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Functional Landscape of Common Variants Associated with Susceptibility to Epithelial Ovarian Cancer

  • Paulo Cilas Morais LyraJr
  • Letícia B. Rangel
  • Alvaro N. A. MonteiroEmail author
Genetic Epidemiology (C Amos, Section Editor)
  • 12 Downloads
Part of the following topical collections:
  1. Topical Collection on Genetic Epidemiology
  2. Topical Collection on Genetic Epidemiology

Abstract

Purpose of the Review

To date, genome-wide association studies (GWASs) have identified 39 genomic loci associated with risk of epithelial ovarian cancer at genome-wide significance level (p ≤ 5 × 10−8) and 13 additional loci using less strict thresholds. Follow-up functional dissection of these loci to uncover the underlining mechanisms driving cancer susceptibility has been challenging.

Recent Findings

In a manner similar to how post-linkage studies led the characterization of then poorly understood cellular pathways, functional analysis of GWAS loci is revealing new mechanisms of ovarian cancer.

Summary

Here, we review recent methodological and conceptual progress relevant to the understanding of how common genetic variation influences the risk of epithelial ovarian cancer.

Keywords

GWAS TWAS Functional analysis Ovarian cancer Common variants SNPs Transcription 

Abbreviations

CCOC

Clear cell ovarian carcinoma

DDR

DNA damage response

ENOC

Endometrioid ovarian carcinoma

EOC

Epithelial ovarian cancer

eQTL

Expression quantitative trait loci

GWAS

Genome-wide association studies

HGSOC

High-grade serous ovarian carcinoma

LD

Linkage disequilibrium

LGSOC

Low-grade serous ovarian carcinoma

MOC

Mucinous ovarian carcinoma

MAF

Minor allele frequency

OCAC

Ovarian Cancer Association Consortium

PARP

Poly ADP ribosyl polymerase

S/MAR

Substrate/matrix attachment region

SNP

Single nucleotide polymorphism

Notes

Acknowledgments

We thank all the women who have donated their time and samples to make possible this work.

