Johnson et al. Breast Cancer Research 2014, 16:R51 http://breast-cancer-research.com/content/16/3/R51

RESEARCH ARTICLE

Open Access

Genetic variation at CYP3A is associated with age at menarche and breast cancer risk: a case-control study
Nichola Johnson1,2*, Frank Dudbridge3, Nick Orr1,2, Lorna Gibson3, Michael E Jones4, Minouk J Schoemaker4, Elizabeth J Folkerd5, Ben P Haynes5, John L Hopper6, Melissa C Southey7, Gillian S Dite6, Carmel Apicella6, Marjanka K Schmidt8, Annegien Broeks8, Laura J Van’t Veer8, Femke Atsma9, Kenneth Muir10, Artitaya Lophatananon10, Peter A Fasching11,12, Matthias W Beckmann11, Arif B Ekici13, Stefan P Renner11, Elinor Sawyer14, Ian Tomlinson15,16, Michael Kerin17, Nicola Miller17, Barbara Burwinkel18,19, Frederik Marme18, Andreas Schneeweiss18, Christof Sohn18,20, Pascal Guénel21,22, Therese Truong21,22, Emilie Cordina21,22, Florence Menegaux21,22, Stig E Bojesen23,24, Børge G Nordestgaard23,24, Henrik Flyger25, Roger Milne26, M Pilar Zamora27, Jose Ignacio Arias Perez28, Javier Benitez29,30, Leslie Bernstein31, Hoda Anton-Culver32, Argyrios Ziogas32, Christina Clarke Dur33, Hermann Brenner34,35, Heiko Müller34, Volker Arndt34, Aida Karina Dieffenbach34,35, Alfons Meindl36, Joerg Heil18, Claus R Bartram37, Rita K Schmutzler38, Hiltrud Brauch39,40, Christina Justenhoven39,40, Yon-Dschun Ko41, The GENICA (Gene Environment Interaction and Breast Cancer in Germany) Network, Heli Nevanlinna42, Taru A Muranen42, Kristiina Aittomäki43, Carl Blomqvist44, Keitaro Matsuo45, Thilo Dörk46, Natalia V Bogdanova47, Natalia N Antonenkova48, Annika Lindblom49, Arto Mannermaa50,51,52, Vesa Kataja50,51,53, Veli-Matti Kosma50,51,52, Jaana M Hartikainen50,51,52, Georgia Chenevix-Trench54, Jonathan Beesley54, kConFab Investigators, Australian Ovarian Cancer Study Group, Anna H Wu55, David Van den Berg55, Chiu-Chen Tseng55, Diether Lambrechts56,57, Dominiek Smeets56,57, Patrick Neven58, Hans Wildiers58, Jenny Chang-Claude59, Anja Rudolph59, Stefan Nickels59, Dieter Flesch-Janys60,61, Paolo Radice62, Paolo Peterlongo62,63, Bernardo Bonanni64, Valeria Pensotti63,65, Fergus J Couch66, Janet E Olson67, Xianshu Wang66, Zachary Fredericksen67, Vernon S Pankratz67, Graham G Giles6,68, Gianluca Severi6,68, Laura Baglietto6,68, Chris Haiman56, Jacques Simard69, Mark S Goldberg69, France Labrèche70, Martine Dumont71, Penny Soucy71, Soo Teo72,73, Cheng Har Yip72, Sze Yee Phuah72,73, Belinda K Cornes74, Vessela N Kristensen75,76, Grethe Grenaker Alnæs76, Anne-Lise Børresen-Dale75,76, Wei Zheng77, Robert Winqvist78, Katri Pylkäs78, Arja Jukkola-Vuorinen79, Mervi Grip80, Irene L Andrulis81,82, Julia A Knight81,83, Gord Glendon81,84, Anna Marie Mulligan85,86, Peter Devillee87, Jonine Figueroa88, Stephen J Chanock88, Jolanta Lissowska89, Mark E Sherman88, Per Hall90, Nils Schoof90, Maartje Hooning91, Antoinette Hollestelle92, Rogier A Oldenburg93, Madeleine Tilanus-Linthorst93, Jianjun Liu94, Angie Cox95, Ian W Brock95, Malcolm WR Reed96, Simon S Cross97, William Blot77,98, Lisa B Signorello99,100,101, Paul DP Pharoah102, Alison M Dunning102, Mitul Shah103, Daehee Kang103, Dong-Young Noh103, Sue K Park104,105,106, Ji-Yeob Choi103, Mikael Hartman107,108,109, Hui Miao99,100, Wei Yen Lim108,109, Anthony Tang110, Ute Hamann111, Asta Försti112,113, Thomas Rüdiger114, Hans Ulrich Ulmer115, Anna Jakubowska116, Jan Lubinski116, Katarzyna Jaworska-Bieniek116,117, Katarzyna Durda116, Suleeporn Sangrajrang118, Valerie Gaborieau119, Paul Brennan119, James McKay119, Susan Slager67, Amanda E Toland120, Celine Vachon67, Drakoulis Yannoukakos121,
* Correspondence: nichola.johnson@icr.ac.uk † Equal contributors 1 Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK 2 Division of Breast Cancer Research, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK Full list of author information is available at the end of the article
© 2014 Johnson et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Chen-Yang Shen122,123, Jyh-Cherng Yu124, Chiun-Sheng Huang125, Ming-Feng Hou126,127, Anna González-Neira29, Daniel C Tessier128, Daniel Vincent128, Francois Bacot128, Craig Luccarini102, Joe Dennis129, Kyriaki Michailidou129, Manjeet K Bolla129, Jean Wang129, Douglas F Easton102,129, Montserrat García-Closas1,2,4, Mitch Dowsett5, Alan Ashworth1,2, Anthony J Swerdlow1,2,4, Julian Peto3, Isabel dos Santos Silva3† and Olivia Fletcher1,2†

