Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in noncoding regions, and causal genes underlying these associations remain largely unknown. Here, we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their cis-predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of P < 2.2 × 10−6, we identified 35 genes, including FZD4 at 11q14.2 (Z = 5.08, P = 3.83 × 10−7, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and three genes remained (P < 1.47 × 10−3). These data identify one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis.

Significance: Transcriptomic analysis of a large cohort confirms earlier GWAS loci and reveals FZD4 as a novel locus associated with EOC risk. Cancer Res; 78(18); 5419–30. ©2018 AACR.

Epithelial ovarian cancer (EOC) has a substantial heritable component with a heritability estimated to be 22% (1). Genome-wide association studies (GWAS) have identified approximately 35 loci associated with EOC risk (2–12). Most reported associations are specific to the most common histologic subtype, serous EOC (2–7, 9–12). Together, known GWAS-identified variants account for approximately 6.4% of EOC risk in the general population (12), indicating that additional susceptibility variants remain to be identified. In addition, genes that underlie the large majority of GWAS-identified risk loci remain unknown; most GWAS-identified variants are located in noncoding genomic regions that may be involved in regulation of gene expression. Recent mechanistic studies have demonstrated that GWAS-identified variants are more frequently located in active chromatin regions, and highly-enriched with expression quantitative trait loci (eQTL; refs. 13, 14). This evidence underscores the importance of transcriptional regulation in influencing human traits and disease susceptibility.

Prior studies on genetically regulated gene expression were largely limited to easily accessible sources, such as adipose tissue and peripheral blood cells (15). Although the sample size in eQTL studies of peripheral blood cells recently reached the thousands, a relatively small number of genes are expressed in blood cells compared with other tissue types (14). Conclusions from eQTL studies in tumor tissue (e.g., TCGA) should also be interpreted with caution due to the inherent complexity of transcriptional regulation caused by acquired somatic alterations (16). The Genotype-Tissue Expression (GTEx) project provides high-density genotype data and RNA sequencing (RNA-seq) transcriptome data from 53 tissues (14). We used these data to build models of genetically regulated expression for 17,121 genes. We investigated the association between these genetically predicted gene expressions and EOC risk using data from 97,898 women including 29,396 EOC cases. We identified 35 genes at 14 loci associated with EOC risk, and provide additional evidence of a potential role for dysregulated ovarian function and imbalanced ovarian hormone production in ovarian carcinogenesis.

### Genomic and transcriptomic data

The GTEx preliminary cleaned genome-wide genotype data and RNA-seq transcriptome data across 53 unique tissues (released on 2015-01-12) were downloaded from dbGaP (accession phs000424.GTEx.v6.p1). It included 183 GTEx donors genotyped on Illumina's Omni 5M and 267 GTEx donors genotyped on Omni 2.5M. Genomic and transcriptomic data were processed according to the GTEx protocol (http://www.gtexportal.org/home/documentationPage). The Omni 2.5M portion of hard-called genotypes from the Omni 2.5M or Omni 5M across all 450 donors were extracted and merged for analysis. We excluded variants with a genotyping call rate < 98%, with differential missingness between Omni 2.5M and Omni 5M arrays, with Hardy–Weinberg equilibrium P < 10−6 (for subjects of European ancestry), or with batch effects. Genotype data were imputed to the Haplotype Reference Consortium reference panel using minimac3 for imputation and SHAPEIT for prephasing (17). Variants with high imputation quality (R2 ≥ 0.8), minor allele frequency (MAF) ≥ 0.05, and inclusion in the HapMap Phase 2 project were used to build predicted expression models.

We used gene level expression in Reads Per Kilobase of transcript per Million mapped reads (RPKM) from RNA-SeQC for gene expression data. For ovarian transcriptomic data, genes were required to have expression in ≥10 individuals with >0.1 RPKM and raw counts >6. For our analysis of cross-tissue derived transcriptomic data (below), genes were filtered on mean expression levels with >0.1 RPKM and RPKM >0 required in at least 3 individuals (18). We performed quantile normalization to transform the expression profile of each sample to the same scale, and performed inverse quantile normalization for each gene to map each set of expression values to a normal distribution. Residual expression was calculated by regressing transformed expression data against three top principal components (PC) derived from common genetic variants (MAF ≥0.05), top 15 or 35 probabilistic estimation of expression residuals (PEER) factors, respectively, for ovarian tissue and cross-tissue derived models (below; ref. 19), sex (for cross-tissue only) to correct for batch effects and other potential experimental confounders.

### European ancestry analysis of GTEx subjects

The ancestral analysis was conducted with 2,836 ancestry informative markers for 450 GTEx individuals and 1,092 individuals included in the 1000 Genome project (Phase 1; ref. 20). Of the individuals with both genotype and transcriptome data available, 369 were clustered together with EUR populations (CEU, FIN, GBR, IBS, and TSI) on the multidimensional scaling plot of the pairwise Identity-By-State distance and were included in the analysis, 68 of whom had transcriptome data available for ovarian tissue.

### Orthogonal tissue decomposition–derived cross tissue estimation

Mixed effect models were used to decompose gene expression levels into subject-specific and subject-by-tissue–specific components (18). GTEx data consisted of expression measurements from multiple tissues for each subject. The expression level of a gene at a given tissue for individual i was considered to be composed of a cross-tissue component represented as YiCT and a tissue-specific component that was estimated as the difference between the expression levels and cross-tissue components given the lack of replicated measurement for a specific tissue/subject pair (18). Z′i represents a vector of covariates that have effects of β on the expression levels of the gene, such as PEER factors, ancestry information derived from the principal component analysis, and sex. The expression of a gene for individual i in tissue t, Yi,t, is modeled as

The mixed effect model parameters were estimated using the lme4 package in R. Posterior modes of the subject level random intercepts were used as estimates of the cross-tissue components (18). Cross-tissue model included gene expression from 6,124 GTEx tissue samples from 369 unique European individuals who had genome-wide genotype data available.

### Ovarian-specific and cross-tissue genetically regulated expression model building

We built an expression prediction model for each gene using the elastic net method as implemented in the glmnet R package, with a ridge-lasso mixing parameter of α = 0.5 and a penalty parameter lambda chosen through 10-fold cross-validation (18, 21, 22). The elastic net method with α = 0.5 is a compromise between the ridge-regression penalty (α = 0) for solutions with many parameters (each of small effects) and the lasso penalty (α = 1) for solutions with fewer parameters (each of large effects; ref. 18). The genetically regulated expression for each gene was estimated by including SNPs within 1 Mb of the gene start or end, as defined by GENCODE V19 gene annotations. Expression prediction models were built for protein-coding genes, long noncoding RNAs (lncRNA), miRNAs, processed transcripts, immunoglobulin genes, and T-cell receptor genes, according to categories described in the GENCODE V19 gene annotation file. Pseudogenes were not included in this study because of potential concerns of inaccurate calling (23). Prediction r2 values (the square of the correlation between predicted and observed expression) were generated to estimate the prediction performance for each gene in our prediction models.

With genome-wide genomic data and RNAseq-based tissue transcriptome data, we built an ovarian tissue cis genetically regulated expression model for 8,580 genes that had predicted performance of r2 > 0.01 and a cross-tissue cis genetically regulated expression model for 14,085 genes that had predicted performance of r2 > 0.01.

