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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    Genetically Predicted Circulating C-Reactive Protein Concentration and Colorectal Cancer Survival: A Mendelian Randomization Consortium Study
    Hua, X ; Dai, JY ; Lindstrom, S ; Harrison, TA ; Lin, Y ; Alberts, SR ; Alwers, E ; Berndt, S ; Brenner, H ; Buchanan, DD ; Campbell, PT ; Casey, G ; Chang-Claude, J ; Gallinger, S ; Giles, GG ; Goldberg, RM ; Gunter, MJ ; Hoffmeister, M ; Jenkins, MA ; Joshi, AD ; Ma, W ; Milne, RL ; Murphy, N ; Pai, RK ; Sakoda, LC ; Schoen, RE ; Shi, Q ; Slattery, ML ; Song, M ; White, E ; Le Marchand, L ; Chan, AT ; Peters, U ; Newcomb, PA (AMER ASSOC CANCER RESEARCH, 2021-07-01)
    BACKGROUND: A positive association between circulating C-reactive protein (CRP) and colorectal cancer survival was reported in observational studies, which are susceptible to unmeasured confounding and reverse causality. We used a Mendelian randomization approach to evaluate the association between genetically predicted CRP concentrations and colorectal cancer-specific survival. METHODS: We used individual-level data for 16,918 eligible colorectal cancer cases of European ancestry from 15 studies within the International Survival Analysis of Colorectal Cancer Consortium. We calculated a genetic-risk score based on 52 CRP-associated genetic variants identified from genome-wide association studies. Because of the non-collapsibility of hazard ratios from Cox proportional hazards models, we used the additive hazards model to calculate hazard differences (HD) and 95% confidence intervals (CI) for the association between genetically predicted CRP concentrations and colorectal cancer-specific survival, overall and by stage at diagnosis and tumor location. Analyses were adjusted for age at diagnosis, sex, body mass index, genotyping platform, study, and principal components. RESULTS: Of the 5,395 (32%) deaths accrued over up to 10 years of follow-up, 3,808 (23%) were due to colorectal cancer. Genetically predicted CRP concentration was not associated with colorectal cancer-specific survival (HD, -1.15; 95% CI, -2.76 to 0.47 per 100,000 person-years; P = 0.16). Similarly, no associations were observed in subgroup analyses by stage at diagnosis or tumor location. CONCLUSIONS: Despite adequate power to detect moderate associations, our results did not support a causal effect of circulating CRP concentrations on colorectal cancer-specific survival. IMPACT: Future research evaluating genetically determined levels of other circulating inflammatory biomarkers (i.e., IL6) with colorectal cancer survival outcomes is needed.
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    Smoking Modifies Pancreatic Cancer Risk Loci on 2q21.3
    Mocci, E ; Kundu, P ; Wheeler, W ; Arslan, AA ; Beane-Freeman, LE ; Bracci, PM ; Brennan, P ; Canzian, F ; Du, M ; Gallinger, S ; Giles, GG ; Goodman, PJ ; Kooperberg, C ; Le Marchand, L ; Neale, RE ; Shu, X-O ; Visvanathan, K ; White, E ; Zheng, W ; Albanes, D ; Andreotti, G ; Babic, A ; Bamlet, WR ; Berndt, S ; Blackford, AL ; Bueno-de-Mesquita, B ; Buring, JE ; Campa, D ; Chanock, SJ ; Childs, EJ ; Duell, EJ ; Fuchs, CS ; Gaziano, JM ; Giovannucci, EL ; Goggins, MG ; Hartge, P ; Hassan, MM ; Holly, EA ; Hoover, RN ; Hung, RJ ; Kurtz, RC ; Lee, I-M ; Malats, N ; Milne, RL ; Ng, K ; Oberg, AL ; Panico, S ; Peters, U ; Porta, M ; Rabe, KG ; Riboli, E ; Rothman, N ; Scelo, G ; Sesso, HD ; Silverman, DT ; Stevens, VL ; Strobel, O ; Thompson, IM ; Tjonneland, A ; Trichopoulou, A ; Van den Eeden, SK ; Wactawski-Wende, J ; Wentzensen, N ; Wilkens, LR ; Yu, H ; Yuan, F ; Zeleniuch-Jacquotte, A ; Amundadottir, LT ; Li, D ; Jacobs, EJ ; Petersen, GM ; Wolpin, BM ; Risch, HA ; Kraft, P ; Chatterjee, N ; Klein, AP ; Stolzenberg-Solomon, R (AMER ASSOC CANCER RESEARCH, 2021-06-01)
    Germline variation and smoking are independently associated with pancreatic ductal adenocarcinoma (PDAC). We conducted genome-wide smoking interaction analysis of PDAC using genotype data from four previous genome-wide association studies in individuals of European ancestry (7,937 cases and 11,774 controls). Examination of expression quantitative trait loci data from the Genotype-Tissue Expression Project followed by colocalization analysis was conducted to determine whether there was support for common SNP(s) underlying the observed associations. Statistical tests were two sided and P < 5 × 10-8 was considered statistically significant. Genome-wide significant evidence of qualitative interaction was identified on chr2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) and upstream of the cyclin T2 (CCNT2). The most significant SNP using the Empirical Bayes method, in this region that included 45 significantly associated SNPs, was rs1818613 [per allele OR in never smokers 0.87, 95% confidence interval (CI), 0.82-0.93; former smokers 1.00, 95% CI, 0.91-1.07; current smokers 1.25, 95% CI 1.12-1.40, P interaction = 3.08 × 10-9). Examination of the Genotype-Tissue Expression Project data demonstrated an expression quantitative trait locus in this region for TMEM163 and CCNT2 in several tissue types. Colocalization analysis supported a shared SNP, rs842357, in high linkage disequilibrium with rs1818613 (r 2 = 0. 94) driving both the observed interaction and the expression quantitative trait loci signals. Future studies are needed to confirm and understand the differential biologic mechanisms by smoking status that contribute to our PDAC findings. SIGNIFICANCE: This large genome-wide interaction study identifies a susceptibility locus on 2q21.3 that significantly modified PDAC risk by smoking status, providing insight into smoking-associated PDAC, with implications for prevention.
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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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    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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    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 ; Balmana, J ; Bandera, E ; Barkardottir, RB ; Barrowdale, D ; Beckmann, MW ; Beeghly-Fadiel, A ; Benitez, J ; Bermisheva, M ; Bernardini, MQ ; Bjorge, L ; Black, A ; Bogdanova, N ; 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 ; 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 ; Dork, T ; du Bois, A ; Durst, M ; Eccles, DM ; Eliassen, HA ; Engel, C ; Evans, GD ; Fasching, PA ; Flanagan, JM ; Fortner, R ; Machackova, E ; Friedman, E ; Ganz, PA ; Garber, J ; Gensini, F ; Giles, GG ; Glendon, G ; Godwin, AK ; Goodman, MT ; Greene, MH ; Gronwald, J ; Group, OS ; AOCSGroup, ; Hahnen, E ; Haiman, CA ; Hakansson, N ; Hamann, U ; Hansen, TVO ; Harris, HR ; Hartman, M ; Heitz, F ; Hildebrandt, MAT ; Hogdall, E ; Hogdall, CK ; Hopper, JL ; Huang, R-Y ; Huff, C ; Hulick, PJ ; Huntsman, DG ; Imyanitov, EN ; 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 ; Mai, PL ; Manoukian, S ; Marks, JR ; KimMatsuno, R ; 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, O ; 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 ; Santamarina, 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 ; BethTerry, M ; 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 ; 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 (SPRINGERNATURE, 2022-01-14)
    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.