Breast Cancer Research (Aug 2023)

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

  • Pooja Middha,
  • Xiaoliang Wang,
  • Sabine Behrens,
  • Manjeet K. Bolla,
  • Qin Wang,
  • Joe Dennis,
  • Kyriaki Michailidou,
  • Thomas U. Ahearn,
  • Irene L. Andrulis,
  • Hoda Anton-Culver,
  • Volker Arndt,
  • Kristan J. Aronson,
  • Paul L. Auer,
  • Annelie Augustinsson,
  • Thaïs Baert,
  • Laura E. Beane Freeman,
  • Heiko Becher,
  • Matthias W. Beckmann,
  • Javier Benitez,
  • Stig E. Bojesen,
  • Hiltrud Brauch,
  • Hermann Brenner,
  • Angela Brooks-Wilson,
  • Daniele Campa,
  • Federico Canzian,
  • Angel Carracedo,
  • Jose E. Castelao,
  • Stephen J. Chanock,
  • Georgia Chenevix-Trench,
  • CTS Consortium,
  • Emilie Cordina-Duverger,
  • Fergus J. Couch,
  • Angela Cox,
  • Simon S. Cross,
  • Kamila Czene,
  • Laure Dossus,
  • Pierre-Antoine Dugué,
  • A. Heather Eliassen,
  • Mikael Eriksson,
  • D. Gareth Evans,
  • Peter A. Fasching,
  • Jonine D. Figueroa,
  • Olivia Fletcher,
  • Henrik Flyger,
  • Marike Gabrielson,
  • Manuela Gago-Dominguez,
  • Graham G. Giles,
  • Anna González-Neira,
  • Felix Grassmann,
  • Anne Grundy,
  • Pascal Guénel,
  • Christopher A. Haiman,
  • Niclas Håkansson,
  • Per Hall,
  • Ute Hamann,
  • Susan E. Hankinson,
  • Elaine F. Harkness,
  • Bernd Holleczek,
  • Reiner Hoppe,
  • John L. Hopper,
  • Richard S. Houlston,
  • Anthony Howell,
  • David J. Hunter,
  • Christian Ingvar,
  • ABCTB Investigators,
  • kConFab Investigators,
  • Karolin Isaksson,
  • Helena Jernström,
  • Esther M. John,
  • Michael E. Jones,
  • Rudolf Kaaks,
  • Renske Keeman,
  • Cari M. Kitahara,
  • Yon-Dschun Ko,
  • Stella Koutros,
  • Allison W. Kurian,
  • James V. Lacey,
  • Diether Lambrechts,
  • Nicole L. Larson,
  • Susanna Larsson,
  • Loic Le Marchand,
  • Flavio Lejbkowicz,
  • Shuai Li,
  • Martha Linet,
  • Jolanta Lissowska,
  • Maria Elena Martinez,
  • Tabea Maurer,
  • Anna Marie Mulligan,
  • Claire Mulot,
  • Rachel A. Murphy,
  • William G. Newman,
  • Sune F. Nielsen,
  • Børge G. Nordestgaard,
  • Aaron Norman,
  • Katie M. O’Brien,
  • Janet E. Olson,
  • Alpa V. Patel,
  • Ross Prentice,
  • Erika Rees-Punia,
  • Gad Rennert,
  • Valerie Rhenius,
  • Kathryn J. Ruddy,
  • Dale P. Sandler,
  • Christopher G. Scott,
  • Mitul Shah,
  • Xiao-Ou Shu,
  • Ann Smeets,
  • Melissa C. Southey,
  • Jennifer Stone,
  • Rulla M. Tamimi,
  • Jack A. Taylor,
  • Lauren R. Teras,
  • Katarzyna Tomczyk,
  • Melissa A. Troester,
  • Thérèse Truong,
  • Celine M. Vachon,
  • Sophia S. Wang,
  • Clarice R. Weinberg,
  • Hans Wildiers,
  • Walter Willett,
  • Stacey J. Winham,
  • Alicja Wolk,
  • Xiaohong R. Yang,
  • M. Pilar Zamora,
  • Wei Zheng,
  • Argyrios Ziogas,
  • Alison M. Dunning,
  • Paul D. P. Pharoah,
  • Montserrat García-Closas,
  • Marjanka K. Schmidt,
  • Peter Kraft,
  • Roger L. Milne,
  • Sara Lindström,
  • Douglas F. Easton,
  • Jenny Chang-Claude

DOI
https://doi.org/10.1186/s13058-023-01691-8
Journal volume & issue
Vol. 25, no. 1
pp. 1 – 13

Abstract

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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.

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