### Association analysis of predicted gene expression with EOC risk

Associations between predicted gene expression levels and EOC risk were evaluated using MetaXcan (22). Briefly, the formula:

was used to estimate the Z-score of the association between predicted gene expression and ovarian cancer risk. Here |${w_{lg}}$| is the weight of SNP |$l$| for predicting the expression of gene |$g$|⁠, |${\hat{\beta }_l}$|⁠, and |${\rm{se}}({\hat{\beta }_l})$| are the association regression coefficient and its standard error for SNP |$l$| in GWAS, and |${\hat{\sigma }_l}$| and|$\ {\hat{\sigma }_g}$| are the estimated variances of SNP |$l$| and the predicted expression of gene |$g$|⁠, respectively. The input variables for the MetaXcan analyses include the weights for gene expression predicting SNPs, GWAS summary statistics results, and correlations between predictor SNPs. We integrated prediction models of gene expression levels with summary statistics from GWAS of EOC risk for 97,898 European women with 29,396 EOC cases from the Ovarian Cancer Association Consortium (OCAC) and Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA; ref. 12) based on the variance and covariance matrix of genetic variants derived from 1000 Genome phase 3 EUR population (N = 503). The performance of MetaXcan has been shown to be similar to PrediXcan that uses individual-level genetic data for the identification of genes with expression that is associated with disease risk (21, 22).

Details of individual contributing studies were previously reported (12). Briefly, the OCAC summary statistics were based on analysis of 40,941 controls and 25,509 population-based EOC cases (22,406 invasive cases and 3,103 borderline cases). OCAC cases included 1,954 serous borderline ovarian cancers, 1,149 mucinous borderline ovarian cancers, 1,417 mucinous invasive ovarian cancer, 1,012 low-grade serous ovarian cancers, 13,037 high-grade serous ovarian cancers, 2,810 endometrioid ovarian cancers, 1,366 clear-cell ovarian cancer and 2,764 other EOC cases. The CIMBA summary statistics were based on the analysis of 19,036 BRCA1 and 12,412 BRCA2 mutation carriers, of whom 2,933 and 954, respectively, were diagnosed with EOC. Details of the genotyping procedure and QC have been described elsewhere (12). In brief, samples were excluded if they had a genotyping call rate < 95%, excessively low or high heterozygosity, if they were not female or had ambiguous sex, or were duplicates (cryptic or intended) (12). SNPs were excluded for a call rate <95%, deviating from Hardy–Weinberg equilibrium (P < 10−7 in controls or unrelated samples in CIMBA and P <10−12 in cases) and concordance <98% among 5,280 duplicate pairs (12). All participants provided written informed consent and each contributing study was approved by the appropriate local institutional ethical review board. The studies were conducted in accordance with Declaration of Helsinki.

We used a Bonferroni-corrected P value threshold of 2.21 × 10−6 (adjusting for 22,665 gene–tissue pairs) to determine a statistically significant association in our analysis. This threshold was conservative as 5,544 genes appeared in both ovarian and cross-tissue models. We did the primary analysis for high-grade serous EOC, as this had the largest sample size. In our secondary analyses, we also evaluated other histotypes or the combined histotypes, even though power to discover novel gene associations was relatively low for some (i.e., clear-cell, endometrioid, or low-grade serous). To determine whether associations identified between genetically predicted gene expression and EOC risk were influenced by variants previously identified by GWAS, we conducted conditional analyses adjusting for index SNPs. Briefly, we performed conditional analyses developed by Yang and colleagues (24) (GCTA-COJO) to calculate association betas and standard errors of SNPs with ovarian cancer risk after adjusting for index SNPs of interest. This was followed by reperforming MetaXcan analyses using updated summary statistics.

### Gene expression prediction model building

We constructed genetically regulated expression models based on genome-wide genotype data and RNA-seq transcriptome data from the GTEx project (Supplementary Fig. S1; ref. 14). Ovarian transcriptome data were available for 68 European individuals, and 8,580 genes achieved a prediction performance of r2 ≥ 0.01 in the ovarian model (Supplementary Table S1). Because a large portion of cis expression regulation is shared across multiple tissues (14, 18), we also used transcriptome data for 6,124 tissue samples from 369 European individuals to build cross-tissue models for 14,085 genes with a prediction performance of r2 ≥ 0.01 (Supplementary Table S1).

### Association analyses between predicted gene expression and EOC risk

We evaluated associations between predicted gene expression levels and EOC risk using MetaXcan (22) with summary statistics for individual GWAS SNPs from 97,898 European women including 29,396 EOC cases from OCAC and CIMBA (Supplementary Fig. S1; ref. 12). Our primary analysis focused on high-grade serous EOC; secondary analyses included other EOC histotypes (Supplementary Fig. S1).

In total, we identified 35 genes with genetically predicted expression that were associated with EOC risk at a Bonferroni-corrected threshold of P < 2.21 × 10−6 (Fig. 1; Supplementary Figs. S2 and S3; Tables 1 and 2; Supplementary Table S2). One gene at 11q14.2 (FZD4), was more than 1 Mb away from any GWAS-identified EOC susceptibility variant (Fig. 1), suggesting a potential novel risk locus for this disease. High predicted FZD4 expression was associated with increased risk of high-grade serous EOC (Z = 5.08, P = 3.83 × 10−7; Fig. 1). The remaining 34 genes were located within 1 Mb of previously identified EOC susceptibility variants (Tables 1 and 2; Supplementary Tables S2 and S3), including 11 genes (at 8 loci) that were previously implicated in EOC risk using functional annotation, bioinformatic prediction, in vitro cellular models or known gene biology. Our study provides additional evidence to support these previous findings (Table 2; Supplementary Table S3). However, 23 genes (at 6 known risk loci) had not been reported to be associated with EOC risk in prior studies (Table 1; Supplementary Table S3). For 31 of these 34 genes, the associations were no longer statistically significant at P < 1.47 × 10−3 (multiple comparisons correction of 0.05/34) after adjustment for the nearest SNP identified by EOC GWAS (Supplementary Table S4), indicating that the previously identified GWAS SNPs for EOC at these 31 regions might regulate the expression of these associated gene to affect EOC risk. Associations for three genes (Z = 6.84 vs. 3.27 for DNALI1, Z = 5.16 vs. 3.81 for HOXD3 and Z = −8.60 vs. −4.18 for CCDC171; Tables 1 and 2; Supplementary Table S4) remained statistically significant at P < 1.47 × 10−3 after adjusting for the nearest EOC risk SNP, although the strength of the association was attenuated. Four loci (2q31.1, 9p22.3, 17q21.31, and 17q21.32) had multiple nearby genes associated with EOC risk (Tables 1 and 2). This may be partially due to coregulated gene expression in these chromosomal regions (Supplementary Table S5 and Supplementary Material).

Figure 1.

Regional plot of OCAC and CIMBA GWAS summary statistics around the FZD4 gene associated with high-grade serous EOC risk (Z = 5.08; P = 3.83 × 10−7 based on the cross-tissue model of r2 = 0.07; see Supplementary Table S2 for details). Each symbol represents the significance (P value on a log10 scale) of a SNP with invasive EOC risk as a function of the SNP's genomic position (NCBI Build 37). The most significantly associated SNP is represented in purple. The color of all other SNPs indicates LD with this SNP (estimated by EUR r2 from the 1000 Genome Project data). Recombination rates were also estimated from 1000 Genome Project data, and gene annotations were obtained from the UCSC Genome Browser. The circle denotes the SNPs included in the model construction of genetically regulated FZD4 expression and the square denotes the SNPs not included in the model construction. The gene model was constructed including SNPs within 1 Mb of the gene start or end, and one SNP included in the model construction was located outside the 1 Mb window size of the locus zoom plot (rs7944482 at chr11:86091532, P = 0.52 for association with high-grade serous EOC risk).

Figure 1.

