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BioMed Central, Breast Cancer Research, 1(17), 2015

DOI: 10.1186/s13058-015-0625-9

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A comprehensive evaluation of interaction between genetic variants and use of menopausal hormone therapy on mammographic density

Journal article published in 2015 by Anja Rudolph ORCID, Peter A. Fasching, Sabine Behrens, Ursula Eilber, Manjeet K. Bolla, Qin Wang, Deborah Thompson, Kamila Czene, Judith S. Brand ORCID, Jingmei Li, Christopher Scott, V. Shane Pankratz, V. Shane Pankratz, Kathleen Brandt, Emily Hallberg and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s13058-015-0625-9 ; Introduction: Mammographic density is an established breast cancer risk factor with a strong genetic component and can be increased in women using menopausal hormone therapy (MHT). Here, we aimed to identify genetic variants that may modify the association between MHT use and mammographic density. Methods: The study comprised 6,298 postmenopausal women from the Mayo Mammography Health Study and nine studies included in the Breast Cancer Association Cons ortium. We selected for evaluation 1327 single nucleotide polymorphisms (SNPs) showing the lowest P-values for interaction ( P_int )in a meta-analysis of genome-wide gene-environment interaction studies with MHT use on risk of breast cancer, 2541 SNPs in candidate genes (AKR1C4 , CYP1A1-CYP1A2 , CYP1B1 , ESR2 , PPARG , PRL , SULT1A1-SULT1A2 and TNF) and ten SNPs (AREG-rs10034692, PRDM6-rs186749, ESR1-rs12665607, ZNF365-rs10995190, 8p11.23-rs7816345, LSP1-rs3817198, IGF1-rs703556, 12q24-rs1265507, TMEM184B-rs7289126, and SGSM3-rs17001868) associated with mammographic density in genome-wide studies. We used multiple linear regression models adjusted for potential confounders to evaluate interactions between SNPs and current use of MHT on mammographic density. Results: No significant interactions were identified after adjustment for multiple testing. The strongest SNP-MHT interaction (unadjusted P_int