Melbourne School of Population and Global Health - Research Publications

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    Genetic insights into biological mechanisms governing human ovarian ageing
    Ruth, KS ; Day, FR ; Hussain, J ; Martinez-Marchal, A ; Aiken, CE ; Azad, A ; Thompson, DJ ; Knoblochova, L ; Abe, H ; Tarry-Adkins, JL ; Gonzalez, JM ; Fontanillas, P ; Claringbould, A ; Bakker, OB ; Sulem, P ; Walters, RG ; Terao, C ; Turon, S ; Horikoshi, M ; Lin, K ; Onland-Moret, NC ; Sankar, A ; Hertz, EPT ; Timshel, PN ; Shukla, V ; Borup, R ; Olsen, KW ; Aguilera, P ; Ferrer-Roda, M ; Huang, Y ; Stankovic, S ; Timmers, PRHJ ; Ahearn, TU ; Alizadeh, BZ ; Naderi, E ; Andrulis, IL ; Arnold, AM ; Aronson, KJ ; Augustinsson, A ; Bandinelli, S ; Barbieri, CM ; Beaumont, RN ; Becher, H ; Beckmann, MW ; Benonisdottir, S ; Bergmann, S ; Bochud, M ; Boerwinkle, E ; Bojesen, SE ; Bolla, MK ; Boomsma, DI ; Bowker, N ; Brody, JA ; Broer, L ; Buring, JE ; Campbell, A ; Campbell, H ; Castelao, JE ; Catamo, E ; Chanock, SJ ; Chenevix-Trench, G ; Ciullo, M ; Corre, T ; Couch, FJ ; Cox, A ; Crisponi, L ; Cross, SS ; Cucca, F ; Czene, K ; Smith, GD ; de Geus, EJCN ; de Mutsert, R ; De Vivo, I ; Demerath, EW ; Dennis, J ; Dunning, AM ; Dwek, M ; Eriksson, M ; Esko, T ; Fasching, PA ; Faul, JD ; Ferrucci, L ; Franceschini, N ; Frayling, TM ; Gago-Dominguez, M ; Mezzavilla, M ; Garcia-Closas, M ; Gieger, C ; Giles, GG ; Grallert, H ; Gudbjartsson, DF ; Gudnason, V ; Guenel, P ; Haiman, CA ; Hakansson, N ; Hall, P ; Hayward, C ; He, C ; He, W ; Heiss, G ; Hoffding, MK ; Hopper, JL ; Hottenga, JJ ; Hu, F ; Hunter, D ; Ikram, MA ; Jackson, RD ; Joaquim, MDR ; John, EM ; Joshi, PK ; Karasik, D ; Kardia, SLR ; Kartsonaki, C ; Karlsson, R ; Kitahara, CM ; Kolcic, I ; Kooperberg, C ; Kraft, P ; Kurian, AW ; Kutalik, Z ; La Bianca, M ; LaChance, G ; Langenberg, C ; Launer, LJ ; Laven, JSE ; Lawlor, DA ; Le Marchand, L ; Li, J ; Lindblom, A ; Lindstrom, S ; Lindstrom, T ; Linet, M ; Liu, Y ; Liu, S ; Luan, J ; Magi, R ; Magnusson, PKE ; Mangino, M ; Mannermaa, A ; Marco, B ; Marten, J ; Martin, NG ; Mbarek, H ; McKnight, B ; Medland, SE ; Meisinger, C ; Meitinger, T ; Menni, C ; Metspalu, A ; Milani, L ; Milne, RL ; Montgomery, GW ; Mook-Kanamori, DO ; Mulas, A ; Mulligan, AM ; Murray, A ; Nalls, MA ; Newman, A ; Noordam, R ; Nutile, T ; Nyholt, DR ; Olshan, AF ; Olsson, H ; Painter, JN ; Patel, AV ; Pedersen, NL ; Perjakova, N ; Peters, A ; Peters, U ; Pharoah, PDP ; Polasek, O ; Porcu, E ; Psaty, BM ; Rahman, I ; Rennert, G ; Rennert, HS ; Ridker, PM ; Ring, SM ; Robino, A ; Rose, LM ; Rosendaal, FR ; Rossouw, J ; Rudan, I ; Rueedi, R ; Ruggiero, D ; Sala, CF ; Saloustros, E ; Sandler, DP ; Sanna, S ; Sawyer, EJ ; Sarnowski, C ; Schlessinger, D ; Schmidt, MK ; Schoemaker, MJ ; Schraut, KE ; Scott, C ; Shekari, S ; Shrikhande, A ; Smith, AV ; Smith, BH ; Smith, JA ; Sorice, R ; Southey, MC ; Spector, TD ; Spinelli, JJ ; Stampfer, M ; Stoeckl, D ; van Meurs, JBJ ; Strauch, K ; Styrkarsdottir, U ; Swerdlow, AJ ; Tanaka, T ; Teras, LR ; Teumer, A ; thorsteinsdottir, U ; Timpson, NJ ; Toniolo, D ; Traglia, M ; Troester, MA ; Truong, T ; Tyrrell, J ; Uitterlinden, AG ; Ulivi, S ; Vachon, CM ; Vitart, V ; Voelker, U ; Vollenweider, P ; Voelzke, H ; Wang, Q ; Wareham, NJ ; Weinberg, CR ; Weir, DR ; Wilcox, AN ; van Dijk, KW ; Willemsen, G ; Wilson, JF ; Wolffenbuttel, BHR ; Wolk, A ; Wood, AR ; Zhao, W ; Zygmunt, M ; Chen, Z ; Li, L ; Franke, L ; Burgess, S ; Deelen, P ; Pers, TH ; Grondahl, ML ; Andersen, CY ; Pujol, A ; Lopez-Contreras, AJ ; Daniel, JA ; Stefansson, K ; Chang-Claude, J ; van der Schouw, YT ; Lunetta, KL ; Chasman, DI ; Easton, DF ; Visser, JA ; Ozanne, SE ; Namekawa, SH ; Solc, P ; Murabito, JM ; Ong, KK ; Hoffmann, ER ; Murray, A ; Roig, I ; Perry, JRB (NATURE PORTFOLIO, 2021-08-04)
    Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.
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    Breast Cancer Risk Factors and Survival by Tumor Subtype: Pooled Analyses from the Breast Cancer Association Consortium
    Morra, A ; Jung, AY ; Behrens, S ; Keeman, R ; Ahearn, TU ; Anton-Culver, H ; Arndt, V ; Augustinsson, A ; Auvinen, PK ; Freeman, LEB ; Becher, H ; Beckmann, MW ; Blomqvist, C ; Bojesen, SE ; Bolla, MK ; Brenner, H ; Briceno, I ; Brucker, SY ; Camp, NJ ; Campa, D ; Canzian, F ; Castelao, JE ; Chanock, SJ ; Choi, J-Y ; Clarke, CL ; Couch, FJ ; Cox, A ; Cross, SS ; Czene, K ; Dork, T ; Dunning, AM ; Dwek, M ; Easton, DF ; Eccles, DM ; Egan, KM ; Evans, DG ; Fasching, PA ; Flyger, H ; Gago-Dominguez, M ; Gapstur, SM ; Garcia-Saenz, JA ; Gaudet, MM ; Giles, GG ; Grip, M ; Guenel, P ; Haiman, CA ; Hakansson, N ; Hall, P ; Hamann, U ; Han, SN ; Hart, SN ; Hartman, M ; Heyworth, JS ; Hoppe, R ; Hopper, JL ; Hunter, DJ ; Ito, H ; Jager, A ; Jakimovska, M ; Jakubowska, A ; Janni, W ; Kaaks, R ; Kang, D ; Kapoor, PM ; Kitahara, CM ; Koutros, S ; Kraft, P ; Kristensen, VN ; Lacey, J ; Lambrechts, D ; Le Marchand, L ; Li, J ; Lindblom, A ; Lush, M ; Mannermaa, A ; Manoochehri, M ; Margolin, S ; Mariapun, S ; Matsuo, K ; Mavroudis, D ; Milne, RL ; Muranen, TA ; Newman, WG ; Noh, D-Y ; Nordestgaard, BG ; Obi, N ; Olshan, AF ; Olsson, H ; Park-Simon, T-W ; Petridis, C ; Pharoah, PDP ; Plaseska-Karanfilska, D ; Presneau, N ; Rashid, MU ; Rennert, G ; Rennert, HS ; Rhenius, V ; Romero, A ; Saloustros, E ; Sawyer, EJ ; Schneeweiss, A ; Schwentner, L ; Scott, C ; Shah, M ; Shen, C-Y ; Shu, X-O ; Southey, MC ; Stram, DO ; Tamimi, RM ; Tapper, W ; Tollenaar, RAEM ; Tomlinson, I ; Torres, D ; Troester, MA ; Truong, T ; Vachon, CM ; Wang, Q ; Wang, SS ; Williams, JA ; Winqvist, R ; Wolk, A ; Wu, AH ; Yoo, K-Y ; Yu, J-C ; Zheng, W ; Ziogas, A ; Yang, XR ; Eliassen, AH ; Holmes, MD ; Garcia-Closas, M ; Teo, SH ; Schmidt, MK ; Chang-Claude, J (AMER ASSOC CANCER RESEARCH, 2021-04-01)
    BACKGROUND: It is not known whether modifiable lifestyle factors that predict survival after invasive breast cancer differ by subtype. METHODS: We analyzed data for 121,435 women diagnosed with breast cancer from 67 studies in the Breast Cancer Association Consortium with 16,890 deaths (8,554 breast cancer specific) over 10 years. Cox regression was used to estimate associations between risk factors and 10-year all-cause mortality and breast cancer-specific mortality overall, by estrogen receptor (ER) status, and by intrinsic-like subtype. RESULTS: There was no evidence of heterogeneous associations between risk factors and mortality by subtype (P adj > 0.30). The strongest associations were between all-cause mortality and BMI ≥30 versus 18.5-25 kg/m2 [HR (95% confidence interval (CI), 1.19 (1.06-1.34)]; current versus never smoking [1.37 (1.27-1.47)], high versus low physical activity [0.43 (0.21-0.86)], age ≥30 years versus <20 years at first pregnancy [0.79 (0.72-0.86)]; >0-<5 years versus ≥10 years since last full-term birth [1.31 (1.11-1.55)]; ever versus never use of oral contraceptives [0.91 (0.87-0.96)]; ever versus never use of menopausal hormone therapy, including current estrogen-progestin therapy [0.61 (0.54-0.69)]. Similar associations with breast cancer mortality were weaker; for example, 1.11 (1.02-1.21) for current versus never smoking. CONCLUSIONS: We confirm associations between modifiable lifestyle factors and 10-year all-cause mortality. There was no strong evidence that associations differed by ER status or intrinsic-like subtype. IMPACT: Given the large dataset and lack of evidence that associations between modifiable risk factors and 10-year mortality differed by subtype, these associations could be cautiously used in prognostication models to inform patient-centered care.