Regional plot of OCAC and CIMBA GWAS summary statistics around the FZD4 gene associated with high-grade serous EOC risk (Z = 5.08; P = 3.83 × 10−7 based on the cross-tissue model of r2 = 0.07; see Supplementary Table S2 for details). Each symbol represents the significance (P value on a log10 scale) of a SNP with invasive EOC risk as a function of the SNP's genomic position (NCBI Build 37). The most significantly associated SNP is represented in purple. The color of all other SNPs indicates LD with this SNP (estimated by EUR r2 from the 1000 Genome Project data). Recombination rates were also estimated from 1000 Genome Project data, and gene annotations were obtained from the UCSC Genome Browser. The circle denotes the SNPs included in the model construction of genetically regulated FZD4 expression and the square denotes the SNPs not included in the model construction. The gene model was constructed including SNPs within 1 Mb of the gene start or end, and one SNP included in the model construction was located outside the 1 Mb window size of the locus zoom plot (rs7944482 at chr11:86091532, P = 0.52 for association with high-grade serous EOC risk).

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Table 1.

Association results for genes in known loci not previously reported in association with epithelial ovarian cancer risk

RegionGeneaZ-scorePr2bHistotypeModelGWAS Index SNPcDistance to the index SNP (kb)d
1p34.3 DNALI1 6.84 7.84E-12 0.29 High-grade serouse Cross-tissue rs58722170 64
9p22.3 CCDC171 −8.60 8.08E-18 0.02 High-grade serouse Ovary rs10962692 854
9p22.3 C9orf92 −5.16 2.45E-07 0.15 High-grade serouse Ovary rs10962692 640
17q21.31 AC091132.1 −7.18 7.02E-13 0.03 High-grade serouse Cross-tissue rs1879586 26
17q21.31 RP11–798G7.8 6.58 4.77E-11 0.05 High-grade serouse Ovary rs1879586 42
17q21.31 CRHR1 8.61 7.23E-18 0.60 High-grade serouse Cross-tissue rs1879586 132
17q21.31 RP11–105N13.4 6.77 1.33E-11 0.05 High-grade serouse Ovary rs1879586 132
17q21.31 MAPT-AS1 7.74 9.60E-15 0.10 High-grade serouse Cross-tissue rs1879586 354
17q21.31 RP11–669E14.6 −8.35 6.64E-17 0.30 High-grade serouse Cross-tissue rs1879586 545
17q21.31 KANSL1-AS1 8.26 1.48E-16 0.85 High-grade serouse Cross-tissue rs1879586 704
17q21.31 LRRC37A 8.38 5.08E-17 0.54 High-grade serouse Ovary rs1879586 803
17q21.31 LRRC37A2 8.26 1.44E-16 0.55 High-grade serouse Ovary rs1879586 1,022
17q21.31 NSF −5.55 2.78E-08 0.02 High-grade serouse Ovary rs1879586 1,101
17q21.32 RP11–138C9.1 5.54 3.04E-08 0.02 High-grade serouse Cross-tissue rs7207826 741
17q21.32 RP11–6N17.6 5.93 3.00E-09 0.19 High-grade serouse Cross-tissue rs7207826 475
17q21.32 PNPO 5.34 9.38E-08 0.30 High-grade serouse Cross-tissue rs7207826 475
17q21.32 PRR15L −4.91 9.18E-07 0.04 High-grade serouse Cross-tissue rs7207826 465
17q21.32 HOXB2 −5.48 4.28E-08 0.40 High-grade serouse Cross-tissue rs7207826 118
17q21.32 HOXB-AS1 −5.15 2.59E-07 0.29 High-grade serouse Cross-tissue rs7207826 120
17q21.32 HOXB3 −5.59 2.30E-08 0.12 High-grade serouse cross-tissue rs7207826 126
18q11.2 RP11–403A21.1 −5.53 3.13E-08 0.11 Low grade/borderline serousf cross-tissue rs8098244 132
19q13.2 ZNF546 7.14 9.07E-13 0.01 Invasive/borderline mucinousf Ovary rs688187 757
RegionGeneaZ-scorePr2bHistotypeModelGWAS Index SNPcDistance to the index SNP (kb)d
1p34.3 DNALI1 6.84 7.84E-12 0.29 High-grade serouse Cross-tissue rs58722170 64
9p22.3 CCDC171 −8.60 8.08E-18 0.02 High-grade serouse Ovary rs10962692 854
9p22.3 C9orf92 −5.16 2.45E-07 0.15 High-grade serouse Ovary rs10962692 640
17q21.31 AC091132.1 −7.18 7.02E-13 0.03 High-grade serouse Cross-tissue rs1879586 26
17q21.31 RP11–798G7.8 6.58 4.77E-11 0.05 High-grade serouse Ovary rs1879586 42
17q21.31 CRHR1 8.61 7.23E-18 0.60 High-grade serouse Cross-tissue rs1879586 132
17q21.31 RP11–105N13.4 6.77 1.33E-11 0.05 High-grade serouse Ovary rs1879586 132
17q21.31 MAPT-AS1 7.74 9.60E-15 0.10 High-grade serouse Cross-tissue rs1879586 354
17q21.31 RP11–669E14.6 −8.35 6.64E-17 0.30 High-grade serouse Cross-tissue rs1879586 545
17q21.31 KANSL1-AS1 8.26 1.48E-16 0.85 High-grade serouse Cross-tissue rs1879586 704
17q21.31 LRRC37A 8.38 5.08E-17 0.54 High-grade serouse Ovary rs1879586 803
17q21.31 LRRC37A2 8.26 1.44E-16 0.55 High-grade serouse Ovary rs1879586 1,022
17q21.31 NSF −5.55 2.78E-08 0.02 High-grade serouse Ovary rs1879586 1,101
17q21.32 RP11–138C9.1 5.54 3.04E-08 0.02 High-grade serouse Cross-tissue rs7207826 741
17q21.32 RP11–6N17.6 5.93 3.00E-09 0.19 High-grade serouse Cross-tissue rs7207826 475
17q21.32 PNPO 5.34 9.38E-08 0.30 High-grade serouse Cross-tissue rs7207826 475
17q21.32 PRR15L −4.91 9.18E-07 0.04 High-grade serouse Cross-tissue rs7207826 465
17q21.32 HOXB2 −5.48 4.28E-08 0.40 High-grade serouse Cross-tissue rs7207826 118
17q21.32 HOXB-AS1 −5.15 2.59E-07 0.29 High-grade serouse Cross-tissue rs7207826 120
17q21.32 HOXB3 −5.59 2.30E-08 0.12 High-grade serouse cross-tissue rs7207826 126
18q11.2 RP11–403A21.1 −5.53 3.13E-08 0.11 Low grade/borderline serousf cross-tissue rs8098244 132
19q13.2 ZNF546 7.14 9.07E-13 0.01 Invasive/borderline mucinousf Ovary rs688187 757

aARHGAP27 and PLEKHM1 were previously considered as potential EOC candidate susceptibility genes by Permuth-Wey and colleagues (10) with an integrated molecular analysis of multiple genes at 17q21.31 locus (see Table 2 and Supplementary Table S3).

br2 of tissue model's correlation to gene's measured transcriptome (prediction performance).

cSee Supplementary Table S4 for detailed information in selecting the GWAS index SNPs.

dIf the GWAS index SNP is located upstream of the gene, the gene start position is used; otherwise, the gene end position was used; LRRC37A2 and NSF are within 1M of reported GWAS SNPs considering the association of all variants with EOC risk at P < 5 × 10−8 at this locus (See text and Supplementary Table S4 for details).

eThe analyses were based on summary statistics for high-grade serous ovarian cancers from Ovarian Cancer Association Consortium (OCAC) and Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA).

fThe analyses were based on summary statistics from OCAC.

