A genome-wide gene-based gene-environment interaction study of breast cancer in more than 90,000 women

Xiaoliang Wang, Hongjie Chen, Pooja Middha Kapoor, Yu-Ru Su, Manjeet K Bolla, Joe Dennis, Alison M Dunning, Michael Lush, Qin Wang, Kyriaki Michailidou, Paul D P Pharoah, John L Hopper, Melissa C Southey, Stella Koutros, Laura E Beane Freeman, Jennifer Stone, Gad Rennert, Rana Shibli, Rachel A Murphy, Kristan AronsonPascal Guénel, Thérèse Truong, Lauren R Teras, James M Hodge, Federico Canzian, Rudolf Kaaks, Hermann Brenner, Volker Arndt, Reiner Hoppe, Sabine Behrens, Arto Mannermaa, Veli-Matti Kosma, Audrey Jung, Heiko Becher, Graham G Giles, Christopher A Haiman, Gertraud Maskarinec, Stacey Winham, Jacques Simard, Mark S Goldberg, Wei Zheng, Jirong Long, Melissa A Troester, Michael I Love, Rulla Tamimi, Heather Eliassen, Montserrat García-Closas, Jonine Figueroa, Thomas Ahearn, Rose Yang, D Gareth Evans, Anthony Howell, Per Hall, Kamila Czene, Alicja Wolk, Dale P Sandler, Jack A Taylor, Anthony J Swerdlow, Nick Orr, James V Lacey, Sophia Wang, Håkan Olsson, Douglas F Easton, Roger L Milne, Li Hsu, Peter Kraft, Jenny Chang-Claude, Sara Lindström

Research output: Contribution to journalArticlepeer-review


BACKGROUND: Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene-environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this.

METHODS: We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P<0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test.

RESULTS: After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (P GXE=4.44×10 -6).

CONCLUSION: In this transcriptome-informed genome-wide gene-environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk.

IMPACT: Our study suggests a limited role of gene-environment interactions in breast cancer risk.

Original languageEnglish
Pages (from-to)211-219
Number of pages9
JournalCancer Research Communications
Issue number4
Publication statusPublished - 8 Apr 2022

Research Beacons, Institutes and Platforms

  • Manchester Cancer Research Centre


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