39% and 17%, respectively (1, 2). Risk is modestly increased by uncommon mutations in genes with moderate penetrance, such as RAD51C/D and BRIP1 (3, 4). That the known genetic risk factors account for less than 50% of the heritable risk of EOC suggests that additional risk alleles await discovery (5). The advent of genome-wide association studies (GWAS) has enabled the international Ovarian Cancer Association Consortium (OCAC) to discover approximately 22 SNPs with mild effects (6–14). Since OCAC includes virtually every large case–control study of EOC in the world, which precludes a substantial increase in sample size, innovative approaches are needed to evaluate the thousands of risk SNPs at sub-genome-wide levels of statistical significance (1 × 10⁻⁵ > P > 5 × 10⁻⁸).

Most risk SNPs identified by GWAS are located in noncoding regions of the genome (3), and the functional biofeatures and target genes remain unknown for many loci. Data suggest that a significant proportion coincide with long noncoding RNAs (lncRNA; ref. 15), a class of transcripts emerging as significant contributors to ovarian carcinogenesis (16–21). We hypothesize that lncRNAs represent functional targets of some EOC risk SNPs and that the integration of genotyping and lncRNA expression datasets will enable identification of additional susceptibility alleles and help unravel the etiology.

Noncoding RNAs (ncRNA) resemble protein-coding transcripts but without functional open reading frames (22) and are typically classified according to size; thus, small ncRNAs are less than 200 nucleotides in length whereas lncRNAs contain at least 200 nucleotides. While small ncRNAs, including microRNAs (miRNA), siRNAs, and PIWI-interacting RNAs (piRNA), have recognized functional importance in carcinogenesis (23), lncRNAs remain understudied even though they are the most common type of transcribed RNAs (24). Recent studies have shown that lncRNAs can act in cis or trans to regulate gene expression and promote tumorigenesis through transcriptional regulation, initiation of chromatin remodeling, modulating alternative splicing, altering protein activity or localization, and genomic imprinting (16, 22, 25–27). Similar to other regulatory elements, lncRNAs exhibit cell-type specificity with varying expression and activity across different tissue types (28).

Given the likely role of lncRNAs in EOC pathogenesis (16–21, 29, 30) and growing evidence implicating inherited variants in lncRNAs with cancer susceptibility (31–34), we sought to systematically test the hypothesis that genetic variants associated with EOC risk are enriched at lncRNA gene regions, particularly those with active epigenomic profiles in ovarian tissue. We further investigated whether lncRNAs represent potential functional target genes of sub-genome-wide EOC risk regions by integrating lncRNA expression data and performing expression quantitative trait loci (eQTL) analyses. Our results suggest that lncRNAs are significantly enriched at EOC risk regions and that variants within these regions have functional effects on lncRNA expression. More comprehensive testing of this hypothesis and candidate lncRNA associated with EOC risk is warranted.

Analyses were based on 4 pooled GWAS totaling 46,213 subjects of European ancestry (15,397 invasive EOC cases, 30,816 controls) from 43 independent studies in the international OCAC (14, 35). A meta-analysis was performed to combine results across studies. Details of the study participants, genotyping, quality control, imputation, and meta-analysis have been previously described (6, 12). Briefly, cases were women with histologic-confirmed primary invasive EOC, fallopian tube cancer, or peritoneal cancer, and for most studies were frequency-matched to controls on age group and self-reported race. Specimens and data were collected according to protocols approved by local institutional review and ethics boards. Genotype data from the contributing GWAS were imputed separately using IMPUTE2 software (36) and 1000 Genomes Project phase 3 as the reference panel with pre-phasing of the genotypes performed using SHAPEIT (37). For each study, log-additive models were fit to estimate SNP associations with EOC risk. The meta-analysis used a fixed-effect model weighted by the inverse variance and only study results for SNPs imputed with r² > 0.25 were included. Association analyses were performed for all invasive EOC cases versus controls and by histologic subtype. A SNP with a significance level of P < 1.0 × 10⁻⁵ was defined as EOC-associated.

