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Int J Epidemiol. 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.

Author information

1
Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany.
2
Real World Insights, CESE, QuintilesIMS, Frankfurt, Germany.
3
Department of Statistics, Sookmyung Women's University, Seoul, Korea.
4
Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK.
5
Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.
6
Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, VIC, Australia.
7
Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.
8
Department of Electron Microscopy/Molecular Pathology, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.
9
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
10
Department of Pathology, University of Melbourne, Melbourne, VIC, Australia.
11
Division of Molecular Pathology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
12
Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.
13
David Geffen School of Medicine, Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA, USA.
14
Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago, IDIS, Complejo Hospitalario Universitario de Santiago, Santiago de Compostela, Spain.
15
Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
16
Oncology and Genetics Unit, Complejo Hospitalario Universitario de Vigo, Instituto de Investigacion Sanitaria Galicia Sur, Vigo, Spain.
17
Center for Research in Epidemiology and Population Health, University Paris-Sud, University Paris-Saclay, Villejuif, France.
18
Copenhagen General Population Study.
19
Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.
20
Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
21
Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.
22
Division of Clinical Epidemiology and Aging Research.
23
Division of Preventive Oncology, National Center for Tumor Diseases (NCT).
24
German Cancer Consortium, German Cancer Research Center (DKFZ), Heidelberg, Germany.
25
Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.
26
Department of Clinical Pharmacology, University of Tübingen, Tübingen, Germany.
27
Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum, Bochum, Germany.
28
Translational Cancer Research Area.
29
Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland.
30
Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland.
31
VIB Center for Cancer Biology, Leuven, Belgium.
32
Laboratory for Translational Genetics, University of Leuven, Leuven, Belgium.
33
Department of Radiation Oncology, University Hospitals Leuven, Leuven, Belgium.
34
Department of Laboratory Medicine and Pathology.
35
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
36
Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
37
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
38
Department of Oncology and Metabolism.
39
Department of Neuroscience, University of Sheffield, Sheffield, UK.
40
Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.
41
Division of Breast Cancer Research, Institute of Cancer Research, London, UK.
42
Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
43
Bloomberg School of Public Health.
44
Department of Oncology, Johns Hopkins University, Baltimore, MD, USA.
45
University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

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.

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