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A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants


We sought to identify susceptibility genes for high-grade serous ovarian cancer (HGSOC) by performing a transcriptome-wide association study of gene expression and splice junction usage in HGSOC-relevant tissue types (N = 2,169) and the largest genome-wide association study available for HGSOC (N = 13,037 cases and 40,941 controls). We identified 25 transcriptome-wide association study significant genes, 7 at the junction level only, including LRRC46 at 19q21.32, (P= 1 × 10−9), CHMP4C at 8q21 (P= 2 × 10−11) and a PRC1 junction at 15q26 (P= 7 × 10−9). In vitro assays for CHMP4C showed that the associated variant induces allele-specific exon inclusion (P = 0.0024). Functional screens in HGSOC cell lines found evidence of essentiality for three of the new genes we identified: HAUS6, KANSL1 and PRC1, with the latter comparable to MYC. Our study implicates at least one target gene for 6 out of 13 distinct genome-wide association study regions, identifying 23 new candidate susceptibility genes for HGSOC.

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Fig. 1: Study schema.
Fig. 2: Ovary-specific TWAS association for ARL17A.
Fig. 3: SpTWAS association at PRC1 implicates a new target gene independent of genetic effects on total expression.
Fig. 4: CHMP4C splicing is associated with the EOC risk allele.
Fig. 5: Functional analyses show evidence of essentiality for three TWAS/spTWAS genes.

Data availability

Code, documentation for all methods and all trained TWAS models for all genes and splice variants have been made available on the TWAS/FUSION website ( Full TWAS association statistics have been made available in an interactive database available at


  1. 1.

    Jones, M. R., Kamara, D., Karlan, B. Y., Pharoah, P. D. P. & Gayther, S. A. Genetic epidemiology of ovarian cancer and prospects for polygenic risk prediction. Gynecol. Oncol. 147, 705–713 (2017).

    CAS  Article  Google Scholar 

  2. 2.

    Lawrenson, K. et al. Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus. Nat. Commun. 7, 12675 (2016).

    Article  Google Scholar 

  3. 3.

    Lawrenson, K. et al. Cis-eQTL analysis and functional validation of candidate susceptibility genes for high-grade serous ovarian cancer. Nat. Commun. 6, 8234 (2015).

    CAS  Article  Google Scholar 

  4. 4.

    Song, H. et al. A genome-wide association study identifies a new ovarian cancer susceptibility locus on 9p22.2. Nat. Genet. 41, 996–1000 (2009).

    CAS  Article  Google Scholar 

  5. 5.

    Bolton, K. L. et al. Common variants at 19p13 are associated with susceptibility to ovarian cancer. Nat. Genet. 42, 880–884 (2010).

    CAS  Article  Google Scholar 

  6. 6.

    Pharoah, P. D. P. et al. GWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer. Nat. Genet. 45, 362–370 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    Phelan, C. M. et al. Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat. Genet. 49, 680–691 (2017).

    CAS  Article  Google Scholar 

  8. 8.

    Kelemen, L. E. et al. Genome-wide significant risk associations for mucinous ovarian carcinoma. Nat. Genet. 47, 888–897 (2015).

    CAS  Article  Google Scholar 

  9. 9.

    Kar, S. P. 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. 6, 1052–1067 (2016).

    CAS  Article  Google Scholar 

  10. 10.

    Chen, K. et al. Genome-wide association study identifies new susceptibility loci for epithelial ovarian cancer in Han Chinese women. Nat. Commun. 5, 4682 (2014).

    CAS  Article  Google Scholar 

  11. 11.

    Kuchenbaecker, K. B. et al. Identification of six new susceptibility loci for invasive epithelial ovarian cancer. Nat. Genet. 47, 164–171 (2015).

    CAS  Article  Google Scholar 

  12. 12.

    Bojesen, S. E. et al. Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer. Nat. Genet. 45, 371–384 (2013).