Coordinates for 13,870 human lncRNAs with biologic functions in eukaryotes were downloaded from the publicly available GENCODE (v19) database based on Genome Build 37 (38). Of the 13,870 lncRNA genes, we excluded 70 genes on the Y chromosome and 4 genes annotated for multiple locations (n = 21 observations) but retained 17 genes that were less than 200 bp in length leaving 13,779 unique gene name and positions for analysis. There were 337 lncRNA genes without variants for a total of 1,757,495 variants mapping to intron and exon coordinates of 13,442 unique lncRNA genes.

As activity of lncRNAs can be tissue-specific (39, 40), we annotated lncRNA genes on the basis of their epigenomic profiles in ovarian tissue from the NIH Epigenome Roadmap (41) to select those with active enhancer, promoter, or transcription profiles (Supplementary Fig. S1). We quantified histone H3 lysine 4 monomethylation (H3K4me1) and trimethylation (H3K4me3) marks in the transcription start site (TSS; ±3 kb) of each gene to identify active enhancers and promoters, respectively (28). To identify transcriptionally active lncRNA, we quantified histone 3 lysine 36 trimethylation (H3K36me3) marks in the gene body (26). For each gene, we computed the average signal density (rpm/bp) for H3K4me1 and H3K4me3 histone marks within the TSS and for H3K36me3 within the gene coordinates using the Genboree Workbench Epigenomic Slicer tool (41). We considered lncRNAs as “active” in ovarian tissue when the H3K4me1, H3K4me3, or H3K36me3 average density was higher than a threshold (7, 4, and 4 rpm/bp, respectively), which was determined by a P value (<0.05) taken from the background model of Poisson distribution for each histone mark, parameterized by the signal density of all lncRNAs (Supplementary Fig. S2). Finally, we similarly computed average H3K4me1, H3K4me3, and H3K36me3 signal density for 11 highly uniform and strongly expressed housekeeping genes as controls (42). All 11 housekeeping genes were defined as active using the above criteria.

To determine whether EOC risk regions were more likely to be found near encoded lncRNAs than expected by chance, we compared the observed proportion of EOC-associated SNPs (P < 1.0 × 10⁻⁵) within lncRNAs to an expected proportion on the basis of the whole genome. A permutation-based approach was used to obtain the expected proportion and the level of significance for enrichment. An empirical P value for the observed proportion of lncRNA risk SNPs was calculated from a background (null) distribution obtained by randomly shuffling lncRNA-sized regions on the chromosome and computing the proportion of risk SNPs for the shuffled regions (10,000 times). We also compared ovarian-active lncRNAs to all lncRNAs by permuting ovarian active/inactive classification 10,000 times to generate the background distribution and calculated empirical p-values.

If an EOC risk region harbors multiple SNPs due to linkage disequilibrium (LD), this can inflate the potential significance of lncRNAs in that region. To correct for that, analyses were performed to identify independent EOC-associated regions and to test for enrichment of lncRNAs within these regions. An independent region was defined as a genomic interval containing at least one EOC-associated SNP at P < 1.0 × 10⁻⁵ (index) and all surrounding, nominally significant LD SNPs (P < 0.05 and r² > 0.2) within a ±250-kb window. We then performed permutation-based testing to determine whether lncRNAs were enriched for overlap with these independent risk regions. Analysis was performed using the gene-set enrichment analysis (GSEA) tool INRICH (INterval enRICHment analysis; ref. 43). Briefly, the number of lncRNA genes that overlap at least one risk region was compared with a null distribution generated by permuting the risk regions to random genomic locations with the constraint that each randomized region matches the original region's number of SNPs tested and SNP density (±10%). In addition, the permuted regions were constrained to lie within the meta-analysis SNP positions. We also compared ovarian-active lncRNA with all lncRNA genes to determine whether the subset of lncRNA active in ovarian tissue was more enriched for overlap with independent regions of association than all lncRNAs. For this comparison, we permuted only the regions that overlapped with lncRNA (e.g., 72 intervals for all invasive analysis) and constrained the permuted regions to overlap the same number of lncRNA genes.

