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Your search keyword '"Prokofyeva, Darya"' showing total 2 results
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2 results on '"Prokofyeva, Darya"'

1. Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

Author

Dareng, Eileen O., Coetzee, Simon G., Tyrer, Jonathan P., Peng, Pei-Chen, Rosenow, Will, Chen, Stephanie, Davis, Brian D., Dezem, Felipe Segato, Seo, Ji-Heui, Nameki, Robbin, Reyes, Alberto L., Aben, Katja K.H., Anton-Culver, Hoda, Antonenkova, Natalia N., Aravantinos, Gerasimos, Bandera, Elisa V., Beane Freeman, Laura E., Beckmann, Matthias W., Beeghly-Fadiel, Alicia, Benitez, Javier, Bernardini, Marcus Q., Bjorge, Line, Black, Amanda, Bogdanova, Natalia V., Bolton, Kelly L., Brenton, James D., Budzilowska, Agnieszka, Butzow, Ralf, Cai, Hui, Campbell, Ian, Cannioto, Rikki, Chang-Claude, Jenny, Chanock, Stephen J., Chen, Kexin, Chenevix-Trench, Georgia, Chiew, Yoke-Eng, Cook, Linda S., DeFazio, Anna, Dennis, Joe, Doherty, Jennifer A., Dörk, Thilo, du Bois, Andreas, Dürst, Matthias, Eccles, Diana M., Ene, Gabrielle, Fasching, Peter A., Flanagan, James M., Fortner, Renée T., Fostira, Florentia, Gentry-Maharaj, Aleksandra, Giles, Graham G., Goodman, Marc T., Gronwald, Jacek, Haiman, Christopher A., Håkansson, Niclas, Heitz, Florian, Hildebrandt, Michelle A.T., Høgdall, Estrid, Høgdall, Claus K., Huang, Ruea-Yea, Jensen, Allan, Jones, Michael E., Kang, Daehee, Karlan, Beth Y., Karnezis, Anthony N., Kelemen, Linda E., Kennedy, Catherine J., Khusnutdinova, Elza K., Kiemeney, Lambertus A., Kjaer, Susanne K., Kupryjanczyk, Jolanta, Labrie, Marilyne, Lambrechts, Diether, Larson, Melissa C., Le, Nhu D., Lester, Jenny, Li, Lian, Lubiński, Jan, Lush, Michael, Marks, Jeffrey R., Matsuo, Keitaro, May, Taymaa, McLaughlin, John R., McNeish, Iain A., Menon, Usha, Missmer, Stacey, Modugno, Francesmary, Moffitt, Melissa, Monteiro, Alvaro N., Moysich, Kirsten B., Narod, Steven A., Nguyen-Dumont, Tu, Odunsi, Kunle, Olsson, Håkan, Onland-Moret, N. Charlotte, Park, Sue K., Pejovic, Tanja, Permuth, Jennifer B., Piskorz, Anna, Prokofyeva, Darya, Riggan, Marjorie J., Risch, Harvey A., Rodríguez-Antona, Cristina, Rossing, Mary Anne, Sandler, Dale P., Setiawan, V. Wendy, Shan, Kang, Song, Honglin, Southey, Melissa C., Steed, Helen, Sutphen, Rebecca, Swerdlow, Anthony J., Teo, Soo Hwang, Terry, Kathryn L., Thompson, Pamela J., Vestrheim Thomsen, Liv Cecilie, Titus, Linda, Trabert, Britton, Travis, Ruth, Tworoger, Shelley S., Valen, Ellen, Van Nieuwenhuysen, Els, Edwards, Digna Velez, Vierkant, Robert A., Webb, Penelope M., Weinberg, Clarice R., Weise, Rayna Matsuno, Wentzensen, Nicolas, White, Emily, Winham, Stacey J., Wolk, Alicja, Woo, Yin-Ling, Wu, Anna H., Yan, Li, Yannoukakos, Drakoulis, Zeinomar, Nur, Zheng, Wei, Ziogas, Argyrios, Berchuck, Andrew, Goode, Ellen L., Huntsman, David G., Pearce, Celeste L., Ramus, Susan J., Sellers, Thomas A., Freedman, Matthew L., Lawrenson, Kate, Schildkraut, Joellen M., Hazelett, Dennis, Plummer, Jasmine T., Kar, Siddhartha, Jones, Michelle R., Pharoah, Paul D.P., and Gayther, Simon A.

