Melbourne School of Population and Global Health - Research Publications

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    Weight is More Informative than Body Mass Index for Predicting Postmenopausal Breast Cancer Risk: Prospective Family Study Cohort (ProF-SC)
    Ye, Z ; Li, S ; Dite, GS ; Nguyen, TL ; MacInnis, RJ ; Andrulis, IL ; Buys, SS ; Daly, MB ; John, EM ; Kurian, AW ; Genkinger, JM ; Chung, WK ; Phillips, K-A ; Thorne, H ; Winship, IM ; Milne, RL ; Dugue, P-A ; Southey, MC ; Giles, GG ; Terry, MB ; Hopper, JL (AMER ASSOC CANCER RESEARCH, 2022-03-01)
    UNLABELLED: We considered whether weight is more informative than body mass index (BMI) = weight/height2 when predicting breast cancer risk for postmenopausal women, and if the weight association differs by underlying familial risk. We studied 6,761 women postmenopausal at baseline with a wide range of familial risk from 2,364 families in the Prospective Family Study Cohort. Participants were followed for on average 11.45 years and there were 416 incident breast cancers. We used Cox regression to estimate risk associations with log-transformed weight and BMI after adjusting for underlying familial risk. We compared model fits using the Akaike information criterion (AIC) and nested models using the likelihood ratio test. The AIC for the weight-only model was 6.22 units lower than for the BMI-only model, and the log risk gradient was 23% greater. Adding BMI or height to weight did not improve fit (ΔAIC = 0.90 and 0.83, respectively; both P = 0.3). Conversely, adding weight to BMI or height gave better fits (ΔAIC = 5.32 and 11.64; P = 0.007 and 0.0002, respectively). Adding height improved only the BMI model (ΔAIC = 5.47; P = 0.006). There was no evidence that the BMI or weight associations differed by underlying familial risk (P > 0.2). Weight is more informative than BMI for predicting breast cancer risk, consistent with nonadipose as well as adipose tissue being etiologically relevant. The independent but multiplicative associations of weight and familial risk suggest that, in terms of absolute breast cancer risk, the association with weight is more important the greater a woman's underlying familial risk. PREVENTION RELEVANCE: Our results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum.
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    Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction
    Li, S ; MacInnis, RJ ; Lee, A ; Nguyen-Dumont, T ; Dorling, L ; Carvalho, S ; Dite, GS ; Shah, M ; Luccarini, C ; Wang, Q ; Milne, RL ; Jenkins, MA ; Giles, GG ; Dunning, AM ; Pharoah, PDP ; Southey, MC ; Easton, DF ; Hopper, JL ; Antoniou, AC (CELL PRESS, 2022-10-06)
    Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%-95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%-1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.
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    The association of age at menarche and adult height with mammographic density in the International Consortium of Mammographic Density
    Ward, S ; Burton, A ; Tamimi, RM ; Pereira, A ; Garmendia, ML ; Pollan, M ; Boyd, N ; dos-Santos-Silva, I ; Maskarinec, G ; Perez-Gomez, B ; Vachon, C ; Miao, H ; Lajous, M ; Lopez-Ridaura, R ; Bertrand, K ; Kwong, A ; Ursin, G ; Lee, E ; Ma, H ; Vinnicombe, S ; Moss, S ; Allen, S ; Ndumia, R ; Vinayak, S ; Teo, S-H ; Mariapun, S ; Peplonska, B ; Bukowska-Damska, A ; Nagata, C ; Hopper, J ; Giles, G ; Ozmen, V ; Aribal, ME ; Schuez, J ; Van Gils, CH ; Wanders, JOP ; Sirous, R ; Sirous, M ; Hipwell, J ; Kim, J ; Lee, JW ; Dickens, C ; Hartman, M ; Chia, K-S ; Scott, C ; Chiarelli, AM ; Linton, L ; Flugelman, AA ; Salem, D ; Kamal, R ; McCormack, V ; Stone, J (BMC, 2022-07-14)
    BACKGROUND: Early age at menarche and tall stature are associated with increased breast cancer risk. We examined whether these associations were also positively associated with mammographic density, a strong marker of breast cancer risk. METHODS: Participants were 10,681 breast-cancer-free women from 22 countries in the International Consortium of Mammographic Density, each with centrally assessed mammographic density and a common set of epidemiologic data. Study periods for the 27 studies ranged from 1987 to 2014. Multi-level linear regression models estimated changes in square-root per cent density (√PD) and dense area (√DA) associated with age at menarche and adult height in pooled analyses and population-specific meta-analyses. Models were adjusted for age at mammogram, body mass index, menopausal status, hormone therapy use, mammography view and type, mammographic density assessor, parity and height/age at menarche. RESULTS: In pooled analyses, later age at menarche was associated with higher per cent density (β√PD = 0.023 SE = 0.008, P = 0.003) and larger dense area (β√DA = 0.032 SE = 0.010, P = 0.002). Taller women had larger dense area (β√DA = 0.069 SE = 0.028, P = 0.012) and higher per cent density (β√PD = 0.044, SE = 0.023, P = 0.054), although the observed effect on per cent density depended upon the adjustment used for body size. Similar overall effect estimates were observed in meta-analyses across population groups. CONCLUSIONS: In one of the largest international studies to date, later age at menarche was positively associated with mammographic density. This is in contrast to its association with breast cancer risk, providing little evidence of mediation. Increased height was also positively associated with mammographic density, particularly dense area. These results suggest a complex relationship between growth and development, mammographic density and breast cancer risk. Future studies should evaluate the potential mediation of the breast cancer effects of taller stature through absolute breast density.
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    Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk
    Li, S ; Nguyen, TL ; Tu, N-D ; Dowty, JG ; Dite, GS ; Ye, Z ; Trinh, HN ; Evans, CF ; Tan, M ; Sung, J ; Jenkins, MA ; Giles, GG ; Hopper, JL ; Southey, MC (MDPI, 2022-06-01)
    Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05-0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the "white but not bright area" (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer.
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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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    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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    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.