For the subset of lncRNAs that contained EOC-associated SNPs, we tested for enrichment of specific biologic processes, pathways, and promoter motifs using the GREAT tool (Genomic Regions Enrichment of Annotations; ref. 44). GREAT uses gene set collections from the Molecular Signatures Database (MSigDB; ref. 45) and calculates a binomial test for enrichment over genomic regions and a hypergeometric test for enrichment over genes within 500 kb of the region. We also tested for enrichment of ENCODE transcription factor (TF)-binding sites using HOMER (Hypergeometric Optimization of Motif EnRichment; ref. 46). For GREAT analyses, we required a false discovery rate (FDR) of 15% for both binomial and hypergeometric tests to determine significance. For HOMER analysis of ENCODE TF-binding sites, we required the more restrictive Bonferroni corrected P value of 1.0 × 10⁻⁴. We also examined the set of significant promoters and TFs, identified by GREAT and HOMER, using the PANTHER classification system and tool set (47) to determine whether they were enriched for specific biologic processes.

We additionally annotated EOC-associated lncRNAs for gene expression in 35 normal ovarian tissue samples from the Genotype-Tissue Expression Project (GTEx; ref. 48) web portal (49) and for tumor tissue expression from 412 high-grade serous (HGS) tumor samples from The Cancer Genome Atlas (TCGA; ref. 50).

For the EOC-associated SNPs within novel sub-genome-wide risk regions, we sought to identify potential lncRNA targets. We performed eQTL analysis of primary ovarian tumor tissues from TCGA (50). Germline genotypes for 402 HGS cases of European ancestry with non-missing stage and grade data were downloaded and imputed to 1,000-genome project phase 3 reference panel (March 2012) using MACH and Minimac software (51–53). Analyses were limited to SNPs with imputation quality r² > 0.3 and with at least 5 minor allele carriers [minor allele frequency (MAF) > 0.0075]. Analysis of lncRNA gene expression was performed with lncRNA RPKM (reads per kilobase per million reads) data for 12,727 intergenic lncRNAs, which was generated from RNA-sequencing reads using GENCODE v19 annotations and was downloaded through the TANRIC platform v1.0 (54). A total of 334 HGS cases with germline genotype and gene expression data were available for analyses. Unadjusted linear regression was used to estimate minor allele dose effect on gene expression (log₂-transformed RPKM) for genes with ≥0.1 RPKM in at least 2 individuals. We performed cis-eQTL analysis for genes within 1 MB of a SNP with a significant association defined by an FDR of less than 5%.

The genome-wide association meta-analysis participants are detailed in Supplementary Table S1. As expected, most cases (62%) had tumors with serous histology, followed by endometrioid (14%) and mucinous (7%) and clear cell (7%). Of the about 15 million genotyped and imputed SNPs, 5,294 (0.035%) were associated with invasive EOC risk (P < 10⁻⁵; Table 1). These SNPs mapped to 130 independent regions, 78 of which are below genome-wide significance and more than 500 kb from previously reported risk SNPs. Fourteen of the 22 reported EOC risk loci associate with invasive EOC and 13 of these were replicated here, the lone exception being a locus identified in high-risk BRCA1/2 mutation carriers (55). In addition, subtype analyses replicated 4 of 4 serous risk loci, 2 of 3 mucinous risk loci, and the sole clear cell risk locus, for a total of 21 of 22 previously reported ovarian cancer risk loci represented in our data.