Abstract
Introduction: We have previously shown that a tag single nucleotide polymorphism (rs10235235), which maps to the CYP3A locus (7q22.1), was associated with a reduction in premenopausal urinary estrone glucuronide levels and a modest reduction in risk of breast cancer in women age ≤50 years. Methods: We further investigated the association of rs10235235 with breast cancer risk in a large case control study of 47,346 cases and 47,570 controls from 52 studies participating in the Breast Cancer Association Consortium. Genotyping of rs10235235 was conducted using a custom Illumina Infinium array. Stratified analyses were conducted to determine whether this association was modified by age at diagnosis, ethnicity, age at menarche or tumor characteristics. Results: We confirmed the association of rs10235235 with breast cancer risk for women of European ancestry but found no evidence that this association differed with age at diagnosis. Heterozygote and homozygote odds ratios (ORs) were OR = 0.98 (95% CI 0.94, 1.01; P = 0.2) and OR = 0.80 (95% CI 0.69, 0.93; P = 0.004), respectively (Ptrend = 0.02). There was no evidence of effect modification by tumor characteristics. rs10235235 was, however, associated with age at menarche in controls (Ptrend = 0.005) but not cases (Ptrend = 0.97). Consequently the association between rs10235235 and breast cancer risk differed according to age at menarche (Phet = 0.02); the rare allele of rs10235235 was associated with a reduction in breast cancer risk for women who had their menarche age ≥15 years (ORhet = 0.84, 95% CI 0.75, 0.94; ORhom = 0.81, 95% CI 0.51, 1.30; Ptrend = 0.002) but not for those who had their menarche age ≤11 years (ORhet = 1.06, 95% CI 0.95, 1.19, ORhom = 1.07, 95% CI 0.67, 1.72; Ptrend = 0.29). Conclusions: To our knowledge rs10235235 is the first single nucleotide polymorphism to be associated with both breast cancer risk and age at menarche consistent with the well-documented association between later age at menarche and a reduction in breast cancer risk. These associations are likely mediated via an effect on circulating hormone levels.

Introduction Family history is a well-established risk factor for breast cancer. First-degree relatives of women with breast cancer have an approximately twofold increased risk of developing the disease relative to the general population [1]. Twin studies are consistent with this familial clustering having, at least in part, a genetic origin [2,3]. Mutations in high-risk susceptibility genes (mainly BRCA1 and BRCA2) explain most large multiple-case families, but account for only 15 to 20% of the excess familial risk [4]. Genome-wide association studies [5,6] have identified more than 70 common variants that are associated with breast cancer susceptibility but they account for only another approximately 15% of the excess familial risk. The so-called ‘missing heritability’ may be explained by common variants with very small effects and/or by rarer variants with larger effects, neither of which can be identified by current genome-wide association studies. A statistically efficient alternative is to increase power by trying to identify variants associated with known quantitative phenotypic markers of susceptibility to breast cancer [7], and then to test them for association with breast cancer risk. This approach might also improve our understanding of the biological mechanisms involved in breast cancer pathogenesis.

Endogenous sex hormones are well-established risk factors for breast cancer in postmenopausal women [8]; the evidence in premenopausal women is less consistent, with some, but not all, studies suggesting an association between higher circulating levels of estrogens and increased breast cancer risk [9-17]. Genetic factors influence the levels of endogenous sex hormones [18] and therefore single nucleotide polymorphisms (SNPs) in genes regulating these hormonal pathways are good candidates for being breast cancer predisposition variants. We have previously studied 642 SNPs tagging 42 genes that might influence sex hormone levels in 729 healthy premenopausal women of European ancestry in relation to cyclic variations in oestrogen levels during the menstrual cycle. We found that the minor allele of rs10273424, which maps 50 kb 3′ to CYP3A5, was associated with a reduction of 22% (95% confidence interval (CI) = –28%, – 15%; P = 10−9) in levels of urinary oestrone glucuronide, a metabolite that is highly correlated with serum oestradiol levels [19]. Analysis of 10,551 breast cancer cases and 17,535 controls of European ancestry demonstrated that the minor allele of rs10235235, a proxy for rs10273424 (r2 = 1.0), was also associated with a weak reduction in

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breast cancer risk but only in women aged 50 years or younger at diagnosis (odds ratio (OR) = 0.91, 95% CI = 0.83, 0.99; P = 0.03) [19]. The aim of the present study was to further investigate an association between rs10235235 and breast cancer risk using a much larger set of subjects – the Breast Cancer Association Consortium (BCAC) – comprising data from 49 additional studies, and to assess whether there was evidence of effect modification by age at diagnosis, ethnicity, age at menarche or tumour characteristics.

Materials and methods
Sample selection

Samples for the case–control analyses were drawn from 52 studies participating in the BCAC: 41 studies from populations of predominantly European ancestry, nine studies of Asian ancestry and two studies of AfricanAmerican ancestry. The majority were population-based or hospital-based case–control studies, but some studies were nested in cohorts, selected samples by age, oversampled for cases with a family history or selected samples on the basis of tumour characteristics (Table S1 in Additional file 1). Studies provided ~2% of samples in duplicate for quality control purposes (see below). Study subjects were recruited on protocols approved by the Institutional Review Boards at each participating institution, and all subjects provided written informed consent (Additional file 2).
Genotyping and post-genotyping quality control

Genotyping for rs10235235 was carried out as part of a collaboration between the BCAC and three other consortia (the Collaborative Oncological Gene-environment Study (COGS)). Full details of SNP selection, array design, genotyping and post-genotyping quality control have been published [5]. Briefly, three categories of SNPs were chosen for inclusion in the array: SNPs selected on the basis of pooled genome-wide association study data; SNPs selected for the fine-mapping of published risk loci; and candidate SNPs selected on the basis of previous analyses or specific hypotheses. rs10235235 was a candidate SNP selected on the basis of our previous analyses [19]. For the COGS project overall, genotyping of 211,155 SNPs in 114,225 samples was conducted using a custom Illumina Infinium array (iCOGS; Illumina, San Diego, CA, USA) in four centres. Genotypes were called using Illumina’s proprietary GenCall algorithm. Standard quality control measures were applied across all SNPs and all samples genotyped as part of the COGS project. Samples were excluded for any of the following reasons: genotypically not female XX (XY, XXY or XO, n = 298); overall call rate <95% (n = 1,656); low or high heterozygosity (P < 10−6, separately for individuals of European, Asian

