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. 2012 Feb 20;14(1):R33. doi: 10.1186/bcr3121

Common variants at 12p11, 12q24, 9p21, 9q31.2 and in ZNF365 are associated with breast cancer risk for BRCA1 and/or BRCA2 mutation carriers

Antonis C Antoniou 1,✉, Karoline B Kuchenbaecker 1, Penny Soucy 2, Jonathan Beesley 3, Xiaoqing Chen 3, Lesley McGuffog 1, Andrew Lee 1, Daniel Barrowdale 1, Sue Healey 3, Olga M Sinilnikova 4, Maria A Caligo 5, Niklas Loman 6, Katja Harbst 6, Annika Lindblom 7, Brita Arver 8, Richard Rosenquist 9, Per Karlsson 10, Kate Nathanson 11, Susan Domchek 11, Tim Rebbeck 11, Anna Jakubowska 12, Jan Lubinski 12, Katarzyna Jaworska 12, Katarzyna Durda 13, Elżbieta Złowowcka-Perłowska 12, Ana Osorio 14, Mercedes Durán 15, Raquel Andrés 16, Javier Benítez 17, Ute Hamann 18, Frans B Hogervorst 19, Theo A van Os 20, Senno Verhoef 21, Hanne EJ Meijers-Heijboer 22, Juul Wijnen 23, Encarna B Gómez Garcia 24, Marjolijn J Ligtenberg 25, Mieke Kriege 26, J Margriet Collée 27, Margreet GEM Ausems 28, Jan C Oosterwijk 29, Susan Peock 1, Debra Frost 1, Steve D Ellis 1, Radka Platte 1, Elena Fineberg 1, D Gareth Evans 31, Fiona Lalloo 31, Chris Jacobs 32, Ros Eeles 33, Julian Adlard 34, Rosemarie Davidson 35, Trevor Cole 36, Jackie Cook 37, Joan Paterson 38, Fiona Douglas 39, Carole Brewer 40, Shirley Hodgson 41, Patrick J Morrison 42, Lisa Walker 43, Mark T Rogers 44, Alan Donaldson 45, Huw Dorkins 46, Andrew K Godwin 47, Betsy Bove 48, Dominique Stoppa-Lyonnet 49, Claude Houdayer 50, Bruno Buecher 51, Antoine de Pauw 52, Sylvie Mazoyer 53, Alain Calender 54, Mélanie Léoné 54, Brigitte Bressac- de Paillerets 55, Olivier Caron 56, Hagay Sobol 57, Marc Frenay 58, Fabienne Prieur 59, Sandra Fert Ferrer 60, Isabelle Mortemousque 61, Saundra Buys 63, Mary Daly 64, Alexander Miron 65, Mary Beth Terry 66, John L Hopper 67, Esther M John 68, Melissa Southey 69, David Goldgar 70, Christian F Singer 71, Anneliese Fink-Retter 71, Muy-Kheng Tea 71, Daphne Geschwantler Kaulich 71, Thomas VO Hansen 72, Finn C Nielsen 72, Rosa B Barkardottir 73, Mia Gaudet 74, Tomas Kirchhoff 75, Vijai Joseph 76, Ana Dutra-Clarke 76, Kenneth Offit 76, Marion Piedmonte 77, Judy Kirk 78, David Cohn 79, Jean Hurteau 80, John Byron 81, James Fiorica 82, Amanda E Toland 83, Marco Montagna 84, Cristina Oliani 85, Evgeny Imyanitov 86, Claudine Isaacs 87, Laima Tihomirova 88, Ignacio Blanco 89, Conxi Lazaro 90, Alex Teulé 89, J Del Valle 90, Simon A Gayther 91, Kunle Odunsi 92, Jenny Gross 93, Beth Y Karlan 93, Edith Olah 94, Soo-Hwang Teo 95, Patricia A Ganz 96, Mary S Beattie 97, Cecelia M Dorfling 98, Elizabeth Jansen van Rensburg 98, Orland Diez 99, Ava Kwong 100, Rita K Schmutzler 101, Barbara Wappenschmidt 101, Christoph Engel 102, Alfons Meindl 103, Nina Ditsch 104, Norbert Arnold 105, Simone Heidemann 106, Dieter Niederacher 107, Sabine Preisler-Adams 108, Dorothea Gadzicki 109, Raymonda Varon-Mateeva 110, Helmut Deissler 111, Andrea Gehrig 112, Christian Sutter 113, Karin Kast 114, Britta Fiebig 115, Dieter Schäfer 116, Trinidad Caldes 117, Miguel de la Hoya 117, Heli Nevanlinna 118, Taru A Muranen 118, Bernard Lespérance 119, Amanda B Spurdle 3, Susan L Neuhausen 121, Yuan C Ding 121, Xianshu Wang 122, Zachary Fredericksen 123, Vernon S Pankratz 123, Noralane M Lindor 124, Paolo Peterlongo 125, Siranoush Manoukian 126, Bernard Peissel 126, Daniela Zaffaroni 126, Bernardo Bonanni 127, Loris Bernard 128, Riccardo Dolcetti 129, Laura Papi 130, Laura Ottini 131, Paolo Radice 125, Mark H Greene 132, Jennifer T Loud 132, Irene L Andrulis 133, Hilmi Ozcelik 134, Anna Marie Mulligan 135, Gord Glendon 136, Mads Thomassen 137, Anne-Marie Gerdes 138, Uffe B Jensen 139, Anne-Bine Skytte 140, Torben A Kruse 137, Georgia Chenevix-Trench 3, Fergus J Couch 141, Jacques Simard 142, Douglas F Easton 1, CIMBA, SWE-BRCA
PMCID: PMC3496151  PMID: 22348646

Abstract

Introduction

Several common alleles have been shown to be associated with breast and/or ovarian cancer risk for BRCA1 and BRCA2 mutation carriers. Recent genome-wide association studies of breast cancer have identified eight additional breast cancer susceptibility loci: rs1011970 (9p21, CDKN2A/B), rs10995190 (ZNF365), rs704010 (ZMIZ1), rs2380205 (10p15), rs614367 (11q13), rs1292011 (12q24), rs10771399 (12p11 near PTHLH) and rs865686 (9q31.2).

Methods

To evaluate whether these single nucleotide polymorphisms (SNPs) are associated with breast cancer risk for BRCA1 and BRCA2 carriers, we genotyped these SNPs in 12,599 BRCA1 and 7,132 BRCA2 mutation carriers and analysed the associations with breast cancer risk within a retrospective likelihood framework.

Results

Only SNP rs10771399 near PTHLH was associated with breast cancer risk for BRCA1 mutation carriers (per-allele hazard ratio (HR) = 0.87, 95% CI: 0.81 to 0.94, P-trend = 3 × 10-4). The association was restricted to mutations proven or predicted to lead to absence of protein expression (HR = 0.82, 95% CI: 0.74 to 0.90, P-trend = 3.1 × 10-5, P-difference = 0.03). Four SNPs were associated with the risk of breast cancer for BRCA2 mutation carriers: rs10995190, P-trend = 0.015; rs1011970, P-trend = 0.048; rs865686, 2df-P = 0.007; rs1292011 2df-P = 0.03. rs10771399 (PTHLH) was predominantly associated with estrogen receptor (ER)-negative breast cancer for BRCA1 mutation carriers (HR = 0.81, 95% CI: 0.74 to 0.90, P-trend = 4 × 10-5) and there was marginal evidence of association with ER-negative breast cancer for BRCA2 mutation carriers (HR = 0.78, 95% CI: 0.62 to 1.00, P-trend = 0.049).

Conclusions

The present findings, in combination with previously identified modifiers of risk, will ultimately lead to more accurate risk prediction and an improved understanding of the disease etiology in BRCA1 and BRCA2 mutation carriers.

Introduction

Pathogenic mutations in BRCA1 and BRCA2 confer high risks of breast and ovarian cancers [1,2]. Several lines of evidence suggest that these risks are modified by other genetic or environmental factors that cluster in families. Direct evidence for genetic modifiers of risk has been provided through studies that investigated the associations between common breast and ovarian cancer susceptibility variants, identified through genome-wide association studies (GWAS) or candidate gene studies in the general population, and cancer risk for BRCA1 and BRCA2 mutation carriers [3-8] and through GWAS in BRCA1 and BRCA2 mutation carriers [9-11]. Six loci (at TOX3, 2q35, 6q25.1, 19p13, CASP8 and wild-type copy of BRCA1) are now known to be associated with breast cancer risk for BRCA1 mutation carriers; a further 10 loci (at FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, 1p11.2, ZNF365 and RAD51) have been associated with breast cancer risk for BRCA2 carriers. The association patterns between these common variants and breast cancer risk for BRCA1 and BRCA2 mutation carriers are in general different, and mostly reflect differences in the associations of these single-nucleotide polymorphism (SNPs) with estrogen receptor (ER) status of breast cancer [12-14].