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    Two-stage Study of Familial Prostate Cancer by Whole-exome Sequencing and Custom Capture Identifies 10 Novel Genes Associated with the Risk of Prostate Cancer
    Schaid, DJ ; McDonnell, SK ; FitzGerald, LM ; DeRycke, L ; Fogarty, Z ; Giles, GG ; MacInnis, RJ ; Southey, MC ; Nguyen-Dumont, T ; Cancel-Tassin, G ; Cussenot, O ; Whittemore, AS ; Sieh, W ; Ioannidis, NM ; Hsieh, C-L ; Stanford, JL ; Schleutker, J ; Cropp, CD ; Carpten, J ; Hoegel, J ; Eeles, R ; Kote-Jarai, Z ; Ackerman, MJ ; Klein, CJ ; Mandal, D ; Cooney, KA ; Bailey-Wilson, JE ; Helfand, B ; Catalona, WJ ; Wiklund, F ; Riska, S ; Bahetti, S ; Larson, MC ; Albright, LC ; Teerlink, C ; Xu, J ; Isaacs, W ; Ostrander, EA ; Thibodeau, SN (ELSEVIER, 2021-02-11)
    BACKGROUND: Family history of prostate cancer (PCa) is a well-known risk factor, and both common and rare genetic variants are associated with the disease. OBJECTIVE: To detect new genetic variants associated with PCa, capitalizing on the role of family history and more aggressive PCa. DESIGN, SETTING, AND PARTICIPANTS: A two-stage design was used. In stage one, whole-exome sequencing was used to identify potential risk alleles among affected men with a strong family history of disease or with more aggressive disease (491 cases and 429 controls). Aggressive disease was based on a sum of scores for Gleason score, node status, metastasis, tumor stage, prostate-specific antigen at diagnosis, systemic recurrence, and time to PCa death. Genes identified in stage one were screened in stage two using a custom-capture design in an independent set of 2917 cases and 1899 controls. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Frequencies of genetic variants (singly or jointly in a gene) were compared between cases and controls. RESULTS AND LIMITATIONS: Eleven genes previously reported to be associated with PCa were detected (ATM, BRCA2, HOXB13, FAM111A, EMSY, HNF1B, KLK3, MSMB, PCAT1, PRSS3, and TERT), as well as an additional 10 novel genes (PABPC1, QK1, FAM114A1, MUC6, MYCBP2, RAPGEF4, RNASEH2B, ULK4, XPO7, and THAP3). Of these 10 novel genes, all but PABPC1 and ULK4 were primarily associated with the risk of aggressive PCa. CONCLUSIONS: Our approach demonstrates the advantage of gene sequencing in the search for genetic variants associated with PCa and the benefits of sampling patients with a strong family history of disease or an aggressive form of disease. PATIENT SUMMARY: Multiple genes are associated with prostate cancer (PCa) among men with a strong family history of this disease or among men with an aggressive form of PCa.
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    Population-based estimates of breast cancer risk for carriers of pathogenic variants identified by gene-panel testing
    Southey, MC ; Dowty, JG ; Riaz, M ; Steen, JA ; Renault, A-L ; Tucker, K ; Kirk, J ; James, P ; Winship, I ; Pachter, N ; Poplawski, N ; Grist, S ; Park, DJ ; Pope, BJ ; Mahmood, K ; Hammet, F ; Mahmoodi, M ; Tsimiklis, H ; Theys, D ; Rewse, A ; Willis, A ; Morrow, A ; Speechly, C ; Harris, R ; Sebra, R ; Schadt, E ; Lacaze, P ; McNeil, JJ ; Giles, GG ; Milne, RL ; Hopper, JL ; Nguyen-Dumont, T (NATURE PORTFOLIO, 2021-12-09)
    Population-based estimates of breast cancer risk for carriers of pathogenic variants identified by gene-panel testing are urgently required. Most prior research has been based on women selected for high-risk features and more data is needed to make inference about breast cancer risk for women unselected for family history, an important consideration of population screening. We tested 1464 women diagnosed with breast cancer and 862 age-matched controls participating in the Australian Breast Cancer Family Study (ABCFS), and 6549 healthy, older Australian women enroled in the ASPirin in Reducing Events in the Elderly (ASPREE) study for rare germline variants using a 24-gene-panel. Odds ratios (ORs) were estimated using unconditional logistic regression adjusted for age and other potential confounders. We identified pathogenic variants in 11.1% of the ABCFS cases, 3.7% of the ABCFS controls and 2.2% of the ASPREE (control) participants. The estimated breast cancer OR [95% confidence interval] was 5.3 [2.1-16.2] for BRCA1, 4.0 [1.9-9.1] for BRCA2, 3.4 [1.4-8.4] for ATM and 4.3 [1.0-17.0] for PALB2. Our findings provide a population-based perspective to gene-panel testing for breast cancer predisposition and opportunities to improve predictors for identifying women who carry pathogenic variants in breast cancer predisposition genes.
