Rudolf, A., Song, M., Brook, M.N. et al. (48 more authors) (2018) Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the Breast Cancer Association Consortium. International Journal of Epidemiology, 47 (2). pp. 526-536. ISSN 0300-5771
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Oxford University Press and US Govt. This is an author produced version of a paper subsequently published in International Journal of Epidemiology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Breast cancer; genetic susceptibility; gene-environment interactions; risk prediction; epidemiology |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > The Medical School (Sheffield) > Division of Genomic Medicine (Sheffield) > Department of Oncology and Metabolism (Sheffield) |
Funding Information: | Funder Grant number CANCER RESEARCH UK (CRUK) C9267/A25152 BREAST CANCER NOW TB2017SHEF |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Nov 2017 15:08 |
Last Modified: | 15 Mar 2024 16:06 |
Status: | Published |
Publisher: | Oxford University Press |
Refereed: | Yes |
Identification Number: | 10.1093/ije/dyx242 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:123418 |