GWAS in the general population have recently identified eight additional breast cancer susceptibility loci which have not been previously investigated in BRCA1 and BRCA2 mutation carriers. Turnbull et al. [15] identified five susceptibility loci on chromosomes 9 (rs1011970), 10 (rs2380205, rs10995190, rs704010) and 11 (rs614367) through a GWAS of breast cancer cases with a family history of the disease and unrelated controls. In a further follow-up of additional promising associations from that GWAS, the Breast Cancer Association Consortium (BCAC) has identified two additional loci at 12p11 (rs10771399) and 12q24 (rs1292011) which were associated with breast cancer risk in the general population [16]. The estimated odds ratios (OR) for ER-positive breast cancer for four of these SNPs (rs1011970 near CDKN2A/CDKN2B at chromosome 9, rs10995190 in ZNF365 at chromosome 10, rs614367 at 11q13 and rs1292011 at 12q24) were higher than the OR estimates for ER-negative breast cancer. In contrast, the OR estimates were similar for ER-positive and ER-negative breast cancer for SNPs rs2380205 (near ANKRD16 and FBXO18), rs704010 (upstream of ZMIZ1) and rs10771399 near PTHLH. In a separate GWAS that included mainly cases with two primary breast cancers or a family history of the disease, SNP rs865686 at 9q31.2 was found to be associated with risk for breast cancer, OR = 0.89 (95% CI: 0.85 to 0.92), but no estimates by ER status were reported [17].

The associations of these eight loci with breast cancer risk for BRCA1 and BRCA2 mutation carriers are still unknown. To evaluate these associations, we genotyped the eight SNPs in BRCA1 and BRCA2 mutation carriers participating in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). We further investigated the associations with the risks of developing ER-positive and ER-negative breast cancer and the risk of ovarian cancer.

Materials and methods

Subjects

All carriers participated in clinical or research studies at the host institutions which have been approved by local ethics committees (list provided in Additional file 1 Table S1). Informed consent was obtained from all study participants. Subjects were BRCA1 and BRCA2 mutation carriers recruited by 40 study centres in 28 countries through CIMBA (Additional file 1 Table 2). The majority of carriers (97.58%) were recruited through cancer genetics clinics offering genetic testing, and enrolled into national or regional studies. Some carriers were identified by population-based sampling of cases (2.38%), and some by community recruitment (0.04%). Eligibility to participate in CIMBA is restricted to female carriers of pathogenic BRCA1 or BRCA2 mutations age 18 years old or older at recruitment. Information collected included the year of birth; mutation description, including nucleotide position and base change; self reported ethnic ancestry, age at last follow-up; ages at breast or ovarian cancer diagnoses; and age or date at bilateral prophylactic mastectomy and oophorectomy. Related individuals were identified through a unique family identifier. Women were included in the analysis if they carried mutations that were pathogenic according to generally recognized criteria [18]. Further details on CIMBA can be found elsewhere [19].

Women who carried pathogenic mutations in both BRCA1 and BRCA2 were excluded from the current analysis. The primary analysis was restricted to women self-reported as "white of European ancestry". The number of mutation carriers of non-white ancestry was too small to allow separate analysis. We investigated possible overlap of carriers between studies by comparing the year of birth, exact mutation description and the reported ages, to identify potential duplicate individuals. Where possible we also used other genotype data on SNPs genotyped in the current round (at least 26 SNPs), in previous genotyping rounds or as part of GWAS to find hidden duplicates. When a potential duplicate was identified, we contacted the relevant centres for further information about these individuals, in a manner that protected the identity of the individuals in question, in order to determine precisely the extent of true overlap in subjects and families appearing more than once in the data set. Duplicate mutation carriers were included only once in the analysis. When in doubt, and when centres could not clarify a potential duplication, one of the samples was excluded from the analysis.

Genotyping

DNA samples (in almost all cases, obtained from blood) were genotyped using the iPLEX Mass Array platform at four genotyping centres (Additional file 1 Table S2); the iPLEX included 26 SNPs as part of a larger study. All centres included at least 2% of the samples in duplicate, no template controls in every plate, and a random mixture of affected and unaffected carriers. Samples that failed for ≥ 20% of all the SNPs typed (that is, five or more) were excluded from the analysis. A study was included in the analysis only if the call rate was over 95%, after samples that failed at multiple SNPs had been excluded. For each study, genotypes for at least 98% of the duplicate samples had to be concordant. To assess the accuracy of genotyping across genotyping centres, the four centres genotyped 95 DNA samples from a standard test plate (Coriell Institute) for all SNPs. If the genotyping was inconsistent for more than one sample in the test plate, all the studies genotyped at the centre were excluded from the analysis of that SNP. No SNPs failed this criterion. The present study included eight SNPs: rs1011970 (9p21, near CDKN2A/B), rs10995190 (10q21, near ZNF365), rs704010 (10q22, near ZMIZ1), rs2380205 (10p15), rs614367 (11q13), rs1292011 (12q24), rs10771399 (12p11 near PTHLH) and rs865686 (9q31.2). Based on the quality control criteria, 4 studies were excluded from the analysis of rs2380205 (one due to low duplicate concordance, 3 due to low call rate), 2 studies were excluded from the analysis of rs704010 (low call rate) and 13 studies were excluded from the analysis of rs1292011 (all due to low call rates). As an additional genotyping quality-control check, we also evaluated the deviation from Hardy-Weinberg equilibrium (HWE) for unrelated subjects separately for each SNP and study. Nine studies had HWE P-values in the range 0.005 to 0.05 (two studies for rs10995190, two studies for rs704010, one study for rs10771399, two for rs1292011 and two for rs865686). Upon examination of the cluster plots for these studies and SNPs, none revealed any unusual patterns and these studies were included in all the analyses. After the above exclusions, a total of 19,731 unique mutation carriers (12,599 BRCA1 and 7,132 BRCA2) from 40 studies had an observed genotype for at least 1 of the SNPs and were included in the primary analysis.

Statistical analysis

The aim of the primary analysis was to evaluate the association between each genotype and breast cancer risk within a survival analysis framework. The time variable for each individual was defined to be the time to breast cancer diagnosis. Each individual was followed until the first breast cancer diagnosis, ovarian cancer diagnosis, or bilateral prophylactic mastectomy or the age at last observation. Only those with a first breast cancer diagnosis were considered as affected in the analysis. Mutation carriers censored at ovarian cancer diagnosis were considered unaffected. Analysis was conducted by modelling the retrospective likelihood of the observed genotypes conditional on the disease phenotypes as previously described [18]. The effect of each SNP was modelled either as a per-allele hazard ratio (HR) (multiplicative model) or as separate HRs for heterozygotes and homozygotes, and these were estimated on the logarithmic scale. The HRs were assumed to be independent of age (that is, we used a Cox proportional-hazards model). The assumption of proportional hazards was tested by adding a "genotype x age" interaction term to the model in order to fit models in which the HR changed with age. Analyses were carried out with the pedigree-analysis software MENDEL [20]; details of this approach have been described previously [18,21]. We examined between-study heterogeneity by comparing the models that allowed for study-specific log-hazard ratios against models in which the same log-hazard ratio was assumed to apply to all studies.

To investigate whether our results were influenced by any of our assumptions we performed additional sensitivity analyses. If a SNP is associated with disease survival, the inclusion of prevalent cases may influence the HR estimates. Current data indicate that five-year survival after a breast cancer diagnosis is over 80% (Cancer Research - UK, Breast cancer survival statistics) and studies have suggested no difference in survival between mutation carriers and non-carriers [22]. We, therefore, repeated our analysis by excluding mutation carriers diagnosed more than five years prior to recruitment into the study. To examine whether SNP associations differed by type of mutation, we classified BRCA1 mutations according to their potential functional effect [23-26]. Class 1 mutations were those likely to lead to the absence of protein expression due to i) reduced transcript level and/or degradation or instability of truncated proteins, or ii) absence of transcription. Class 1 mutations comprise truncating mutations expected to trigger nonsense-mediated mRNA decay (NMD) or translation re-initiation but no production of stable protein, and deletion of transcription regulatory regions. Class 2 mutations were those likely to generate stable mutant proteins with partial or total loss of function that might also have dominant negative effect. Class 2 mutations include missense substitutions, in-frame deletions and insertions, as well as truncating mutations with premature stop codons occurring in the last exon. Mutations whose consequences at transcript or protein level could not be inferred were not considered for this classification. These were mainly mutations located in splice sites but not characterised for their effect at the transcript level, or large deletions or insertions with undetermined boundaries.

The associations of these SNPs with ovarian cancer risk were evaluated within a competing risk analysis framework [8,9,21], by estimating HRs simultaneously for breast and ovarian cancers. In this model, each individual was at risk of developing either breast or ovarian cancer, by assuming that the probabilities of developing each disease were independent conditional on the underlying genotype. A different censoring process was used for the competing risk analysis, whereby individuals were followed up to the age of the first breast or ovarian cancer diagnosis and were considered to have developed the corresponding disease. No follow-up was considered after the first cancer diagnosis. Individuals were censored for breast cancer at the age of bilateral prophylactic mastectomy and for ovarian cancer at the age of bilateral oophorectomy and were assumed to be unaffected for the corresponding disease. The remaining individuals were censored at the age at last observation and were assumed to be unaffected for both diseases.

We further evaluated the associations of these SNPs with breast cancer subtypes defined by the estrogen receptor (ER) status of the tumours in BRCA1 and BRCA2 mutation carriers. The analysis was carried out by an extension of the retrospective likelihood approach to model the simultaneous effect of each SNP on more than one tumour subtype [14]. Briefly, this involves modelling the conditional likelihood of the observed SNP genotypes and tumour subtypes, given the disease phenotypes. Within this framework it is possible to estimate simultaneously the HRs for each tumour subtype and test for heterogeneity in the associations. Only studies that provided tumour pathology information and had genotype information were included in the analysis. To maximise the available information, genotyped mutation carriers that were missing information on tumour characteristics (within each study) were included in the analysis, and their disease subtype was assumed to be missing at random [14]. This is a reasonable assumption given that more than 90% of mutation carriers in our sample were recruited prior to 2007, when it was uncommon to use tumour pathology in selecting individuals for BRCA1 and BRCA2 mutation screening.