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    Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk.
    Dareng, EO ; Tyrer, JP ; Barnes, DR ; Jones, MR ; Yang, X ; Aben, KKH ; Adank, MA ; Agata, S ; Andrulis, IL ; Anton-Culver, H ; Antonenkova, NN ; Aravantinos, G ; Arun, BK ; Augustinsson, A ; Balmaña, J ; Bandera, EV ; Barkardottir, RB ; Barrowdale, D ; Beckmann, MW ; Beeghly-Fadiel, A ; Benitez, J ; Bermisheva, M ; Bernardini, MQ ; Bjorge, L ; Black, A ; Bogdanova, NV ; Bonanni, B ; Borg, A ; Brenton, JD ; Budzilowska, A ; Butzow, R ; Buys, SS ; Cai, H ; Caligo, MA ; Campbell, I ; Cannioto, R ; Cassingham, H ; Chang-Claude, J ; Chanock, SJ ; Chen, K ; Chiew, Y-E ; Chung, WK ; Claes, KBM ; Colonna, S ; GEMO Study Collaborators, ; GC-HBOC Study Collaborators, ; EMBRACE Collaborators, ; Cook, LS ; Couch, FJ ; Daly, MB ; Dao, F ; Davies, E ; de la Hoya, M ; de Putter, R ; Dennis, J ; DePersia, A ; Devilee, P ; Diez, O ; Ding, YC ; Doherty, JA ; Domchek, SM ; Dörk, T ; du Bois, A ; Dürst, M ; Eccles, DM ; Eliassen, HA ; Engel, C ; Evans, GD ; Fasching, PA ; Flanagan, JM ; Fortner, RT ; Machackova, E ; Friedman, E ; Ganz, PA ; Garber, J ; Gensini, F ; Giles, GG ; Glendon, G ; Godwin, AK ; Goodman, MT ; Greene, MH ; Gronwald, J ; OPAL Study Group, ; AOCS Group, ; Hahnen, E ; Haiman, CA ; Håkansson, N ; Hamann, U ; Hansen, TVO ; Harris, HR ; Hartman, M ; Heitz, F ; Hildebrandt, MAT ; Høgdall, E ; Høgdall, CK ; Hopper, JL ; Huang, R-Y ; Huff, C ; Hulick, PJ ; Huntsman, DG ; Imyanitov, EN ; KConFab Investigators, ; HEBON Investigators, ; Isaacs, C ; Jakubowska, A ; James, PA ; Janavicius, R ; Jensen, A ; Johannsson, OT ; John, EM ; Jones, ME ; Kang, D ; Karlan, BY ; Karnezis, A ; Kelemen, LE ; Khusnutdinova, E ; Kiemeney, LA ; Kim, B-G ; Kjaer, SK ; Komenaka, I ; Kupryjanczyk, J ; Kurian, AW ; Kwong, A ; Lambrechts, D ; Larson, MC ; Lazaro, C ; Le, ND ; Leslie, G ; Lester, J ; Lesueur, F ; Levine, DA ; Li, L ; Li, J ; Loud, JT ; Lu, KH ; Lubiński, J ; Mai, PL ; Manoukian, S ; Marks, JR ; Matsuno, RK ; Matsuo, K ; May, T ; McGuffog, L ; McLaughlin, JR ; McNeish, IA ; Mebirouk, N ; Menon, U ; Miller, A ; Milne, RL ; Minlikeeva, A ; Modugno, F ; Montagna, M ; Moysich, KB ; Munro, E ; Nathanson, KL ; Neuhausen, SL ; Nevanlinna, H ; Yie, JNY ; Nielsen, HR ; Nielsen, FC ; Nikitina-Zake, L ; Odunsi, K ; Offit, K ; Olah, E ; Olbrecht, S ; Olopade, OI ; Olson, SH ; Olsson, H ; Osorio, A ; Papi, L ; Park, SK ; Parsons, MT ; Pathak, H ; Pedersen, IS ; Peixoto, A ; Pejovic, T ; Perez-Segura, P ; Permuth, JB ; Peshkin, B ; Peterlongo, P ; Piskorz, A ; Prokofyeva, D ; Radice, P ; Rantala, J ; Riggan, MJ ; Risch, HA ; Rodriguez-Antona, C ; Ross, E ; Rossing, MA ; Runnebaum, I ; Sandler, DP ; Santamariña, M ; Soucy, P ; Schmutzler, RK ; Setiawan, VW ; Shan, K ; Sieh, W ; Simard, J ; Singer, CF ; Sokolenko, AP ; Song, H ; Southey, MC ; Steed, H ; Stoppa-Lyonnet, D ; Sutphen, R ; Swerdlow, AJ ; Tan, YY ; Teixeira, MR ; Teo, SH ; Terry, KL ; Terry, MB ; OCAC Consortium, ; CIMBA Consortium, ; Thomassen, M ; Thompson, PJ ; Thomsen, LCV ; Thull, DL ; Tischkowitz, M ; Titus, L ; Toland, AE ; Torres, D ; Trabert, B ; Travis, R ; Tung, N ; Tworoger, SS ; Valen, E ; van Altena, AM ; van der Hout, AH ; Van Nieuwenhuysen, E ; van Rensburg, EJ ; Vega, A ; Edwards, DV ; Vierkant, RA ; Wang, F ; Wappenschmidt, B ; Webb, PM ; Weinberg, CR ; Weitzel, JN ; Wentzensen, N ; White, E ; Whittemore, AS ; Winham, SJ ; Wolk, A ; Woo, Y-L ; Wu, AH ; Yan, L ; Yannoukakos, D ; Zavaglia, KM ; Zheng, W ; Ziogas, A ; Zorn, KK ; Kleibl, Z ; Easton, D ; Lawrenson, K ; DeFazio, A ; Sellers, TA ; Ramus, SJ ; Pearce, CL ; Monteiro, AN ; Cunningham, J ; Goode, EL ; Schildkraut, JM ; Berchuck, A ; Chenevix-Trench, G ; Gayther, SA ; Antoniou, AC ; Pharoah, PDP (Springer Science and Business Media LLC, 2022-05)
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    A Genome-Wide Gene-Based Gene–Environment Interaction Study of Breast Cancer in More than 90,000 Women
    Wang, X ; Chen, H ; Kapoor, PM ; Su, Y-R ; Bolla, MK ; Dennis, J ; Dunning, AM ; Lush, M ; Wang, Q ; Michailidou, K ; Pharoah, PDP ; Hopper, JL ; Southey, MC ; Koutros, S ; Freeman, LEB ; Stone, J ; Rennert, G ; Shibli, R ; Murphy, RA ; Aronson, K ; Guénel, P ; Truong, T ; Teras, LR ; Hodge, JM ; Canzian, F ; Kaaks, R ; Brenner, H ; Arndt, V ; Hoppe, R ; Lo, W-Y ; Behrens, S ; Mannermaa, A ; Kosma, V-M ; Jung, A ; Becher, H ; Giles, GG ; Haiman, CA ; Maskarinec, G ; Scott, C ; Winham, S ; Simard, J ; Goldberg, MS ; Zheng, W ; Long, J ; Troester, MA ; Love, MI ; Peng, C ; Tamimi, R ; Eliassen, H ; García-Closas, M ; Figueroa, J ; Ahearn, T ; Yang, R ; Evans, DG ; Howell, A ; Hall, P ; Czene, K ; Wolk, A ; Sandler, DP ; Taylor, JA ; Swerdlow, AJ ; Orr, N ; Lacey, JV ; Wang, S ; Olsson, H ; Easton, DF ; Milne, RL ; Hsu, L ; Kraft, P ; Chang-Claude, J ; Lindström, S (American Association for Cancer Research (AACR), 2022-04-08)
    Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene–environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P &lt; 0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE = 4.44 × 10−6). In this transcriptome-informed genome-wide gene–environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Our study suggests a limited role of gene–environment interactions in breast cancer risk.