Table 2.

Association results for genes in known loci previously reported in association with ovarian cancer risk

RegionGeneZ-scorePr2aHistotypeModelGWAS Index SNPbDistance to the index SNP (kb)c
2q31.1 HOXD3 5.16 2.42E-07 0.04 Borderline mucinouse Cross-tissue rs711830
2q31.1 HOXD1 6.07 1.31E-09 0.04 High-grade serousf Cross-tissue rs711830 16
3q25.31 LEKR1 −5.81 6.24E-09 0.46 High-grade serousf Cross-tissue rs62274041 108
8q21.13 CHMP4C −6.69 2.24E-11 0.47 High-grade serouse Cross-tissue rs11782652
9q34.2 ABO 5.44 5.37E-08 0.49 High-grade serousf Ovary rs635634
10q24.33 OBFC1 −5.09 3.66E-07 0.01 Borderline serouse Cross-tissue rs7902587 16
15q26.1 RCCD1 −5.46 4.64E-08 0.59 High-grade serouse Cross-tissue rs8037137
17q21.31d PLEKHM1 4.80 1.59E-06 0.01 High-grade serousf Cross-tissue rs1879586
17q21.31d KANSL1 4.74 2.15E-06 0.18 High-grade serousf Ovary rs1879586 540
17q21.31d WNT3 6.81 9.82E-12 0.40 High-grade serousf Cross-tissue rs1879586 1,273
19p13.11 ABHD8 4.79 1.69E-06 0.23 High-grade serousf Cross-tissue rs4808075 13
RegionGeneZ-scorePr2aHistotypeModelGWAS Index SNPbDistance to the index SNP (kb)c
2q31.1 HOXD3 5.16 2.42E-07 0.04 Borderline mucinouse Cross-tissue rs711830
2q31.1 HOXD1 6.07 1.31E-09 0.04 High-grade serousf Cross-tissue rs711830 16
3q25.31 LEKR1 −5.81 6.24E-09 0.46 High-grade serousf Cross-tissue rs62274041 108
8q21.13 CHMP4C −6.69 2.24E-11 0.47 High-grade serouse Cross-tissue rs11782652
9q34.2 ABO 5.44 5.37E-08 0.49 High-grade serousf Ovary rs635634
10q24.33 OBFC1 −5.09 3.66E-07 0.01 Borderline serouse Cross-tissue rs7902587 16
15q26.1 RCCD1 −5.46 4.64E-08 0.59 High-grade serouse Cross-tissue rs8037137
17q21.31d PLEKHM1 4.80 1.59E-06 0.01 High-grade serousf Cross-tissue rs1879586
17q21.31d KANSL1 4.74 2.15E-06 0.18 High-grade serousf Ovary rs1879586 540
17q21.31d WNT3 6.81 9.82E-12 0.40 High-grade serousf Cross-tissue rs1879586 1,273
19p13.11 ABHD8 4.79 1.69E-06 0.23 High-grade serousf Cross-tissue rs4808075 13

ar2 of tissue model's correlation to gene's measured transcriptome (prediction performance).

bSee Supplementary Table S4 for detailed information in selecting the GWAS index SNPs.

cIf the GWAS index SNP is located upstream of the gene, the gene start position is used; otherwise, the gene end position was used; WNT3 is within 1M of reported GWAS SNPs considering the association of all variants with EOC risk at P < 5 × 10−8 at this locus (See text and Supplementary Table S4 for details).

dEleven novel genes associated with EOC risk at this locus were presented in Table 1.

eThe analyses were based on summary statistics from OCAC.

fThe analyses were based on summary statistics for high-grade serous ovarian cancers from Ovarian Cancer Association Consortium (OCAC) and Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA).

Consistent with the etiologic heterogeneity of EOC (25), GWAS-identified risk variants differed across histologic subtypes (12). Therefore, we investigated associations between genes with P < 2.21 × 10−6 across all major histotypes of EOC (Supplementary Table S6). The majority of identified genes were associated with high-grade serous EOC risk, likely due to the large number of cases in our primary analysis. A few additional histotype specific associations were identified from secondary analyses. HOXD3 at 2q31.1 was associated with borderline mucinous EOC risk (Table 2; Supplementary Table S6: Z = 5.16, P = 2.42 × 10−7). RP11-403A21.1 at 18q11.2 was associated with low-grade or borderline serous EOC risk (Table 1; Supplementary Table S6: Z = −5.53, P = 3.13 × 10−8). ZNF546 at 19q13.2 was associated with mucinous EOC risk (Table 1; Supplementary Table S6: Z = 7.14, P = 9.07 × 10−13 for invasive/borderline mucinous EOC combined; Z = 5.99 and P = 2.14 × 10−9 for borderline mucinous EOC only). HOXD1 at 2q31.1 was associated with both invasive serous (Supplementary Table S6: Z = 4.92, P = 8.55 × 10−7) and borderline mucinous (Supplementary Table S6: Z = 5.24, P = 1.59 × 10−7) EOC risk.

Evidence from previous eQTL analyses of identified EOC susceptibility risk variants supports several currently identified gene associations (Table 2; Supplementary Table S3). Reduced OBFC1 expression was associated with risk allele of GWAS identified EOC SNP at 10q24.33 (12), and we found that higher predicted OBFC1 expression was associated with lower EOC risk. Similarly, reduced RCCD1 expression was associated with risk allele of GWAS identified EOC SNP (11), and we found that higher predicted RCCD1 expression was associated with reduced EOC risk at 15q26.1. In addition, multiple lines of evidence support our finding between higher predicted ABHD8 and increased EOC risk at 19p13.11. Increased ABHD8 expression was associated with risk allele of GWAS identified EOC SNP (26). Copy number variant analysis indicated that 46% of high-grade serous EOC had amplification at 19p13.11 that contains ABHD8 (3).

In this large transcriptome-wide association study (TWAS) among 97,898 women of European ancestry, we identified 35 genes with genetically predicted expression levels associated with EOC risk. One of these genes (FZD4) is located more than 1 Mb away from any previously identified GWAS EOC variant (25 Mb away from the nearest reported EOC risk variant; ref. 11), suggesting it is a potential novel risk locus. All other 34 genes identified were located within 1 Mb of known GWAS loci, including 23 genes at 6 loci that had not previously been associated with EOC risk. After adjustment for nearby known EOC GWAS-identified variants, the associations for 3 of the 34 genes retained.

FZD4 is a member of the frizzled gene family that encodes seven-transmembrane domain proteins (Fz) as the receptors for the secreted Wnts signaling ligands. Several Wnts and Fzs (including Fzd4 and Wnt4), as well as downstream targets of the canonical WNT signaling pathway, are expressed at different stages of ovarian follicular development, ovulation, and luteinization, suggesting specific functions for these signaling molecules in the mature ovary (27). Recent studies using transgenic mouse models demonstrated that Wnt4, Fzd4, and Ctnnb1 are required for normal folliculogenesis, luteogenesis and steroidogenesis, and that dysregulated WNT signaling leads to granulosa cell tumor development (27, 28). FZD4-null female mice are infertile and exhibit reduced progesterone production, reduced luteinization-associated gene expression, impaired corpora lutea formation and function, and impaired vascular development (28). Interestingly, WNT4 (1p36.12) encodes a potential Fzd4-binding ligand, which was also recently identified as a potentially causal gene underlying EOC risk by GWAS (Supplementary Table S3; ref. 7). Aberrant activation of WNT signaling in adult tissues has been implicated in the pathogenesis of several types of cancer, including colorectal cancer (29). The positive association between FZD4 expression and invasive serous EOC risk suggests that dysregulated corpus luteum function and/or progesterone production may contribute to EOC pathogenesis.