and African-American ancestry, n = 670); individuals not concordant with previous genotyping within the BCAC (n = 702); individuals where genotypes for the duplicate sample appeared to be from a different individual (n = 42); cryptic duplicates within studies where the phenotypic data indicated that the individuals were different, or between studies where genotype data indicated samples were duplicates (n = 485); first-degree relatives (n = 1,981); phenotypic exclusions (n = 527); or concordant replicates (n = 2,629). Ethnic outliers were identified by multidimensional scaling, combining the iCOGS array data with the three Hapmap2 populations, based on a subset of 37,000 uncorrelated markers that passed quality control (including ~1,000 selected as ancestry informative markers). Most studies were predominantly of a single ancestry (European or Asian), and women with >15% minority ancestry, based on the first two components, were excluded (n = 1,244). Two studies from Singapore (SGBCC) and Malaysia (MYBRCA; see Table S1 in Additional file 1 for all full study names) contained a substantial fraction of women of mixed European/Asian ancestry (probably of South Asian ancestry). For these studies, no exclusions for ethnic outliers were made, but principal components analysis (see below) was used to adjust for inflation in these studies. Similarly, for the two African-American studies (NBHS and SCCS), no exclusions for ethnic outliers were made. Principal component analyses were carried out separately for the European, Asian and African-American subgroups, based on a subset of 37,000 uncorrelated SNPs. For the analyses of European subjects, we included the first six principal components as covariates, together with a seventh component derived specific to one study (LMBC) for which there was substantial inflation not accounted for by the components derived from the analysis of all studies. Addition of further principal components did not reduce inflation further. Two principal components were included for the studies conducted in Asian populations and two principal components were included for the African-American studies. For the main analyses of rs10235235 and breast cancer risk, we excluded women from three studies (BBCS, BIGGS and UKBGS) that were genotyped in the hypothesis-generating study (n = 5,452) [19] and women with non-invasive cancers (ductal carcinoma in situ/lobular carcinoma in situ, n = 2,663) or cancers of uncertain status (n = 960)). After exclusions there were 47,346 invasive breast cancer case samples and 47,570 control samples from 49 studies (38 from populations of predominantly European ancestry, nine Asian and two African-American) used in the analysis (Tables S1 and S2 in Additional file 1). After quality control exclusions (above) the call rate for rs10235235 was 100% (one no call in 94,916 samples), and for the controls there was no evidence of deviation from

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Hardy–Weinberg equilibrium in any of the contributing studies (Table S2 in Additional file 1). We did not test for an association between rs10235235 and age at menarche in our hypothesis-generating study [19]. Therefore, to maximise our power to detect an association, we included menarche data from BBCS cases (n = 2,508) and controls (n = 1,650) and from UKBGS cases (n = 3,388) and controls (n = 4,081) in this analysis. Age at menarche was not available for samples from BIGGS. Full details of genotyping of rs10235235 in BBCS and UKBGS samples have been published previously [19]. Briefly, genotyping was carried out using competitive allele-specific polymerase chain reaction KASPar chemistry (KBiosciences Ltd, Hoddesdon, Hertfordshire, UK). Call rates were 98.0% (BBCS) and 96.6% (UKBGS); there was no evidence for deviation from Hardy–Weinberg equilibrium (P = 0.29 (BBCS); P = 0.92 (UKBGS)), and the duplicate concordance based on a 1% (BBCS) and 5% (UKBGS) random sample of duplicates was 100% for both studies.
Statistical analysis

We estimated per-allele and genotypic log odds ratios (ORs) for the European, Asian and African-American subgroups separately using logistic regression, adjusted for principal components and study [5]. To test for departure from a multiplicative model we compared multiplicative and unconstrained models using a one degree of freedom likelihood ratio test. Heterogeneity in ORs between studies within each subgroup (European, Asian and African-American), and between subgroups, was assessed using the Cochrane Q statistic and quantified using the I2 measure [20]. Analyses stratified by oestrogen receptor status (+/–), progesterone receptor status (+/–), morphology (ductal or lobular), grade (1,2,3), lymph node involvement (+/–) or age at diagnosis (≤50 and >50 years) were restricted to studies of European ancestry due to the small number of studies of Asian and African-American ancestry. In addition, studies were excluded if they had selected cases on the basis of the stratifying variable, or had collected data on that variable for less than 5% of cases or less than 10 cases in total. Availability of data for each of the stratifying variables in each study is shown in Table S3 in Additional file 1. To assess the relationship between each of the stratifying variables and genotype, stratumspecific ORs were calculated using logistic regression. Cases in each stratum were compared with all control subjects, adjusted for study and principal components. Case-only logistic regression was used to test for heterogeneity between strata (binary stratifying variables) or across strata (stratifying variables with three or more strata). P values were estimated using likelihood ratio tests with one degree of freedom.

We assessed whether rs10235235 was associated with age at menarche in cases and controls separately. Studies that had not collected data on age at menarche in both cases and controls were excluded (Table S4 in Additional file 1). We used linear regression, adjusted for principal components and study, to estimate the relationship between age at menarche (years) and rs10235235 genotype (0, 1, 2 rare alleles) and logistic regression adjusted for principal components and study to estimate the association between age at menarche and breast cancer risk. To test for effect modification of an association between rs10235235 and breast cancer risk by age at menarche, we used logistic regression adjusted for principal components, study and age at menarche (grouped as ≤11, 12, 13, 14 and ≥15 years) with and without an interaction term(s). We considered four models: no interaction (zero interaction terms); assuming a linear interaction between genotype and menarche group (one interaction term); assuming a linear interaction between genotype and menarche group but allowing the linear term to differ between women who were heterozygous and those who were homozygous for the rare allele (two interaction terms); and one interaction term for each possible genotype/menarche group combination (eight interaction terms). Nested models were compared using likelihood ratio tests. All statistical analyses were performed using STATA version 11.0 (StataCorp, College Station, TX, USA). All P values reported are two-sided.