To ensure a sufficiently large number of mutation carriers within each stratum, we grouped studies from the same country. All analyses were stratified by country of residence and used calendar-year- and cohort-specific cancer incidences for BRCA1 and BRCA2 [27]. For sensitivity analyses, strata with small numbers of mutation carriers were grouped. We used a robust variance-estimation approach to allow for the non-independence among related carriers [28].

Results

The analysis included 12,599 BRCA1 and 7,132 BRCA2 mutation carriers who were genotyped successfully for at least one of the eight SNPs. Table 1 summarises the characteristics of the mutation carriers used in the analysis. In evaluating associations with breast cancer, 10,200 mutation carriers had been diagnosed with a first breast cancer diagnosis, 1,869 were censored at an ovarian cancer diagnosis, 561 at age of bilateral prophylactic mastectomy and 7,101 at the age at last observation.

Table 1.

Summary characteristics for the 19731 eligible BRCA1 and BRCA2 carriers* used in the analysis

Characteristic BRCA1 BRCA2
Unaffected Breast cancer Unaffected Breast cancer
Number 6,209 6,390 3,322 3,810
Person-years follow-up 264,903 263,068 147,053 168,201
Median age at censure (IQR1) 42 (34 to 50) 40 (34 to 47) 43 (34 to 53) 43 (37 to 50)
Age at censure, N (%)
< 30 1,189 (19.2) 691 (10.8) 611 (18.4) 306 (8.0)
30 to 39 1,161 (26.8) 2,445 (38.3) 834 (25.1) 1,141 (30.0)
40 to 49 1,765 (28.4) 2,191 (34.3) 865 (26.0) 1,394 (36.6)
50 to 59 1,058 (17.0) 812 (12.7) 566 (17.0) 687 (18.0)
60 to 69 380 (6.1) 198 (3.1) 302 (9.1) 226 (5.9)
70+ 156 (2.5) 53 (0.8) 144 (4.3) 56 (1.5)
Year of birth, N (%)
< 1920 28 (0.5) 30 (0.5) 23 (0.7) 44 (1.2)
1920 to 1929 131 (2.1) 196 (3.1) 99 (3.0) 167 (4.4)
1930 to 1939 369 (5.9) 516 (8.1) 232 (7.0) 430 (11.3)
1940 to 1949 832 (13.4) 1,341 (21.0) 458 (13.8) 896 (23.5)
1950 to 1959 1,409 (22.7) 1,989 (31.1) 691 (20.8) 1,160 (30.5)
1960 to 1969 1,703 (27.4) 1,666 (26.1) 902 (27.2) 868 (22.8)
1970+ 1,737 (28.0) 652 (10.2) 917 (27.6) 245 (6.4)
Mutation Class, N (%)
Class 12 4,063 (65.4) 3,878 (60.7) 3,114 (93.7) 3,520 (92.4)
Class 22 1,780 (28.7) 1,973 (30.9) 72 (2.2) 100 (2.6)
Other 366 (5.9) 539 (8.4) 136 (4.1) 190 (5.0)
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1 IQR: Interquartile Range

2 See methods for definitions

* Carriers of self-reported white European ancestry only.

Associations with cancer risk for BRCA1 mutation carriers

Of the eight SNPs, only rs10771399 in PTHLH was associated with breast cancer risk for BRCA1 mutation carriers (P-trend = 3 × 10-4, Table 2). The association was consistent with a multiplicative model in which each copy of the minor allele was estimated to confer a HR of 0.87 (95% CI: 0.81 to 0.94). There was no evidence of heterogeneity in the HR estimates across studies (P-het = 0.24, Additional file 1 Supplementary Figure 1). There was no evidence that the HRs varied with age (P = 0.68). The association remained significant, with a similar HR estimate (HR = 0.85, 95% CI: 0.77 to 0.93, P-trend = 6 × 10-4, Table 3), when long-term survivors were excluded from the analysis, suggesting no evidence of survival bias. Interestingly, the association was restricted to BRCA1 carriers of Class 1 mutations (HR = 0.82, 95% CI: 0.74 to 0.90, P-trend = 3 × 10-5, Table 3) with no evidence of association for Class 2 mutation carriers (HR = 1.00, 0.87 to 1.15, P-trend = 0.99, P-difference between Class 1 and Class 2 = 0.03).

Table 2.

SNP genotype distributions and associations with breast cancer risk.

Mutation Genotype Unaffected
N (%)		 Affecteda
N (%)		 HR 95% CI P-value
CDK2NA/B - rs1011970
BRCA1 GG 4,318 (69.7) 4,460 (70.0) 1
GT 1,698 (27.4) 1,719 (27.0) 1.01 0.94 to 1.09
TT 180 (2.9) 195 (3.1) 1.11 0.91 to 1.35
2-df test 0.61
per allele 1.03 0.96 to 1.09 0.45
BRCA2 GG 2,279 (68.7) 2,586 (67.9) 1
GT 943 (28.4) 1,098 (28.9) 1.08 0.98 to 1.19
TT 94 (2.8) 123 (3.2) 1.23 0.95 to 1.59
2-df test 0.12
per allele 1.09 1.00 to 1.18 0.048
ZNF365 - rs10995190
BRCA1 GG 4,394 (70.9) 4,556 (71.5) 1
GA 1,656 (26.7) 1,662 (26.1) 0.98 0.91 to 1.06
AA 147 (2.4) 156 (2.5) 0.98 0.79 to 1.20
2-df test 0.89
per allele 0.99 0.93 to 1.05 0.64
BRCA2 GG 2,334 (70.4) 2,802 (73.7) 1
GA 913 (27.5) 923 (24.3) 0.86 0.78 to 0.96
AA 68 (20.1) 79 (2.1) 0.96 0.69 to 1.34
2-df test 0.019
per allele 0.90 0.82 to 0.98 0.015
ZMIZ1 - rs704010
BRCA1 CC 2,476 (40.3) 2,504 (39.8) 1
CT 2,814 (45.8) 2,894 (46.0) 1.03 0.96 to 1.10
TT 855 (13.9) 888 (14.1) 1.04 0.93 to 1.15
2-df test 0.69
per allele 1.02 0.97 to 1.07 0.42
BRCA2 CC 1,286 (39.3) 1,443 (38.4) 1
CT 1,496 (45.7) 1,779 (47.3) 1.07 0.97 to 1.18
TT 494 (15.8) 539 (14.3) 0.99 0.86 to 1.14
2-df test 0.32
per allele 1.01 0.95 to 1.08 0.73
10p15 - rs2380205
BRCA1 CC 1,609 (32.5) 1,710 (32.1) 1
CT 2,410 (48.7) 2,625 (49.3) 1.01 0.93 to 1.09
TT 933 (18.8) 990 (18.6) 1.02 0.92 to 1.13
2-df test 0.95
per allele 1.01 0.96 to 1.06 0.75
BRCA2 CC 1,013 (32.8) 1,163 (31.8) 1
CT 1,516 (49.1) 1,816 (49.7) 1.05 0.95 to 1.16
TT 560 (18.1) 681 (18.6) 1.03 0.90 to 1.17
2-df test 0.63
per allele 1.02 0.96 to 1.09 0.57
11q13 - rs614367
BRCA1 CC 4,516 (73.2) 4,581 (72.1) 1
CT 1,511 (24.5) 1,618 (25.5) 1.05 0.98 to 1.14
TT 146 (2.4) 154 (2.4) 1.07 0.87 to 1.32
2-df test 0.34
per allele 1.05 0.98 to 1.12 0.15
BRCA2 CC 2,432 (73.6) 2,723 (71.8) 1
CT 799 (24.1) 983 (26.0) 1.06 0.96 to 1.17
TT 76 (2.3) 83 (2.2) 0.97 0.72 to 1.30
2-df test 0.54
per allele 1.03 0.95 to 1.12 0.46
12q24 - rs1292011
BRCA1 AA 1,292 (34.3) 1,331 (35.4) 1
AG 1,825 (48.4) 1,775 (47.3) 0.98 0.89 to 1.07
GG 653 (17.3) 649 (17.3) 1.01 0.90 to 1.14
2-df test 0.80
per allele 1.00 0.94 to 1.06 0.99
BRCA2 AA 824 (35.2) 908 (35.9) 1
AG 1,095 (46.7) 1,225 (48.4) 1.03 0.92 to 1.16
GG 423 (18.1) 397 (15.7) 0.84 0.72 to 0.99
2-df test 0.03
per allele 0.94 0.87 to 1.01 0.10
PTHLH - rs10771399
BRCA1 AA 4,913 (79.4) 5,221 (82.0) 1
AG 1,194 (19.3) 1,082 (17.0) 0.87 0.80 to 0.95
GG 83 (1.3) 65 (1.0) 0.77 0.57 to 1.04
2-df test 1.5 × 10-3
per allele 0.87 0.81 to 0.94 3.2 × 10-4
BRCA2 AA 2,649 (80.0) 3,085 (81.2) 1
AG 620 (18.7) 679 (17.9) 0.95 0.85 to 1.07
GG 45 (1.4) 34 (0.9) 0.74 0.47 to 1.15
2-df test 0.31
per allele 0.93 0.84 to 1.04 0.20
9q31.2 - rs865686
BRCA1 TT 2,521 (40.1) 2,640 (41.4) 1
TG 2,872 (46.4) 2,849 (44.7) 0.95 0.88 to 1.01
GG 799 (12.9) 880 (13.8) 1.05 0.95 to 1.17
2-df test 0.06
per allele 1 0.96 to 1.05 0.85
BRCA2 TT 1,277 (38.6) 1,581 (41.6) 1
TG 1,610 (48.6) 1,717 (45.2) 0.86 0.78 to 0.95
GG 425 (12.8) 501 (13.2) 0.96 0.84 to 1.11
2-df test 7.3 × 10-3
per allele 0.95 0.89 to 1.01 0.10
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a Breast Cancer

HR, hazard ratio

Table 3.