Globally, 1.76 million SNPs (12%) mapped to 13,442 lncRNA genes and nearly all (97.55%) lncRNA genes contained genotyped or imputed SNPs. Most (75%) of the 13,442 lncRNAs annotated were greater than 1,700 bp in length and classified as long-intergenic ncRNAs (lincRNAs; n = 7,048), followed by antisense (n = 5,257), sense intronic (n = 741), sense overlapping (n = 202), processed transcript (n = 511), and 3′ overlapping (n = 21). We additionally identified a subset of 5,401 lncRNAs (∼40%) with active histone modification profiles in ovarian tissue and annotated them as “ovarian-active” (Supplementary Figs. S1 and S2). Just more than 457,000 SNPs (26% of lncRNA SNPs and 3% of all genotyped or imputed SNPs) mapped to 5,287 of the ovarian-active lncRNAs and most were antisense (n = 2,926) or lincRNA (n = 1,651).

Of the 130 independent regions associated with EOC risk, 72 (55%) overlapped encoded lncRNAs, of which 39 regions are novel (>500 kb from previously reported loci). These 72 regions consisted of 28% (n = 1,464) of the SNP hits which directly mapped to 53 unique lncRNA genes and an additional 3,484 (66%) SNP hits in LD (r² > 0.2) with SNPs located in 115 lncRNAs. The proportion of risk SNPs that mapped to lncRNA gene coordinates was approximately 2 times higher than the proportion observed across the whole genome (0.083% vs. 0.035%; Fig. 1A) and was significantly higher-than-the-expected proportion on the basis of a random distribution across the genome (P_PERM = 0.047; Supplementary Fig. S3). We also compared the number of independent regions that overlapped with lncRNA genes to a random distribution of regions across the genome and the observed overlap (55%) was significantly higher than expected (P_INRICH = 0.0005), providing further evidence for enrichment even when accounting for LD structure. To determine whether the r² threshold used to define LD regions had any undue influence on our results, we repeated analyses with a more stringent r² = 0.8 criterion and while the observed overlap with lncRNA was lower (28%), it remained significantly higher than expected (P_INRICH = 0.01).

The subset of 5,287 ovarian-active lncRNAs encompassed 60% (n = 873) of the lncRNA SNP hits and overlapped 64% (n = 46) of lncRNA independent regions of association. When limiting to ovarian-active lncRNAs, the enrichment for EOC-associated SNPs was significantly increased from 0.083% to 0.20% (P_PERM = 0.009; Fig. 1A); a 5-fold higher proportion of SNP hits than observed across the whole genome. Similarly, enrichment for independent risk regions was higher for ovarian-active lncRNAs versus all lncRNAs (P_INRICH = 0.04; Table 1). Analyses stratified by tumor histology revealed that SNPs associated with HGS tumors, the most predominant subtype, are enriched in lncRNA (Table 1), but not other, less common histologic subtypes (Fig. 1A; Supplementary Table S2) or low-grade serous histology (Fig. 1B). Although SNPs associated with mucinous EOC were not enriched within all lncRNA, when subset to ovarian-active lncRNAs the difference became more pronounced (P_PERM = 0.0001; Supplementary Table S2).

Having determined that the proportion of EOC-associated SNPs was overrepresented at genomic regions harboring lncRNA, we sought to assess whether this enrichment was influenced by the length (kB) or SNP coverage (# tested SNPs) of the lncRNA regions. The lncRNAs containing SNP hits were comparable in length and SNP coverage to the overall catalogue of lncRNA genes, suggesting an absence of bias due to gene coverage (Supplementary Fig. S4). Moreover, we compared the density of SNP hits between lncRNA regions and whole genome to assess enrichment while accounting for coverage. This analysis demonstrated a significant enrichment of EOC-associated SNPs in lncRNA regions compared with whole genome and protein-coding genes (223 vs. 536 kB/hit and 339.1 KB/hit, respectively) and further supported our findings overall and by histologic subtype (Supplemental Methods and Supplementary Tables S3 and S4).