Funding Information

Funding for ovarian cancer research in the Monteiro Lab came from NIH awards U19 CA148112, R01 CA116167, and U54 CA163068 and from the Rivkin Center and the Moffitt Foundation. This study was funded in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES)—Finance Code 001 and Fundação de Amparo à Pesquisa do Estado do Espírito Santo (FAPES).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.Google Scholar
  2. 2.
    SEER Stats fact sheets: ovary cancer: National Cancer Institute; 2019. Available from: https://seer.cancer.gov/statfacts/html/ovary.html.
  3. 3.
    Prat J. Ovarian carcinomas: five distinct diseases with different origins, genetic alterations, and clinicopathological features. Virchows Archiv. 2012;460(3):237–49.PubMedCrossRefGoogle Scholar
  4. 4.
    Karnezis AN, Cho KR, Gilks CB, Pearce CL, Huntsman DG. The disparate origins of ovarian cancers: pathogenesis and prevention strategies. Nat Rev Cancer. 2016;17:65.PubMedCrossRefGoogle Scholar
  5. 5.
    Kim J, Park EY, Kim O, Schilder JM, Coffey DM, Cho CH, et al. Cell origins of high-grade serous ovarian cancer. Cancers (Basel). 2018;10(11).PubMedCentralCrossRefPubMedGoogle Scholar
  6. 6.
    Labidi-Galy SI, Papp E, Hallberg D, Niknafs N, Adleff V, Noe M, et al. High grade serous ovarian carcinomas originate in the fallopian tube. Nat Commun. 2017;8(1):1093.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Shih Ie M, Kurman RJ. Ovarian tumorigenesis: a proposed model based on morphological and molecular genetic analysis. Am J Pathol. 2004;164(5):1511–8.PubMedCrossRefGoogle Scholar
  8. 8.
    Kurman RJ, Shih IM. Pathogenesis of ovarian cancer: lessons from morphology and molecular biology and their clinical implications. Int J Gynecol Pathol. 2008;27(2):151–60.PubMedPubMedCentralGoogle Scholar
  9. 9.
    Kurman RJ, Shih IM. The origin and pathogenesis of epithelial ovarian cancer: a proposed unifying theory. Am J Surg Pathol. 2010;34(3):433–43.PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Teer JK, Yoder S, Gjyshi A, Nicosia SV, Zhang C, Monteiro ANA. Mutational heterogeneity in non-serous ovarian cancers. Sci Rep. 2017;7(1):9728.PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Ahmed AA, Etemadmoghadam D, Temple J, Lynch AG, Riad M, Sharma R, et al. Driver mutations in TP53 are ubiquitous in high grade serous carcinoma of the ovary. J Pathol. 2010;221(1):49–56.PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Ho ES, Lai CR, Hsieh YT, Chen JT, Lin AJ, Hung MH, et al. p53 mutation is infrequent in clear cell carcinoma of the ovary. Gynecol Oncol. 2001;80(2):189–93.PubMedCrossRefGoogle Scholar
  13. 13.
    Kuo KT, Mao TL, Jones S, Veras E, Ayhan A, Wang TL, et al. Frequent activating mutations of PIK3CA in ovarian clear cell carcinoma. Am J Pathol. 2009;174(5):1597–601.PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Cancer Genome Atlas Research N. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609–15.CrossRefGoogle Scholar
  15. 15.
    Bonome T, Lee JY, Park DC, Radonovich M, Pise-Masison C, Brady J, et al. Expression profiling of serous low malignant potential, low-grade, and high-grade tumors of the ovary. Cancer Res. 2005;65(22):10602–12.PubMedCrossRefGoogle Scholar
  16. 16.
    Bast RC Jr, Hennessy B, Mills GB. The biology of ovarian cancer: new opportunities for translation. Nat Rev Cancer. 2009;9(6):415–28.PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Patch A-M, Christie EL, Etemadmoghadam D, Garsed DW, George J, Fereday S, et al. Whole–genome characterization of chemoresistant ovarian cancer. Nature. 2015;521:489.PubMedCrossRefGoogle Scholar
  18. 18.
    Reid BM, Permuth JB, Sellers TA. Epidemiology of ovarian cancer: a review. Cancer Biol Med. 2017;14(1):9–32.PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Webb PM, Jordan SJ. Epidemiology of epithelial ovarian cancer. Best Pract Res Clin Obstet Gynaecol. 2017;41:3–14.PubMedCrossRefGoogle Scholar
  20. 20.
    Pharoah PD, Ponder BA. The genetics of ovarian cancer. Best Pract Res Clin Obstet Gynaecol. 2002;16(4):449–68.PubMedCrossRefGoogle Scholar
  21. 21.
    Stratton JF, Pharoah P, Smith SK, Easton D, Ponder BA. A systematic review and meta-analysis of family history and risk of ovarian cancer. Br J Obstet Gynaecol. 1998;105(5):493–9.PubMedCrossRefGoogle Scholar
  22. 22.
    Jones MR, Kamara D, Karlan BY, Pharoah PDP, Gayther SA. Genetic epidemiology of ovarian cancer and prospects for polygenic risk prediction. Gynecol Oncol. 2017;147(3):705–13.PubMedCrossRefGoogle Scholar
  23. 23.
    Lilyquist J, LaDuca H, Polley E, Davis BT, Shimelis H, Hu C, et al. Frequency of mutations in a large series of clinically ascertained ovarian cancer cases tested on multi-gene panels compared to reference controls. Gynecol Oncol. 2017;147(2):375–80.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Pharoah PD, Dunning AM, Ponder BA, Easton DF. Association studies for finding cancer-susceptibility genetic variants. Nat Rev Cancer. 2004;4(11):850–60.PubMedCrossRefGoogle Scholar
