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Int J Cancer. 2018 Nov 29. doi: 10.1002/ijc.32029. [Epub ahead of print]

A Comprehensive Gene-Environment Interaction Analysis in Ovarian Cancer using Genome-wide Significant Common Variants.

Author information

1
Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
2
Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
3
Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark.
4
Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
5
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
6
Department of Health Science, California State University, Fullerton, Fullerton, CA, USA.
7
University of Texas MD Anderson Cancer Center, Houston, TX, USA.
8
Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA.
9
Research Institute and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
10
Department of Health Research and Policy - Epidemiology, Stanford University School of Medicine, Stanford, CA, USA.
11
Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
12
Department of Epidemiology, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA.
13
Department of Genetics and Genomic Sciences, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
14
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
15
Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC, USA.
16
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
17
Cancer Prevention and Control, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
18
Community and Population Health Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
19
Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
20
Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
21
Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
22
Department of Epidemiology, University of Washington, Seattle, WA, USA.
23
Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
24
Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.
25
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
26
Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
27
Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, Boston, MA, USA.
28
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
29
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
30
Gynaecological Cancer Research Centre, Women's Cancer, Institute for Women's Health, University College London, London, UK.
31
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
32
Center for Cancer Prevention and Translational Genomics, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
33
Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
34
Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, Canada.
35
Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada.
36
Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.
37
University of New Mexico Health Sciences Center, University of New Mexico, Albuquerque, NM, USA.
38
Division of Cancer Care, Department of Population Health Research, Alberta Health Services, Calgary, AB, Canada.
39
Department of Gynaecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
40
Ovarian Cancer Center of Excellence, Womens Cancer Research Program, Magee-Womens Research Institute and Hillman Cancer Center, Pittsburgh, PA, USA.
41
Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.
42
Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
43
Research Group Genetic Cancer Epidemiology, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
44
Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
45
Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada.
46
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
47
Molecular Unit, Department of Pathology, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark.
48
British Columbia's Ovarian Cancer Research (OVCARE) program, Vancouver General Hospital, BC Cancer Agency and University of British Columbia.
49
Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
50
Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA, USA.
51
Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

Abstract

As a follow-up to genome-wide association analysis of common variants associated with ovarian carcinoma (cancer), this study considers seven well-known ovarian cancer risk factors and their interactions with 28 genome-wide significant common genetic variants. The interaction analyses were based on data from 9,971 ovarian cancer cases and 15,566 controls from 17 case-control studies. Likelihood ratio and Wald tests for multiplicative interaction and for relative excess risk due to additive interaction were used. The top multiplicative interaction was noted between oral contraceptive pill (OCP) use (ever vs never) and rs13255292 (P-value = 3.48 x 10-4 ). Among women with the TT genotype for this variant, the odds ratio for OCP use was 0.53 (95% CI=0.46-0.60) compared to 0.71 (95%CI=0.66-0.77) for women with the CC genotype. When stratified by duration of OCP use, women with 1-5 years of OCP use exhibited differential protective benefit across genotypes. However, no interaction on either the multiplicative or additive scale was found to be statistically significant after multiple testing correction. The results suggest that OCP use may offer increased benefit for women who are carriers of the T allele in rs13255292. On the other hand, for women carrying the C allele in this variant, longer (5+ years) use of OCP may reduce the impact of carrying the risk allele of this SNP. Replication of this finding is needed. The study presents a comprehensive analytic framework for conducting gene-environment analysis in ovarian cancer. This article is protected by copyright. All rights reserved.

KEYWORDS:

G x E; additive interaction; genetics; ovarian cancer

PMID:
30499236
DOI:
10.1002/ijc.32029

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