Results The case–control analysis comprised genotype data for 47,346 invasive breast cancer cases and 47,569 controls from 49 studies, including 80,518 (84.8%) subjects of selfreported European ancestry, 12,419 (13.1%) of selfreported Asian ancestry and 1,978 (2.1%) of self-reported African-American ancestry. The mean (± standard deviation) age at diagnosis was 56.1 (± 11.6) years for European cases, 51.1 (± 10.5) years for Asian cases and 53.1 (± 10.7) years for African-American cases. There were ethnic differences in the estimated minor allele frequency (MAF) of rs10235235 (Q = 7317.1, two degrees of freedom; P for heterogeneity (Phet) = 0). The overall MAF for European control women was 0.089 (95% CI = 0.087, 0.091), but with strong evidence of between-study heterogeneity (Phet = 1 × 10−22) that was accounted for by the three Finnish studies (HEBCS, MAF = 0.15; KBCP, MAF = 0.21; and OBCS, MAF = 0.15; Phet = 0.01); no evidence of heterogeneity remained after taking account of these studies (MAF = 0.087 (95% CI = 0.085, 0.089); Phet = 0.23). Relative to Europeans, the overall MAF was higher for African-Americans (0.213, 95% CI = 0.195, 0.232; Phet = 0.26) but much lower for Asians (0.002; 95% CI = 0.001, 0.002), with strong evidence of between-study heterogeneity for the latter (Phet = 4 × 10−14).

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The case–control analysis was consistent with a modest association between rs10235235 and breast cancer risk for women of European ancestry, with an estimated per-allele OR of 0.96 (95% CI = 0.93, 0.99; P for linear trend (Ptrend) = 0.02). Genotype-specific ORs were 0.98 (95% CI = 0.94, 1.01; P = 0.21) for AG versus AA (Figure 1A) and 0.80 (95% CI = 0.69, 0.93; P = 0.004) for GG versus AA (Figure 1B), with no evidence of between-study heterogeneity for either OR estimate (Phet = 0.44, I2 = 1.9% and Phet = 0.76, I2 = 0.0% for heterozygote and homozygote OR estimates respectively). There was, however, marginally significant evidence that the genotypic OR estimates departed from those expected under a multiplicative model with the inverse association of the GG genotype being more than the square of that of the AG genotype (test for deviation from multiplicative model, P = 0.04). Data for rs10235235 in women of Asian or AfricanAmerican ancestry were more limited, with just two African-American studies (1,046 cases and 932 controls) and nine Asian studies (5,795 cases and 6,624 controls). In addition, this SNP was sufficiently rare in Asian populations (MAF = 0.002) that we were unable to estimate the heterozygote OR in two Asian studies (SEBCS, one carrier among 1,114 cases and no carriers among 1,129 controls; TWBCS, one carrier among 236 controls and no carriers among 774 cases; Table S2 in Additional file 1) and we could not estimate a homozygote OR for any

Asian study (Table S2 in Additional file 1). There was no clear evidence that this SNP was associated with breast cancer risk for women of Asian ancestry (heterozygote OR = 1.06, 95% CI = 0.76, 1.49) or African-American ancestry (heterozygote and homozygote ORs were OR = 1.09, 95% CI = 0.90, 1.32 and OR = 0.94, 95% CI = 0.62, 1.42 respectively; Figure S1 in Additional file 1). This analysis, however, had low power to detect associations in non-Europeans and these OR estimates were not inconsistent with the magnitude of the observed OR estimates for European women (Phet = 0.51). Stratifying cases by oestrogen receptor (Phet = 0.83) or progesterone receptor (Phet = 0.19) status, tumour grade (Phet = 0.63) or nodal involvement at diagnosis (Phet = 0.51) showed no evidence of effect modification (Table 1). There was some evidence of effect modification by morphology (Phet = 0.03). For ductal cancers we estimated a very modest reduction of risk for heterozygotes (ORhet = 0.98, 95% CI = 0.93, 1.02; P = 0.30) and a stronger, significant reduction for homozygotes (ORhom = 0.74, 95% CI = 0.61, 0.90; P = 0.003). For lobular cancers there was no such trend (ORhet = 1.07, 95% CI = 0.98, 1.17; P = 0.14 and ORhom = 0.91, 95% CI = 0.64, 1.27; P = 0.57). The SNP rs10235235 maps to a locus (CYP3A) that has been considered an a priori candidate for involvement in determining age at menopause and age at menarche [21,22]. Stratifying cases by age at diagnosis (≤50

Study

Cases

Controls

MAF 0.09 0.08 0.08 0.09 0.09 0.09 0.10 0.09 0.07 0.10 0.08 0.15 0.10 0.08 0.21 0.09 0.09 0.08 0.09 0.09 0.10 0.08 0.06 0.06 0.09 0.15 0.08 0.07 0.09 0.09 0.09 0.07 0.09 0.08 0.09 0.09 0.08 0.08

OR (95% CI) 0.91 (0.67, 1.23) 0.98 (0.79, 1.21) 1.07 (0.76, 1.52) 0.98 (0.75, 1.28) 1.08 (0.84, 1.39) 1.07 (0.94, 1.22) 0.82 (0.63, 1.06) 0.65 (0.25, 1.66) 1.24 (0.65, 2.37) 0.79 (0.56, 1.12) 1.09 (0.75, 1.57) 1.09 (0.91, 1.29) 1.08 (0.65, 1.78) 1.33 (0.98, 1.80) 0.98 (0.69, 1.39) 0.70 (0.50, 0.98) 1.08 (0.85, 1.37) 0.98 (0.81, 1.18) 0.95 (0.59, 1.52) 0.95 (0.79, 1.14) 0.85 (0.62, 1.16) 1.03 (0.77, 1.37) 1.16 (0.78, 1.71) 1.64 (0.36, 7.56) 0.79 (0.37, 1.70) 0.90 (0.67, 1.22) 1.12 (0.83, 1.51) 1.02 (0.65, 1.58) 0.83 (0.48, 1.42) 0.99 (0.71, 1.39) 0.90 (0.81, 1.01) 1.39 (1.02, 1.91) 0.38 (0.12, 1.21) 0.93 (0.74, 1.16) 0.75 (0.57, 1.00) 0.94 (0.87, 1.02) 1.20 (0.60, 2.40) 1.36 (0.87, 2.14) 0.98 (0.94, 1.01)

Study

Cases

Controls

MAF 0.09 0.08 0.08 0.09 0.09 0.09 0.10 0.09 0.07 0.10 0.08 0.15 0.10 0.08 0.21 0.09 0.09 0.08 0.09 0.09 0.10 0.08 0.06 0.06 0.09 0.15 0.08 0.07 0.09 0.09 0.09 0.07 0.09 0.08 0.09 0.09 0.08 0.08

