Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Multicenter Study
. 2018 Apr 1;47(2):526-536.
doi: 10.1093/ije/dyx242.

Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the Breast Cancer Association Consortium

Anja Rudolph  1   2 , Minsun Song  3 , Mark N Brook  4 , Roger L Milne  5   6 , Nasim Mavaddat  7 , Kyriaki Michailidou  7   8 , Manjeet K Bolla  7 , Qin Wang  7 , Joe Dennis  7 , Amber N Wilcox  9 , John L Hopper  6 , Melissa C Southey  10 , Renske Keeman  11 , Peter A Fasching  12   13 , Matthias W Beckmann  12 , Manuela Gago-Dominguez  14   15 , Jose E Castelao  16 , Pascal Guénel  17 , Thérèse Truong  17 , Stig E Bojesen  18   19   20 , Henrik Flyger  21 , Hermann Brenner  22   23   24 , Volker Arndt  22 , Hiltrud Brauch  24   25   26 , Thomas Brüning  27 , Arto Mannermaa  28   29   30 , Veli-Matti Kosma  28   29   30 , Diether Lambrechts  31   32 , Machteld Keupers  33 , Fergus J Couch  34 , Celine Vachon  35 , Graham G Giles  5   6 , Robert J MacInnis  5   6 , Jonine Figueroa  9   36 , Louise Brinton  9 , Kamila Czene  37 , Judith S Brand  37 , Marike Gabrielson  37 , Keith Humphreys  37 , Angela Cox  38 , Simon S Cross  39 , Alison M Dunning  40 , Nick Orr  41 , Anthony Swerdlow  4   41 , Per Hall  37 , Paul D P Pharoah  7   40 , Marjanka K Schmidt  11   42 , Douglas F Easton  7   40 , Nilanjan Chatterjee  9   43   44 , Jenny Chang-Claude  1   45 , Montserrat García-Closas  9
Affiliations
Multicenter Study

Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the Breast Cancer Association Consortium

Anja Rudolph et al. Int J Epidemiol. .

Abstract

Background: Polygenic risk scores (PRS) for breast cancer can be used to stratify the population into groups at substantially different levels of risk. Combining PRS and environmental risk factors will improve risk prediction; however, integrating PRS into risk prediction models requires evaluation of their joint association with known environmental risk factors.

Methods: Analyses were based on data from 20 studies; datasets analysed ranged from 3453 to 23 104 invasive breast cancer cases and similar numbers of controls, depending on the analysed environmental risk factor. We evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS with reproductive history, alcohol consumption, menopausal hormone therapy (MHT), height and body mass index (BMI). We tested the null hypothesis of multiplicative joint associations for PRS and each of the environmental factors, and performed global and tail-based goodness-of-fit tests in logistic regression models. The outcomes were breast cancer overall and by estrogen receptor (ER) status.

Results: The strongest evidence for a non-multiplicative joint associations with the 77-SNP PRS was for alcohol consumption (P-interaction = 0.009), adult height (P-interaction = 0.025) and current use of combined MHT (P-interaction = 0.038) in ER-positive disease. Risk associations for these factors by percentiles of PRS did not follow a clear dose-response. In addition, global and tail-based goodness of fit tests showed little evidence for departures from a multiplicative risk model, with alcohol consumption showing the strongest evidence for ER-positive disease (P = 0.013 for global and 0.18 for tail-based tests).

Conclusions: The combined effects of the 77-SNP PRS and environmental risk factors for breast cancer are generally well described by a multiplicative model. Larger studies are required to confirm possible departures from the multiplicative model for individual risk factors, and assess models specific for ER-negative disease.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Odds ratios and 95% confidence intervals for breast cancer risk factors by percentiles of the 77-SNP polygenic risk score (PRS) specific for ER-positive breast cancer, based on population-based and non-population-based studies. FFTP, first full-term pregnancy.
See this image and copyright information in PMC

References

    1. Colditz GA, Baer HJ, Tamimi RM. Breast cancer. In: Schottenfeld D, Fraumeni JF (eds). Cancer Epidemiology and Prevention. New York, NY: Oxford University Press, 2006.
    1. Pharoah PD, Antoniou A, Bobrow M, Zimmern RL, Easton DF, Ponder BA. Polygenic susceptibility to breast cancer and implications for prevention. Nat Genet 2002;31:33–36. - PubMed
    1. Pharoah PD, Antoniou AC, Easton DF, Ponder BA. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med 2008;358:2796–803. - PubMed
    1. Mavaddat N, Pharoah PD, Michailidou K, et al. Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst 2015, Apr 8;107. doi: 10.1093/jnci/djv036. - PMC - PubMed
    1. Maas P, Barrdahl M, Joshi AD, et al. Breast cancer risk from modifiable and nonmodifiable risk factors among White women in the United States. JAMA Oncol 2016;2:1295–302. - PMC - PubMed

Publication types