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Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Author

Listed:
  • Sara Lindström

    (Program in Genetic Epidemiology and Statistical Genetics, Harvard School Of Public Health
    Harvard School Of Public Health)

  • Deborah J. Thompson

    (Centre for Genetic Epidemiology, University of Cambridge
    University of Cambridge)

  • Andrew D. Paterson

    (Program in Genetics and Genome Biology, The Hospital for Sick Children)

  • Jingmei Li

    (Karolinska Institutet)

  • Gretchen L. Gierach

    (Hormonal and Reproductive Epidemiology Branch, National Cancer Institute)

  • Christopher Scott

    (Mayo Clinic)

  • Jennifer Stone

    (Centre for Genetic Origins of Health and Disease, University of Western Australia)

  • Julie A. Douglas

    (University of Michigan Medical School)

  • Isabel dos-Santos-Silva

    (Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine)

  • Pablo Fernandez-Navarro

    (Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública—CIBERESP))

  • Jajini Verghase

    (Centre for Genetic Epidemiology, University of Cambridge
    University of Cambridge
    Plastic Surgery Unit, Royal Free Hospital)

  • Paula Smith

    (Centre for Genetic Epidemiology, University of Cambridge
    University of Cambridge)

  • Judith Brown

    (Centre for Genetic Epidemiology, University of Cambridge
    University of Cambridge)

  • Robert Luben

    (Centre for Genetic Epidemiology, University of Cambridge)

  • Nicholas J. Wareham

    (Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge)

  • Ruth J. F. Loos

    (Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge
    The Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute)

  • John A. Heit

    (Mayo Clinic)

  • V. Shane Pankratz

    (Mayo Clinic)

  • Aaron Norman

    (Mayo Clinic)

  • Ellen L. Goode

    (Mayo Clinic)

  • Julie M. Cunningham

    (Mayo Clinic)

  • Mariza deAndrade

    (Mayo Clinic)

  • Robert A. Vierkant

    (Mayo Clinic)

  • Kamila Czene

    (Karolinska Institutet)

  • Peter A. Fasching

    (Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN
    University of California at Los Angeles, David Geffen School of Medicine)

  • Laura Baglietto

    (Cancer Epidemiology Centre, Cancer Council Victoria
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne)

  • Melissa C. Southey

    (University of Melbourne)

  • Graham G. Giles

    (Cancer Epidemiology Centre, Cancer Council Victoria
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne)

  • Kaanan P. Shah

    (University of Michigan Medical School)

  • Heang-Ping Chan

    (University of Michigan Medical School)

  • Mark A. Helvie

    (University of Michigan Medical School)

  • Andrew H. Beck

    (Beth Israel Deaconess Medical Center and Harvard Medical School)

  • Nicholas W. Knoblauch

    (Beth Israel Deaconess Medical Center and Harvard Medical School)

  • Aditi Hazra

    (Program in Genetic Epidemiology and Statistical Genetics, Harvard School Of Public Health
    Harvard School Of Public Health
    Brigham and Women’s Hospital)

  • David J. Hunter

    (Program in Genetic Epidemiology and Statistical Genetics, Harvard School Of Public Health
    Harvard School Of Public Health
    Brigham and Women’s Hospital)

  • Peter Kraft

    (Program in Genetic Epidemiology and Statistical Genetics, Harvard School Of Public Health
    Harvard School Of Public Health
    Harvard School Of Public Health)

  • Marina Pollan

    (Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health
    Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública—CIBERESP))

  • Jonine D. Figueroa

    (Hormonal and Reproductive Epidemiology Branch, National Cancer Institute)

  • Fergus J. Couch

    (Mayo Clinic
    Mayo Clinic)

  • John L. Hopper

    (Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne)

  • Per Hall

    (Karolinska Institutet)

  • Douglas F. Easton

    (Centre for Genetic Epidemiology, University of Cambridge
    University of Cambridge
    University of Cambridge)

  • Norman F. Boyd

    (Campbell Family Institute for Breast Cancer Research, Ontario Cancer Institute)

  • Celine M. Vachon

    (Mayo Clinic)

  • Rulla M. Tamimi

    (Program in Genetic Epidemiology and Statistical Genetics, Harvard School Of Public Health
    Harvard School Of Public Health
    Brigham and Women’s Hospital)

Abstract

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P

Suggested Citation

  • Sara Lindström & Deborah J. Thompson & Andrew D. Paterson & Jingmei Li & Gretchen L. Gierach & Christopher Scott & Jennifer Stone & Julie A. Douglas & Isabel dos-Santos-Silva & Pablo Fernandez-Navarro, 2014. "Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk," Nature Communications, Nature, vol. 5(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6303
    DOI: 10.1038/ncomms6303
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    Cited by:

    1. Hui Chen & Zeyang Wang & Lihai Gong & Qixuan Wang & Wenyan Chen & Jia Wang & Xuelian Ma & Ruofan Ding & Xing Li & Xudong Zou & Mireya Plass & Cheng Lian & Ting Ni & Gong-Hong Wei & Wei Li & Lin Deng &, 2024. "A distinct class of pan-cancer susceptibility genes revealed by an alternative polyadenylation transcriptome-wide association study," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Padraig Dixon & Richard M. Martin & Sean Harrison, 2024. "Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer," Medical Decision Making, , vol. 44(3), pages 283-295, April.

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