OR (95% CI) 1.21 (0.35, 4.18) 1.39 (0.47, 4.07) 2.24 (0.59, 8.49) 1.45 (0.52, 4.07) 1.34 (0.51, 3.50) 0.73 (0.43, 1.24) 0.33 (0.11, 1.06)

ABCFS 790 551 1429 ABCS 1256 BBCC 554 458 BSUCH 815 954 999 CECILE 900 CGPS 2811 4086 876 CNIO-BCS 867 71 CTS 68 DEMOKRITOS 413 95 502 ESTHER 471 GENICA 465 427 HEBCS 1517 1234 130 HMBCS 688 662 KARBAC 722 251 KBCP 411 897 kConFab/AOCS 410 1388 LMBC 2616 1778 MARIE 1656 MBCSG 189 400 MCBCS 1546 1931 MCCS 614 510 MEC 705 741 MTLGEBCS 489 436 NBCS 22 70 118 NBHS 125 OBCS 500 414 511 OFBCR 1156 327 ORIGO 335 OSU 207 203 424 PBCS 519 5537 pKARMA 4553 RBCS 620 699 RPCI 47 126 1378 1163 SASBAC SBCS 751 848 SEARCH 9097 8069 SKKDKFZS 134 168 SZBCS 303 315 Overall (I-squared = 1.9%, phet = 0.436)

ABCFS 790 551 ABCS 1256 1429 458 BBCC 554 954 BSUCH 815 999 CECILE 900 4086 CGPS 2811 CNIO-BCS 867 876 CTS 68 71 DEMOKRITOS 413 95 ESTHER 471 502 GENICA 465 427 HEBCS 1517 1234 130 HMBCS 688 KARBAC 722 662 411 251 KBCP 897 kConFab/AOCS 410 1388 LMBC 2616 1778 MARIE 1656 MBCSG 189 400 1931 MCBCS 1546 510 MCCS 614 741 MEC 705 MTLGEBCS 489 436 NBCS 22 70 NBHS 125 118 OBCS 500 414 511 OFBCR 1156 ORIGO 335 327 207 203 OSU PBCS 519 424 pKARMA 4553 5537 RBCS 620 699 126 RPCI 47 1378 SASBAC 1163 848 SBCS 751 SEARCH 9097 8069 SKKDKFZS 134 168 315 SZBCS 303 Overall (I-squared = 0.0%, phet = 0.76)

1.00 (0.28, 3.56) 0.59 (0.10, 3.57) 0.72 (0.44, 1.18) 0.44 (0.08, 2.32) 0.61 (0.21, 1.75) 0.28 (0.11, 0.69) 0.64 (0.21, 1.96) 0.78 (0.31, 1.94) 3.80 (0.34, 42.75) 0.86 (0.40, 1.85) 0.89 (0.26, 3.12) 2.42 (0.74, 7.93) 1.79 (0.22, 14.35)

0.92 (0.36, 2.30) 1.03 (0.18, 5.79) 0.47 (0.04, 5.27) 6.55 (0.54, 79.13) 2.01 (0.39, 10.51) 0.80 (0.51, 1.26) 1.37 (0.45, 4.14) 0.20 (0.04, 0.91) 0.35 (0.10, 1.30) 0.81 (0.58, 1.14) 0.52 (0.05, 5.30) 0.67 (0.16, 2.90) 0.80 (0.69, 0.93)

A

.5

1

2

B

.1

.5

1

2

5

Figure 1 Association of rs10235235 with breast cancer risk for women of European ancestry. Forest plots of the association of the rs10235235 AG (heterozygote) genotype (A) and GG (homozygote) genotype (B) with breast cancer risk for women of European ancestry. Horizontal lines, 95% confidence intervals (CIs); square boxes, study-specific fixed-effects estimates; diamond, combined, fixed-effects estimate of the odds ratio (OR) and 95% CI. Vertical line, null effect (OR = 1.0); dashed vertical line, estimated heterozygote OR (A) and estimated homozygote OR (B). Homozygote ORs for six studies (CTS, DEMOKRITOS, kConFab/AOCS, NBCS, NBHS and RPCI) could not be estimated because there were no GG homozygotes among cases or among controls in each of these studies (see Table S2 in Additional file 1).

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Table 1 Association of rs10235235 with risk of breast cancer for women of European ancestry: stratified analysis
Cases ER status ER-positive ER-negative NK Total PR status PR-positive PR-negative NK Total Morphology Ductal Lobular Other and NK Total Grade Grade 1 Grade 2 Grade 3 NK Total Nodal status Node-negative Node-positive NK Total 17,463 10,746 9,359 37,568 37,836 0.98 0.94, 1.02 0.31 0.81 0.68, 0.96 0.02 0.51 37,836 37,836 0.98 0.98 0.93, 1.03 0.92, 1.04 0.47 0.46 0.86 0.72 0.71, 1.04 0.57, 0.93 0.12 0.01 5,944 13,427 8,638 8,769 36,778 37,285 0.99 0.95, 1.03 0.56 0.76 0.64, 0.90 0.001 0.63 37,285 37,285 37,285 0.97 1.00 0.98 0.90, 1.05 0.95, 1.06 0.92, 1.05 0.46 0.92 0.58 0.86 0.80 0.61 0.65, 1.15 0.63, 0.98 0.46, 0.82 0.31 0.04 0.001 22,123 3,921 5,995 32,039 31,803 0.99 0.95, 1.04 0.64 0.77 0.64, 0.92 0.004 0.03 31,803 31,803 0.98 1.07 0.93, 1.02 0.98, 1.17 0.30 0.14 0.74 0.91 0.61, 0.90 0.64, 1.27 0.003 0.57 18,497 8,193 12,111 38,801b 39,033 0.99 0.94, 1.03 0.52 0.80 0.67, 0.95 0.01 0.19 39,033 39,033 0.98 1.02 0.93, 1.02 0.96, 1.09 0.32 0.53 0.82 0.74 0.67, 0.99 0.56, 0.98 0.04 0.03 24,780 5,851 8,339 38,970a 38,739 0.99 0.95, 1.03 0.74 0.79 0.67, 0.94 0.006 0.83 38,739 38,739 0.99 1.02 0.95, 1.03 0.95, 1.10 0.61 0.60 0.83 0.60 0.70, 0.99 0.43, 0.86 0.04 0.005 Controls ORhet 95% CI P1 ORhom 95% CI P1 Phet