  25. 25.
    Song H, Dicks E, Ramus SJ, Tyrer JP, Intermaggio MP, Hayward J, et al. Contribution of germline mutations in the RAD51B, RAD51C, and RAD51D genes to ovarian cancer in the population. J Clin Oncol. 2015;33(26):2901–7.PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Ramus SJ, Song H, Dicks E, Tyrer JP, Rosenthal AN, Intermaggio MP, et al. Germline mutations in the BRIP1, BARD1, PALB2, and NBN genes in women with ovarian cancer. J Natl Cancer Inst. 2015;107(11).Google Scholar
  27. 27.
    Jervis S, Song H, Lee A, Dicks E, Tyrer J, Harrington P, et al. Ovarian cancer familial relative risks by tumour subtypes and by known ovarian cancer genetic susceptibility variants. J Med Genet. 2014;51(2):108–13.PubMedCrossRefGoogle Scholar
  28. 28.
    Lu Y, Ek WE, Whiteman D, Vaughan TL, Spurdle AB, Easton DF, et al. Most common ‘sporadic’ cancers have a significant germline genetic component. Hum Mol Genet. 2014;23(22):6112–8.PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Amos CIDJ, Wang Z, Byun J, Schumacher FR, Gayther SA, Hunter DJ, et al. The OncoArray Consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiology Biomarkers & Prevention. 2016; under review.Google Scholar
  30. 30.
    Bahcall OG. iCOGS collection provides a collaborative model. Foreword Nature genetics. 2013;45(4):343.PubMedCrossRefGoogle Scholar
  31. 31.
    Houlston RS, Peto J. The search for low-penetrance cancer susceptibility alleles. Oncogene. 2004;23(38):6471–6.PubMedCrossRefGoogle Scholar
  32. 32.
    •• 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. Nature genetics. 2017;49(5):680–91 This paper describes the largest GWAS analysis of Epithelial Ovarian Cancer risk using samples from the Ovarian Cancer Association Consortium (OCAC) and the Consotium of Investigators of Modifiers of BRCA1/2 (CIMBA). It identifies 12 new susceptibility loci. PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Bryant HE, Schultz N, Thomas HD, Parker KM, Flower D, Lopez E, et al. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature. 2005;434(7035):913–7.PubMedCrossRefGoogle Scholar
  34. 34.
    Farmer H, McCabe N, Lord CJ, Tutt AN, Johnson DA, Richardson TB, et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature. 2005;434(7035):917–21.PubMedCrossRefGoogle Scholar
  35. 35.
    Ciccia A, Elledge SJ. The DNA damage response: making it safe to play with knives. Mol Cell. 2010;40(2):179–204.PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Kennedy RD, D’Andrea AD. DNA repair pathways in clinical practice: lessons from pediatric cancer susceptibility syndromes. J Clin Oncol. 2006;24(23):3799–808.PubMedCrossRefGoogle Scholar
  37. 37.
    Fong PC, Boss DS, Yap TA, Tutt A, Wu P, Mergui-Roelvink M, et al. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N Engl J Med. 2009;361(2):123–34.PubMedCrossRefGoogle Scholar
  38. 38.
    Fong PC, Yap TA, Boss DS, Carden CP, Mergui-Roelvink M, Gourley C, et al. Poly(ADP)-ribose polymerase inhibition: frequent durable responses in BRCA carrier ovarian cancer correlating with platinum-free interval. J Clin Oncol. 2010;28(15):2512–9.PubMedCrossRefGoogle Scholar
  39. 39.
    Kar SP, Berchuck A, Gayther SA, Goode EL, Moysich KB, Pearce CL, et al. Common genetic variation and susceptibility to ovarian cancer: current insights and future directions. Cancer Epidemiol Biomarkers Prev. 2017.Google Scholar
  40. 40.
    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(9):996–1000.PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Chen K, Ma H, Li L, Zang R, Wang C, Song F, et al. Genome-wide association study identifies new susceptibility loci for epithelial ovarian cancer in Han Chinese women. Nat Commun. 2014;5:4682.PubMedCrossRefGoogle Scholar
  42. 42.
    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(4):362–70.PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    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(10):874–9.PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    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(2):164–71.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    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(10):880–4.PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Lawrenson K, Kar S, MccCue 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.PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    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.PubMedCrossRefGoogle Scholar
  48. 48.