A total of 53 lncRNA genes contained EOC-associated SNP(s) within their coordinates. These genes were located within 36 of the 72 independent risk regions and contained 1,464 EOC-associated SNPs. Most of the 53 genes (83%) were expressed in normal (GTeX; n = 35) or tumor tissues (TCGA; n = 412) with the majority showing expression in both (57%) or in tumor tissues only (30%; Fig. 2A). Roughly half (n = 25) had active epigenomic profiles in normal ovarian tissue.

Pathway analysis of the 53 lncRNA genes revealed significant enrichment for multiple embryonic development and morphogenesis pathways as well as positive regulation of hormone/steroid biosynthesis (FDR < 15%; Fig. 2B). The lncRNA regions were enriched for 5 predicted promoter motifs, including androgen receptor (AR; P = 3.6 × 10⁻⁶), STAT3 (P = 5.8×10⁻⁵), and paired box 8 (PAX8; P = 1.3 × 10⁻³) and 5 TF-binding sites were overrepresented within their sequences, including n-MYC and c-MYC (Supplementary Table S5). Taken together, these promoter and transcription factors were enriched for regulatory pathways of transcription, cell differentiation, and epithelial development (Supplementary Table S6).

To potentially inform biologic significance, we conducted eQTL analyses of primary tumor tissue for EOC-associated SNPs within the 39 novel sub-genome-wide regions that overlapped lncRNA genes. TCGA gene expression data were available for 334 HGS EOC cases with genotype data imputed to 1KGP density. A total of 8,763 lncRNAs were at least minimally expressed (≥0.1 RPKM in ≥2 individuals) in the tumor tissues and were retained for analysis. The 39 novel regions contained 158 EOC-associated SNPs located within or in LD to 78 lncRNA genes; of these, we analyzed 143 SNPs that met the inclusion criteria (imputation r² > 0.3 and ≥5 minor allele carriers). Cis-eQTL analysis revealed that 5 novel regions (24 SNPs) were associated with expression of 6 lncRNAs in tumor tissue (FDR < 5%; Table 2). Expression of the lncRNA at 4 of these loci was associated with reduced EOC risk (11p15, 11p13, 16q21, 16q22.1) and 1 (19q13.12-13) was associated with an increase in EOC risk. The locus 19q13.12-13 where risk alleles were associated with increased expression contained 9 SNPs with the top signal observed for chr19:38451511 TA>T [OR, 1.12; 95% confidence interval (CI), 1.09–1.15; P = 5.74 × 10⁻⁶; Supplementary Fig. S5A]. Seven of the 9 SNPs were associated with differential expression of AC012309.5 (P = 0.0003; Fig. 3A) that is located 695 kB from the top regional SNP. The reduced risk locus 16q21 exhibited the strongest SNP association (P = 8.57 × 10⁻⁸) located within the coordinates of RP11-410D17.2 (Supplementary Fig. S5B) and eQTL analysis revealed 4 SNPs with minimal allele-specific effects on 2 distal lncRNAs (Fig. 3B and C). The 11p15.5 locus contained the only other significant SNP associations located within an encoded lncRNA. The strongest signal was seen for rs3741205 A>C [OR (95% CI) = 0.93 (0.91–0.94), P = 3.94 × 10⁻⁶] located within an exon of IGF2-AS and introns of IGF2 and INS-IGF2 (Supplementary Fig. S5C) that associated with differential expression of FAM99A (P = 0.008; Fig. 3D). Two other loci were also associated with reduced EOC risk, 11p13 and 16q22.1, and contained eQTL SNPs associated with the expression of proximal lncRNA genes (<50 kb; Supplementary Fig. S5D and S5E).