Association of rs10235235 with risk of breast cancer for women of European ancestry stratified by oestrogen receptor (ER) status, progesterone receptor (PR) status, morphology, grade and nodal status. ORhet, odds ratio comparing rs10235235 AG genotype versus AA genotype; H0, null hypothesis; NK, not known; ORhom, odds ratio comparing rs10235235 GG genotype versus AA genotype; P1, test of H0 no association between rs10235235 and breast cancer risk; Phet, test of H0 no difference between stratum specific estimates for variables with two strata or test of H0 no linear trend in stratum specific estimates for variables with three strata. aExcludes seven studies that selected all ER-negative cases (CTS, DEMOKRITOS, NBCS, NBHS, OSU, RPCI and SKKDKFZS) and one study (PBCS) that selected all ER-positive cases. bExcludes seven studies that selected all PR-negative cases (CTS, DEMOKRITOS, NBCS, NBHS, OSU, RPCI and SKKDKFZS).

or >50 years) as a proxy for menopausal status at diagnosis showed no evidence of effect modification (Phet = 0.89; Table 2), and excluding cases who were diagnosed between age 46 and 55 as potentially perimenopausal did not alter this result (Phet = 0.28). Data on age at menarche were available for 21,736 cases and 22,686 controls (Table S4 in Additional file 1); to increase the power of the analysis we included additional data from BBCS and UKBGS (5,737 cases, 5,572 controls; Table S4 in Additional file 1) [19]. There was a 1.5% (95% CI = 0.5%, 2.7%; P = 0.004) reduction in breast cancer risk associated with each additional year’s increase in age at menarche. Mean age at menarche was positively associated with number of copies of the minor allele of rs10235235 for controls (Ptrend = 0.005; Table 3) but not for cases (Ptrend = 0.97; Table 3). Consequently, there was an inverse trend in

the magnitude of the heterozygote and homozygote breast cancer ORs with mean age at menarche (Phet = 0.02; Table 4); being a carrier of one or two rare alleles of rs10235235 was associated with an estimated 16% (ORhet = 0.84, 95% CI = 0.75, 0.94; P = 0.003) or 19% (ORhom = 0.81, 95% CI = 0.51, 1.30; P = 0.39) (Ptrend = 0.002) reduction in breast cancer risk for women who had their menarche at ages ≥15 years but there was no evidence of reduction for those with a menarche at age ≤11 years (ORhet = 1.06, 95% CI = 0.95, 1.19; P = 0.30 and ORhom = 1.07, 95% CI = 0.67, 1.72; P = 0.78) (Ptrend = 0.29). There was no evidence that the inverse trend in the magnitude of ORs with mean age at menarche differed between heterozygous and homozygous carriers (P = 0.97) and no evidence that the trend was nonlinear (P = 0.70).

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Table 2 rs10235235 and risk of breast cancer for women of European ancestry by age at diagnosis
Age at diagnosis ≤ 50 years > 50 years NK Total
a

Casesa 11,794 23,264 554 35,612

Controlsa 34,988 34,988

ORhet 0.99 0.97

95% CI 0.93, 1.05 0.93, 1.02

P1 0.69 0.24

ORhom 0.68 0.84

95% CI 0.53, 0.86 0.70, 1.00

P1 0.003 0.04

Phet

34,988

0.98

0.94, 1.02

0.23

0.79

0.67, 0.92

0.003

0.89

Five studies (ABCFS, MARIE, MEC, MTLGEBCS and SASBAC) that selected all cases on the basis of age at diagnosis (Table S3 in Additional file 1) were excluded from this stratified analysis; two small studies (CTS and NBCS) that had no heterozygote or rare homozygote cases in one of the age stratum were also excluded. H0, null hypothesis; NK, not known; ORhet, odds ratio comparing rs10235235 AG genotype versus AA genotype; ORhom, odds ratio comparing rs10235235 GG genotype versus AA genotype; P1, test of H0 no association between rs10235235 and breast cancer risk; Phet, test of H0 no difference between stratum specific estimates.

Discussion This study of more than 47,000 breast cancer cases and 47,000 controls has confirmed that rs10235235, mapping to 7q22.1 (CYP3A), is associated with a reduction in breast cancer risk for women of European ancestry. Previously, our hypothesis-generating study of 10,000 breast cancer cases and 17,000 controls found a per-allele OR estimate of 0.96 (95% CI = 0.90, 1.02; P = 0.2), with marginally significant evidence of an inverse association for breast cancer diagnosed age 50 years or younger (OR = 0.91, 95% CI = 0.83, 0.99; P = 0.03) but no evidence of an association for breast cancer at later ages (OR = 1.01, 95% CI = 0.93, 1.10; P = 0.82) [19]. In this considerably larger study, we found a heterozygote OR estimate of 0.98 (95% CI = 0.94, 1.01; P = 0.21) and a homozygote OR estimate of 0.80 (95% CI = 0.69, 0.93; P = 0.004) with marginally significant evidence that the inverse association for homozygotes is greater than predicted by a multiplicative model (P = 0.04). To our knowledge, rs10235235 is the first SNP to be associated with both breast cancer risk and age at menarche, consistent with the well-documented association between later age at menarche and a reduction in breast cancer risk [23]. Genome-wide association studies have identified more than 70 breast cancer risk variants [5,6] and more than 30 variants associated with age at menarche [22], none of which map to the CYP3A locus. rs10235235 was originally identified on the basis of a highly significant association with hormone levels, accounting for 4.9% of the variation in premenopausal urinary oestrone glucuronide levels [19]. In this current analysis, rs10235235 accounted for only 0.01% of the variation across controls in age at menarche and we estimate that this SNP explains just 0.01% of the familial

