Large-scale genome-wide association study of 398,238 women unveils seven novel loci associated with high-grade serous epithelial ovarian cancer risk

Daniel R Barnes, Jonathan P Tyrer, Joe Dennis, Goska Leslie, Manjeet K Bolla, Michael Lush, Amber M Aeilts, Kristiina Aittomäki, Nadine Andrieu, Irene L Andrulis, Hoda Anton-Culver, Adalgeir Arason, Banu K Arun, Judith Balmaña, Elisa V Bandera, Rosa B Barkardottir, Lieke P V Berger, Amy Berrington de Gonzalez, Pascaline Berthet, Katarzyna BialkowskaLine Bjørge, Amie M Blanco, Marinus J Blok, Kristie A Bobolis, Natalia V Bogdanova, James D Brenton, Henriett Butz, Saundra S Buys, Maria A Caligo, Ian Campbell, Carmen Castillo, Kathleen B M Claes, Sarah V Colonna, Linda S Cook, Mary B Daly, Agnieszka Dansonka-Mieszkowska, Miguel de la Hoya, Anna deFazio, Allison DePersia, Yuan Chun Ding, Susan M Domchek, Thilo Dörk, Zakaria Einbeigi, Christoph Engel, D Gareth Evans, Lenka Foretova, Renée T Fortner, Florentia Fostira, GEMO Study Collaborators, EMBRACE Collaborators, KConFab Investigators

Research output: Working paper / PreprintPreprint

Abstract

BACKGROUND: Nineteen genomic regions have been associated with high-grade serous ovarian cancer (HGSOC). We used data from the Ovarian Cancer Association Consortium (OCAC), Consortium of Investigators of Modifiers of (CIMBA), UK Biobank (UKBB), and FinnGen to identify novel HGSOC susceptibility loci and develop polygenic scores (PGS). METHODS: We analyzed >22 million variants for 398,238 women. Associations were assessed separately by consortium and meta-analysed. OCAC and CIMBA data were used to develop PGS which were trained on FinnGen data and validated in UKBB and BioBank Japan. RESULTS: Eight novel variants were associated with HGSOC risk. An interesting discovery biologically was finding that 3'-UTR SNP rs78378222 was associated with HGSOC (per T allele relative risk (RR)=1.44, 95%CI:1.28-1.62, P=1.76×10 ). The optimal PGS included 64,518 variants and was associated with an odds ratio of 1.46 (95%CI:1.37-1.54) per standard deviation in the UKBB validation (AUROC curve=0.61, 95%CI:0.59-0.62). CONCLUSIONS: This study represents the largest GWAS for HGSOC to date. The results highlight that improvements in imputation reference panels and increased sample sizes can identify HGSOC associated variants that previously went undetected, resulting in improved PGS. The use of updated PGS in cancer risk prediction algorithms will then improve personalized risk prediction for HGSOC.
Original languageEnglish
PublisherMedRxiv
DOIs
Publication statusPublished - 4 Mar 2024

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