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

Barnes, D. R.; Tyrer, J. P.; Dennis, J.; Leslie, G.; Bolla, M. K.; Lush, M.; Aeilts, A. M.; Aittomaki, K.; Andrieu, N.; Andrulis, I. L.; Anton-Culver, H.; Arason, A.; Arun, B. K.; Balmana, J.; Bandera, E. V.; Barkardottir, R. B.; Berger, L. P. V.; Berrington de Gonzalez, A.; Berthet, P.; Bialkowska, K.; Bjorge, L.; Blanco, A. M.; Blok, M. J.; Bobolis, K. A.; Bogdanova, N. V.; Brenton, J. D.; Butz, H.; Buys, S. S.; Caligo, M. A.; Campbell, I.; Castillo, C.; Claes, K. B. M.; GEMO Study Collaborators, ; EMBRACE Collaborators, ; Colonna, S. V.; Cook, L. S.; Daly, M. B.; Dansonka-Mieszkowska, A.;

2024-03-04 genetic and genomic medicine
10.1101/2024.02.29.24303243 medRxiv
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BackgroundNineteen 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 BRCA1/BRCA2 (CIMBA), UK Biobank (UKBB), and FinnGen to identify novel HGSOC susceptibility loci and develop polygenic scores (PGS). MethodsWe 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 ResultsEight novel variants were associated with HGSOC risk. An interesting discovery biologically was finding that TP53 3-UTR SNP rs78378222 was associated with HGSOC (per T allele relative risk (RR)=1.44, 95%CI:1.28-1.62, P=1.76x10-9). 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). ConclusionsThis 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.

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