Associations with breast cancer risk, after excluding prevalent breast cancer cases, and BRCA1 mutation class.

Unaffected, N Affected, N HR 95% CI P-value
Excluding prevalent breast cancer cases
CDK2NA/B -rs1011970
BRCA1 6,200 3,152 1.05 0.98 to 1.14 0.18
BRCA2 3,319 1,950 1.10 1.00 to 1.22 0.05
ZNF365 - rs10995190
BRCA1 6,201 3,151 0.96 0.89 to 1.04 0.34
BRCA2 3,318 1,949 0.90 0.81 to 1.00 0.05
ZMIZ1 - rs704010
BRCA1 6,149 3,094 1.02 0.96 to 1.08 0.53
BRCA2 3,276 1,919 0.98 0.91 to 1.06 0.64
10p15 - rs2380205
BRCA1 4,955 2,764 1.02 0.95 to 1.08 0.64
BRCA2 3,092 1,884 1.00 0.93 to 1.08 0.92
11q13 - rs614367
BRCA1 6,177 3,144 1.01 0.94 to 1.10 0.73
BRCA2 3,310 1,944 0.99 0.89 to 1.10 0.88
12q24 - rs1292011
BRCA1 3,773 1,798 1.04 0.97 to 1.12 0.29
BRCA2 2,345 1,220 0.96 0.88 to 1.06 0.41
PTHLH - rs10771399
BRCA1 6,194 3,152 0.85 0.77 to 0.93 5.8 × 10-4
BRCA2 3,317 1,944 0.89 0.78 to 1.00 0.06
9q31.2 - rs865686
BRCA1 6,196 3,149 1.01 0.95 to 1.07 0.72
BRCA2 3,315 1,946 0.94 0.87 to 1.02 0.15
BRCA1 analysis by mutation class
CDK2NA/B -rs1011970
Class1 4,040 3,843 1.01 0.94 to 1.10 0.72
Class2 1,771 1,958 1.03 0.91 to 1.16 0.66
ZNF365 - rs10995190
Class1 4,058 3,844 .99 0.92 to 1.07 0.80
Class2 1,774 1,957 0.97 0.86 to 1.09 0.59
ZMIZ1 - rs704010
Class1 3,998 3,787 1.04 0.98 to 1.10 0.22
Class2 1,767 1,936 1.01 0.92 to 1.11 0.85
10p15 - rs2380205
Class1 3,664 3,538 1.01 0.95 to 1.07 0.82
Class2 931 1,263 1.03 0.91 to 1.15 0.67
11q13 - rs614367
Class1 4,024 3,833 1.10 1.02 to 1.19 0.02
Class2 1,764 1,948 0.94 0.84 to 1.06 0.32
12q24 - rs1292011
Class1 2,812 2,521 0.99 0.92 to 1.06 0.71
Class2 642 797 0.97 0.84 to 1.12 0.68
PTHLH - rs10771399
Class1 4,035 3,841 0.82 0.74 to 0.90 3.1 × 10-5
Class2 1,770 1,953 1.00 0.87 to 1.15 0.99
9q31.2 - rs865686
Class1 4,038 3,840 0.98 0.92 to 1.04 0.48
Class2 1,769 1,957 1.03 0.94 to 1.14 0.49
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HR, hazard ratio

We found no evidence of association between breast cancer risk for BRCA1 mutation carriers and any of the other SNPs under the trend models (P-trend > 0.15). There was, however, some suggestion of an association under the genotype specific model for rs865686 (2df P = 0.06, Table 2), reflecting a lower HR for heterozygous carriers than for either homozygote genotype. There was marginal evidence of heterogeneity in the HRs across countries for rs704010 and rs865686 (P-het = 0.04 for both), but examination of the forest plots revealed that in each case this was mainly due to a single study/country of relatively small sample size, with the majority of the HR estimates being close to 1 (Additional file 1 Supplementary Figure 1). There was no evidence that the HRs varied by age for any of the SNPs (P > 0.08 for all).

We further evaluated the SNP associations with breast and ovarian cancer risk simultaneously (Table 4). The associations with breast cancer risk remained essentially unchanged in the competing risk analysis, with only the PTHLH SNP rs10771399 being significantly associated with breast cancer risk. There was some suggestion of a possible association between this SNP and ovarian cancer risk for BRCA1 mutation carriers with risk in the opposite direction (HR for ovarian cancer = 1.14, 95% CI: 1.00 to 1.30, P-trend = 0.06) especially among rare homozygotes (ovarian cancer HR for GG = 1.67, 95% CI: 1.05 to 2.64, P-homozygotes = 0.03). This analysis also provided some weak evidence for an association between SNP rs614367 at 11q13 and ovarian cancer risk for BRCA1 mutation carriers under the genotype-specific model (2df P-value = 0.03). There was no evidence that any of the other SNPs are associated with ovarian cancer risk for BRCA1 mutation carriers.

Table 4.

Competing risk analysis*.

