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Int J Cancer. 2017 Nov 1;141(9):1830-1840. doi: 10.1002/ijc.30859. Epub 2017 Aug 11.

Gene-environment interactions involving functional variants: Results from the Breast Cancer Association Consortium.

Author information

1
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
2
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.
3
Department of Pathology, The University of Melbourne, Melbourne, VIC, Australia.
4
Division of Molecular Pathology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
5
Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.
6
David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA.
7
Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, Servicio Galego de Saúde, SERGAS, Santiago De Compostela, Spain.
8
Moores Cancer Center, University of California San Diego, La Jolla, CA.
9
Oncology and Genetics Unit, Instituto de Investigación Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain.
10
CESP-Cancer and Environment team, INSERM U1018, Université Paris-Sud, Université Paris-Saclay, Villejuif, France.
11
Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.
12
Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.
13
Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
14
Epidemiology Research Program, American Cancer Society, Atlanta, GA.
15
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
16
Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.
17
German Cancer Consortium (DKTK), Heidelberg, Germany.
18
Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.
19
University of Tübingen, Tübingen, Germany.
20
Molecular Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
21
Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland.
22
Pathology and Forensic Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
23
Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland.
24
Vesalius Research Center, VIB, Leuven, Belgium.
25
Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium.
26
Leuven Multidisciplinary Breast Center, Department of Oncology, KU Leuven and Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium.
27
Institute for Medical Biometrics and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
28
Department of Cancer Epidemiology, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
29
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN.
30
Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.
31
Genomics Center, Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec City, QC, Canada.
32
Department of Medicine, McGill University, Montréal, QC, Canada.
33
Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montréal, QC, Canada.
34
Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, Teviot Place Edinburgh, Edinburgh, United Kingdom.
35
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD.
36
Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Worts Causeway, Cambridge, United Kingdom.
37
Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, Nicosia.
38
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
39
Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom.
40
Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom.
41
Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, London, United Kingdom.
42
Division of Breast Cancer Research, The Institute of Cancer Research, Sutton, London, United Kingdom.
43
Department of Oncology, University of Cambridge, Worts Causeway, Centre for Cancer Genetic Epidemiology, Cambridge, United Kingdom.
44
Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
45
Research Group Genetic Cancer Epidemiology, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

Abstract

Investigating the most likely causal variants identified by fine-mapping analyses may improve the power to detect gene-environment interactions. We assessed the interplay between 70 single nucleotide polymorphisms identified by genetic fine-scale mapping of susceptibility loci and 11 epidemiological breast cancer risk factors in relation to breast cancer. Analyses were conducted on up to 58,573 subjects (26,968 cases and 31,605 controls) from the Breast Cancer Association Consortium, in one of the largest studies of its kind. Analyses were carried out separately for estrogen receptor (ER) positive (ER+) and ER negative (ER-) disease. The Bayesian False Discovery Probability (BFDP) was computed to assess the noteworthiness of the results. Four potential gene-environment interactions were identified as noteworthy (BFDP < 0.80) when assuming a true prior interaction probability of 0.01. The strongest interaction result in relation to overall breast cancer risk was found between CFLAR-rs7558475 and current smoking (ORint  = 0.77, 95% CI: 0.67-0.88, pint  = 1.8 × 10-4 ). The interaction with the strongest statistical evidence was found between 5q14-rs7707921 and alcohol consumption (ORint =1.36, 95% CI: 1.16-1.59, pint  = 1.9 × 10-5 ) in relation to ER- disease risk. The remaining two gene-environment interactions were also identified in relation to ER- breast cancer risk and were found between 3p21-rs6796502 and age at menarche (ORint  = 1.26, 95% CI: 1.12-1.43, pint =1.8 × 10-4 ) and between 8q23-rs13267382 and age at first full-term pregnancy (ORint  = 0.89, 95% CI: 0.83-0.95, pint  = 5.2 × 10-4 ). While these results do not suggest any strong gene-environment interactions, our results may still be useful to inform experimental studies. These may in turn, shed light on the potential interactions observed.

KEYWORDS:

Breast Cancer Association Consortium; breast cancer; gene-environment; interaction; single nucleotide polymorphism

PMID:
28670784
PMCID:
PMC5601244
DOI:
10.1002/ijc.30859
[Indexed for MEDLINE]
Free PMC Article

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