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Genome-wide association studies (GWAS) have identified approximately 100 breast cancer risk loci. Translating these findings into a greater understanding of the mechanisms that influence disease risk requires identification of the genes or non-coding RNAs that mediate these associations. Here, we use Capture Hi-C (CHi-C) to annotate 63 loci; we identify 110 putative target genes at 33 loci. To assess the support for these target genes in other data sources we test for associations between levels of expression and SNP genotype (eQTLs), disease-specific survival (DSS), and compare them with somatically mutated cancer genes. 22 putative target genes are eQTLs, 32 are associated with DSS and 14 are somatically mutated in breast, or other, cancers. Identifying the target genes at GWAS risk loci will lead to a greater understanding of the mechanisms that influence breast cancer risk and prognosis. Risk loci for breast cancer have been identified by genome-wide association studies. Here, the authors use Capture Hi-C to identify 110 putative target genes at 33 loci and assessed associations of gene expression, SNP genotype, and survival, providing evidence of mechanisms that may influence the prognosis and risk of breast cancer.

The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane . The capture Hi-C assay and its variants require specialized statistical methods for identification of high-confidence contacts. This protocol presents a versatile computational pipeline for detecting interactions and performing downstream analyses.

BackgroundBRCA1 interacting protein C-terminal helicase 1 (BRIP1) is one of the Fanconi Anaemia Complementation (FANC) group family of DNA repair proteins. Biallelic mutations in BRIP1 are responsible for FANC group J, and previous studies have also suggested that rare protein truncating variants in BRIP1 are associated with an increased risk of breast cancer. These studies have led to inclusion of BRIP1 on targeted sequencing panels for breast cancer risk prediction.MethodsWe evaluated a truncating variant, p.Arg798Ter (rs137852986), and 10 missense variants of BRIP1, in 48 144 cases and 43 607 controls of European origin, drawn from 41 studies participating in the Breast Cancer Association Consortium (BCAC). Additionally, we sequenced the coding regions of BRIP1 in 13 213 cases and 5242 controls from the UK, 1313 cases and 1123 controls from three population-based studies as part of the Breast Cancer Family Registry, and 1853 familial cases and 2001 controls from Australia.ResultsThe rare truncating allele of rs137852986 was observed in 23 cases and 18 controls in Europeans in BCAC (OR 1.09, 95% CI 0.58 to 2.03, p=0.79). Truncating variants were found in the sequencing studies in 34 cases (0.21%) and 19 controls (0.23%) (combined OR 0.90, 95% CI 0.48 to 1.70, p=0.75).ConclusionsThese results suggest that truncating variants in BRIP1, and in particular p.Arg798Ter, are not associated with a substantial increase in breast cancer risk. Such observations have important implications for the reporting of results from breast cancer screening panels.

The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane.

The Breast Cancer Association Consortium (BCAC) has been established to conduct combined case-control analyses with augmented statistical power to try to confirm putative genetic associations with breast cancer. We genotyped nine SNPs for which there was some prior evidence of an association with breast cancer: CASP8 D302H (rs1045485), IGFBP3 −202 C → A (rs2854744), SOD2 V16A (rs1799725), TGFB1 L10P (rs1982073), ATM S49C (rs1800054), ADH1B 3′ UTR A → G (rs1042026), CDKN1A S31R (rs1801270), ICAM5 V301I (rs1056538) and NUMA1 A794G (rs3750913). We included data from 9–15 studies, comprising 11,391–18,290 cases and 14,753–22,670 controls. We found evidence of an association with breast cancer for CASP8 D302H (with odds ratios (OR) of 0.89 (95% confidence interval (c.i.): 0.85–0.94) and 0.74 (95% c.i.: 0.62–0.87) for heterozygotes and rare homozygotes, respectively, compared with common homozygotes; P trend = 1.1 × 10 −7 ) and weaker evidence for TGFB1 L10P (OR = 1.07 (95% c.i.: 1.02–1.13) and 1.16 (95% c.i.: 1.08–1.25), respectively; P trend = 2.8 × 10 −5 ). These results demonstrate that common breast cancer susceptibility alleles with small effects on risk can be identified, given sufficiently powerful studies. NOTE: In the version of this article initially published, there was an error that affected the calculations of the odds ratios, confidence intervals, between-study heterogeneity, trend test and test for association for SNP ICAM5 V301I in Table 1 (ICAM5 V301I); genotype counts in Supplementary Table 2 (ICAM5; ICR_FBCS and Kuopio studies) and minor allele frequencies, trend test and odds ratios for heterozygotes and rare homozygotes in Supplementary Table 3 (ICAM5; ICR_FBCS and Kuopio studies). The errors in Table 1 have been corrected in the PDF version of the article. The errors in supplementary information have been corrected online.