A locus 17q21.31 was previously identified by GWAS as associated with EOC risk (10, 30). This region contains a 900-kb inversion in Europeans that has extensive linkage disequilibrium likely due to restriction from crossovers in individuals who are heterozygous with respect to inversion (31). The H2 haplotype is less frequent (20% in Europeans) and is associated with higher number of children born to women (31). Interestingly, minor alleles of genetic variants in this region were almost universally associated with reduced breast cancer risk but increased EOC risk at genome-wide significance levels (Supplementary Table S7; Supplementary Material; refs. 10, 30). Permuth-Wey and colleagues (10) investigated several of these genes, including KIF18B, C1QL1, DCAKD, NMT1, PLCD3, ACBD4, HEXIM1, HEXIM2, FMNL1, C17orf46, MAP3K14, ARHGAP27, PLEKHM1, CRHR1, IMP5, and MAPT; extensive functional analysis suggested that ARHGAP27 and PLEKHM1 may be EOC susceptibility genes (10). One of the other candidate genes at this region, CRHR1, is involved in regulating ovarian function; it is expressed in ovarian thecal cells, granulosa cells and luteal cells (32), and upregulated in EOC (10). High CRHR1 expression was almost universally associated with minor alleles of multiple genetic variants in this chromosome 17 region (Supplementary Table S8; Supplementary Material; ref. 33). Enhanced CRHR1 activation in the ovary leads to reduced production of testosterone (32) and estrogen (32, 34–36), but increased progesterone accumulation and production (32). This may explain the lower breast cancer risk associated with variants in this region from lower estrogen exposure and higher progesterone exposure associated with multiparity (31, 37). Similarly, this also suggests that imbalanced estrogen and/or progesterone production contributes to EOC pathogenesis.

Two of the candidate genes at the 17q21.32 locus, HOXB2 and HOXB3, belong to the homeobox gene family, which is important for normal vertebrate limb and organ development. This gene family was also recently shown to be enriched for genes underlying serous EOC risk by GWAS (38). Inconsistent tumorigenic effects of HOXB2 and HOXB3 were reported across several types of cancers (breast, pancreatic, lung, cervical cancer, and acute myeloid leukemia; refs. 39–43). This may be due to context-dependent effects from specific tumor microenvironments (39, 43). With regard to ovarian cancer, increased HOXB2 and HOXB3 expression were associated with reduced EOC risk; potential molecular mechanisms underlying HOXB suppressive effect on EOC warrant further investigation.

Several additional findings from this study are noteworthy. The precise function of DNALI1 at 1p34.3 is not known. It is a potential candidate gene for primary cilia syndrome or Kartagener syndrome, in which the action of cilia lining the respiratory tract and fallopian tube is compromised (44). A marked reduction in fertility was observed in female patients with Kartagener syndrome due to dysfunction of the oviductal cilia (45). The predicted expression of CCDC171 at 9p22.3 was associated with reduced EOC risk. CCDC171 was shown to interact with KRAS by a stringent screening for Ras synthetic lethal genes (46). Several lncRNAs were associated with EOC risk, including RP11-403A21.1 at 18q11.2 (Table 1). Little is known about their particular function in either tumor initiation or tumor development, but lncRNAs have been increasingly implicated in many classic cancer biology pathways (47). In addition to HOXD3 and HOXD1 at 2q31.1 (Table 2; Supplementary Table S3; refs. 4, 8), ZNF546 at 19q13.2 was identified as a novel candidate gene for mucinous EOC. Enrichment for expression in gonadal tissues (14) supports a potential role in EOC pathogenesis. Because of the complexity of mucinous EOC, and undetermined cell/tissue of origin, identification of associated genetic variants and/or genes is particularly important (8, 25).

The tissue samples used in building gene expression models in GTEx (V6) came most from the people who recently died of traumatic injury (for these young donors) or cardio-cerebrovascular diseases (for the old donors). There were no overlaps between the tissues used in building gene expression models and the samples used in EOC GWAS in OCAC or CIMBA. Our ability to detect genes significantly associated with EOC risk is affected by tissue specificity and the sample size of the dataset used to build genetic prediction models for gene expression. Four genes were identified from both ovarian and cross-tissue models; 8 genes were only identified on the basis of ovarian models, and 23 genes were only identified from cross-tissue models (Supplementary Table S2). The ovarian tissue transcriptome that we used to model gene expression was potentially derived from multiple ovarian cell types, including surface epithelial cells, oocytes, granulosa cells, Theca cells, luteal cells, and other interstitial cells. Because of the importance of tissue or cell-specific regulators (i.e., transcription factors or epigenomic features) in governing development and function, the ovarian-specific model should best capture transcriptional regulatory mechanisms of the ovary. However, in light of abundant shared cis regulation of expression across multiple tissues (14, 18), we also pooled constitutive variant-dependent regulatory information across tissues and built cross-tissue gene expression models. We would expect this model to yield greater power as the number of tissues in which a variant is functional increases. By coupling both tissue-specific and cross-tissue models, we aimed to robustly capture genetically regulated genes expression using a large sample size. Because of insufficient samples in the GTEx project, we did not build Fallopian tube–specific models.

In summary, we identified one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, and these findings may help improve our mechanistic understanding of EOC pathogenesis. In line with tentative observations of increased borderline EOC risk from ovarian hormone dysregulation for women who received fertility drug treatment with in vitro fertilization (48–50), the known biology of FZD4 and CRHR1 in the ovary implicates the potential of long-term dysregulated ovarian function or imbalanced ovarian hormone production as a possible mechanism underlying EOC pathogenesis.

No potential conflicts of interest were disclosed.

Conception and design: Y. Lu, J.M. Schildkraut, H. Anton-Culver, B.K. Arun, P.A. Fasching, R.L. Nussbaum, P.H.M. Peeters, V.W. Setiawan, M.C. Southey, S.A. Gayther, A. Berchuck, W. Zheng, J. Long

Development of methodology: Y. Lu, X. Guo, H.K. Im, C.F. Singer, W. Zheng

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Y. Lu, L. Wu, J.M. Schildkraut, B.M. Reid, K.B. Moysich, I.L. Andrulis, H. Anton-Culver, B.K. Arun, E.V. Bandera, R.B. Barkardottir, J. Benitez, L. Bjorge, J. Brenton, R. Butzow, T. Caldes, M.A. Caligo, I.G. Campbell, J. Chang-Claude, K.B.M. Claes, F.J. Couch, D.W. Cramer, M.B. Daly, A. DeFazio, O. Diez, S.M. Domchek, T. Dork, D.F. Easton, D.M. Eccles, P.A. Fasching, R.T. Fortner, G. Fountzilas, E. Friedman, P.A. Ganz, J. Garber, G.G. Giles, A.K. Godwin, D.E. Goldgar, M.T. Goodman, M.H. Greene, J. Gronwald, U. Hamann, F. Heitz, M.A.T. Hildebrandt, C.K. Høgdall, A. Hollestelle, P.J. Hulick, E.N. Imyanitov, C. Isaacs, A. Jakubowska, B.Y. Karlan, L.E. Kelemen, L.A. Kiemeney, S.K. Kjaer, A. Kwong, N.D. Le, F. Lesueur, D.A. Levine, A. Mattiello, T. May, I.A. McNeish, F. Modugno, M. Montagna, S.L. Neuhausen, H. Nevanlinna, L. Nikitina-Zake, R.L. Nussbaum, K. Offit, E. Olah, O.I. Olopade, S.H. Olson, H. Olsson, A. Osorio, S.K. Park, P.H.M. Peeters, T. Pejovic, P. Peterlongo, C.M. Phelan, M.A. Pujana, S.J. Ramus, G. Rennert, H. Risch, G.C. Rodriguez, C. Rodríguez-Antona, M.A. Rookus, M.A. Rossing, I.K. Rzepecka, D.P. Sandler, R.K. Schmutzler, V.W. Setiawan, P. Sharma, W. Sieh, J. Simard, C.F. Singer, H. Song, M.C. Southey, A.B. Spurdle, R. Sutphen, A.J. Swerdlow, M.R. Teixeira, S.H. Teo, M. Thomassen, M. Tischkowitz, A. Trichopoulou, N. Tung, S.S. Tworoger, E.J. van Rensburg, A. Vega, D.V. Edwards, P.M. Webb, J.N. Weitzel, N. Wentzensen, E. White, A. Wolk, A.H. Wu, D. Yannoukakos, K.K. Zorn, S.A. Gayther, A.C. Antoniou, E.L. Goode, G. Chenevix-Trench, T.A. Sellers, P.D.P. Pharoah, W. Zheng

