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

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

doi:10.1186/s13058-019-1138-8

Abstract

BackgroundMammographic 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.MethodsUsing 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.ResultsAdjusted 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.ConclusionsThe 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.

Highlights

Large consortia have identified multiple common genetic susceptibility markers associated with risk of breast cancer [1,2,3,4]
Adjusted percent density (PD) and dense area (DA) measures were positively associated with breast cancer across all studies (Additional file 1: Table S3)
For adjusted PD, there was a 1.45-fold increased risk per Standard deviation (SD) of the adjusted PD (Table 2; χ2 = 156, P < 0.001 compared to baseline model)

Summary

Introduction

Large consortia have identified multiple common genetic susceptibility markers associated with risk of breast cancer [1,2,3,4]. Several studies have shown that the PRS is a strong risk factor for young women [7], those with family history [8], BRCA1 and BRCA2 mutation carriers [7, 9,10,11], and for women with contralateral breast cancer [12]. 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

Results

Conclusion