1. The contributions of breast density and common genetic variation to breast cancer risk.
- Author
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Vachon CM, Pankratz VS, Scott CG, Haeberle L, Ziv E, Jensen MR, Brandt KR, Whaley DH, Olson JE, Heusinger K, Hack CC, Jud SM, Beckmann MW, Schulz-Wendtland R, Tice JA, Norman AD, Cunningham JM, Purrington KS, Easton DF, Sellers TA, Kerlikowske K, Fasching PA, and Couch FJ
- Subjects
- Adult, Aged, Area Under Curve, Breast Density, Breast Neoplasms diagnostic imaging, Breast Neoplasms pathology, Case-Control Studies, Female, Genetic Predisposition to Disease, Genetic Variation, Germany epidemiology, Humans, Logistic Models, Mammary Glands, Human abnormalities, Middle Aged, Odds Ratio, Radiography, Risk Assessment, Risk Factors, United States epidemiology, Breast pathology, Breast Neoplasms epidemiology, Breast Neoplasms genetics, Polymorphism, Single Nucleotide
- Abstract
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 (2(nd) 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., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)
- Published
- 2015
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