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Your search keyword '"Huang, Ruea-Yea"' showing total 12 results
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12 results on '"Huang, Ruea-Yea"'

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

Author

Dareng, Eileen, Coetzee, Simon, Tyrer, Jonathan, Peng, Pei-Chen, Rosenow, Will, Chen, Stephanie, Davis, Brian, Dezem, Felipe, Seo, Ji-Heui, Nameki, Robbin, Reyes, Alberto, Aben, Katja, Anton-Culver, Hoda, Antonenkova, Natalia, Aravantinos, Gerasimos, Bandera, Elisa, Beane Freeman, Laura, Beckmann, Matthias, Beeghly-Fadiel, Alicia, Benitez, Javier, Bernardini, Marcus, Bjorge, Line, Black, Amanda, Bogdanova, Natalia, Bolton, Kelly, Brenton, James, Budzilowska, Agnieszka, Butzow, Ralf, Cai, Hui, Campbell, Ian, Cannioto, Rikki, Chang-Claude, Jenny, Chanock, Stephen, Chen, Kexin, Chenevix-Trench, Georgia, Chiew, Yoke-Eng, Cook, Linda, DeFazio, Anna, Dennis, Joe, Doherty, Jennifer, Dörk, Thilo, du Bois, Andreas, Dürst, Matthias, Eccles, Diana, Ene, Gabrielle, Fasching, Peter, Flanagan, James, Fortner, Renée, Fostira, Florentia, Gentry-Maharaj, Aleksandra, Giles, Graham, Goodman, Marc, Gronwald, Jacek, Haiman, Christopher, Håkansson, Niclas, Heitz, Florian, Hildebrandt, Michelle, Høgdall, Estrid, Høgdall, Claus, Huang, Ruea-Yea, Jensen, Allan, Jones, Michael, Kang, Daehee, Karlan, Beth, Karnezis, Anthony, Kelemen, Linda, Kennedy, Catherine, Khusnutdinova, Elza, Kiemeney, Lambertus, Kjaer, Susanne, Kupryjanczyk, Jolanta, Labrie, Marilyne, Lambrechts, Diether, Larson, Melissa, Le, Nhu, Lester, Jenny, Li, Lian, Lubiński, Jan, Lush, Michael, Marks, Jeffrey, Matsuo, Keitaro, May, Taymaa, McLaughlin, John, McNeish, Iain, Menon, Usha, Missmer, Stacey, Modugno, Francesmary, Moffitt, Melissa, Monteiro, Alvaro, Moysich, Kirsten, Narod, Steven, Nguyen-Dumont, Tu, Odunsi, Kunle, Olsson, Håkan, Onland-Moret, N, Park, Sue, Pejovic, Tanja, Permuth, Jennifer, Piskorz, Anna, and Prokofyeva, Darya

Subjects
GWAS, epithelial ovarian cancer risk, fine mapping, functional mechanisms, Humans, Female, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Ovarian Neoplasms, Genetic Predisposition to Disease, Carcinoma, Ovarian Epithelial, Transcriptome, Risk Factors, Genomics, Case-Control Studies, Multiomics
Abstract

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value

Published
2024

2. Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Author

Dareng, Eileen O, Tyrer, Jonathan P, Barnes, Daniel R, Jones, Michelle R, Yang, Xin, Aben, Katja KH, Adank, Muriel A, Agata, Simona, Andrulis, Irene L, Anton-Culver, Hoda, Antonenkova, Natalia N, Aravantinos, Gerasimos, Arun, Banu K, Augustinsson, Annelie, Balmaña, Judith, Bandera, Elisa V, Barkardottir, Rosa B, Barrowdale, Daniel, Beckmann, Matthias W, Beeghly-Fadiel, Alicia, Benitez, Javier, Bermisheva, Marina, Bernardini, Marcus Q, Bjorge, Line, Black, Amanda, Bogdanova, Natalia V, Bonanni, Bernardo, Borg, Ake, Brenton, James D, Budzilowska, Agnieszka, Butzow, Ralf, Buys, Saundra S, Cai, Hui, Caligo, Maria A, Campbell, Ian, Cannioto, Rikki, Cassingham, Hayley, Chang-Claude, Jenny, Chanock, Stephen J, Chen, Kexin, Chiew, Yoke-Eng, Chung, Wendy K, Claes, Kathleen BM, Colonna, Sarah, Cook, Linda S, Couch, Fergus J, Daly, Mary B, Dao, Fanny, Davies, Eleanor, de la Hoya, Miguel, de Putter, Robin, Dennis, Joe, DePersia, Allison, Devilee, Peter, Diez, Orland, Ding, Yuan Chun, Doherty, Jennifer A, Domchek, Susan M, Dörk, Thilo, du Bois, Andreas, Dürst, Matthias, Eccles, Diana M, Eliassen, Heather A, Engel, Christoph, Evans, Gareth D, Fasching, Peter A, Flanagan, James M, Fortner, Renée T, Machackova, Eva, Friedman, Eitan, Ganz, Patricia A, Garber, Judy, Gensini, Francesca, Giles, Graham G, Glendon, Gord, Godwin, Andrew K, Goodman, Marc T, Greene, Mark H, Gronwald, Jacek, Hahnen, Eric, Haiman, Christopher A, Håkansson, Niclas, Hamann, Ute, Hansen, Thomas VO, Harris, Holly R, Hartman, Mikael, Heitz, Florian, Hildebrandt, Michelle AT, Høgdall, Estrid, Høgdall, Claus K, Hopper, John L, Huang, Ruea-Yea, Huff, Chad, Hulick, Peter J, Huntsman, David G, Imyanitov, Evgeny N, Isaacs, Claudine, Jakubowska, Anna, James, Paul A, and Janavicius, Ramunas