    CAS  Article  Google Scholar 

  13. 13.

    Goode, E. L. et al. A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24. Nat. Genet. 42, 874–879 (2010).

    CAS  Article  Google Scholar 

  14. 14.

    Li, Q. et al. Expression QTL-based analyses reveal candidate causal genes and loci across five tumor types. Hum. Mol. Genet. 23, 5294–5302 (2014).

    CAS  Article  Google Scholar 

  15. 15.

    Li, Q. et al. Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 152, 633–641 (2013).

    CAS  Article  Google Scholar 

  16. 16.

    Mancuso, N. et al. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am. J. Hum. Genet. 100, 473–487 (2017).

    CAS  Article  Google Scholar 

  17. 17.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    Xu, Z., Wu, C., Wei, P. & Pan, W. A powerful framework for integrating eQTL and GWAS summary data. Genetics 207, 893–902 (2017).

    CAS  Article  Google Scholar 

  20. 20.

    Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

    CAS  Article  Google Scholar 

  21. 21.

    Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51, 592–599 (2019).

    CAS  Article  Google Scholar 

  22. 22.

    Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

    CAS  Article  Google Scholar 

  23. 23.

    Leeper, K. et al. Pathologic findings in prophylactic oophorectomy specimens in high-risk women. Gynecol. Oncol. 87, 52–56 (2002).

    Article  Google Scholar 

  24. 24.

    Paley, P. J. et al. Occult cancer of the fallopian tube in BRCA-1 germline mutation carriers at prophylactic oophorectomy: a case for recommending hysterectomy at surgical prophylaxis. Gynecol. Oncol. 80, 176–180 (2001).

    CAS  Article  Google Scholar 

  25. 25.

    Carcangiu, M. L. et al. Atypical epithelial proliferation in fallopian tubes in prophylactic salpingo-oophorectomy specimens from BRCA1 and BRCA2 germline mutation carriers. Int. J. Gynecol. Pathol. 23, 35–40 (2004).

    Article  Google Scholar 

  26. 26.

    Callahan, M. J. et al. Primary fallopian tube malignancies in BRCA-positive women undergoing surgery for ovarian cancer risk reduction. J. Clin. Oncol. 25, 3985–3990 (2007).

    Article  Google Scholar 

  27. 27.

    Gilks, C. B. et al. Incidental nonuterine high-grade serous carcinomas arise in the fallopian tube in most cases: further evidence for the tubal origin of high-grade serous carcinomas. Am. J. Surg. Pathol. 39, 357–364 (2015).

    Article  Google Scholar 

  28. 28.

    Auersperg, N. et al. Expression of two mucin antigens in cultured human ovarian surface epithelium: influence of a family history of ovarian cancer. Am. J. Obstet. Gynecol. 173, 558–565 (1995).

    CAS  Article  Google Scholar 

  29. 29.

    Dyck, H. G. et al. Autonomy of the epithelial phenotype in human ovarian surface epithelium: changes with neoplastic progression and with a family history of ovarian cancer. Int. J. Cancer 69, 429–436 (1996).

    CAS  Article  Google Scholar 

  30. 30.

    He, Q.-Y. et al. Proteomic analysis of a preneoplastic phenotype in ovarian surface epithelial cells derived from prophylactic oophorectomies. Gynecol. Oncol. 98, 68–76 (2005).

    CAS  Article  Google Scholar 

  31. 31.

    Casey, M. J. et al. Histology of prophylactically removed ovaries from BRCA1 and BRCA2 mutation carriers compared with noncarriers in hereditary breast ovarian cancer syndrome kindreds. Gynecol. Oncol. 78, 278–287 (2000).

    CAS  Article  Google Scholar 

  32. 32.

    Lu, K. H. et al. Occult ovarian tumors in women with BRCA1 or BRCA2 mutations undergoing prophylactic oophorectomy. J. Clin. Oncol. 18, 2728–2732 (2000).

