2015
DOI: 10.1158/0008-5472.can-14-2012
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Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Abstract: Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute non-dense area adjusted for study, age and BMI using mixed linear modeling. We found strong support for established… Show more

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Cited by 56 publications
(49 citation statements)
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“…Associations between ESR1 SNPs and mammographic density have previously been reported 2527 , but, in this detailed analysis, only signal 2 was significantly associated with mammographic dense area ( P = 1.7 × 10 −5 ), although signal 1 also showed some evidence of an effect in the conditional analysis ( P = 0.017). Although adjusting the breast cancer analysis of signal 2 for mammographic dense area produced some attenuation of the associated effect, the lead SNP remained significantly associated with breast cancer risk (unconditional OR = 1.30, 95% CI = 1.13–1.49; P = 0.00024; OR conditional on dense area = 1.24, 95% CI = 1.08–1.43; P = 0.0025), suggesting either that the mechanism by which the signal 2 candidate causal variant affects breast cancer risk is not mediated through mammographic density or, alternatively, that dense area, as measured here, is unable to capture the association with breast composition that is most relevant to risk.…”
Section: Discussionmentioning
confidence: 48%
“…Associations between ESR1 SNPs and mammographic density have previously been reported 2527 , but, in this detailed analysis, only signal 2 was significantly associated with mammographic dense area ( P = 1.7 × 10 −5 ), although signal 1 also showed some evidence of an effect in the conditional analysis ( P = 0.017). Although adjusting the breast cancer analysis of signal 2 for mammographic dense area produced some attenuation of the associated effect, the lead SNP remained significantly associated with breast cancer risk (unconditional OR = 1.30, 95% CI = 1.13–1.49; P = 0.00024; OR conditional on dense area = 1.24, 95% CI = 1.08–1.43; P = 0.0025), suggesting either that the mechanism by which the signal 2 candidate causal variant affects breast cancer risk is not mediated through mammographic density or, alternatively, that dense area, as measured here, is unable to capture the association with breast composition that is most relevant to risk.…”
Section: Discussionmentioning
confidence: 48%
“…(5658) Improving outcomes for women with dense breasts may require dual consideration of risk and density,(58) and/or use of technology that employs alternative approaches to tissue visualization, such as tomosynthesis (59,60) or breast-specific gamma imaging,(61,62) or identification of genetic risk markers. (6365)…”
Section: Discussionmentioning
confidence: 99%
“…Age, BMI and other breast cancer risk factors explain about 30% of the variation in mammographic density, and over 60% of the residual variation appears to be accounted for by additive genetic factors . Genome‐wide association studies (GWAS) have identified variants at specific genetic loci associated with mammographic density, and about 15% of the genetic variants known to be associated with breast cancer risk are also associated with mammographic density measures, although they explain only a small fraction of the latter. Despite these known determinants, the molecular mechanisms underlying variation in mammographic density are not well understood, nor is how mammographic density translates into breast cancer risk at the biological level …”
Section: Introductionmentioning
confidence: 99%