Breast cancer Ovarian cancer
Unaffected
N (%)		 Breast
cancer
N (%)		 Ovarian
cancer
N (%)		 HR 95% CI P-value HR 95% CI P-value
CDK2NA/B - rs1011970
BRCA1 GG 3,328 (69.6) 4,424 (69.9) 1,026 (70.1) 1 1
GT 1,309 (27.4) 1,710 (27.0) 398 (27.2) 1.03 0.95 to 1.11 1.10 0.95 to 1.26
TT 142 (3.0) 194 (3.1) 39 (2.7) 1.09 0.88 to 1.35 0.90 0.61 to 1.32
2-df test 0.57 0.35
per allele 1.04 0.91 to 1.11 0.30 1.04 0.93 to 1.17 0.48
BRCA2 GG 1,972 (68.6) 2578 (67.9) 315 (69.5) 1 1
GT 815 (28.4) 1097 (28.9) 129 (28.5) 1.09 0.98 to 1.21 1.07 0.85 to 1.35
TT 86 (3.0) 122 (3.2) 9 (2.0) 1.19 0.91 to 1.57 0.84 0.40 to 1.77
2-df test 0.15 0.74
per allele 1.09 1.00 to 1.19 0.05 1.03 0.84 to 1.25 0.81
ZNF365 - rs10995190
BRCA1 GG 3,408 (71.3) 4,523 (71.5) 1,019 (69.6) 1 1
GA 1,258 (26.3) 1,650 (26.1) 410 (28.0) 1.00 0.93 to 1.08 1.12 0.98 to 1.28
AA 113 (2.4) 155 (2.5) 35 (2.4) 0.96 0.78 to 1.20 0.90 0.61 to 1.33
2-df test 0.94 0.23
per allele 0.99 0.93 to 1.06 0.88 1.06 0.95 to 1.19 0.32
BRCA2 GG 2,033 (70.4) 2,795 (73.7) 318 (70.2) 1 1
GA 794 (27.7) 920 (24.3) 122 (26.9) 0.86 0.78 to 0.96 0.99 0.78 to 1.25
AA 55 (1.9) 79 (2.1) 13 (2.9) 1.03 0.74 to 1.43 1.58 0.83 to 3.03
2-df test 0.02 0.37
per allele 0.90 0.82 to 0.99 0.03 1.06 0.87 to 1.31 0.55
ZMIZ1 - rs704010
BRCA1 CC 1,904 (40.2) 2,493 (40.0) 583 (40.1) 1 1
CT 2,172 (45.9) 2,871 (46.0) 665 (45.7) 1.02 0.95 to 1.10 1.01 0.89 to 1.15
TT 660 (13.9) 877 (14.1) 206 (14.2) 1.02 0.92 to 1.14 1.01 0.84 to 1.22
2-df test 0.67 0.99
per allele 1.01 0.97 to 1.07 0.58 1.01 0.92 to 1.10 0.90
BRCA2 CC 1,109 (39.0) 1,439 (38.4) 181 (40.1) 1 1
CT 1,306 (46.0) 1,774 (47.3) 192 (43.4) 1.06 0.96 to 1.17 0.96 0.76 to 1.20
TT 426 (15.0) 538 (14.3) 69 (15.6) 1.00 0.86 to 1.15 1.05 0.77 to 1.43
2-df test 0.46 0.81
per allele 1.01 0.95 to 1.08 0.72 1.01 0.87 to 1.18 0.90
10p15 - rs2380205
BRCA1 CC 1,183 (32.2) 1,698 (32.1) 438 (33.2) 1 1
CT 1,796 (48.9) 2,605 (49.3) 634 (48.1) 0.99 0.91 to 1.08 0.91 0.79 to 1.05
TT 696 (18.9) 981 (18.6) 246 (18.7) 1.00 0.90 to 1.12 0.94 0.78 to 1.14
2-df test 0.96 0.45
per allele 1.00 0.95 to 1.06 0.98 0.96 0.87 to 1.06 0.40
BRCA2 CC 872 (32.6) 1,161 (31.8) 143 (33.7) 1 1
CT 1,321 (49.4) 1,812 (49.6) 199 (46.9) 1.04 0.94 to 1.16 0.94 0.74 to 1.19
TT 481 (18.0) 678 (18.6) 82 (19.3) 1.03 0.90 to 1.18 1.01 0.75 to 1.38
2-df test 0.74 0.82
per allele 1.02 0.95 to 1.09 0.61 1.00 0.85 to 1.16 0.98
11q13 - rs614367
BRCA1 CC 3,439 (72.3) 4,547 (72.1) 1,111 (76.2) 1 1
CT 1,212 (25.5) 1,606 (25.5) 311 (21.3) 1.02 0.94 to 1.11 0.83 0.72 to 0.96
TT 109 (2.3) 154 (2.4) 37 (2.5) 1.12 0.91 to 1.39 1.20 0.81 to 1.76
2-df test 0.52 0.03
per allele 1.03 0.91 to 1.10 0.35 0.91 0.80 to 1.03 0.13
BRCA2 CC 2,106 (73.5) 2,716 (71.9) 333 (73.8) 1 1
CT 693 (24.2) 981 (26.0) 108 (24.0) 1.05 0.95 to 1.16 0.95 0.74 to 1.21
TT 66 (2.3) 83 (2.2) 10 (2.2) 0.96 0.71 to 1.28 0.87 0.46 to 1.63
2-df test 0.62 0.84
per allele 1.03 0.94 to 1.12 0.56 0.94 0.77 to 1.15 0.56
12q24 - rs1292011
BRCA1 AA 997 (34.1) 1,321 (35.4) 305 (34.9) 1 1
AG 1,406 (48.2) 1,765 (47.3) 429 (49.1) 1.00 0.90 to 1.10 1.11 0.93 to 1.31
GG 517 (17.7) 645 (17.3) 140 (16.0) 1.01 0.89 to 1.15 0.98 0.78 to 1.24
2-df test 0.97 0.39
per allele 1.00 0.94 to 1.07 0.91 1.01 0.91 to 1.13 0.82
BRCA2 AA 715 (35.0) 907 (35.9) 110 (36.5) 1 1
AG 961 (47.0) 1222 (48.4) 137 (45.5) 1.02 0.90 to 1.15 0.94 0.71 to 1.25
GG 370 (18.1) 396 (15.7) 54 (17.9) 0.83 0.70 to 0.97 0.89 0.61 to 1.28
2-df test 0.03 0.80
per allele 0.93 0.86 to 1.00 0.07 0.94 0.78 to 1.13 0.51
PTHLH - rs10771399
BRCA1 AA 3,810 (79.8) 5,179 (81.9) 1,145 (78.4) 1 1
AG 909 (19.0) 1078 (17.1) 289 (19.8) 0.89 0.81 to 0.97 1.09 0.93 to 1.26
GG 56 (1.2) 65 (1.0) 27 (1.9) 0.86 0.63 to 1.16 1.67 1.05 to 2.64
2-df test 0.02 0.06
per allele 0.90 0.83 to 0.97 6.4 × 10-3 1.14 1.00 to 1.30 0.06
BRCA2 AA 2,289 (79.7) 3,076 (81.2) 369 (81.5) 1 1
AG 545 (19.0) 678 (17.9) 76 (16.8) 0.94 0.84 to 1.06 0.88 0.67 to 1.16
GG 37 (1.3) 34 (0.9) 8 (1.8) 0.79 0.49 to 1.26 1.48 0.63 to 3.46
2-df test 0.38 0.43
per allele 0.93 0.84 to 1.04 0.19 0.96 0.75 to 1.23 0.75
9q31.2 - rs865686
BRCA1 TT 1,935 (40.5) 2,621 (41.5) 605 (41.3) 1 1
TG 2,206 (46.2) 2,825 (44.7) 690 (47.1) 0.94 0.88 to 1.01 0.99 0.87 to 1.12
GG 633 (13.3) 877 (13.9) 169 (11.5) 1.03 0.93 to 1.15 0.85 0.70 to 1.03
2-df test 0.12 0.23
per allele 1.00 0.95 to 1.05 0.88 0.94 0.86 to 1.03 0.17
BRCA2 TT 1,103 (38.4) 1,576 (41.6) 179 (39.6) 1 1
TG 1400 (48.8) 1712 (45.2) 215 (47.6) 0.85 0.77 to 0.94 0.91 0.73 to 1.14
GG 367 (12.8) 501 (13.2) 58 (12.8) 0.97 0.84 to 1.12 0.98 0.71 to 1.35
2-df test 4.6 × 10-3 0.70
per allele 0.94 0.88 to 1.01 0.10 0.97 0.83 to 1.13 0.67
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Associations with breast and ovarian cancer risk for BRCA1 and BRCA2 carriers.

*Censoring process described in the methods

HR, hazard ratio

Associations with cancer risk for BRCA2 mutation carriers

There was evidence of association with breast cancer risk for BRCA2 mutation carriers for four SNPs (Table 2). The minor allele of rs10995190 in ZNF365 was associated with a reduced risk of breast cancer, where each copy of allele "A" was estimated to confer a HR of 0.90 (95% CI: 0.82 to 0.98, P-trend = 0.015). There was also some marginal evidence that the minor allele of rs1011970 near CDKN2A/CDKN2B was associated with increased breast cancer risk (HR = 1.09, 95% CI: 1.00 to 1.18, P-trend = 0.048). None of the other polymorphisms was associated with breast cancer risk for BRCA2 mutation carriers under the multiplicative model. However, SNPs rs865686 and rs1292011 were associated with risk under the genotype specific model (2df-P = 0.007 and 0.03 respectively, Table 2). There was some evidence of heterogeneity in the HRs across countries for rs1011970 (P-het = 0.005). This appeared to be mainly due to the USA stratum. The heterogeneity was no longer significant after removal of that stratum (P-het = 0.42) and the HR estimate for the association with breast cancer risk increased to 1.20 (95% CI: 1.09 to 1.32, P-trend = 1 × 10-4). There was no heterogeneity for any of the other SNPs (P-het > 0.12 for all, Additional file 1 Supplementary Figure 2). The HR estimates for the four associated SNPs were similar when long-term survivors were excluded from the analysis (Table 3). Consistent with the results of the main analysis, rs10995190 in ZNF365 and rs1011970 near CDKN2A/CDKN2B provided marginal evidence of association using the trend-test statistic (P-trend = 0.05 for both) and SNPs rs865686 was associated with breast cancer risk under the genotype specific model (2df-P = 0.03). SNP rs1292011 was not associated with breast cancer risk in this analysis. A somewhat smaller HR estimate was obtained for the PTHLH SNP rs10771399 compared to the main analysis (per-allele HR = 0.89, 95% CI: 0.78 to 1.00, P-trend = 0.06). The attenuation of the association in the overall analysis could have occurred if the SNP is also associated with prognosis. However, the difference in the HRs was small. The results for the remaining SNPs were similar and non-significant. None of SNPs were associated with ovarian cancer risk for BRCA2 mutation carriers (Table 4).

Associations by tumour ER-status

Table 5 summarises the associations of the eight SNPs with breast cancer ER status in BRCA1 and BRCA2 mutation carriers. Only the PTHLH SNP rs10771399 was associated with ER-negative breast cancer for BRCA1 mutation carriers (ER-negative HR = 0.81, 95% CI: 0.74 to 0.90, P-trend = 3.8 × 10-5). There was also marginal evidence that SNP rs704010 near ZMIZ1 was associated with ER-positive breast cancer for BRCA1 mutation carriers (ER-positive HR = 1.12, 95% CI: 1.00 to 1.26, P-trend = 0.046). However, the associations between ER-negative and ER-positive breast cancer among BRCA1 mutation carriers were only significantly different for SNP rs1292011 at 12q24 (P-heterogeneity = 0.045).

Table 5.

Associations with estrogen receptor-positive and estrogen receptor-negative breast cancer risk for BRCA1 and BRCA2 carriers.

ER-positive ER-negative
Unaffected
N		 ER-positive N ER-negative N ER status unknown N HR 95% CI P-value HR 95% CI P-value P-dif
CDK2NA/B - rs1011970
BRCA1 4,893 559 1,888 2,841 0.95 0.81 to 1.12 0.56 1.03 0.95 to 1.11 0.47 0.41
BRCA2 2,928 1,372 424 1,649 1.10 1.00 to 1.22 0.05 1.15 0.96 to 1.37 0.12 0.70
ZNF365 - rs10995190
BRCA1 4,895 559 1,887 2,843 0.88 0.74 to 1.04 0.14 1.01 0.94 to 1.10 0.75 0.16
BRCA2 2,927 1,370 406 1,648 0.89 0.80 to 1.00 0.043 0.87 0.71 to 1.07 0.19 0.84
ZMIZ1 - rs704010
BRCA1 4,842 548 1,846 2,811 1.12 1.00 to 1.26 0.046 1.00 0.94 to 1.06 0.91 0.08
BRCA2 2,887 1,347 401 1,636 1.01 0.93 to 1.09 0.91 1.00 0.87 to 1.14 0.95 0.91
10p15 - rs2380205
BRCA1 4,465 540 1,812 2,513 0.90 0.80 to 1.01 0.08 1.02 0.96 to 1.09 0.46 0.06
BRCA2 2,701 1,341 396 1,543 1.02 0.95 to 1.10 0.60 0.94 0.82 to 1.08 0.39 0.31
11q13 - rs614367
BRCA1 4,879 557 1,886 2,832 1.09 0.93 to 1.29 0.30 1.04 0.96 to 1.12 0.40 0.59
BRCA2 2,921 1,365 405 1,639 1.06 0.96 to 1.17 0.26 0.84 0.69 to 1.04 0.10 0.05
12q24 - rs1292011
BRCA1 3,429 308 1,043 2,031 0.87 0.74 to 1.02 0.09 1.05 0.98 to 1.13 0.12 0.046
BRCA2 2,065 813 239 1,170 0.95 0.86 to 1.04 0.28 0.98 0.82 to 1.16 0.78 0.79
PTHLH - rs10771399
BRCA1 4,889 557 1,887 2,842 0.94 0.78 to 1.13 0.52 0.81 0.74 to 0.90 3.8 × 10-5 0.20
BRCA2 2,926 1,366 406 1,648 0.97 0.86 to 1.10 0.68 0.78 0.62 to 1.00 0.049 0.12
9q31.2 - rs865686
BRCA1 4,892 559 1,888 2,836 0.92 0.81 to 1.03 0.15 1.01 0.95 to 1.08 0.68 0.16
BRCA2 2,924 1,370 405 1,645 0.91 0.84 to 0.99 0.028 1.07 0.92 to 1.25 0.40 0.08
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ER, estrogen receptor; HR, hazard ratio