    CAS  Article  Google Scholar 

  33. 33.

    Adler, E., Mhawech-Fauceglia, P., Gayther, S. A. & Lawrenson, K. PAX8 expression in ovarian surface epithelial cells. Hum. Pathol. 46, 948–956 (2015).

    CAS  Article  Google Scholar 

  34. 34.

    Bell, D. et al. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

    CAS  Article  Google Scholar 

  35. 35.

    Ross-Adams, H. et al. HNF1B variants associate with promoter methylation and regulate gene networks activated in prostate and ovarian cancer. Oncotarget 7, 74734–74746 (2016).

    Article  Google Scholar 

  36. 36.

    Aguet, F. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  Google Scholar 

  37. 37.

    Permuth-Wey, J. et al. Identification and molecular characterization of a new ovarian cancer susceptibility locus at 17q21.31. Nat. Commun. 4, 1627 (2013).

    Article  Google Scholar 

  38. 38.

    Goecks, J. et al. Open pipelines for integrated tumor genome profiles reveal differences between pancreatic cancer tumors and cell lines. Cancer Med. 4, 392–403 (2015).

    CAS  Article  Google Scholar 

  39. 39.

    Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  Google Scholar 

  40. 40.

    Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

    Article  Google Scholar 

  41. 41.

    Schumacher, F. R. et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet. 50, 928–936 (2018).

    CAS  Article  Google Scholar 

  42. 42.

    Reyes-González, J. M. et al. Targeting c-MYC in platinum-resistant ovarian cancer. Mol. Cancer Ther. 14, 2260–2269 (2015).

    Article  Google Scholar 

  43. 43.

    Baskin, R. et al. Functional analysis of the 11q23.3 glioma susceptibility locus implicates PHLDB1 and DDX6 in glioma susceptibility. Sci. Rep. 5, 17367 (2015).

    CAS  Article  Google Scholar 

  44. 44.

    French, J. D. et al. Functional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers. Am. J. Hum. Genet. 92, 489–503 (2013).

    CAS  Article  Google Scholar 

  45. 45.

    Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–143 (2014).

    CAS  Article  Google Scholar 

  46. 46.

    Fujita, K. et al. Proteomic analysis of urinary extracellular vesicles from high Gleason score prostate cancer. Sci. Rep. 7, 42961 (2017).

    CAS  Article  Google Scholar 

  47. 47.

    Nikolova, D. N. et al. Genome-wide gene expression profiles of ovarian carcinoma: identification of molecular targets for the treatment of ovarian carcinoma. Mol. Med. Rep. 2, 365–384 (2009).

    CAS  PubMed  Google Scholar 

  48. 48.

    Lu, Y. et al. A transcriptome-wide association study among 97,898 women to identify candidate susceptibility genes for epithelial ovarian cancer risk. Cancer Res. 78, 5419–5430 (2018).

    CAS  Article  Google Scholar 

  49. 49.

    Lawrenson, K. et al. In vitro three-dimensional modelling of human ovarian surface epithelial cells. Cell Prolif. 42, 385–393 (2009).

    CAS  Article  Google Scholar 

  50. 50.

    Karst, A. M., Levanon, K. & Drapkin, R. Modeling high-grade serous ovarian carcinogenesis from the fallopian tube. Proc. Natl Acad. Sci. USA 108, 7547–7552 (2011).

    CAS  Article  Google Scholar 

  51. 51.

    Ardlie, K. G. et al. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  Google Scholar 

  52. 52.

    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. 7, 500–507 (2012).

    CAS  Article  Google Scholar 

  53. 53.

    Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 8971 (2015).

    CAS  Article  Google Scholar 

  54. 54.

    Haseman, J. K. & Elston, R. C. The investigation of linkage between a quantitative trait and a marker locus. Behav. Genet. 2, 3–19 (1972).

    CAS  Article  Google Scholar 

  55. 55.

    Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  Article  Google Scholar 

  56. 56.

    Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics 4th edn (Pearson Prentice Hall, 1996).

  57. 57.

    Wheeler, H. E. et al. Survey of the heritability and sparse architecture of gene expression traits across human tissues. PLoS Genet. 12, e1006423 (2016).

    Article  Google Scholar 

  58. 58.

    Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    CAS  Article  Google Scholar 

  59. 59.

    Li, N. F. et al. A modified medium that significantly improves the growth of human normal ovarian surface epithelial (OSE) cells in vitro. Lab. Invest. 84, 923–931 (2004).

    Article  Google Scholar 

  60. 60.

    Hsiao, Y.-H. E. et al. Alternative splicing modulated by genetic variants demonstrates accelerated evolution regulated by highly conserved proteins. Genome Res. 26, 440–450 (2016).

    CAS  Article  Google Scholar 

  61. 61.

    Buckley, M. et al. Enhancer scanning to locate regulatory regions in genomic loci. Nat. Protoc. 11, 46–60 (2016).

    CAS  Article  Google Scholar 

  62. 62.

    Lawrenson, K. et al. Senescent fibroblasts promote neoplastic transformation of partially transformed ovarian epithelial cells in a three-dimensional model of early stage ovarian cancer. Neoplasia 12, 317–325 (2010).

    CAS  Article  Google Scholar 

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This work was supported by multiple grants: an NIH/NCI R21 award (grant no. CA22007801); an NIH/NCI U19 award as part of the Genetic Mechanisms in Oncology (GAME-ON) consortium (grant no. CA148112); an NIH/NCI R01 award (grant no. CA211707); an NIH/NCI R01 award (grant no. CA207456); an NIH/NCI R01 award (grant no. CA204954); and an NIH/NCI R01 award (grant no. CA227237). S.A.G. is additionally supported by the Barth Family Chair in Cancer Genetics at Cedars-Sinai Medical Center. K.L. is supported in part by a K99/R00 Pathway to Independence Award from the NIH (grant no. R00CA184415) and institutional support from the Samuel Oschin Comprehensive Cancer Institute at Cedars-Sinai Medical Center. H.N. and M.A.S.F. are supported by grant nos. 2015/07925-5 and 2017/08211-1 from São Paulo Research Foundation. H.N. is also supported by an institutional grant (Henry Ford Hospital, A30935). This work was supported in part by the Ovarian Cancer Research Fund Alliance Program Project Development Grant (grant no. 373356; Co-Evolution of Epithelial Ovarian Cancer and Tumor Stroma). Additional support for this work came from NIH/NCI grant nos. 1R01CA211707 and 1R01CA207456 and Ovarian Cancer Research Foundation award no. 258807. The results shown in this article are in part based on data generated by the TCGA Research Network ( Some of the normal tissue specimens were collected as part of the USC Jean Richardson Gynecologic Tissue and Fluid Repository, which is supported by a grant from the USC Department of Obstetrics & Gynecology and the NCT Cancer Center Shared Grant award no. P30 CA014089 (to the Norris Comprehensive Cancer Center). A.G. is supported by R01-CA227237 and the Claudia Adams Barr Award. B.P. is supported by R01-HG009120, R21-CA220078 and U01-CA194393.

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A.G., K.L., P.D.P.P., B.P. and S.A.G. designed and performed the experiments, analyzed the data and wrote the paper. X.L., P.C.L., S.K., K.C.V., F.S., M.A.S.F., J.M.L., T.P. and G.L. designed and performed the experiments. A.G., K.L., B.Y.K., M.L.F., H.N., A.N.M., P.D.P.P., B.P. and S.A.G. participated in designing the study and supervised the project.

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Correspondence to Alexander Gusev.

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Supplementary Tables 1–26

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Gusev, A., Lawrenson, K., Lin, X. et al. A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants. Nat Genet 51, 815–823 (2019).

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