The contributions of breast density and common genetic variation to breast cancer risk

Celine M. Vachon, V. Shane Pankratz, Christopher G. Scott, Lothar Haeberle, Elad Ziv, Matthew R. Jensen, Kathleen R. Brandt, Dana H. Whaley, Janet E. Olson, Katharina Heusinger, Carolin C. Hack, Sebastian M. Jud, Matthias W. Beckmann, Ruediger Schulz-Wendtland, Jeffrey A. Tice, Aaron D. Norman, Julie M. Cunningham, Kristen S. Purrington, Douglas F. Easton, Thomas A. SellersKarla Kerlikowske, Peter A. Fasching, Fergus J. Couch

Research output: Contribution to journalArticlepeer-review

110 Scopus citations


We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction =. 23). Relative to those with scattered fibroglandular densities and average PRS (2nd quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P <. 001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.

Original languageEnglish (US)
JournalJournal of the National Cancer Institute
Issue number5
StatePublished - May 1 2015

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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