TY - JOUR
T1 - A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry
AU - CTS Consortium
AU - ABCTB Investigators
AU - KConFab investigators
AU - Middha, Pooja
AU - Wang, Xiaoliang
AU - Behrens, Sabine
AU - Bolla, Manjeet K.
AU - Wang, Qin
AU - Dennis, Joe
AU - Michailidou, Kyriaki
AU - Ahearn, Thomas U.
AU - Andrulis, Irene L.
AU - Anton-Culver, Hoda
AU - Arndt, Volker
AU - Aronson, Kristan J.
AU - Auer, Paul L.
AU - Augustinsson, Annelie
AU - Baert, Thaïs
AU - Freeman, Laura E.Beane
AU - Becher, Heiko
AU - Beckmann, Matthias W.
AU - Benitez, Javier
AU - Bojesen, Stig E.
AU - Brauch, Hiltrud
AU - Brenner, Hermann
AU - Brooks-Wilson, Angela
AU - Campa, Daniele
AU - Canzian, Federico
AU - Carracedo, Angel
AU - Castelao, Jose E.
AU - Chanock, Stephen J.
AU - Chenevix-Trench, Georgia
AU - Cordina-Duverger, Emilie
AU - Couch, Fergus J.
AU - Cox, Angela
AU - Cross, Simon S.
AU - Czene, Kamila
AU - Dossus, Laure
AU - Dugué, Pierre Antoine
AU - Eliassen, A. Heather
AU - Eriksson, Mikael
AU - Evans, D. Gareth
AU - Fasching, Peter A.
AU - Figueroa, Jonine D.
AU - Fletcher, Olivia
AU - Flyger, Henrik
AU - Gabrielson, Marike
AU - Gago-Dominguez, Manuela
AU - Giles, Graham G.
AU - Olson, Janet E.
AU - Ruddy, Kathryn J.
AU - Vachon, Celine M.
AU - Winham, Stacey J.
N1 - Publisher Copyright:
© 2023, BioMed Central Ltd., part of Springer Nature.
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - Breast cancer
KW - European ancestry
KW - Gene-environment interactions
KW - Genetic epidemiology
UR - http://www.scopus.com/inward/record.url?scp=85167531154&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85167531154&partnerID=8YFLogxK
U2 - 10.1186/s13058-023-01691-8
DO - 10.1186/s13058-023-01691-8
M3 - Article
C2 - 37559094
AN - SCOPUS:85167531154
SN - 1465-5411
VL - 25
JO - Breast Cancer Research
JF - Breast Cancer Research
IS - 1
M1 - 93
ER -