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Hum Mol Genet. 2014 Oct 1;23(19):5260-70. doi: 10.1093/hmg/ddu223. Epub 2014 May 8.

Post-GWAS gene-environment interplay in breast cancer: results from the Breast and Prostate Cancer Cohort Consortium and a meta-analysis on 79,000 women.

Author information

1
Division of Cancer Epidemiology and.
2
Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg D-69120, Germany.
3
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
4
Cancer Epidemiology Unit, University of Oxford, Oxford OX3 7LF, UK.
5
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA School of Public Health, University of Wisconsin, Milwaukee, WI 1240, USA.
6
Department of Epidemiology, American Cancer Society, Atlanta, GA 30303, USA.
7
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
8
Cancer Center, University of Hawaii, Honolulu, HI 96813, USA.
9
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
10
Cancer Epidemiology Centre Melbourne, Cancer Council Victoria, Carlton South, VIC 3004, Australia Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of Population Health, The University of Melbourne, Melbourne, VIC 3010, Australia Faculty of Medicine, Monash University, Melbourne, VIC 3800, Australia.
11
Cancer Epidemiology Centre Melbourne, Cancer Council Victoria, Carlton South, VIC 3004, Australia Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of Population Health, The University of Melbourne, Melbourne, VIC 3010, Australia.
12
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA Division of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003-9304, USA.
13
INSERM, Centre for Research in Epidemiology and Population Health, Institut Gustave Roussy, Villejuif F-94805, France Paris South University, Villejuif F-94807, France.
14
School of Public Health, Imperial College London, London SW7 2AZ, UK Université de Lyon, Université Lyon 1, ISPB, Lyon F-69007, France INSERM U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon F-69008, France Centre Léon Bérard, Lyon F-69008, France.
15
Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, UK.
16
Institute of Community Medicine, University of Tromsø, Tromsø N-9037, Norway.
17
Molecular and Genetic Epidemiology Unit, Human Genetics Foundation Torino, Torino I-10126, Italy.
18
School of Public Health, Imperial College London, London SW7 2AZ, UK Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht 3584 CS, The Netherlands.
19
Public Health Directorate, Asturias ES-CP33006, Spain.
20
School of Public Health, Imperial College London, London SW7 2AZ, UK.
21
Department of Surgical and Perioperative Sciences, Surgery, Umeå University, Umeå S-90185, Sweden.
22
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA Bureau of Epidemiologic Research, Academy of Athens, Athens 10679, Greece Hellenic Health Foundation, Athens 11527, Greece.
23
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
24
Division of Cancer Epidemiology and d.campa@dkfz.de.

Abstract

We studied the interplay between 39 breast cancer (BC) risk SNPs and established BC risk (body mass index, height, age at menarche, parity, age at menopause, smoking, alcohol and family history of BC) and prognostic factors (TNM stage, tumor grade, tumor size, age at diagnosis, estrogen receptor status and progesterone receptor status) as joint determinants of BC risk. We used a nested case-control design within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), with 16 285 BC cases and 19 376 controls. We performed stratified analyses for both the risk and prognostic factors, testing for heterogeneity for the risk factors, and case-case comparisons for differential associations of polymorphisms by subgroups of the prognostic factors. We analyzed multiplicative interactions between the SNPs and the risk factors. Finally, we also performed a meta-analysis of the interaction ORs from BPC3 and the Breast Cancer Association Consortium. After correction for multiple testing, no significant interaction between the SNPs and the established risk factors in the BPC3 study was found. The meta-analysis showed a suggestive interaction between smoking status and SLC4A7-rs4973768 (Pinteraction = 8.84 × 10(-4)) which, although not significant after considering multiple comparison, has a plausible biological explanation. In conclusion, in this study of up to almost 79 000 women we can conclusively exclude any novel major interactions between genome-wide association studies hits and the epidemiologic risk factors taken into consideration, but we propose a suggestive interaction between smoking status and SLC4A7-rs4973768 that if further replicated could help our understanding in the etiology of BC.

PMID:
24895409
PMCID:
PMC4159150
DOI:
10.1093/hmg/ddu223
[Indexed for MEDLINE]
Free PMC Article

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