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

Research output: Contribution to journalJournal articleResearchpeer-review

Anja Rudolph, Minsun Song, Mark N. Brook, Roger L. Milne, Nasim Mavaddat, Kyriaki Michailidou, Manjeet K. Bolla, Qin Wang, Joe Dennis, Amber N. Wilcox, John L. Hopper, Melissa C. Southey, Renske Keeman, Peter A. Fasching, Matthias W. Beckmann, Manuela Gago-Dominguez, Jose E. Castelao, Pascal Guénel, Thérèse Truong, Stig E. Bojesen & 31 others Henrik Flyger, Hermann Brenner, Volker Arndt, Hiltrud Brauch, Thomas Brüning, Arto Mannermaa, Veli Matti Kosma, Diether Lambrechts, Machteld Keupers, Fergus J. Couch, Celine Vachon, Graham G. Giles, Robert J. MacInnis, Jonine Figueroa, Louise Brinton, Kamila Czene, Judith S. Brand, Marike Gabrielson, Keith Humphreys, Angela Cox, Simon S. Cross, Alison M. Dunning, Nick Orr, Anthony Swerdlow, Per Hall, Paul D.P. Pharoah, Marjanka K. Schmidt, Douglas F. Easton, Nilanjan Chatterjee, Jenny Chang-Claude, Montserrat García-Closas

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.

Original languageEnglish
JournalInternational Journal of Epidemiology
Volume47
Issue number2
Pages (from-to)526-536
Number of pages11
ISSN0300-5771
DOIs
Publication statusPublished - 2018

    Research areas

  • Breast cancer, Epidemiology, Gene-environment interactions, Genetic susceptibility, Risk prediction

ID: 220860619