Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk | Breast Cancer Research | Full Text

Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk | Breast Cancer Research | Full Text



Breast Cancer Research

Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk

• Celine M. VachonEmail authorView ORCID ID profile,
• Christopher G. Scott,
• Rulla M. Tamimi,
• Deborah J. Thompson,
• Peter A. Fasching,
• Jennifer Stone,
• Melissa C. Southey,
• Stacey Winham,
• Sara Lindström,
• Jenna Lilyquist,
• Graham G. Giles,
• Roger L. Milne,
• Robert J. MacInnis,
• Laura Baglietto,
• Jingmei Li,
• Kamila Czene,
• Manjeet K. Bolla,
• Qin Wang,
• Joe Dennis,
• Lothar Haeberle,
• Mikael Eriksson,
• Peter Kraft,
• Robert Luben,
• Nick Wareham,
• Janet E. Olson,
• Aaron Norman,
• Eric C. Polley,
• Gertraud Maskarinec,
• Loic Le Marchand,
• Christopher A. Haiman,
• John L. Hopper,
• Fergus J. Couch,
• Douglas F. Easton,
• Per Hall,
• Nilanjan Chatterjee and
• Montse Garcia-Closas

Breast Cancer Research201921:68

https://doi.org/10.1186/s13058-019-1138-8

©  The Author(s). 2019

• Received: 27 November 2018
• Accepted: 15 April 2019
• Published: 22 May 2019

Abstract

Background

Mammographic breast density, adjusted for age and body mass index, and a polygenic risk score (PRS), comprised of common genetic variation, are both strong risk factors for breast cancer and increase discrimination of risk models. Understanding their joint contribution will be important to more accurately predict risk.

Methods

Using 3628 breast cancer cases and 5126 controls of European ancestry from eight case-control studies, we evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS and quantitative mammographic density measures with breast cancer. Mammographic percent density and absolute dense area were evaluated using thresholding software and examined as residuals after adjusting for age, 1/BMI, and study. PRS and adjusted density phenotypes were modeled both continuously (per 1 standard deviation, SD) and categorically. We fit logistic regression models and tested the null hypothesis of multiplicative joint associations for PRS and adjusted density measures using likelihood ratio and global and tail-based goodness of fit tests within the subset of six cohort or population-based studies.

Results

Adjusted percent density (odds ratio (OR) = 1.45 per SD, 95% CI 1.38–1.52), adjusted absolute dense area (OR = 1.34 per SD, 95% CI 1.28–1.41), and the 77-SNP PRS (OR = 1.52 per SD, 95% CI 1.45–1.59) were associated with breast cancer risk. There was no evidence of interaction of the PRS with adjusted percent density or dense area on risk of breast cancer by either the likelihood ratio (P > 0.21) or goodness of fit tests (P > 0.09), whether assessed continuously or categorically. The joint association (OR) was 2.60 in the highest categories of adjusted PD and PRS and 0.34 in the lowest categories, relative to women in the second density quartile and middle PRS quintile.

Conclusions

The combined associations of the 77-SNP PRS and adjusted density measures are generally well described by multiplicative models, and both risk factors provide independent information on breast cancer risk.

Keywords

• Breast density
• Breast cancer risk
• Polygenic risk score
• Genetic variation
• Risk models