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Int J Epidemiol. 2019 Oct 12. pii: dyz193. doi: 10.1093/ije/dyz193. [Epub ahead of print]

Assessment of interactions between 205 breast cancer susceptibility loci and 13 established risk factors in relation to breast cancer risk, in the Breast Cancer Association Consortium.

Collaborators (134)

Ahearn T, Andrulis IL, Anton-Culver H, Arndt V, Aronson KJ, Auer PL, Augustinsson A, Freeman LEB, Beckmann MW, Benitez J, Bernstein L, Berrandou T, Bojesen SE, Brauch H, Brenner H, Brock IW, Broeks A, Brooks-Wilson A, Butterbach K, Cai Q, Campa D, Canzian F, Carter BD, Castelao JE, Chanock SJ, Chenevix-Trench G, Cheng TD, Clarke CL, Cordina-Duverger E, Couch FJ, Cox A, Cross SS, Czene K, Dai JY, Dite GS, Earp HS, Eliassen AH, Eriksson M, Evans DG, Fasching PA, Figueroa J, Flyger H, Fritschi L, Gabrielson M, Gago-Dominguez M, Gapstur SM, Gaudet MM, Giles GG, González-Neira A, Grundy A, Guénel P, Haeberle L, Haiman CA, Håkansson N, Hall P, Hamann U, Hankinson SE, Harkness EF, Harstad T, He W, Heyworth J, Hoover RN, Hopper JL, Humphreys K, Hunter DJ, Marrón PI, John EM, Jones ME, Jung A, Kaaks R, Keeman R, Kitahara CM, Ko YD, Koutros S, Krüger U, Lambrechts D, Marchand LL, Lee E, Lejbkowicz F, Linet M, Lissowska J, Llaneza A, Lo WY, Makalic E, Martinez ME, Maurer T, Muñoz-Garzon VM, Neuhausen SL, Neven P, Newman WG, Nielsen SF, Nordestgaard BG, Norman A, O'Brien KM, Olshan AF, Olson JE, Olsson H, Orr N, Perou CM, Pinchev M, Prentice R, Rennert G, Rennert HS, Ruddy KJ, Sandler DP, Schneider MO, Schoemaker MJ, Schöttker B, Scott RJ, Scott C, Sherman ME, Shrubsole MJ, Shu XO, Southey MC, Spinelli JJ, Stone J, Swerdlow AJ, Tamimi RM, Taylor JA, Thöne K, Troester MA, Truong T, Vachon CM, van Ongeval C, van Veen EM, Wagner P, Weinberg CR, Wildiers H, Willett W, Winham SJ, Wolk A, Yang XR, Zheng W, Ziogas A.

Author information

1
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany .
2
Faculty of Medicine, University of Heidelberg, Heidelberg, Germany.
3
Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA.
4
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
5
Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
6
Department of Electron Microscopy/Molecular Pathology and Cyprus School of Molecular Medicine, Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus.
7
Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
8
Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands .
9
Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
10
Program in Genetic Epidemiology and Statistical Genetics.
11
Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA .
12
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
13
Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK .
14
Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia .
15
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.
16
Precision Medicine, Monash University, Clayton, VIC, Australia.
17
Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), Hamburg, Germany.

Abstract

BACKGROUND:

Previous gene-environment interaction studies of breast cancer risk have provided sparse evidence of interactions. Using the largest available dataset to date, we performed a comprehensive assessment of potential effect modification of 205 common susceptibility variants by 13 established breast cancer risk factors, including replication of previously reported interactions.

METHODS:

Analyses were performed using 28 176 cases and 32 209 controls genotyped with iCOGS array and 44 109 cases and 48 145 controls genotyped using OncoArray from the Breast Cancer Association Consortium (BCAC). Gene-environment interactions were assessed using unconditional logistic regression and likelihood ratio tests for breast cancer risk overall and by estrogen-receptor (ER) status. Bayesian false discovery probability was used to assess the noteworthiness of the meta-analysed array-specific interactions.

RESULTS:

Noteworthy evidence of interaction at ≤1% prior probability was observed for three single nucleotide polymorphism (SNP)-risk factor pairs. SNP rs4442975 was associated with a greater reduction of risk of ER-positive breast cancer [odds ratio (OR)int = 0.85 (0.78-0.93), Pint = 2.8 x 10-4] and overall breast cancer [ORint = 0.85 (0.78-0.92), Pint = 7.4 x 10-5) in current users of estrogen-progesterone therapy compared with non-users. This finding was supported by replication using OncoArray data of the previously reported interaction between rs13387042 (r2 = 0.93 with rs4442975) and current estrogen-progesterone therapy for overall disease (Pint = 0.004). The two other interactions suggested stronger associations between SNP rs6596100 and ER-negative breast cancer with increasing parity and younger age at first birth.

CONCLUSIONS:

Overall, our study does not suggest strong effect modification of common breast cancer susceptibility variants by established risk factors.

KEYWORDS:

Europeans; Gene-environment interaction; breast cancer; epidemiology; risk factors; single nucleotide polymorphism

PMID:
31605532
DOI:
10.1093/ije/dyz193
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