excess breast cancer risk. Our data thus illustrate the potential statistical efficiency of studies of intermediate phenotypes in the identification of rarer (MAF < 10%) risk alleles with modest associations. Our analysis shows some inconsistency with a recent genome-wide study of circulating oestradiol, testosterone and sex hormonebinding globulin in postmenopausal women [24]. In that study there was no genome-wide significant association observed with plasma oestradiol levels in either the primary analysis of approximately 1,600 postmenopausal women who were not taking postmenopausal hormones at blood draw or the secondary analysis that included approximately 900 current postmenopausal hormone users. Further studies will be needed to determine whether the lack of an association between CYP3A variants and postmenopausal plasma oestradiol levels reflects a difference in the menopausal status of the study subjects, the hormone/metabolite that was analysed or chance. One possible explanation for the apparent effect modification of the rs10235235–breast cancer risk association by age at menarche is that this is a function of genotyping a marker SNP rather than the true causal variant. For example, if rs10235235 was perfectly correlated with a causal variant, SNP X, with a MAF substantially lower than that of rs10235235 (D′ ~ 1.0, r2 < 1.0), then there would be three types of chromosome in the population: type i, chromosomes carrying the common allele of rs10235235 and the common allele of SNP X; type ii, chromosomes carrying the rare allele of rs10235235 and the common allele of SNP X; and type iii, chromosomes carrying the rare allele of rs10235235 and the rare (protective) allele of SNP X. Only chromosomes carrying the rare allele of rs10235235 and the rare (protective) allele of

Table 3 Association of rs10235235 with age at menarche for women of European ancestry by case-control status
rs10235235 genotype AA AG GG Total Cases 22,954 4,312 207 27,473 Age at menarche (years) 12.83 12.83 12.83 12.83 0.97 Ptrend Controls 23,383 4,627 248 28,258 Age at menarche (years) 12.95 13.02 13.05 12.96 0.005 Ptrend

H0, null hypothesis; Ptrend, test of H0 no linear trend in age at menarche according to rs10235235 genotype.

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Table 4 rs10235235 and risk of breast cancer for women of European ancestry by age at menarche
Age at menarche (years) ≤11 12 13 14 ≥15 Total Cases 4,818 5,655 7,308 5,307 4,385 27,473 Controls 4,749 5,720 7,379 5,743 4,667 28,258 ORhet 1.06 0.92 0.93 0.96 0.84 0.94 95% CI 0.95, 1.19 0.83, 1.02 0.85, 1.02 0.86, 1.06 0.75, 0.94 0.90, 0.98 P1 0.30 0.10 0.11 0.42 0.003 0.007 ORhom 1.07 0.83 0.77 0.69 0.81 0.81 95% CI 0.67, 1.72 0.54, 1.28 0.54, 1.09 0.45, 1.06 0.51, 1.30 0.67, 0.98 P1 0.78 0.41 0.14 0.09 0.39 0.03 0.02 Phet

H0, null hypothesis; ORhet, odds ratio comparing rs10235235 AG genotype versus AA genotype; ORhom, odds ratio comparing rs10235235 GG genotype versus AA genotype; P1, test of H0 no association between rs10235235 and breast cancer risk; Phet, test of H0 no linear trend in stratum specific estimates.

SNP X (type iii) would be enriched in controls. Genotyping the marker (rs10235235) rather than the causal variant leads to misclassification. As the causal variant is associated with a protective effect on breast cancer risk, the proportion of chromosomes carrying both the rare allele of the causal variant and the marker (type iii) compared with the common allele of the causal variant and the rare allele of the marker (type ii) will be greater in controls than in cases such that the extent of misclassification will be greater for cases than controls. This will attenuate the association between genotype and age at menarche to a greater extent in cases than in controls creating an apparent effect modification. Fine mapping and functional studies will be required to identify the causal variant and to determine the true relationship between the causal variant, age at menarche and breast cancer risk. Despite our original finding of a strong association between rs10235235 and hormone levels, we found no evidence that the association between this SNP and breast cancer risk differed by the hormone receptor status of the tumour, and nor did we find any evidence that the association differed by stage, grade or lymph node involvement. There was marginally significant evidence that the association between rs10235235 and breast cancer risk differed between ductal and lobular cancers (Phet = 0.03). Given the number of stratified analyses that we carried out (six stratifying variables) and given that there is no biological basis to support an interaction between rs10235235 and morphology, this is probably a chance observation. In contrast to our earlier study [19], we found no evidence of an interaction with age at diagnosis when we stratified cases by age ≤/>50 years, either including or excluding cases diagnosed between age 46 and 55 years as potentially perimenopausal. We used age at diagnosis as a proxy for menopausal status at diagnosis because menopausal status at diagnosis is difficult to determine by questionnaire, especially given the use of hormone replacement therapies; while information on age at diagnosis was available for all but 1.4% (n = 554) of cases, information on age at natural menopause was missing for 65.6% (n = 26,552) of cases of European

ancestry. Similarly, although rs10235235 is a plausible candidate for association with age at menopause, we did not test this due to the limited amount of data on age at natural menopause for controls of European ancestry (n = 11,294, 28.2%) and the difficulty in ascertaining whether treatment for breast cancer had influenced reported age at menopause for cases. The strengths of our study include the large size of this combined analysis, and the availability of information on tumour characteristics for the majority of cases and on age at menarche for the majority of cases and controls. Limitations include low power of the study to examine an association between genotype and breast cancer risk for non-Europeans.

Conclusions In summary, we have confirmed that rs10235235 is associated with breast cancer, have shown for the first time that rs10235235 is associated with age at menarche in controls and have suggested a potential mechanism for these associations. rs10235235, which maps to the CYP3A locus, probably tags a causal variant that affects expression of one or more CYP3A genes. Additional files
Additional file 1: Contains Table S1 presenting details of participating BCAC studies; Table S2 presenting rs10235235 genotypes for breast cancer cases and controls from 49 BCAC studies; Table S3 presenting availability of data on age at diagnosis, hormone receptor status, morphology, grade and nodal status for breast cancer cases from 38 European BCAC studies; Table S4 presenting availability of data on age at menarche for breast cancer cases and controls from 40 European BCAC studies; and Figure S1 showing association of the rs10235235-AG genotype with breast cancer risk for women of Asian and African-American ancestry. Additional file 2: Presents details of ethical committees that approved each study.

Abbreviations BCAC: Breast Cancer Association Consortium; CI: confidence interval; COGS: Collaborative Oncological Gene-environment Study; MAF: minor allele frequency; OR: odds ratio; Ptrend: P value for linear trend; SNP: single nucleotide polymorphism.