Evidence for a prominent role of lncRNA in carcinogenesis is rapidly accumulating (56). This study represents the first genome-wide evaluation of germline lncRNA variants in EOC susceptibility. We performed an enrichment analysis of genome-wide association data from 46,213 subjects and show that lncRNA regions are significantly enriched for EOC risk loci (P < 10⁻⁵) with a 2-fold higher proportion of risk variants than across the whole genome. Moreover, among the 40% of lncRNA genes with active epigenetic regulation in ovarian tissue, the risk variant enrichment was 5-fold higher than whole genome. This high concentration of risk loci at ovarian-active lncRNAs aligns with previous studies that have shown an overrepresentation of disease-associated variants at enhancers (57) and within tissue-specific long-intergenic ncRNAs (lincRNAs; ref. 28). Similar to these studies (28, 57), we focused on identifying lncRNA activity on the basis of epigenomic profiles and did not focus on analyzing lncRNAs expressed in ovarian tissue that did not have active histone modification marks. Although this approach may have missed areas of lncRNA risk SNP enrichment, the subset of ovarian-active lncRNAs we analyzed was regulatory specific and potentially included overlapping enhancer and lncRNA sequences that could be functionally interrelated given that lncRNA expression associates with tissue-specific enhancers (27) and lncRNAs can mediate enhancer function (26, 58, 59). Taken together, our findings provide further support for a predominant regulatory role of EOC risk variants (57, 60) and reveal that lncRNAs may account for a significant proportion of such variation, particularly where tissue-specific regulatory elements are present. Further mechanistic studies are needed to confirm these findings.

Our pathway analysis demonstrates that lncRNAs containing EOC risk SNPs within their coordinates are enriched for developmental and regulatory pathways relevant to ovarian cancer pathogenesis. While the majority of pathways were developmental, most EOC-associated lncRNAs were expressed in adult tumor and/or normal ovarian tissues, suggesting that their role in EOC development likely extends beyond developmental pathways. Our “upstream” enrichment analyses revealed that EOC-associated lncRNAs were enriched for putative targets of AR, STAT3, and PAX8 TF, all of which have been implicated in EOC pathogenesis. Androgen receptor is expressed in most ovarian tumors, and androgens promote ovarian tumor growth (61, 62), although prospective studies have not identified a clear association between androgens and EOC risk (63). STAT3 is overactivated in ovarian cancer cells and inhibition is subsequently accompanied by tumor growth suppression (64). In addition, the Jak/STAT3 pathway has been linked with cancer cell survival and chemoresistance (65, 66), and recent work suggests that germline polymorphisms within STAT3 predict poor response to platinum-based therapy (67). Finally, PAX8 is a member of the paired box family of transcription factors (PAX1-9) that are primarily expressed in the embryo with persistent expression observed in ovarian tumors (68). PAX8 specifically is expressed in fallopian tube secretory epithelial and ovarian surface epithelial cells (69), and in vitro knockdown of PAX8 expression reduces ovarian cancer cell proliferation, migration, and invasion (70). Importantly, a recent GSEA of genome-wide association data revealed enrichment of putative PAX8 targets near serous EOC risk loci (71). Our analysis correlates well with the GSEA finding and shows that this enrichment of putative PAX8 targets can also be observed near the subset of invasive EOC risk loci that overlap lncRNAs. Given that several lncRNAs can alter the binding and/or activity of transcription factors as well as interact with them directly (72), it is possible that EOC-associated lncRNA variants may influence these transcription factors. Further studies evaluating the role of lncRNA risk variants could help elucidate the underlying etiology of EOC susceptibility and possibly identify opportunities for therapeutic intervention.

