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Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk

Author

Listed:
  • Weiva Sieh

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai)

  • Joseph H. Rothstein

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai)

  • Robert J. Klein

    (Icahn School of Medicine at Mount Sinai)

  • Stacey E. Alexeeff

    (Division of Research, Kaiser Permanente Northern California)

  • Lori C. Sakoda

    (Division of Research, Kaiser Permanente Northern California)

  • Eric Jorgenson

    (Division of Research, Kaiser Permanente Northern California)

  • Russell B. McBride

    (Icahn School of Medicine at Mount Sinai)

  • Rebecca E. Graff

    (University of California San Francisco)

  • Valerie McGuire

    (Stanford University School of Medicine)

  • Ninah Achacoso

    (Division of Research, Kaiser Permanente Northern California)

  • Luana Acton

    (Division of Research, Kaiser Permanente Northern California)

  • Rhea Y. Liang

    (Stanford University School of Medicine)

  • Jafi A. Lipson

    (Stanford University School of Medicine)

  • Daniel L. Rubin

    (Stanford University School of Medicine
    Stanford University School of Medicine
    Stanford University School of Medicine)

  • Martin J. Yaffe

    (University of Toronto)

  • Douglas F. Easton

    (University of Cambridge)

  • Catherine Schaefer

    (Division of Research, Kaiser Permanente Northern California)

  • Neil Risch

    (Division of Research, Kaiser Permanente Northern California
    University of California San Francisco
    University of California San Francisco)

  • Alice S. Whittemore

    (Stanford University School of Medicine
    Stanford University School of Medicine)

  • Laurel A. Habel

    (Division of Research, Kaiser Permanente Northern California)

Abstract

Mammographic density (MD) phenotypes are strongly associated with breast cancer risk and highly heritable. In this GWAS meta-analysis of 24,192 women, we identify 31 MD loci at P

Suggested Citation

  • Weiva Sieh & Joseph H. Rothstein & Robert J. Klein & Stacey E. Alexeeff & Lori C. Sakoda & Eric Jorgenson & Russell B. McBride & Rebecca E. Graff & Valerie McGuire & Ninah Achacoso & Luana Acton & Rhe, 2020. "Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18883-x
    DOI: 10.1038/s41467-020-18883-x
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    Cited by:

    1. Xiaoyu Song & Jiayi Ji & Joseph H. Rothstein & Stacey E. Alexeeff & Lori C. Sakoda & Adriana Sistig & Ninah Achacoso & Eric Jorgenson & Alice S. Whittemore & Robert J. Klein & Laurel A. Habel & Pei Wa, 2023. "MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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