Published
2024
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2. Large-scale genome-wide association study of 398,238 women unveils seven novel loci associated with high-grade serous epithelial ovarian cancer risk.

Author

Barnes DR, Tyrer JP, Dennis J, Leslie G, Bolla MK, Lush M, Aeilts AM, Aittomäki K, Andrieu N, Andrulis IL, Anton-Culver H, Arason A, Arun BK, Balmaña J, Bandera EV, Barkardottir RB, Berger LPV, de Gonzalez AB, Berthet P, Białkowska K, Bjørge L, Blanco AM, Blok MJ, Bobolis KA, Bogdanova NV, Brenton JD, Butz H, Buys SS, Caligo MA, Campbell I, Castillo C, Claes KBM, Colonna SV, Cook LS, Daly MB, Dansonka-Mieszkowska A, de la Hoya M, deFazio A, DePersia A, Ding YC, Domchek SM, Dörk T, Einbeigi Z, Engel C, Evans DG, Foretova L, Fortner RT, Fostira F, Foti MC, Friedman E, Frone MN, Ganz PA, Gentry-Maharaj A, Glendon G, Godwin AK, González-Neira A, Greene MH, Gronwald J, Guerrieri-Gonzaga A, Hamann U, Hansen TVO, Harris HR, Hauke J, Heitz F, Hogervorst FBL, Hooning MJ, Hopper JL, Huff CD, Huntsman DG, Imyanitov EN, Izatt L, Jakubowska A, James PA, Janavicius R, John EM, Kar S, Karlan BY, Kennedy CJ, Kiemeney LALM, Konstantopoulou I, Kupryjanczyk J, Laitman Y, Lavie O, Lawrenson K, Lester J, Lesueur F, Lopez-Pleguezuelos C, Mai PL, Manoukian S, May T, McNeish IA, Menon U, Milne RL, Modugno F, Mongiovi JM, Montagna M, Moysich KB, Neuhausen SL, Nielsen FC, Noguès C, Oláh E, Olopade OI, Osorio A, Papi L, Pathak H, Pearce CL, Pedersen IS, Peixoto A, Pejovic T, Peng PC, Peshkin BN, Peterlongo P, Powell CB, Prokofyeva D, Pujana MA, Radice P, Rashid MU, Rennert G, Richenberg G, Sandler DP, Sasamoto N, Setiawan VW, Sharma P, Sieh W, Singer CF, Snape K, Sokolenko AP, Soucy P, Southey MC, Stoppa-Lyonnet D, Sutphen R, Sutter C, Teixeira MR, Terry KL, Thomsen LCV, Tischkowitz M, Toland AE, Van Gorp T, Vega A, Velez Edwards DR, Webb PM, Weitzel JN, Wentzensen N, Whittemore AS, Winham SJ, Wu AH, Yadav S, Yu Y, Ziogas A, Berchuck A, Couch FJ, Goode EL, Goodman MT, Monteiro AN, Offit K, Ramus SJ, Risch HA, Schildkraut JM, Thomassen M, Simard J, Easton DF, Jones MR, Chenevix-Trench G, Gayther SA, Antoniou AC, and Pharoah PDP

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 BRCA1/BRCA2 (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 TP53 3'-UTR SNP rs78378222 was associated with HGSOC (per T allele relative risk (RR)=1.44, 95%CI:1.28-1.62, P=1.76×10 -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)., 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.

Published
2024
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