Our integration of GWAS, GENCODE, and TCGA gene expression data identified 5 novel sub-genome-wide regions with suggested functional effects on lncRNA targets. These novel regions contained common SNPs (MAF > 0.10) with small effect sizes that may represent true associations previously undetected because of limited power. Studies well-powered (≥80% power) to detect an OR ≥ 1.1 among common SNPs (assuming a rare disease prevalence of 1.4%) would require a sample size of 110,176 matched cases and controls, almost double the sample size of previous EOC GWAS (6, 12). Thus, for such small effects, sample size is a rate-limiting step and we demonstrate an integrative approach to select and provide biologic support for candidate loci in the absence of increased sample sizes. Our in silico biologic investigations consisted of eQTL analyses of lncRNA expression data to identify candidate loci, although this was also limited by sample size. As expected, the 12,727 lincRNAs analyzed were expressed at lower levels (average, 0.29 RPKM; median, 0.03 RPKM) compared with mRNAs (average, 21.09 RPKM; median, 3.88 RPKM) which makes detection of cis-eQTL more difficult (73). We also observed relatively small yet potentially relevant fold changes in lncRNA expression that further hampered eQTL detection. A more comprehensive eQTL analysis of lncRNA expression with the larger samples sizes needed to overcome the difficulty of low expression and small fold changes is warranted; including adjustment for copy number variation, methylation, and/or batch effects. Confirmation of candidate regions will require functional validation through analysis of allele-specific effects on the lncRNA and in vivo and in vitro studies to determine the lncRNAs role in the initiation and development of EOC.

In summary, the current study implicates SNPs in lncRNAs as plausible candidates for risk regions that show evidence of EOC association but fail to reach genome-wide statistical significance. Integrative molecular studies provide biologic support for the hypothesis and reveal connections between germline and tissue-level expression.

No potential conflicts of interest were disclosed.

Conception and design: B.M. Reid, J.B. Permuth, Y.A. Chen, A. Berchuck, T.A. Sellers

Development of methodology: B.M. Reid, A. Berchuck

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J.B. Permuth, A. Berchuck, E.L. Goode, P.D. Pharoah, C.M. Phelan, S.J. Ramus, M.A. Rossing, J.M. Schildkraut, S.A. Gayther, T.A. Sellers

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): B.M. Reid, Y.A. Chen, J.K. Teer, A.N. Monteiro, Z. Chen, J.P. Tyrer, G. Chenevix-Trench, E.S. Iversen, P.D. Pharoah, M.A. Rossing, T.A. Sellers

Writing, review, and/or revision of the manuscript: B.M. Reid, J.B. Permuth, Y.A. Chen, J.K. Teer, A.N. Monteiro, J.P. Tyrer, A. Berchuck, G. Chenevix-Trench, J.A. Doherty, K. Lawrenson, C.L. Pearce, P.D. Pharoah, C.M. Phelan, S.J. Ramus, M.A. Rossing, J.Q. Cheng, S.A. Gayther, T.A. Sellers

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): Z. Chen, A. Berchuck, E.S. Iversen, S.J. Ramus

Study supervision: T.A. Sellers

We thank all of the women who participated in this research along with all of the researchers, clinicians, and staff who have contributed to the participating studies.

