Citations

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 nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10−5). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk. Cancer Res; 75(12); 2457–67. ©2015 AACR.

A large genotyping project within the Breast Cancer Association Consortium (BCAC) recently identified 41 associations between single nucleotide polymorphisms (SNPs) and overall breast cancer (BC) risk. We investigated whether the effects of these 41 SNPs, as well as six SNPs associated with estrogen receptor (ER) negative BC risk are modified by 13 environmental risk factors for BC. Data from 22 studies participating in BCAC were pooled, comprising up to 26,633 cases and 30,119 controls. Interactions between SNPs and environmental factors were evaluated using an empirical Bayes‐type shrinkage estimator. Six SNPs showed interactions with associated p ‐values ( p int ) <1.1 × 10 −3 . None of the observed interactions was significant after accounting for multiple testing. The Bayesian False Discovery Probability was used to rank the findings, which indicated three interactions as being noteworthy at 1% prior probability of interaction. SNP rs6828523 was associated with increased ER‐negative BC risk in women ≥170 cm (OR = 1.22, p = 0.017), but inversely associated with ER‐negative BC risk in women <160 cm (OR = 0.83, p = 0.039, p int = 1.9 × 10 −4 ). The inverse association between rs4808801 and overall BC risk was stronger for women who had had four or more pregnancies (OR = 0.85, p = 2.0 × 10 −4 ), and absent in women who had had just one (OR = 0.96, p = 0.19, p int = 6.1 × 10 −4 ). SNP rs11242675 was inversely associated with overall BC risk in never/former smokers (OR = 0.93, p = 2.8 × 10 −5 ), but no association was observed in current smokers (OR = 1.07, p = 0.14, p int = 3.4 × 10 −4 ). In conclusion, recently identified BC susceptibility loci are not strongly modified by established risk factors and the observed potential interactions require confirmation in independent studies.

ABSTRACTGenes that alter disease risk only in combination with certain environmental exposures may not be detected in genetic association analysis. By using methods accounting for gene‐environment (G × E) interaction, we aimed to identify novel genetic loci associated with breast cancer risk. Up to 34,475 cases and 34,786 controls of European ancestry from up to 23 studies in the Breast Cancer Association Consortium were included. Overall, 71,527 single nucleotide polymorphisms (SNPs), enriched for association with breast cancer, were tested for interaction with 10 environmental risk factors using three recently proposed hybrid methods and a joint test of association and interaction. Analyses were adjusted for age, study, population stratification, and confounding factors as applicable. Three SNPs in two independent loci showed statistically significant association: SNPs rs10483028 and rs2242714 in perfect linkage disequilibrium on chromosome 21 and rs12197388 in ARID1B on chromosome 6. While rs12197388 was identified using the joint test with parity and with age at menarche (P‐values = 3 × 10−07), the variants on chromosome 21 q22.12, which showed interaction with adult body mass index (BMI) in 8,891 postmenopausal women, were identified by all methods applied. SNP rs10483028 was associated with breast cancer in women with a BMI below 25 kg/m2 (OR = 1.26, 95% CI 1.15–1.38) but not in women with a BMI of 30 kg/m2 or higher (OR = 0.89, 95% CI 0.72–1.11, P for interaction = 3.2 × 10−05). Our findings confirm comparable power of the recent methods for detecting G × E interaction and the utility of using G × E interaction analyses to identify new susceptibility loci.

Abstract Part of the substantial unexplained familial aggregation of breast cancer may be due to interactions between common variants, but few studies have had adequate statistical power to detect interactions of realistic magnitude. We aimed to assess all two-way interactions in breast cancer susceptibility between 70 917 single nucleotide polymorphisms (SNPs) selected primarily based on prior evidence of a marginal effect. Thirty-eight international studies contributed data for 46 450 breast cancer cases and 42 461 controls of European origin as part of a multi-consortium project (COGS). First, SNPs were preselected based on evidence (P &lt; 0.01) of a per-allele main effect, and all two-way combinations of those were evaluated by a per-allele (1 d.f.) test for interaction using logistic regression. Second, all 2.5 billion possible two-SNP combinations were evaluated using Boolean operation-based screening and testing, and SNP pairs with the strongest evidence of interaction (P &lt; 10−4) were selected for more careful assessment by logistic regression. Under the first approach, 3277 SNPs were preselected, but an evaluation of all possible two-SNP combinations (1 d.f.) identified no interactions at P &lt; 10−8. Results from the second analytic approach were consistent with those from the first (P &gt; 10−10). In summary, we observed little evidence of two-way SNP interactions in breast cancer susceptibility, despite the large number of SNPs with potential marginal effects considered and the very large sample size. This finding may have important implications for risk prediction, simplifying the modelling required. Further comprehensive, large-scale genome-wide interaction studies may identify novel interacting loci if the inherent logistic and computational challenges can be overcome.

The cytochrome P450 2B6 (CYP2B6) is involved in the metabolism of testosterone. Functional changes in this enzyme may influence endogenous hormone exposure, which has been associated with risk of breast cancer. To assess potential associations between two functional polymorphisms CYP2B6_516_G>T (rs3745274) and CYP2B6_785_A>G (rs2279343) and breast cancer risk, we established a specific matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry assay. The GENICA breast cancer case–control study showed associations between the variant genotypes CYP2B6_516_TT and CYP2B6_785_GG and breast cancer risk with odds ratios (ORs) of 1.34 (p = 0.001) and 1.31 (p = 0.002), respectively. A similar effect was observed for carriers of the CYP2B6_516_T allele in a validation study including four independent studies from Germany, Sweden and USA. In a pooled analysis of all five studies involving 4,638 breast cancer cases and 3,594 controls of European ancestry, carriers of the CYP2B6_516_G and the CYP2B6_785_G variant had an increased breast cancer risk with ORs of 1.10 (p = 0.027) and 1.10 (p = 0.031), respectively. We conclude that the genetic variants CYP2B6_516_G and CYP2B6_785_G (designated CYP2B6*6), which are known to decrease activity of the CYP2B6 enzyme, contribute to an increased breast cancer risk.