Despite the small number of BRCA2 ER-negative breast cancers, there was a suggestion that the minor allele of the PTHLH SNP rs10771399 is protective for ER-negative breast cancer for BRCA2 mutation carriers (HR for ER-negative = 0.78, 95% CI: 0.62 to 1.00, P-trend = 0.049), but there was no association with ER-positive breast cancer. There was evidence that SNPs rs10995190 near ZNF365, rs865686 at 9q31.2 and rs1011970 near CDKN2A/B are associated with ER-positive breast cancer for BRCA2 mutation carriers (P-trend = 0.043, 0.028 and 0.05 respectively). However, the HR estimates were not significantly different from those for ER-negative breast cancer.

Discussion

We have investigated eight novel breast cancer susceptibility loci identified through breast cancer GWAS [15-17] for their associations with breast cancer risk for BRCA1 and BRCA2 mutation carriers using data from the CIMBA. The estimated per-allele ORs associated with the minor allele of each SNP from the population-based studies varied from 0.85 to 1.15, and only four of the eight SNPs had ORs of less than 0.90 or greater than 1.10 (rs10995190, rs614367, rs865686 and rs10771399) [15-17]. For BRCA1 mutation carriers, only SNP rs10771399 at 12p11 was associated with the overall risk of breast cancer, whereas SNPs rs10995190 at 10q21, rs1011970 at 9p21, rs865686 at 9q31.2 and rs1292011 at 12q24 were associated with breast cancer risk for BRCA2 mutation carriers. The magnitude of the estimated HRs for all these SNPs were consistent with the OR estimates for the risk of breast cancer in the general population. The power to detect associations with SNPs conferring relative risks in the range of 0.90 to 1.10 was limited by our sample size, particularly among BRCA2 mutation carriers [29].

Based on the HR estimates and associated 95% confidence intervals, given our sample size of BRCA1 mutation carriers, it is unlikely that the relative risks for overall BRCA1 breast cancer risk are of similar magnitude to those estimated in the general population for SNPs rs10995190 at 10q21 (estimated odds ratio (OR) from the replication stage of the GWAS = 0.76), rs2380205 at 10p15 (OR = 0.94), rs614367 at 11q13 (OR = 1.15), rs1292011 at 12q24 (OR = 0.92) and rs865686 at 9q31.2 (OR = 0.89), since the 95% confidence intervals for the HRs do not include the estimated OR from the population-based studies. Similarly, the HRs for BRCA2 breast cancer risk exclude the ORs from the general population for SNPs rs2380205 at 10p15 and rs614367 at 11q13. Taken together, these findings suggest that SNPs rs2380205 at 10p15 and rs614367 at 11q13 do not modify breast cancer risk in either BRCA1 or BRCA2 mutation carriers. A replication study by BCAC, involving close to 50,000 breast cancer cases and 50,000 controls, found only weak evidence for association of rs2380205 at 10p15 with breast cancer risk in the general population [Lambrechts and Easton personal communication, manuscript submitted] suggesting that the original finding (OR = 0.94, P = 5 × 10-7 [15]) may have been a false positive. If this were true, the absence of an association in carriers would be expected. The lack of evidence for an association with the 11q13 SNP rs614367 with BRCA1 and BRCA2 breast cancer risk is more surprising since the association in the general population is relatively strong and consistently replicated (OR 1.21, 95% CI 1.17 to 1.25 in the recent BCAC analysis [Lambrechts and Easton personal communication, manuscript submitted]). The association in the general population appears to be restricted to ER-positive disease, which would explain the lack of association for BRCA1 carriers but not BRCA2 carriers. This is perhaps the clearest evidence so far of a departure from a multiplicative interaction between a common susceptibility locus and a BRCA2 mutation on the risk of developing breast cancer. The lack of an association in BRCA1 carriers for rs1292011 and rs865686 is also consistent with the observation that these associations are stronger for ER-positive disease in the general population [16]. The absence of an association for ZNF365 rs10995190 in BRCA1 carriers is more surprising since this association appears to be unrelated to ER status in the general population [Lambrechts and Easton personal communication, manuscript submitted] [30].

Of the eight SNPs investigated, the strongest association was found between SNP rs10771399 at 12p11 and breast cancer risk for BRCA1 mutation carriers. Other loci previously found to be associated with BRCA1 breast cancer risk include the 19p13 and 6q25.1 loci [6,9], TOX3 and CASP8 [3,5,7]. Analysis by tumour ER-status revealed that rs10771399 at 12p11 has a stronger association with ER-negative than ER-positive breast cancer for both BRCA1 and BRCA2 mutation carriers. The ER-specific HRs were similar for both genes, suggesting that this SNP is primarily associated with ER-negative breast cancer, although results from the general population suggested similar ORs for ER-positive and ER-negative breast cancer (0.87 for ER-positive disease, 0.85 for ER-negative disease [16]). Interestingly, the association among BRCA1 mutation carriers was restricted to those carrying mutations proven or predicted to lead to absence of protein expression (Class 1) with no evidence for an association in carriers of BRCA1 mutations likely to generate stable mutant proteins (Class 2) (P-diff = 0.03). This observation suggests that the modifying effect of SNP rs10771399 at 12p11 might be attenuated for tumours that retain residual BRCA1 function or that retain the capacity to bind to some of its partners. rs10771399 lies in a region at 12p11 that contains PTHLH (parathyroid hormone-like hormone isoform 1, also known as PTHRP - parathyroid hormone-related protein) and CCDC91. PTHLH is a plausible candidate cancer susceptibility gene. It encodes a protein that regulates endochondral bone development and epithelial-mesenchymal interactions during the formation of the mammary glands. The receptor of this hormone, PTHR1, is responsible for most cases of humoral hypercalcemia of malignancy [31]. It is produced by various types of carcinomas [32], and is an important factor in the development of bone metastasis [33].

We found that SNP rs10995190 ZNF365 is associated with BRCA2 breast cancer risk. A different SNP (rs16917302) in ZNF365, which is only weakly correlated with rs10995190 (pairwise r2 is approximately 0.10 in the present sample) was previously identified via a GWAS of breast cancer in BRCA2 mutation carriers [10]. These results suggest that there could be a causal associated variant correlated with both rs10995190 and rs16917302, or alternatively more than one causal disease variant in this locus. SNP rs10995190 has also recently been found to be associated with mammographic density in the general population [34]. Previous studies found that mammographic density modifies breast cancer risk for BRCA2 mutation carriers [35], raising the possibility that this locus modifies breast cancer risk for BRCA2 mutation carriers through its influence on mammographic density. However, mammographic density has also been shown to modify the breast cancer risk for BRCA1 carriers, which also makes the absence of association for rs10995190 in BRCA1 carriers somewhat surprising. Mammographic density data are not available in the CIMBA sample to test this hypothesis explicitly.

There was no evidence of association with ovarian cancer risk for BRCA1 or BRCA2 mutation carriers for any of the SNPs, with the exception of some weak evidence for SNPs rs10771399 and rs614367 for BRCA1 carriers. This is not surprising, since all SNPs were selected on the basis of prior evidence of association with breast cancer risk in the general population and none of these SNPs have so far been found to be associated with ovarian cancer in general population through the ongoing GWAS [36-38].

Conclusions

The per-allele HRs estimated for each of the associated loci in the present report are modest, and in isolation would have only a small impact on the absolute risks of developing breast cancer. However, we have shown previously that modifier SNPs in combination can result in large differences in the absolute risk of developing breast cancer for carriers at the extreme percentiles of the combined SNP distribution [5,39]. Furthermore, the causal variants underlying these loci may confer larger relative risks. Considering all reported modifying loci by the CIMBA consortium, there are now six loci in total that are associated with breast cancer risk for BRCA1 mutation carriers (19p13, 6q25.1, 12p11, TOX3, 2q35 and CASP8) and 13 loci which are known to be associated with BRCA2 breast cancer risk (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, 1p11.2, ZNF365, CDKN2A/B, 9q31.2, 12q24 and RAD51). Ongoing GWAS in BRCA1 and BRCA2 mutation carriers and in the general population are likely to identify further modifier loci and taken together, they may lead to more accurate risk predictions in mutation carriers with implications for clinical management, and to a better understanding of the biology of tumour development in mutation carriers.