Nick Orr and colleagues report a genome-wide association study for male breast cancer. They identify a new susceptibility locus at RAD51B and examine association evidence for known female breast cancer loci in these cohorts. We conducted a genome-wide association study of male breast cancer comprising 823 cases and 2,795 controls of European ancestry, with validation in independent sample sets totaling 438 cases and 474 controls. A SNP in RAD51B at 14q24.1 was significantly associated with male breast cancer risk ( P = 3.02 × 10 −13 ; odds ratio (OR) = 1.57). We also refine association at 16q12.1 to a SNP within TOX3 ( P = 3.87 × 10 −15 ; OR = 1.50).

Background: Endogenous sex hormones are well-established risk factors for breast cancer; the contribution of specific oestrogen metabolites (EMs) and/or ratios of specific EMs is less clear. We have previously identified a CYP3A7*1C allele that is associated with lower urinary oestrone (E 1 ) levels in premenopausal women. The purpose of this analysis was to determine whether this allele was associated with specific pathway EMs. Methods: We measured successfully 12 EMs in mid-follicular phase urine samples from 30 CYP3A7*1C carriers and 30 non-carriers using HPLC-MS/MS. Results: In addition to having lower urinary E 1 levels, CYP3A7*1C carriers had significantly lower levels of four of the 2-hydroxylation pathway EMs that we measured (2-hydroxyestrone, P =1.1 × 10 −12 ; 2-hydroxyestradiol, P =2.7 × 10 −7 ; 2-methoxyestrone, P =1.9 × 10 −12 ; and 2-methoxyestradiol, P =0.0009). By contrast, 16α-hydroxylation pathway EMs were slightly higher in carriers and significantly so for 17-epiestriol ( P =0.002). Conclusions: The CYP3A7*1C allele is associated with a lower urinary E 1 levels, a more pronounced reduction in 2-hydroxylation pathway EMs and a lower ratio of 2-hydroxylation:16α-hydroxylation EMs in premenopausal women. To further characterise the association between parent oestrogens, EMs and subsequent risk of breast cancer, characterisation of additional genetic variants that influence oestrogen metabolism and large prospective studies of a broad spectrum of EMs will be required.

Background It is well established that estrogens and other hormonal factors influence breast cancer susceptibility. We hypothesized that a woman’s total lifetime estrogen exposure accumulates changes in DNA methylation, detectable in the blood, which could be used in risk assessment for breast cancer. Methods An estimated lifetime estrogen exposure (ELEE) model was defined using epidemiological data from EPIC-Italy ( n  = 31,864). An epigenome-wide association study (EWAS) of ELEE was performed using existing Illumina HumanMethylation450K Beadchip (HM450K) methylation data obtained from EPIC-Italy blood DNA samples ( n  = 216). A methylation index (MI) of ELEE based on 31 CpG sites was developed using HM450K data from EPIC-Italy and the Generations Study and evaluated for association with breast cancer risk in an independent dataset from the Generations Study ( n  = 440 incident breast cancer cases matched to 440 healthy controls) using targeted bisulfite sequencing. Lastly, a meta-analysis was conducted including three additional cohorts, consisting of 1187 case-control pairs. Results We observed an estimated 5% increase in breast cancer risk per 1-year longer ELEE (OR = 1.05, 95% CI 1.04–1.07, P  = 3 × 10 −12 ) in EPIC-Italy. The EWAS identified 694 CpG sites associated with ELEE (FDR Q  < 0.05). We report a DNA methylation index (MI) associated with breast cancer risk that is validated in the Generations Study targeted bisulfite sequencing data (OR Q4_vs_Q1  = 1.77, 95% CI 1.07–2.93, P  = 0.027) and in the meta-analysis (OR Q4_vs_Q1  = 1.43, 95% CI 1.05–2.00, P  = 0.024); however, the correlation between the MI and ELEE was not validated across study cohorts. Conclusion We have identified a blood DNA methylation signature associated with breast cancer risk in this study. Further investigation is required to confirm the interaction between estrogen exposure and DNA methylation in the blood.