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
  • 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
  • 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 (Geteilte/r Letztautor/in)
  • Jenny Chang-Claude (Geteilte/r Letztautor/in)
  • CTS Consortium
  • ABCTB Investigators
  • KConFab Investigators

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.

Bibliografische Daten

OriginalspracheEnglisch
Aufsatznummer93
ISSN1465-5411
DOIs
StatusVeröffentlicht - 09.08.2023

Anmerkungen des Dekanats

© 2023. BioMed Central Ltd., part of Springer Nature.

PubMed 37559094