Abbreviations

BCAC: Breast Cancer Association Consortium; CIMBA: Consortium of Investigators of Modifiers of BRCA1/2; ER: estrogen receptor; GWAS: genome-wide association studies; HR: hazard ratio; HWE: Hardy-Weinberg equilibrium; NMD: nonsense-mediated mRNA decay; OR: odds ratio; SNPs: single-nucleotide polymorphism.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

ACA, KBK and DFE wrote the manuscript. KBK performed the statistical analysis. ACA supervised the statistical analysis and data management. ACA, GCT and DFE developed the study design. LM and DB are the CIMBA database managers. AL wrote computer programs for the analysis. SH and OMS reviewed, recoded and classified the BRCA1 and BRCA2 mutations in CIMBA. GCT initiated and coordinates CIMBA. PS, JB, XC and YCD performed the genotyping. AJ, SLN, GCT and JS supervised the genotyping of samples. MAC, NL, KH, AL, BA, RR, PK, KN, SD, TR, AJ, JL, KJ, KD, EZ, AO, MD, RA, JB, UH, FBH, TAvO, SV, HEJMH, JW, EBGG, MJL, MK, JMC, MGEMA, JCO, SP, DF, SDE, RP, EF, DGE, FL, CJ, RE, JA, RD, TC, JC, JP, FD, CB, SH, PJM, LW, MTR, AD, HD, AKG, BB, DS, CH, BB, AdP, SM, AC, ML, BBdeP, OC, HS, MF, FP, SFF, IM, SB, MD, AM, MBT, JLH, EMJ, MS, DG, CFS, AFR, MKT, DGK, TvOH, FCN, RBB, MG, TK, VJ, ADC, KO, MP, JK, DC, JH, JB, JF, AET, MM, CO, EI, CI, LT, IB, CL, AT, JDV, SAG, KO, JG, BYK, EO, SHT, PAG, MSB, CMD, EJvR, OD, AK, RKS, BW, CE, AM, ND, NA, SH, DN, SPA, DG, RVM, HD, AG, CS, KK, BF, DS, TC, MdlH, HN, TAM, BL, ABS, SLN, YCD, XW, ZF, VSP, NML, PR, MHG, JTL, ILA, HO, AMM, GG, MT, AMG, UBJ, ABS, TAK, GCT and FJC acquired phenotypic data and DNA samples or designed the centre-specific studies. All authors read and approved the final manuscript for publication.

Supplementary Material

Additional file 1

Supplementary tables and figures. Table S1 List of local ethics committees that granted approval for the access and use of the data in current study. Supplementary figure 1 Forest plot of the country-specific per-allele HR estimates for breast cancer for BRCA1 mutation carriers. Supplementary figure 2 Forest plot of the country-specific per-allele HR estimates for breast cancer for BRCA2 mutation carriers.

Click here for file (135.7KB, DOCX)

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Acknowledgements

This work was supported by Cancer Research UK grants C12292/A11174 and C1287/A10118. The research leading to these results has received funding from the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175), from the Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program and by the Canadian Breast Cancer Research Alliance-grant #019511. This research was also supported by NIH grant CA128978, an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a U.S. Department of Defence Ovarian Cancer Idea award (W81XWH-10-1-0341) and grants from the Breast Cancer Research Foundation and the Komen Foundation for the Cure. ACA is a CR-UK Senior Cancer Research Fellow, DFE is CR-UK Principal Research Fellow, GCT is a NHMRC Senior Principal Research Fellow, J.S. is Chairholder of the Canada Research Chair in Oncogenetics.

Study specific

Baltic Familial Breast and Ovarian Cancer Consortium

We acknowledge the Genome Database of Latvian Population, Latvian Biomedical

Research and Study Centre and Ramunas Janavicius (Vilnius University Hospital Santariskiu Clinics, Lithuania) for data and DNA samples for BFBOCC. The work was supported in part by a grant from the European Social Fund Nr.2009/0220/1DP/1.1.1.2.0/09/APIA/VIAA/016.

BMBSA was supported by grants from the Cancer Association of South Africa (CANSA) to Elizabeth J. van Rensburg.

Breast Cancer Family Registry (BCFR)

This work was supported by the National Cancer Institute, National Institutes of Health under RFA-CA-06-503 and through cooperative agreements with members of the Breast Cancer Family Registry (BCFR) and Principal Investigators, including Cancer Care Ontario (U01 CA69467), Columbia University (U01 CA69398), Fox Chase Cancer Center (U01 CA69631), Huntsman Cancer Institute (U01 CA69446), Cancer Prevention Institute of California (formerly the Northern California Cancer Center) (U01 CA69417), University of Melbourne (U01 CA69638), and Research Triangle Institute Informatics Support Center (RFP No. N02PC45022-46). Samples from the FCCC, HCI and CPIC were processed and distributed by the Coriell Institute for Medical Research. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centres in the BCFR, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government or the BCFR.

CNIO

The research leading to these results has been partially funded by Mutua Madrileña Foundation, "Red de Investigación en Cáncer RD06/0020/1160" and Spanish Ministry of Science and Innovation (FIS PI08 1120 and SAF2010-20493).

Copenhagen Breast Cancer Study (CBCS)

We would like to thank Bent Ejlertsen for clinical data and acknowledge the NEYE foundation for financial support.

Deutsches Krebsforschungszentrum (DKFZ) study

The DKFZ study was supported by the DKFZ.

Epidemiological study of BRCA1 & BRCA2 mutation carriers (EMBRACE)

Douglas F. Easton is the PI of the study. EMBRACE Collaborating Centres are: Coordinating Centre, Cambridge: Susan Peock, Debra Frost, Steve D. Ellis, Elena Fineberg, Radka Platte, Clare Oliver. North of Scotland Regional Genetics Service, Aberdeen: Zosia Miedzybrodzka, Helen Gregory. Northern Ireland Regional Genetics Service, Belfast: Patrick Morrison, Lisa Jeffers. West Midlands Regional Clinical Genetics Service, Birmingham: Trevor Cole, Kai-ren Ong, Jonathan Hoffman. South West Regional Genetics Service, Bristol: Alan Donaldson, Margaret James. East Anglian Regional Genetics Service, Cambridge: Joan Paterson, Sarah Downing, Amy Taylor. Medical Genetics Services for Wales, Cardiff: Alexandra Murray, Mark T. Rogers, Emma McCann. St James's Hospital, Dublin and National Centre for Medical Genetics, Dublin: M. John Kennedy, David Barton. South East of Scotland Regional Genetics Service, Edinburgh: Mary Porteous, Sarah Drummond. Peninsula Clinical Genetics Service, Exeter: Carole Brewer, Emma Kivuva, Anne Searle, Selina Goodman, Kathryn Hill. West of Scotland Regional Genetics Service, Glasgow: Rosemarie Davidson, Victoria Murday, Nicola Bradshaw, Lesley Snadden, Mark Longmuir, Catherine Watt, Sarah Gibson, Eshika Haque, Ed Tobias, Alexis Duncan. South East Thames Regional Genetics Service, Guy's Hospital London: Louise Izatt, Chris Jacobs, Caroline Langman. North West Thames Regional Genetics Service, Harrow: Huw Dorkins. Leicestershire Clinical Genetics Service, Leicester: Julian Barwell. Yorkshire Regional Genetics Service, Leeds: Julian Adlard, Gemma Serra-Feliu. Cheshire & Merseyside Clinical Genetics Service, Liverpool: Ian Ellis, Catherine Houghton. Manchester Regional Genetics Service, Manchester: D Gareth Evans, Fiona Lalloo, Jane Taylor. North East Thames Regional Genetics Service, NE Thames, London: Lucy Side, Alison Male, Cheryl Berlin. Nottingham Centre for Medical Genetics, Nottingham: Jacqueline Eason, Rebecca Collier. Northern Clinical Genetics Service, Newcastle: Fiona Douglas, Oonagh Claber, Irene Jobson. Oxford Regional Genetics Service, Oxford: Lisa Walker, Diane McLeod, Dorothy Halliday, Sarah Durell, Barbara Stayner. The Institute of Cancer Research and Royal Marsden NHS Foundation Trust: Ros Eeles, Susan Shanley, Nazneen Rahman, Richard Houlston, Elizabeth Bancroft, Elizabeth Page, Audrey Ardern-Jones, Kelly Kohut, Jennifer Wiggins, Elena Castro, Anita Mitra, Lisa Robertson. North Trent Clinical Genetics Service, Sheffield: Jackie Cook, Oliver Quarrell, Cathryn Bardsley. South West Thames Regional Genetics Service, London: Shirley Hodgson, Sheila Goff, Glen Brice, Lizzie Winchester, Charlotte Eddy, Vishakha Tripathi, Virginia Attard. Wessex Clinical Genetics Service, Princess Anne Hospital, Southampton: Diana Eccles, Anneke Lucassen, Gillian Crawford, Donna McBride, Sarah Smalley. EMBRACE is supported by Cancer Research UK Grants C1287/A10118 and C1287/A11990. D. Gareth Evans and Fiona Lalloo are supported by an NIHR grant to the Biomedical Research Centre, Manchester. The Investigators at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust are supported by an NIHR grant to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Ros Eeles is supported by Cancer Research UK Grant C5047/A8385.