Subjects
GEMO Study Collaborators, GC-HBOC Study Collaborators, EMBRACE Collaborators, OPAL Study Group, AOCS Group, KConFab Investigators, HEBON Investigators, OCAC Consortium, CIMBA Consortium, Genetics, Clinical Sciences, Genetics & Heredity
Published
2022

3. Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Author

Dareng, Eileen O, Tyrer, Jonathan P, Barnes, Daniel R, Jones, Michelle R, Yang, Xin, Aben, Katja KH, Adank, Muriel A, Agata, Simona, Andrulis, Irene L, Anton-Culver, Hoda, Antonenkova, Natalia N, Aravantinos, Gerasimos, Arun, Banu K, Augustinsson, Annelie, Balmaña, Judith, Bandera, Elisa V, Barkardottir, Rosa B, Barrowdale, Daniel, Beckmann, Matthias W, Beeghly-Fadiel, Alicia, Benitez, Javier, Bermisheva, Marina, Bernardini, Marcus Q, Bjorge, Line, Black, Amanda, Bogdanova, Natalia V, Bonanni, Bernardo, Borg, Ake, Brenton, James D, Budzilowska, Agnieszka, Butzow, Ralf, Buys, Saundra S, Cai, Hui, Caligo, Maria A, Campbell, Ian, Cannioto, Rikki, Cassingham, Hayley, Chang-Claude, Jenny, Chanock, Stephen J, Chen, Kexin, Chiew, Yoke-Eng, Chung, Wendy K, Claes, Kathleen BM, Colonna, Sarah, Cook, Linda S, Couch, Fergus J, Daly, Mary B, Dao, Fanny, Davies, Eleanor, de la Hoya, Miguel, de Putter, Robin, Dennis, Joe, DePersia, Allison, Devilee, Peter, Diez, Orland, Ding, Yuan Chun, Doherty, Jennifer A, Domchek, Susan M, Dörk, Thilo, du Bois, Andreas, Dürst, Matthias, Eccles, Diana M, Eliassen, Heather A, Engel, Christoph, Evans, Gareth D, Fasching, Peter A, Flanagan, James M, Fortner, Renée T, Machackova, Eva, Friedman, Eitan, Ganz, Patricia A, Garber, Judy, Gensini, Francesca, Giles, Graham G, Glendon, Gord, Godwin, Andrew K, Goodman, Marc T, Greene, Mark H, Gronwald, Jacek, Hahnen, Eric, Haiman, Christopher A, Håkansson, Niclas, Hamann, Ute, Hansen, Thomas VO, Harris, Holly R, Hartman, Mikael, Heitz, Florian, Hildebrandt, Michelle AT, Høgdall, Estrid, Høgdall, Claus K, Hopper, John L, Huang, Ruea-Yea, Huff, Chad, Hulick, Peter J, Huntsman, David G, Imyanitov, Evgeny N, Isaacs, Claudine, Jakubowska, Anna, James, Paul A, and Janavicius, Ramunas

Subjects
Prevention, Cancer, Ovarian Cancer, Rare Diseases, Good Health and Well Being, Bayes Theorem, Breast Neoplasms, Carcinoma, Ovarian Epithelial, Female, Genetic Predisposition to Disease, Humans, Male, Ovarian Neoplasms, Polymorphism, Single Nucleotide, Prospective Studies, Risk Factors, GEMO Study Collaborators, GC-HBOC Study Collaborators, EMBRACE Collaborators, OPAL Study Group, AOCS Group, KConFab Investigators, HEBON Investigators, OCAC Consortium, CIMBA Consortium, Genetics, Clinical Sciences, Genetics & Heredity
Abstract

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

Published
2022

12. Whole-Genome Sequencing Identifies PPARGC1A as a Putative Modifier of Cancer Risk in BRCA1/2 Mutation Carriers

Author

Zhu, Qianqian, primary, Wang, Jie, additional, Yu, Han, additional, Hu, Qiang, additional, Bateman, Nicholas W., additional, Long, Mark, additional, Rosario, Spencer, additional, Schultz, Emily, additional, Dalgard, Clifton L., additional, Wilkerson, Matthew D., additional, Sukumar, Gauthaman, additional, Huang, Ruea-Yea, additional, Kaur, Jasmine, additional, Lele, Shashikant B., additional, Zsiros, Emese, additional, Villella, Jeannine, additional, Lugade, Amit, additional, Moysich, Kirsten, additional, Conrads, Thomas P., additional, Maxwell, George L., additional, and Odunsi, Kunle, additional

Published
2022
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