학술논문
A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry
Document Type
Author
Middha, Pooja; Wang, Xiaoliang; Behrens, Sabine; Bolla, Manjeet K; Wang, Qin; Dennis, Joe; Michailidou, Kyriaki; Ahearn, Thomas U; Andrulis, Irene L; Anton-Culver, Hoda; Arndt, Volker; Aronson, Kristan J; Auer, Paul L; Augustinsson, Annelie; Baert, Thaïs; Freeman, Laura E Beane; Becher, Heiko; Beckmann, Matthias W; Benitez, Javier; Bojesen, Stig E; Brauch, Hiltrud; Brenner, Hermann; Brooks-Wilson, Angela; Campa, Daniele; Canzian, Federico; Carracedo, Angel; Castelao, Jose E; Chanock, Stephen J; Chenevix-Trench, Georgia; Cordina-Duverger, Emilie; Couch, Fergus J; Cox, Angela; Cross, Simon S; Czene, Kamila; Dossus, Laure; Dugué, Pierre-Antoine; Eliassen, A Heather; Eriksson, Mikael; Evans, D Gareth; Fasching, Peter A; Figueroa, Jonine D; Fletcher, Olivia; Flyger, Henrik; Gabrielson, Marike; Gago-Dominguez, Manuela; Hall, Per; Ingvar, Christian; Isaksson, Karolin; Jernström, Helena
Source
Breast cancer research : BCR EpiHealth: Epidemiology for Health. 25(1):1-13
Subject
Language
English
ISSN
1465-5411
Abstract
BACKGROUND: Genome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.METHODS: Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.RESULTS: Assuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88-0.94).CONCLUSIONS: Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.