GEORGETOWN

CI received support from the Familial Cancer Registry and the Tissue Culture Shared Registry at Georgetown University (NIH/NCI grant P30-CA051008), the Cancer Genetics Network (HHSN261200744000C), and Swing Fore the Cure.

Gynecologic Oncology Group (GOG)

This study was supported by National Cancer Institute grants to the Gynecologic Oncology Group (GOG) Administrative Office and the GOG Tissue Bank (CA 27469), and to the GOG Statistical and Data Center (CA 37517 and CA 101165). We thank the investigators of the Australia New Zealand Gynaecological Oncology Group (ANZGOG). GOG's participation was sponsored by GOG's Cancer Prevention and Control Committee, and supported through funding provided by both intramural (Clinical Genetics Branch, DCEG) and extramural (Community Oncology and Prevention Trials Program - COPTRG) NCI programs.

Hospital Clinico San Carlos (HCSC)

The HCSC study was partially supported by Instituto de Salud Carlos III; RD06/0020/0021. We wish to thank Dr. Pedro Perez-Segura and Dr. Atocha Romero for their contribution to this study.

The Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON)

HEBON Collaborating Centres: Coordinating center: Netherlands Cancer Institute, Amsterdam, NL: F.B.L. Hogervorst, S. Verhoef, M. Verheus, L.J. van 't Veer, F.E. van Leeuwen, M.A. Rookus; Erasmus Medical Center, Rotterdam, NL: M. Collée, A.M.W. van den Ouweland, A. Jager, M.J. Hooning, M.M.A. Tilanus-Linthorst, C. Seynaeve; Leiden University Medical Center, NL, Leiden: C.J. van Asperen, J.T. Wijnen, M.P. Vreeswijk, R.A. Tollenaar, P. Devilee; Radboud University Nijmegen Medical Center, Nijmegen, NL: M.J. Ligtenberg, N. Hoogerbrugge; University Medical Center Utrecht, Utrecht, NL: M.G. Ausems, R.B. van der Luijt; Amsterdam Medical Center, NL: C.M. Aalfs, T.A. van Os; VU University Medical Center, Amsterdam, NL: J.J.P. Gille, Q. Waisfisz, H.E.J. Meijers-Heijboer; University Hospital Maastricht, Maastricht, NL: E.B. Gomez-Garcia, C.E. van Roozendaal, Marinus J. Blok, B. Caanen; University Medical Center Groningen University, NL: J.C. Oosterwijk, A.H. van der Hout, M.J. Mourits; The Netherlands Foundation for the detection of hereditary tumours, Leiden, NL: H.F. Vasen. The HEBON study is supported by the Dutch Cancer Society grants NKI1998-1854, NKI2004-3088, NKI2007-3756 and the ZonMW grant 91109024.

Helsinki Breast Cancer Study (HEBCS)

HEBCS acknowledge Drs. Kristiina Aittomäki, Kirsimari Aaltonen and Carl Blomqvist and Tuomas Heikkinen and research nurse Irja Erkkilä for their help with the patient data and samples. The HEBCS study has been financially supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (132473), the Finnish Cancer Society, and the Sigrid Juselius Foundation.

ICO

Contract grant sponsor: Asociación Española Contra el Cáncer, Spanish Health Research Fund; Carlos III Health Institute; Catalan Health Institute and Autonomous Government of Catalonia. Contract grant numbers: ISCIIIRETIC RD06/0020/1051, PI10/01422, PI10/31488 and 2009SGR290.

ILUH

The ILUH group was supported by the Icelandic Association "Walking for Breast Cancer Research" and by the Landspitali University Hospital Research Fund.

INHERIT

We would like to thank Stéphane Dubois, Dr Martine Dumont, Martine Tranchant (Cancer Genomics Laboratory, CRCHUQ) for sample management and skillful technical assistance, Sylvie Desjardins and Marc-André Rodrigue (Plateforme de séquençage et de génotypage des génome du CRCHUL/CHUQ) for iPLEX genotyping and Pascal Belleau for data quality control analyses.

Istituto Oncologico Veneto Hereditary Breast and Ovarian Cancer Study (IOVHBOCS)

This study was supported by Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR) and "Ministero della Salute" ("Progetto Tumouri Femminili and grant numbers RFPS 2006-5-341353, ACC2/R6.9")

kConFab

We wish to thank Heather Thorne, Eveline Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics, and the Clinical Follow Up Study (funded 2001-2009 by NHMRC and currently by the National Breast Cancer Foundation and Cancer Australia #628333) for their contributions to this resource, and the many families who contribute to kConFab. kConFab is supported by grants from the National Breast Cancer Foundation, the National Health and Medical Research Council (NHMRC) and by the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. ABS is funded by an NHMRC Senior Research Fellowship.

Memorial Sloan-Kettering Cancer Center (MSKCC)

We acknowledge the Starr Cancer Consortium, the Breast Cancer Research Foundation, the Norman and Carol Stone Cancer Research Initiative, the Kate and Robert Niehaus Clinical Cancer Research Initiative, the Lymphoma Foundation, and the Sabin Family Research Initiative.

National Cancer Institute (NCI)

The research of Drs. Greene and Loud was supported by the Intramural Research Program of the US National Cancer Institute at the National Institutes of Health, and by support services contracts NO2-CP-11019-50 and N02-CP-65504 with Westat, Inc, Rockville, MD.

N.N. Petrov Institute of Oncology (NNPIO)

This work has been supported by the Russian Federation for Basic Research (grants 10-04-92601, 10-04-92110, 11-04-00227) the Federal Agency for Science and Innovations (contract 16.512.11.2237) and through a Royal Society International Joint grant (JP090615).

The Hong Kong Hereditary Breast Cancer Family Registry thank Dr. Ellen Li Charitable Foundation for their support

Hungarian Breast and Ovarian Cancer Study (HUNBOCS)

The study was supported by Norwegian EEA Financial Mechanism (HU0115/NA/2008-3/ÖP-9) and by Hungarian Research Grant KTIA-OTKA (CK-80745).

Ohio State University Clinical Cancer Genetics (OSU-CCG)

Leigha Senter and Kevin Sweet were instrumental in accrual of study participants, ascertainment of medical records and database management. We thank the Human Genetics Sample Bank for preparation of samples. This study was supported by the Ohio State University Comprehensive Cancer Center.

Ontario Cancer Genetics Network Study (OCGN)

This work was supported by Cancer Care Ontario, the "CIHR Team in Familial Risks of Breast Cancer" program, and the US National Cancer Institute, National Institutes of Health under RFA # CA- 06-503 and through cooperative agreements with members of the Breast Cancer Family Registry (BCFR) and Principal Investigators. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centres in the BCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. We wish to thank Teresa Selander, Nayana Weerasooriya and members of the Ontario Cancer Genetics Network for their contributions to the study.

Beckman Research Institute of City of Hope (BRICOH)

The study was supported by the National Institutes of Health (R01 CA74415 to SLN)) and the Morris and Horowitz Families Endowment.

SWE-BRCA

SWE-BRCA collaborators: Per Karlsson, Margareta Nordling, Annika Bergman and Zakaria Einbeigi, Gothenburg, Sahlgrenska University Hospital; Marie Stenmark-Askmalm and Sigrun Liedgren, Linköping University Hospital; Åke Borg, Niklas Loman, Håkan Olsson, Maria Soller, Helena Jernström, Katja Harbst and Karin Henriksson, Lund University Hospital; Annika Lindblom, Brita Arver, Anna von Wachenfeldt, Annelie Liljegren, Gisela Barbany-Bustinza and Johanna Rantala, Stockholm, Karolinska University Hospital; Beatrice Melin, Henrik Grönberg, Eva-Lena Stattin and Monica Emanuelsson, Umeå University Hospital; Hans Ehrencrona, Richard Rosenquist and Niklas Dahl, Uppsala University Hospital.

U.K. and Gilda Radner Familial Ovarian Cancer Registry (UKGRFOCR)

UKFOCR was supported by a project grant from CRUK to Paul Pharoah. We thank Paul Pharoah, Susan Ramus, Carole Pye, Patricia Harrington and Eva Wozniak for their contributions towards the UKFOCR. We would like to acknowledge the Roswell Park Alliance Foundation for their continued support of the Gilda Radner Ovarian Family Cancer Registry. GRFOCR would like to acknowledge Kirsten Moysich and Lara Sucheston (Department of Cancer Prevention and Control).

University of Kansas Medical Center (KUMC)

We thank Ms. JoEllen Weaver for her help collecting patient data and samples. AKG was funded by U01CA69631, 5U01CA113916, and the Eileen Stein Jacoby Fund while at FCCC. The author acknowledges support from The University of Kansas Cancer Center and the Kansas Bioscience Authority Eminent Scholar Program. AKG is the Chancellors Distinguished Chair in Biomedical Sciences endowed Professor.

University of Pennsylvania (UPENN)

This research was supported by the Breast Cancer Research Foundation (to KLN) and the Komen Foundation for the Cure (to SMD).

Women's Cancer Program - Cedars-Sinai Medical Center (WCRI)

This work is supported by funding from the American Cancer Society Clinical Research Professorship (SIOP-06-258-COUN).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional file 1

Supplementary tables and figures. Table S1 List of local ethics committees that granted approval for the access and use of the data in current study. Supplementary figure 1 Forest plot of the country-specific per-allele HR estimates for breast cancer for BRCA1 mutation carriers. Supplementary figure 2 Forest plot of the country-specific per-allele HR estimates for breast cancer for BRCA2 mutation carriers.

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