The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith. Funding for this project was supported by the National Institute of Health and the Genetic Associations and Mechanisms in Oncology (GAME-ON), a NCI Cancer Post-GWAS Initiative (U19-CA148112 to T.A. Sellers). This study made use of data generated by the Wellcome Trust Case Control consortium that was funded by the Wellcome Trust under award 076113. In addition, we acknowledge the following agencies for funding of constituent studies: AOC/ACS: U.S. Army Medical Research and Materiel Command (DAMD17-01-1-0729, to D. Bowtell and A. Green), National Health & Medical Research Council of Australia, Cancer Councils of New South Wales, Victoria, Queensland, South Australia and Tasmania, Cancer Foundation of Western Australia; National Health and Medical Research Council of Australia (199600 and 400281) BAV: ELAN funds of the University of Erlangen-Nuremberg. BEL: Nationaal Kankerplan; DOV: U.S. National Cancer Institute (NCI;R01-CA112523 and R01-CA87538, to M.A. Rossing); GER: German Federal Ministry of Education and Research, Programme of Clinical Biomedical Research (01 GB 9401) and the German Cancer Research Center; GRR: Roswell Park Cancer Institute Alliance Foundation (P30 CA016056, to K. Odunsi). HAW: U.S. National Institutes of Health (R01-CA58598, N01-CN-55424 and N01-PC-67001 to M.T. Goodman); HJO and HMO: Intramural funding; Rudolf-Bartling Foundation; HOC: Helsinki University Research Fund; HOP: US Army Medical Research and Material Command (DAMD17-02-1-0669 to R.B. Ness); US NCI (K07-CA080668 to F. Modugno; R01-CA095023 to R.B. Ness; P50-CA159981, to K.B. Moysich); NIH/National Center for Research Resources/General Clinical Research Center (M01-RR000056 to F. Modugno, R.B. Ness); LAX: American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN to B.Y. Karlan); National Center for Advancing Translational Sciences (NCATS;UL1TR000124 to S.M. Dubinett); MAL: US NCI (R01- CA61107 to S. K. Kjaer); Danish Cancer Society (94-222-52); Mermaid I project; MAY: US NCI (R01-CA122443, P30-CA15083, P50-CA136393 to E.L. Goode); Mayo Foundation; Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation; MCC: Cancer Council Victoria; NHMRC (209057, 251533, 396414, and 504715, to G. Giles); MDA: US DOD Ovarian Cancer Research Program (W81XWH-07-0449 to M. Hildebrandt); NEC: US NCI (R01-CA54419 and P50-CA105009 to D. Cramer); US DOD (W81XWH-10-1-02802 to K.L. Terry); NHS: US NCI (UM1-CA176726 and R01-CA67262 to W. C. Willett); NJO: US NCI (K07 CA095666 and K22-CA138563 to E.V. Bandera, R01-CA83918 to S. Olson, and Rutgers Cancer Institute of New Jersey Cancer Center Support GrantP30-CA072720) NOR: Helse Vest; The Norwegian Cancer Society; The Research Council of Norway; NTH: Radboud University Medical Centre; ORE: OHSU Foundation; OVA: Canadian Institutes of Health Research (MOP-86727 to N. Le); US NCI (R01CA160669 to L.S. Cook); POC: Pomeranian Medical University; POL: Intramural Research Program of the NCI; PVD: Herlev Hospitals Forskningsrad; Herlev Hospitals Forskningsrad; Danish Cancer Society; RMH: Cancer Research UK; SEA: Cancer Research UK (C490/A10119 and C490/A10124 to P.D.P. Pharoah); UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge; SRO: Cancer Research UK (C536/A13086 and C536/A6689 to S. Banerjee); Imperial Experimental Cancer Research Centre (C1312/A15589 to S. Banerjee) STA: US NCI (U01-CA71966 and U01-CA69417 to A.S. Whittemore, R01-CA16056 to K.B. Moysich, K07-CA143047 to W. Sieh); TOR: US NCI (R01-CA063678 to S.A. Narod, R01-CA063682 to H.A. Risch); UCI: US NCI (R01-CA058860 to H.A. Anton-Culver); Lon V Smith Foundation (LVS-39420 to H.A. Anton-Culver); UKO: The Eve Appeal (The Oak Foundation); National Institute for Health Research University College London Hospitals Biomedical Research Centre; UKR: Cancer Research UK (C490/A6187 to P.D.P. Pharoah); UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge; USC: US NIH (P01-CA17054 to A.H. Wu, P30-CA14089 (to C.L. Pearce and S.J. Ramus), R01-CA61132 to M.C. Pike, N01-PC67010 and N01-CN025403 to R.K. Ross, R03-CA113148 and R03-CA115195 to C.L. Pearce); California Cancer Research Program (00-01389V-20170, 2II0200); WOC: National Science Centre (N N301 5645 40); The Maria Sklodowska-Curie Memorial Cancer Center; Institute of Oncology (Warsaw, Poland).

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