    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.PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    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(4):371–84.PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    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(3):e1003212.PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    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(8):888–97.PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Lawrenson K, Song F, Hazelett DJ, Kar SP, Tyrer J, Phelan CM, et al. Genome-wide association studies identify susceptibility loci for epithelial ovarian cancer in east Asian women. Gynecol Oncol. 2019;153(2):343–55.PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Krzywinski M, Altman N. Comparing samples—part II. Nat Methods. 2014;11:355.CrossRefGoogle Scholar
  54. 54.
    Wakefield J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am J Hum Genet. 2007;81(2):208–27.PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Manichaikul A, Peres LC, Wang XQ, Barnard ME, Chyn D, Sheng X, et al. Identification of novel epithelial ovarian cancer loci in women of African Ancestry. Int J Cancer Journal international du cancer. 2019.Google Scholar
  56. 56.
    Jervis S, Song H, Lee A, Dicks E, Harrington P, Baynes C, et al. A risk prediction algorithm for ovarian cancer incorporating BRCA1, BRCA2, common alleles and other familial effects. J Med Genet. 2015;52(7):465–75.PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Turnbull C, Sud A, Houlston RS. Cancer genetics, precision prevention and a call to action. Nat Genet. 2018;50(9):1212–8.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Freedman ML, Monteiro AN, Gayther SA, Coetzee GA, Risch A, Plass C, et al. Principles for the post-GWAS functional characterization of cancer risk loci. Nat Genet. 2011;43(6):513–8.PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Monteiro AN, Freedman ML. Lessons from postgenome-wide association studies: functional analysis of cancer predisposition loci. J Intern Med. 2013;274(5):414–24.PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Sur I, Tuupanen S, Whitington T, Aaltonen LA, Taipale J. Lessons from functional analysis of genome-wide association studies. Cancer Res. 2013;73(14):4180–4.PubMedCrossRefGoogle Scholar
  61. 61.
    Knight JC. Approaches for establishing the function of regulatory genetic variants involved in disease. Genome Med. 2014;6(10):92.PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Fachal L, Dunning AM. From candidate gene studies to GWAS and post-GWAS analyses in breast cancer. Curr Opin Genet Dev. 2015;30:32–41.PubMedCrossRefGoogle Scholar
  63. 63.
    Rivandi M, Martens JWM, Hollestelle A. Elucidating the underlying functional mechanisms of breast cancer susceptibility through post-GWAS analyses. Front Genet. 2018;9:280.PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Cannon ME, Mohlke KL. Deciphering the emerging complexities of molecular mechanisms at GWAS loci. Am J Hum Genet. 2018;103(5):637–53.PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Nishizaki SS, Boyle AP. Mining the unknown: assigning function to noncoding single nucleotide polymorphisms. Trends Genet. 2017;33(1):34–45.PubMedCrossRefGoogle Scholar
  66. 66.
    Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101(1):5–22.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Paul DS, Soranzo N, Beck S. Functional interpretation of non-coding sequence variation: concepts and challenges. Bioessays. 2014;36(2):191–9.PubMedCrossRefGoogle Scholar
  68. 68.
    Edwards SL, Beesley J, French JD, Dunning AM. Beyond GWASs: illuminating the dark road from association to function. Am J Hum Genet. 2013;93(5):779–97.PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Gallagher MD, Chen-Plotkin AS. The post-GWAS era: from association to function. Am J Hum Genet. 2018;102(5):717–30.PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, et al. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57–74.CrossRefGoogle Scholar
  71. 71.
    UCSC GB. Available from: genome.ucsc.edu.
  72. 72.
    Coetzee SG, Rhie SK, Berman BP, Coetzee GA, Noushmehr H. FunciSNP: an R/bioconductor tool integrating functional non-coding data sets with genetic association studies to identify candidate regulatory SNPs. Nucleic Acids Res. 2012.Google Scholar
  73. 73.
    Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40(Database issue):D930–4.PubMedCrossRefGoogle Scholar
  74. 74.
    Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22(9):1790–7.PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Buckley M, Gjyshi A, Mendoza-Fandino G, Baskin R, Carvalho RS, Carvalho MA, et al. Enhancer scanning to locate regulatory regions in genomic loci. Nat Protoc. 2016;11(1):46–60.PubMedCrossRefGoogle Scholar
  76. 76.
    • Buckley MA, Woods NT, Tyrer JP, Mendoza-Fandino G, Lawrenson K, Hazelett DJ, et al. Functional analysis and fine mapping of the 9p22.2 ovarian cancer susceptibility locus. Cancer research. 2019;79(3):467–81 This paper which describes functional dissection of the 9p22.2 risk locus identifies a scaffold/matrix attachment region as a possible new mechanism for regulation of gene expression in susceptibility loci. PubMedCrossRefGoogle Scholar