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Y. Lu, A. Beeghly-Fadiel, L. Wu, X. Guo, B. Li, Y.A. Chen, J.K. Teer, K.B. Moysich, D.R. Barnes, D.R. Barnes, S.M. Domchek, R.T. Fortner, J. Gronwald, N.D. Le, K. Offit, H. Risch, G.C. Rodriguez, I. Romieu, W. Sieh, C.F. Singer, M.C. Southey, A.E. Toland, N. Tung, A. Vanderstichele, N. Wentzensen, A.C. Antoniou, A. Berchuck, E.L. Goode, W. Zheng

Writing, review, and/or revision of the manuscript: Y. Lu, A. Beeghly-Fadiel, L. Wu, B. Li, H.K. Im, J.B. Permuth, B.M. Reid, J.K. Teer, K.B. Moysich, I.L. Andrulis, H. Anton-Culver, B.K. Arun, E.V. Bandera, R.B. Barkardottir, J. Benitez, L. Bjorge, J. Brenton, I.G. Campbell, J. Chang-Claude, K.B.M. Claes, F.J. Couch, D.W. Cramer, M.B. Daly, A. DeFazio, O. Diez, S.M. Domchek, T. Dork, D.F. Easton, D.M. Eccles, P.A. Fasching, R.T. Fortner, G. Fountzilas, E. Friedman, P.A. Ganz, J. Garber, G.G. Giles, A.K. Godwin, M.T. Goodman, M.H. Greene, J. Gronwald, U. Hamann, F. Heitz, C.K. Høgdall, A. Hollestelle, P.J. Hulick, E.N. Imyanitov, C. Isaacs, A. Jakubowska, P. James, B.Y. Karlan, L.E. Kelemen, L.A. Kiemeney, S.K. Kjaer, A. Kwong, N.D. Le, A. Mattiello, T. May, M.A. Merritt, F. Modugno, S.L. Neuhausen, R.L. Nussbaum, E. Olah, O.I. Olopade, H. Olsson, A. Osorio, S.K. Park, P.H.M. Peeters, S.J. Ramus, G. Rennert, H. Risch, G.C. Rodriguez, I. Romieu, M.A. Rossing, D.P. Sandler, R.K. Schmutzler, V.W. Setiawan, P. Sharma, W. Sieh, J. Simard, C.F. Singer, H. Song, M.C. Southey, A.B. Spurdle, R. Sutphen, A.J. Swerdlow, S.H. Teo, M. Tischkowitz, A.E. Toland, A. Trichopoulou, N. Tung, S.S. Tworoger, E.J. van Rensburg, A. Vega, P.M. Webb, J.N. Weitzel, N. Wentzensen, E. White, A. Wolk, A.H. Wu, D. Yannoukakos, S.A. Gayther, A.C. Antoniou, A. Berchuck, E.L. Goode, G. Chenevix-Trench, T.A. Sellers, P.D.P. Pharoah, W. Zheng, J. Long

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): Y. Lu, L. Wu, K.B. Moysich, J. Dennis, T. Dork, M.A.T. Hildebrandt, B.Y. Karlan, L.E. Kelemen, L.A. Kiemeney, G. Leslie, F. Lesueur, L. McGuffog, F. Modugno, K. Offit, O.I. Olopade, M.T. Parsons, S.J. Ramus, M.A. Rookus, P. Sharma, M.C. Southey, J.N. Weitzel, E. White, A.C. Antoniou, A. Berchuck, W. Zheng

Study supervision: P.J. Hulick, D.G. Huntsman, L.A. Kiemeney, C.F. Singer, M.C. Southey, R. Sutphen, S.A. Gayther, W. Zheng, J. Long

