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A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

Middha, Pooja ; Wang, Xiaoliang ; Behrens, Sabine ; Bolla, Manjeet K ; Wang, Qin ; Dennis, Joe ; Michailidou, Kyriaki ; Ahearn, Thomas U ; Andrulis, Irene L and Anton-Culver, Hoda , et al. (2023) In Breast cancer research : BCR 25(1). p.1-13
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Adult, Female, Humans, Gene-Environment Interaction, Genetic Predisposition to Disease, Breast Neoplasms/etiology, Bayes Theorem, Genome-Wide Association Study, Risk Factors, Polymorphism, Single Nucleotide, Case-Control Studies
in
Breast cancer research : BCR
volume
25
issue
1
article number
93
pages
1 - 13
publisher
BioMed Central (BMC)
external identifiers
  • pmid:37559094
  • scopus:85167531154
ISSN
1465-5411
DOI
10.1186/s13058-023-01691-8
language
English
LU publication?
yes
additional info
© 2023. BioMed Central Ltd., part of Springer Nature.
id
b8dadb9e-1cf1-4fff-97ea-fdc8c1d52be8
date added to LUP
2023-11-11 16:00:21
date last changed
2025-01-15 23:05:37
@article{b8dadb9e-1cf1-4fff-97ea-fdc8c1d52be8,
  abstract     = {{<p>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.</p><p>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.</p><p>RESULTS: Assuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability &lt; 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).</p><p>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.</p>}},
  author       = {{Middha, Pooja and Wang, Xiaoliang and Behrens, Sabine and Bolla, Manjeet K and Wang, Qin and Dennis, Joe and Michailidou, Kyriaki and Ahearn, Thomas U and Andrulis, Irene L and Anton-Culver, Hoda and Arndt, Volker and Aronson, Kristan J and Auer, Paul L and Augustinsson, Annelie and Baert, Thaïs and Freeman, Laura E Beane and Becher, Heiko and Beckmann, Matthias W and Benitez, Javier and Bojesen, Stig E and Brauch, Hiltrud and Brenner, Hermann and Brooks-Wilson, Angela and Campa, Daniele and Canzian, Federico and Carracedo, Angel and Castelao, Jose E and Chanock, Stephen J and Chenevix-Trench, Georgia and Cordina-Duverger, Emilie and Couch, Fergus J and Cox, Angela and Cross, Simon S and Czene, Kamila and Dossus, Laure and Dugué, Pierre-Antoine and Eliassen, A Heather and Eriksson, Mikael and Evans, D Gareth and Fasching, Peter A and Figueroa, Jonine D and Fletcher, Olivia and Flyger, Henrik and Gabrielson, Marike and Gago-Dominguez, Manuela and Hall, Per and Ingvar, Christian and Isaksson, Karolin and Jernström, Helena}},
  issn         = {{1465-5411}},
  keywords     = {{Adult; Female; Humans; Gene-Environment Interaction; Genetic Predisposition to Disease; Breast Neoplasms/etiology; Bayes Theorem; Genome-Wide Association Study; Risk Factors; Polymorphism, Single Nucleotide; Case-Control Studies}},
  language     = {{eng}},
  month        = {{08}},
  number       = {{1}},
  pages        = {{1--13}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Breast cancer research : BCR}},
  title        = {{A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry}},
  url          = {{http://dx.doi.org/10.1186/s13058-023-01691-8}},
  doi          = {{10.1186/s13058-023-01691-8}},
  volume       = {{25}},
  year         = {{2023}},
}