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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; [...]

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

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  • Media type: E-Article
  • Title: A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry
  • Contributor: 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; [...]
  • imprint: Springer Science and Business Media LLC, 2023
  • Published in: Breast Cancer Research
  • Language: English
  • DOI: 10.1186/s13058-023-01691-8
  • ISSN: 1465-542X
  • Origination:
  • Footnote:
  • Description: <jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>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.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>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.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>Assuming a 1 × 10<jats:sup>–5</jats:sup> 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)-<jats:italic>LINGO2</jats:italic> and adult height in association with overall breast cancer risk (OR<jats:sub>int</jats:sub> = 0.94, 95% CI 0.92–0.96), and rs4770552(13q12)-<jats:italic>SPATA13</jats:italic> and age at menarche for ER + breast cancer risk (OR<jats:sub>int</jats:sub> = 0.91, 95% CI 0.88–0.94).</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>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.</jats:p> </jats:sec>
  • Access State: Open Access