Other (variant classification): A.B. Spurdle

Acknowledgements for individual studies: AOV: We thank Jennifer Koziak, Mie Konno, Michelle Darago, Faye Chambers and the Tom Baker Cancer Centre Translational Laboratories; AUS: The AOCS also acknowledges the cooperation of the participating institutions in Australia and acknowledges the contribution of the study nurses, research assistants and all clinical and scientific collaborators to the study. The complete AOCS Study Group can be found at www.aocstudy.org. We would like to thank all of the women who participated in these research programs; BEL: We would like to thank Gilian Peuteman, Thomas Van Brussel, Annick Van den Broeck and Joke De Roover for technical assistance; BGS: The BGS is funded by Breast Cancer Now and the Institute of Cancer Research (ICR). ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. We thank the Study staff, study participants, doctors, nurses, health care providers and health information sources who have contributed to the study; BVU: The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center's BioVU, which is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the Vanderbilt CTSA grant UL1TR000445 from NCATS/NIH; CAM: This work was supported by Cancer Research UK; the University of Cambridge; National Institute for Health Research Cambridge Biomedical Research Centre; CHA: Innovative Research Team in University (PCSIRT) in China (IRT1076); CHN: To thank all members of Department of Obstetrics and Gynaecology, Hebei Medical University, Fourth Hospital and Department of Molecular Biology, Hebei Medical University, Fourth Hospital; COE: Gynecologic Cancer Center of Excellence (W81XWH-11-2-0131); CON: The cooperation of the 32 Connecticut hospitals, including Stamford Hospital, in allowing patient access, is gratefully acknowledged. This study was approved by the State of Connecticut Department of Public Health Human Investigation Committee. Certain data used in this study were obtained from the Connecticut Tumor Registry in the Connecticut Department of Public Health. The authors assume full responsibility for analyses and interpretation of these data; DKE: OCRF; EPC: To thank all members and investigators of the Rotterdam Ovarian Cancer Study. Dutch Cancer Society (EMC 2014-6699); GER: The German Ovarian Cancer Study (GER) thank Ursula Eilber for competent technical assistance; HOC: The study was supported by the Helsinki University Research Fund; JGO: JSPS KAKENHI grant; KRA: This study (Ko-EVE) was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), and the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (HI16C1127; 0920010); LUN: ERC -2011-AdG, Swedish Cancer Society, Swedish Research Council; MAS: We would like to thank Famida Zulkifli and Ms Moey for assistance in patient recruitment, data collection and sample preparation. The Malaysian Ovarian Cancer Genetic Study is funded by research grants from the Malaysian Ministry of Higher Education (UM.C/HIR/MOHE/06) and charitable funding from Cancer Research Initiatives Foundation; MCC: MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database; MOF: the Total Cancer Care Protocol and the Collaborative Data Services and Tissue Core Facilities at the H. Lee Moffitt Cancer Center & Research Institute, an NCI designated Comprehensive Cancer Center (P30-CA076292), Merck Pharmaceuticals and the state of Florida; NHS: The NHS/NHSII studies thank the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY; NJO: L. Paddock, M. King, U. Chandran, A. Samoila, and Y. Bensman; OPL: Members of the OPAL Study Group (http://opalstudy.qimrberghofer.edu.au/); RPC: National Institute of Health (P50 CA159981, R01CA126841); SEA: SEARCH team, Craig Luccarini, Caroline Baynes, Don Conroy; SIS: The Sister Study (SISTER) is supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES049033); SON: National Health Research and Development Program, Health Canada, grant 6613-1415-53; SRO: To thank all members of Scottish Gynaecological Clinical Trails group and SCOTROC1 investigators; SWE: Swedish Cancer Foundation, WeCanCureCancer and årKampMotCancer Foundation; SWH: The SWHS is supported primarily by NIH grant R37-CA070867. We thank the participants and the research staff of the Shanghai Women's Health Study for making this study possible; UCI: The UCI Ovarian Cancer Study is supported by the National Institutes of Health, National Cancer Institute grants CA58860, and the Lon V Smith Foundation grant LVS-39420; UHN: Princess Margaret Cancer Centre Foundation-Bridge for the Cure; UKO: We particularly thank I. Jacobs, M.Widschwendter, E. Wozniak, A. Ryan, J. Ford and N. Balogun for their contribution to the study; UKR: Carole Pye; VAN: BC Cancer Foundation, VGH & UBC Hospital Foundation; WMH: We thank the Gynaecological Oncology Biobank at Westmead, a member of the Australasian Biospecimen Network-Oncology group, which is funded by the National Health and Medical Research Council Enabling Grants ID 310670 & ID 628903 and the Cancer Institute NSW Grants 12/RIG/1-17 & 15/RIG/1-16.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Lichtenstein
P
,
Holm
NV
,
Verkasalo
PK
,
A
,
Kaprio
J
,
Koskenvuo
M
, et al
Environmental and heritable factors in the causation of cancer–analyses of cohorts of twins from Sweden, Denmark, and Finland
.
N Engl J Med
2000
;
343
:
78
85
.
2.
Song
H
,
Ramus
SJ
,
Tyrer
J
,
Bolton
KL
,
Gentry-Maharaj
A
,
Wozniak
E
, et al
A genome-wide association study identifies a new ovarian cancer susceptibility locus on 9p22.2
.
Nat Genet
2009
;
41
:
996
1000
.
3.
Bolton
KL
,
Tyrer
J
,
Song
H
,
Ramus
SJ
,
Notaridou
M
,
Jones
C
, et al
Common variants at 19p13 are associated with susceptibility to ovarian cancer
.
Nat Genet
2010
;
42
:
880
4
.
4.
Goode
EL
,
Chenevix-Trench
G
,
Song
H
,
Ramus
SJ
,
Notaridou
M
,
Lawrenson
K
, et al
A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24
.
Nat Genet
2010
;
42
:
874
9
.
5.
Pharoah
PD
,
Tsai
YY
,
Ramus
SJ
,
Phelan
CM
,
Goode
EL
,
Lawrenson
K
, et al
GWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer
.
Nat Genet
2013
;
45
:
362
70
.
6.
Bojesen
SE
,
Pooley
KA
,
Johnatty
SE
,
Beesley
J
,
Michailidou
K
,
Tyrer
JP
, et al
Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer
.
Nat Genet
2013
;
45
:
371
84
.
7.
Kuchenbaecker
KB
,
Ramus
SJ
,
Tyrer
J
,
Lee
A
,
Shen
HC
,
Beesley
J
, et al
Identification of six new susceptibility loci for invasive epithelial ovarian cancer
.
Nat Genet
2015
;
47
:
164
71
.
8.
Kelemen
LE
,
Lawrenson
K
,
Tyrer
J
,
Li
Q
,
Lee
JM
,
Seo
JH
, et al
Genome-wide significant risk associations for mucinous ovarian carcinoma
.
Nat Genet
2015
;
47
:
888
97
.
9.
Shen
H
,
Fridley
BL
,
Song
H
,
Lawrenson
K
,
Cunningham
JM
,
Ramus
SJ
, et al
Epigenetic analysis leads to identification of HNF1B as a subtype-specific susceptibility gene for ovarian cancer
.
Nat Commun
2013
;
4
:
1628
.
10.
Permuth-Wey
J
,
Lawrenson
K
,
Shen
HC
,
Velkova
A
,
Tyrer
JP
,
Chen
Z
, et al
Identification and molecular characterization of a new ovarian cancer susceptibility locus at 17q21.31
.
Nat Commun
2013
;
4
:
1627
.
11.
Kar
SP
,
Beesley
J
,
Amin Al Olama
A
,
Michailidou
K
,
Tyrer
J
,
Kote-Jarai
Z
, et al
Genome-wide meta-analyses of breast, ovarian, and prostate cancer association studies identify multiple new susceptibility loci shared by at least two cancer types