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    Genome-wide interaction analysis of menopausal hormone therapy use and breast cancer risk among 62,370 women.
    Wang, X ; Kapoor, PM ; Auer, PL ; Dennis, J ; Dunning, AM ; Wang, Q ; Lush, M ; Michailidou, K ; Bolla, MK ; Aronson, KJ ; Murphy, RA ; Brooks-Wilson, A ; Lee, DG ; Cordina-Duverger, E ; Guénel, P ; Truong, T ; Mulot, C ; Teras, LR ; Patel, AV ; Dossus, L ; Kaaks, R ; Hoppe, R ; Lo, W-Y ; Brüning, T ; Hamann, U ; Czene, K ; Gabrielson, M ; Hall, P ; Eriksson, M ; Jung, A ; Becher, H ; Couch, FJ ; Larson, NL ; Olson, JE ; Ruddy, KJ ; Giles, GG ; MacInnis, RJ ; Southey, MC ; Le Marchand, L ; Wilkens, LR ; Haiman, CA ; Olsson, H ; Augustinsson, A ; Krüger, U ; Wagner, P ; Scott, C ; Winham, SJ ; Vachon, CM ; Perou, CM ; Olshan, AF ; Troester, MA ; Hunter, DJ ; Eliassen, HA ; Tamimi, RM ; Brantley, K ; Andrulis, IL ; Figueroa, J ; Chanock, SJ ; Ahearn, TU ; García-Closas, M ; Evans, GD ; Newman, WG ; van Veen, EM ; Howell, A ; Wolk, A ; Håkansson, N ; Anton-Culver, H ; Ziogas, A ; Jones, ME ; Orr, N ; Schoemaker, MJ ; Swerdlow, AJ ; Kitahara, CM ; Linet, M ; Prentice, RL ; Easton, DF ; Milne, RL ; Kraft, P ; Chang-Claude, J ; Lindström, S (Springer Science and Business Media LLC, 2022-04-13)
    Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values < 5 × 10-8 as genome-wide significant, and p-values < 1 × 10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants. None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values < 1 × 105. The strongest evidence was found for rs4674019 (p-value = 2.27 × 10-7), which showed genome-wide significant interaction (p-value = 3.8 × 10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT-breast cancer risk association.
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    Genome-wide and transcriptome-wide association studies of mammographic density phenotypes reveal novel loci
    Chen, H ; Fan, S ; Stone, J ; Thompson, DJ ; Douglas, J ; Li, S ; Scott, C ; Bolla, MK ; Wang, Q ; Dennis, J ; Michailidou, K ; Li, C ; Peters, U ; Hopper, JL ; Southey, MC ; Nguyen-Dumont, T ; Nguyen, TL ; Fasching, PA ; Behrens, A ; Cadby, G ; Murphy, RA ; Aronson, K ; Howell, A ; Astley, S ; Couch, F ; Olson, J ; Milne, RL ; Giles, GG ; Haiman, CA ; Maskarinec, G ; Winham, S ; John, EM ; Kurian, A ; Eliassen, H ; Andrulis, I ; Evans, DG ; Newman, WG ; Hall, P ; Czene, K ; Swerdlow, A ; Jones, M ; Pollan, M ; Fernandez-Navarro, P ; McConnell, DS ; Kristensen, VN ; Rothstein, JH ; Wang, P ; Habel, LA ; Sieh, W ; Dunning, AM ; Pharoah, PDP ; Easton, DF ; Gierach, GL ; Tamimi, RM ; Vachon, CM ; Lindstrom, S (BMC, 2022-04-12)
    BACKGROUND: Mammographic density (MD) phenotypes, including percent density (PMD), area of dense tissue (DA), and area of non-dense tissue (NDA), are associated with breast cancer risk. Twin studies suggest that MD phenotypes are highly heritable. However, only a small proportion of their variance is explained by identified genetic variants. METHODS: We conducted a genome-wide association study, as well as a transcriptome-wide association study (TWAS), of age- and BMI-adjusted DA, NDA, and PMD in up to 27,900 European-ancestry women from the MODE/BCAC consortia. RESULTS: We identified 28 genome-wide significant loci for MD phenotypes, including nine novel signals (5q11.2, 5q14.1, 5q31.1, 5q33.3, 5q35.1, 7p11.2, 8q24.13, 12p11.2, 16q12.2). Further, 45% of all known breast cancer SNPs were associated with at least one MD phenotype at p < 0.05. TWAS further identified two novel genes (SHOX2 and CRISPLD2) whose genetically predicted expression was significantly associated with MD phenotypes. CONCLUSIONS: Our findings provided novel insight into the genetic background of MD phenotypes, and further demonstrated their shared genetic basis with breast cancer.
