- (1) Real World Insights, CESE, QuintilesIMS, Frankfurt, Germany.
- (2) German Cancer Research Center, grid.7497.d
- (3) Sookmyung Women's University, grid.412670.6
- (4) Institute of Cancer Research, grid.18886.3f
- (5) University of Melbourne, grid.1008.9
- (6) Cancer Council Victoria, grid.3263.4
- (7) University of Cambridge, grid.5335.0
- (8) Cyprus Institute of Neurology and Genetics, grid.417705.0
- (9) National Cancer Institute, grid.48336.3a
- (10) Antoni van Leeuwenhoek Hospital, grid.430814.a
- (11) University of California, Los Angeles, grid.19006.3e
- (12) Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.
- (13) Complejo Hospitalario Universitario de Santiago, grid.411048.8
- (14) University of California, San Diego, grid.266100.3
- (15) University Hospital Complex Of Vigo, grid.411855.c
- (16) University of Paris-Sud, grid.5842.b
- (17) Copenhagen General Population Study,
- (18) Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.
- (19) University of Copenhagen, grid.5254.6, KU
- (20) Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.
- (21) Division of Preventive Oncology, National Center for Tumor Diseases (NCT).
- (22) Division of Clinical Epidemiology and Aging Research,
- (23) Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, grid.502798.1
- (24) University of Tübingen, grid.10392.39
- (25) Ruhr University Bochum, grid.5570.7
- (26) Kuopio University Hospital, grid.410705.7
- (27) Translational Cancer Research Area.
- (28) University of Eastern Finland, grid.9668.1
- (29) KU Leuven, grid.5596.f
- (30) VIB Center for Cancer Biology, Leuven, Belgium.
- (31) Universitair Ziekenhuis Leuven, grid.410569.f
- (32) Department of Laboratory Medicine and Pathology.
- (33) Mayo Clinic, grid.66875.3a
- (34) University of Edinburgh, grid.4305.2
- (35) Karolinska Institute, grid.4714.6
- (36) Department of Oncology and Metabolism.
- (37) University of Sheffield, grid.11835.3e
- (38) Bloomberg School of Public Health.
- (39) Johns Hopkins University, grid.21107.35
- (40) University Cancer Center Hamburg, grid.412315.0
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.