.
Cancer Discov
2016
;
6
:
1052
67
.
12.
Phelan
CM
,
Kuchenbaecker
KB
,
Tyrer
JP
,
Kar
SP
,
Lawrenson
K
,
Winham
SJ
, et al
Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer
.
Nat Genet
2017
;
49
:
680
91
.
13.
Nicolae
DL
,
Gamazon
E
,
Zhang
W
,
Duan
S
,
Dolan
ME
,
Cox
NJ
.
Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS
.
PLos Genet
2010
;
6
:
e1000888
.
14.
Consortium
GT
.
Human genomics. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans
.
Science
2015
;
348
:
648
60
.
15.
Westra
HJ
,
Peters
MJ
,
Esko
T
,
Yaghootkar
H
,
Schurmann
C
,
Kettunen
J
, et al
Systematic identification of trans eQTLs as putative drivers of known disease associations
.
Nat Genet
2013
;
45
:
1238
43
.
16.
Li
Q
,
Seo
JH
,
Stranger
B
,
McKenna
A
,
Pe'er
I
,
Laframboise
T
, et al
Integrative eQTL-based analyses reveal the biology of breast cancer risk loci
.
Cell
2013
;
152
:
633
41
.
17.
Das
S
,
Forer
L
,
Schonherr
S
,
Sidore
C
,
Locke
AE
,
Kwong
A
, et al
Next-generation genotype imputation service and methods
.
Nat Genet
2016
;
48
:
1284
7
.
18.
Wheeler
HE
,
Shah
KP
,
Brenner
J
,
Garcia
T
,
Aquino-Michaels
K
,
Consortium
GT
, et al
Survey of the heritability and sparse architecture of gene expression traits across human tissues
.
PLos Genet
2016
;
12
:
e1006423
.
19.
Stegle
O
,
Parts
L
,
Piipari
M
,
Winn
J
,
Durbin
R
.
Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses
.
Nat Protoc
2012
;
7
:
500
7
.
20.
Genomes Project
C
,
Abecasis
GR
,
Auton
A
,
Brooks
LD
,
DePristo
MA
,
Durbin
RM
, et al
An integrated map of genetic variation from 1,092 human genomes
.
Nature
2012
;
491
:
56
65
.
21.
Gamazon
ER
,
Wheeler
HE
,
Shah
KP
,
Mozaffari
SV
,
Aquino-Michaels
K
,
Carroll
RJ
, et al
A gene-based association method for mapping traits using reference transcriptome data
.
Nat Genet
2015
;
47
:
1091
8
.
22.
Barbeira
AN
,
Dickinson
SP
,
Bonazzola
R
,
Zheng
J
,
Wheeler
HE
,
Torres
JM
, et al
Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics.
Nat Commun
2018 May 8
;
9
:
1825
.
doi: 10.1038/s41467-018-03621-1
.
23.
Guo
X
,
Lin
M
,
Rockowitz
S
,
Lachman
HM
,
Zheng
D
.
Characterization of human pseudogene-derived non-coding RNAs for functional potential
.
PLoS One
2014
;
9
:
e93972
.
24.
Yang
J
,
Ferreira
T
,
Morris
AP
,
Medland
SE
Genetic Investigation of ATC
Replication
DIG
, et al
Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits
.
Nat Genet
2012
;
44
:
369
75
.
25.
Prat
J
.
New insights into ovarian cancer pathology
.
Ann Oncol
2012
;
23
:
x111
7
.
26.
Lawrenson
K
,
Kar
S
,
McCue
K
,
Kuchenbaeker
K
,
Michailidou
K
,
Tyrer
J
, et al
Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus
.
Nat Commun
2016
;
7
:
12675
.
27.
Boyer
A
,
Goff
AK
,
Boerboom
D
.
WNT signaling in ovarian follicle biology and tumorigenesis
.
Trends Endocrinol Metab
2010
;
21
:
25
32
.
28.
Hsieh
M
,
Boerboom
D
,
M
,
Lo
Y
,
Parlow
AF
,
Luhmann
UF
, et al
Mice null for Frizzled4 (Fzd4-/-) are infertile and exhibit impaired corpora lutea formation and function
.
Biol Reprod
2005
;
73
:
1135
46
.
29.
Zhan
T
,
Rindtorff
N
,
Boutros
M
.
Wnt signaling in cancer
.
Oncogene
2017
;
36
:
1461
73
.
30.
Couch
FJ
,
Wang
X
,
McGuffog
L
,
Lee
A
,
Olswold
C
,
Kuchenbaecker
KB
, et al
Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk
.
PLos Genet
2013
;
9
:
e1003212
.
31.
Stefansson
H
,
Helgason
A
,
Thorleifsson
G
,
Steinthorsdottir
V
,
Masson
G
,
Barnard
J
, et al
A common inversion under selection in Europeans
.
Nat Genet
2005
;
37
:
129
37
.
32.
Liang
B
,
Wei
DL
,
Cheng
YN
,
Yuan
HJ
,
Lin
J
,
Cui
XZ
, et al
Restraint stress impairs oocyte developmental potential in mice: role of CRH-induced apoptosis of ovarian cells
.
Biol Reprod
2013
;
89
:
64
.
33.
de Jong
S
,
Chepelev
I
,
Janson
E
,
Strengman
E
,
van den Berg
LH
,
Veldink
JH
, et al
Common inversion polymorphism at 17q21.31 affects expression of multiple genes in tissue-specific manner
.
BMC Genomics
2012
;
13
:
458
.
34.
Calogero
AE
,
Burrello
N
,
Negri-Cesi
P
,
Papale
L
,
Palumbo
MA
,
Cianci
A
, et al
Effects of corticotropin-releasing hormone on ovarian estrogen production in vitro
.
Endocrinology
1996
;
137
:
4161
6
.
35.
Yu
C
,
Li
M
,
Wang
Y
,
Liu
Y
,
Yan
C
,
Pan
J
, et al
MiR-375 Mediates CRH signaling pathway in inhibiting E2 synthesis in porcine ovary
.
Reproduction
2017
;
153
:
63
73
.
36.
Ghizzoni
L
,
Mastorakos
G
,
Vottero
A
,
Barreca
A
,
Furlini
M
,
Cesarone
A
, et al
Corticotropin-releasing hormone (CRH) inhibits steroid biosynthesis by cultured human granulosa-lutein cells in a CRH and interleukin-1 receptor-mediated fashion
.
Endocrinology
1997
;
138
:
4806
11
.
37.
Barrett
ES
,
Parlett
LE
,
Windham
GC
,
Swan
SH
.
Differences in ovarian hormones in relation to parity and time since last birth
.
Fertil Steril
2014
;
101
:
1773
80
.
38.
Kar
SP
,
Tyrer
JP
,
Li
Q
,
Lawrenson
K
,
Aben
KK
,
Anton-Culver
H
, et al
Network-based integration of GWAS and gene expression identifies a HOX-centric network associated with serous ovarian cancer risk
.
Cancer Epidemiol Biomark Prev
2015
;
24
:
1574
84
.
39.
O
,
Chougule
RA
,
Moharram
SA
,
Kabir
NN
,
Sun
J
,
Kazi
JU
, et al
The role of HOXB2 and HOXB3 in acute myeloid leukemia
.
Biochem Biophys Res Commun
2015
;
467
:
742
7
.
40.
Inamura
K
,
Togashi
Y
,
Okui
M
,
Ninomiya
H
,
Hiramatsu
M
,
Satoh
Y
, et al
HOXB2 as a novel prognostic indicator for stage I lung adenocarcinomas
.
J Thorac Oncol
2007
;
2
:
802
7
.
41.
Lopez
R
,
Garrido
E
,
Pina
P
,
Hidalgo
A
,
Lazos
M
,
Ochoa
R
, et al
HOXB homeobox gene expression in cervical carcinoma
.
Int J Gynecol Cancer
2006
;
16
:
329
35
.
42.
Segara
D
,
Biankin
AV
,
Kench
JG
,
Langusch
CC
,
Dawson
AC
,
Skalicky
DA
, et al
Expression of HOXB2, a retinoic acid signaling target in pancreatic cancer and pancreatic intraepithelial neoplasia
.
Clin Cancer Res
2005
;
11
:
3587
96
.
43.
Boimel
PJ
,
Cruz
C
,
Segall
JE
.
A functional in vivo screen for regulators of tumor progression identifies HOXB2 as a regulator of tumor growth in breast cancer
.
Genomics
2011
;
98
:
164
72
.
44.
Loges
NT
,
Olbrich
H
,
Becker-Heck
A
,
Haffner
K
,
Heer
A
,
Reinhard
C
, et al
Deletions and point mutations of LRRC50 cause primary ciliary dyskinesia due to dynein arm defects
.
Am J Hum Genet
2009
;
85
:
883
9
.
45.
McComb
P
,
Langley
L
,
Villalon
M
,
Verdugo
P
.
The oviductal cilia and Kartagener's syndrome
.
Fertil Steril
1986
;
46
:
412
6
.
46.
Luo
J
,
Emanuele
MJ
,
Li
D
,
Creighton
CJ
,
Schlabach
MR
,
Westbrook
TF
, et al
A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene
.
Cell
2009
;
137
:
835
48
.
47.
Evans
JR
,
Feng
FY
,
Chinnaiyan
AM
.
The bright side of dark matter: lncRNAs in cancer
.
J Clin Invest
2016
;
126
:
2775
82
.
48.
van Leeuwen
FE
,
Klip
H
,
Mooij
TM
,
van de Swaluw
AM
,
Lambalk
CB
,
Kortman
M
, et al
Risk of borderline and invasive ovarian tumours after ovarian stimulation for in vitro fertilization in a large Dutch cohort
.
Hum Reprod
2011
;
26
:
3456
65
.
49.
Kessous
R
,
Davidson
E
,
Meirovitz
M
,
Sergienko
R
,
Sheiner
E
.
The risk of female malignancies after fertility treatments: a cohort study with 25-year follow-up
.
J Cancer Res Clin Oncol
2016
;
142
:
287
93
.
50.
Stewart
LM
,
Holman
CD
,
Finn
JC
,
Preen
DB
,
Hart
R
.
In vitro fertilization is associated with an increased risk of borderline ovarian tumours
.
Gynecol Oncol
2013
;
129
:
372
6
.