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    Population-based estimates of age-specific cumulative risk of breast cancer for pathogenic variants in ATM
    Renault, A-L ; Dowty, JG ; Steen, JA ; Li, S ; Winship, IM ; Giles, GG ; Hopper, JL ; Southey, MC ; Nguyen-Dumont, T (BMC, 2022-04-01)
    BACKGROUND: Multigene panel tests for breast cancer predisposition routinely include ATM as it is now a well-established breast cancer predisposition gene. METHODS: We included ATM in a multigene panel test applied to the Australian Breast Cancer Family Registry (ABCFR), a population-based case-control-family study of breast cancer, with the purpose of estimating the prevalence and penetrance of heterozygous ATM pathogenic variants from the family data, using segregation analysis. RESULTS: The estimated breast cancer hazard ratio for carriers of pathogenic ATM variants in the ABCFR was 1.32 (95% confidence interval 0.45-3.87; P = 0.6). The estimated cumulative risk of breast cancer to age 80 years for heterozygous ATM pathogenic variant carriers was estimated to be 13% (95% CI 4.6-30). CONCLUSIONS: Although ATM has been definitively identified as a breast cancer predisposition gene, further evidence, such as variant-specific penetrance estimates, are needed to inform risk management strategies for carriers of pathogenic variants to increase the clinical utility of population testing of this gene.
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    Familial Aspects of Mammographic Density Measures Associated with Breast Cancer Risk
    Nguyen, TL ; Li, S ; Dowty, JG ; Dite, GS ; Ye, Z ; Nguyen-Dumont, T ; Trinh, HN ; Evans, CF ; Tan, M ; Sung, J ; Jenkins, MA ; Giles, GG ; Southey, MC ; Hopper, JL (MDPI, 2022-03-01)
    Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) for breast cancer based on mammographic density defined in effect by different levels of pixel brightness and adjusted for age and body mass index. We measured these MRS from digitized film mammograms for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated the correlations in relatives (r) and the proportion of variance due to genetic factors (heritability) using the software FISHER and predicted the familial risk ratio (FRR) associated with each MRS. The ρ estimates ranged from: 0.41 to 0.60 (standard error [SE] 0.02) for MZ pairs, 0.16 to 0.26 (SE 0.05) for DZ pairs, and 0.19 to 0.29 (SE 0.02) for sister pairs (including pairs of a twin and her non-twin sister), respectively. Heritability estimates were 39% to 69% under the classic twin model and 36% to 56% when allowing for shared non-genetic factors specific to MZ pairs. The FRRs were 1.08 to 1.17. These MRSs are substantially familial, due mostly to genetic factors that explain one-quarter to one-half as much of the familial aggregation of breast cancer that is explained by the current best polygenic risk score.