Novel associations between common breast cancer susceptibility variants and risk-predicting mammographic density measures

Jennifer L Stone, Deborah J Thompson, Isabel Dos Santos Silva, Christopher Scott, Rulla M. Tamimi, Sara Lindstrom, Peter Kraft, Aditi Hazra, Jingmei Li, Louise Eriksson, Kamila Czene, Per Hall, Matt Jensen, Julie Cunningham, Janet E Olson, Kristen S. Purrington, Fergus J Couch, Judith Brown, Jean Leyland, Ruth M L WarrenRobert N. Luben, Kay-Tee Khaw, Paula Smith, Nicholas J. Wareham, Sebastian M Jud, Katharina Heusinger, Matthias W. Beckmann, Julie A. Douglas, Kaanan P. Shah, Heang Ping Chan, Mark A. Helvie, Loic Le Marchand, Laurence N Kolonel, Christy Woolcott, Gertraud Maskarinec, Christopher A Haiman, Graham G. Giles, Laura Baglietto, Kavitha Krishnan, Melissa C. Southey, Carmel Apicella, Irene L Andrulis, Julia A Knight, Giske Ursin, Grethe I.Grenaker Alnaes, Vessela N. Kristensen, Anne-Lise Borresen-Dale, Inger Torhild Gram, Manjeet K. Bolla, Qin Wang, Kyriaki Michailidou, Joe Dennis, Jacques Simard, Paul D P Pharoah, Alison M Dunning, Douglas F Easton, Peter A. Fasching, V. Shane Pankratz, John L. Hopper, Celine M Vachon

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47 Citations (Scopus)


Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210(ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10-5). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679(EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.

Original languageEnglish
Pages (from-to)2457-2467
Number of pages11
JournalCancer Research
Issue number12
Publication statusPublished - 15 Jun 2015
Externally publishedYes

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