  77. 77.
    Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337(6099):1190–5.PubMedPubMedCentralCrossRefGoogle Scholar
  78. 78.
    Lawrenson K, Li Q, Kar S, Seo JH, Tyrer J, Spindler TJ, et al. Cis-eQTL analysis and functional validation of candidate susceptibility genes for high-grade serous ovarian cancer. Nat Commun. 2015;6:8234.PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009;326(5950):289–93.PubMedPubMedCentralCrossRefGoogle Scholar
  80. 80.
    Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science. 2002;295(5558):1306–11.PubMedCrossRefGoogle Scholar
  81. 81.
    Sur IK, Hallikas O, Vaharautio A, Yan J, Turunen M, Enge M, et al. Mice lacking a Myc enhancer that includes human SNP rs6983267 are resistant to intestinal tumors. Science. 2012;338(6112):1360–3.PubMedCrossRefGoogle Scholar
  82. 82.
    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(9):1091–8.PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48(3):245–52.PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    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;9(1):1825.PubMedPubMedCentralCrossRefGoogle Scholar
  85. 85.
    Consortium GT. Human genomics. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348(6235):648–60.CrossRefGoogle Scholar
  86. 86.
    Lu Y, Beeghly-Fadiel A, Wu L, Guo X, Li B, Schildkraut JM, et al. A transcriptome-wide association study among 97,898 women to identify candidate susceptibility genes for epithelial ovarian cancer risk. Cancer Res. 2018;78(18):5419–30.PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Gusev A, Lawrenson K, Lin X, Lyra PC Jr, Kar S, Vavra KC, et al. A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants. Nat Genet. 2019;51(5):815–23.PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Hrycaj SM, Wellik DM. Hox genes and evolution. F1000Res. 2016;5.CrossRefGoogle Scholar
  89. 89.
    Taylor HS, Vanden Heuvel GB, Igarashi P. A conserved Hox axis in the mouse and human female reproductive system: late establishment and persistent adult expression of the Hoxa cluster genes. Biol Reprod. 1997;57(6):1338–45.PubMedCrossRefGoogle Scholar
  90. 90.
    Cheng W, Liu J, Yoshida H, Rosen D, Naora H. Lineage infidelity of epithelial ovarian cancers is controlled by HOX genes that specify regional identity in the reproductive tract. Nat Med. 2005;11(5):531–7.PubMedCrossRefGoogle Scholar
  91. 91.
    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 Biomarkers Prev. 2015;24(10):1574–84.PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Hiyama E, Gollahon L, Kataoka T, Kuroi K, Yokoyama T, Gazdar AF, et al. Telomerase activity in human breast tumors. J Natl Cancer Inst. 1996;88(2):116–22.PubMedCrossRefGoogle Scholar
  93. 93.
    Levy D, Neuhausen SL, Hunt SC, Kimura M, Hwang SJ, Chen W, et al. Genome-wide association identifies OBFC1 as a locus involved in human leukocyte telomere biology. Proc Natl Acad Sci U S A. 2010;107(20):9293–8.PubMedPubMedCentralCrossRefGoogle Scholar
  94. 94.
    Casteel DE, Zhuang S, Zeng Y, Perrino FW, Boss GR, Goulian M, et al. A DNA polymerase-{alpha}{middle dot}primase cofactor with homology to replication protein A-32 regulates DNA replication in mammalian cells. J Biol Chem. 2009;284(9):5807–18.PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    Wan M, Qin J, Songyang Z, Liu D. OB fold-containing protein 1 (OBFC1), a human homolog of yeast Stn1, associates with TPP1 and is implicated in telomere length regulation. J Biol Chem. 2009;284(39):26725–31.PubMedPubMedCentralCrossRefGoogle Scholar
  96. 96.
    Telomeres Mendelian Randomization C, Haycock PC, Burgess S, Nounu A, Zheng J, Okoli GN, et al. Association between telomere length and risk of cancer and non-neoplastic diseases: a Mendelian randomization study. JAMA Oncol. 2017;3(5):636–51.CrossRefGoogle Scholar
  97. 97.
    Choi J, Brown KM. A dynamic cis-regulation pattern underlying epithelial ovarian cancer susceptibility. Cancer Res. 2019;79(3):439–40.PubMedCrossRefGoogle Scholar
  98. 98.
    Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J, Gignoux CR, et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature. 2019;570(7762):514–8.PubMedCrossRefGoogle Scholar
  99. 99.
    Dutil JCZ, Monteiro AN, Teer JK, Eschrich SA. An interactive resource to probe genetic diversity and estimated ancestry in cancer cell lines. Cancer Res. 2018; Provisionally accepted.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Paulo Cilas Morais LyraJr
    • 1
  • Letícia B. Rangel
    • 1
  • Alvaro N. A. Monteiro
    • 2
    Email author
  1. 1.Biotechnology/RENORBIO ProgramFederal University of Espírito SantoVitóriaBrazil
  2. 2.Cancer Epidemiology ProgramH. Lee Moffitt Cancer Center and Research InstituteTampaUSA

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