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Competing interests The authors state that they have no competing interests. Authors’ contributions OF, FD and NO performed the statistical analyses. OF, IdSS and NJ drafted the manuscript. NJ, FD, NO, LG, MEJ, MJS, EJF, BPH, MG-C, MDo, AA, AJS, JP, IdSS and OF comprised the writing group that was responsible for the interpretation of the results and for critically reviewing the manuscript. AC, AJ, AHW, AMa, BBu, C-YS, DL, ES, GC-T, HN, HBre, HBra, ILA, JC-C, J-YC, JLH, LBa, MKB, HMi, PAF, PR, RW, SEB, TD, MKS and UH also significantly contributed to the interpretation of the results. OF, IdSS, NJ, JP, LG, DFE, MKB and JW conceived of the original design of the study and participated in subject recruitment and in acquisition of data. JBen, AG-N, RM, DCT, DV, FB, CL, JD, JS and KMi carried out the genotyping and/or data analysis. FD, NO, MEJ, MJS, EJF, BPH, JLH, MCS, GSD, CA, MKS, AB, LJVV, FA, KMu, ALo, PAF, MWB, ABE, SPR, ES, IT, MK, NM, BBu, FMa, AS, CS, PG, TT, EC, FMe, SEB, BGN, HF, RMi, MPZ, JIAP, JBen, LBe, HA-C, AZ, CCD, HBre, HMü, VA, AKD, AMe, JH, CRB, RKS, HBra, CJ, Y-DK, The GENICA Network, HN, TAM, KA, CB, KMa, TD, NVB, NNA, ALi, AMa, VK, V-MK, JMH, GC-T, JBee, kConFab Investigators, Australian Ovarian Cancer Study Group, AHW, DVdB, C-CT, DL, DS, PN, HW, JC-C, AR, SN, DF-J, PR, PP, BBo, VP, FJC, JEO, XW, ZF, VSP, GGG, GS, LBa, CH, JS, MSG, FL, MDu, PS, ST, CHY, SYP, BKC, VNK, GGA, A-LB-D, WZ, RW, KP, AJ-V, MG, ILA, JAK, GG, AMM, PD, JF, SJC, JLis, MES, PH, NS, MHo, AH, RAO, MT-L, JLiu, AC, IWB, MWRR, SSC, WB, LBS, PDPP, AMD, MS, DK, D-YN, SKP, J-YC, MHa, HMi, WYL, AT, UH, AF, TR, HUU, AJ, JLu, KJ-B, KD, SSa, VG, PB, JM, SSl, AET, CV, DY, C-YS, J-CY, C-SH, M-FH, AG-N, DCT, DV, FB, CL, JD, KMi, MKB, JW, DFE, MG-C, MDo, AA and AJS made substantial contributions in recruiting subjects and acquiring data, and in critically reviewing the manuscript. All authors take responsibility for the work and read and approved the final version of the manuscript. Acknowledgements The authors thank all of the individuals who took part in these studies and all of the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. ABCFS would like to thank Maggie Angelakos, Judi Maskiell and Gillian Dite. ABCS would like to thank Ellen van der Schoot and Sanquin Amsterdam. The ACP study wishes to thank the participants in the Thai Breast Cancer study. Special thanks also go to the Thai Ministry of Public Health (MOPH) doctors and nurses who helped with the data collection process. The study would like to thank Dr Prat Boonyawongviroj, the former Permanent Secretary of MOPH and Dr Pornthep Siriwanarungsan, the Department Director-General of Disease Control who have supported the study throughout. BBCS would like to thank Eileen Williams, Elaine Ryder-Mills and Kara Sargus. BIGGS would like to thank Niall McInerney, Gabrielle Colleran, Andrew Rowan and Angela Jones. CNIO-BCS would like to thank Charo Alonso, Tais Moreno, Guillermo Pita, Primitiva Menendez and Anna González-Neira. The authors would like to acknowledge the contribution of the staff of the Génome Québec-genotyping unit under the supervision of Dr Sylvie LaBoissière, as well as Frédérick Robidoux from the McGill University and Génome Québec Innovation Centre. ESTHER would like to thank Hartwig Ziegler, Sonja Wolf and Volker Hermann. GC-HBOC would like to thank Bernd Frank. HEBCS would like to thank Dr Sofia Khan, Dr Kirsimari Aaltonen and Dr Karl von Smitten, and research nurses Irja Erkkilä and Virpi Palola. KBCP would like to thank Eija Myöhänen and Helena Kemiläinen. kConFab/AOCS would like to thank Heather Thorne, Eveline Niedermayr, the AOCS Management Group (D Bowtell, G Chenevix-Trench, A deFazio, D Gertig, A Green, P Webb) and the ACS Management Group (A Green, P Parsons, N Hayward, P Webb, D Whiteman). LAABC thanks all of the study participants and the entire data collection team, especially Annie Fung and June Yashiki. LMBC would like to thank Gilian Peuteman, Dominiek Smeets, Thomas Van Brussel and Kathleen Corthouts. MARIE would like to thank Tracy Slanger, Elke Mutschelknauss, Ramona Salazar, S Behrens, R Birr, W Busch, U Eilber, B Kaspereit, N Knese and K Smit. MBCSG would like to thank Siranoush Manokian, Bernard Peissel and Daniela Zaffaroni of the Fondazione Istituto Nazionale dei Tumori, Milan, Monica Barile of the Istituto Europeo di Oncologia, Milan and Loris Bernard and personnel of the Cogentech Cancer Genetic Test Laboratory, Milan, Italy. MTLGEBCS would like to thank Martine Tranchant (Cancer Genomics Laboratory, CRCHUQ), Marie-France Valois, Annie Turgeon and Lea Heguy (McGill University Health Center, Royal Victoria Hospital; McGill University) for DNA extraction, sample management and skillful technical assistance. JS is Chairholder of the Canada Research Chair in Oncogenetics. MYBRCA would

like to thank Phuah Sze Yee, Peter Kang, Kang In Nee, Kavitta Sivanandan, Shivaani Mariapun, Yoon Sook-Yee, Daphne Lee, Teh Yew Ching and Nur Aishah Mohd Taib for DNA Extraction and patient recruitment. NBHS thanks study participants and research staff for their contributions and commitment to the study. OBCS would like to thank Meeri Otsukka and Kari Mononen. OFBCR would like to thank Teresa Selander and Nayana Weerasooriya. ORIGO thanks E Krol-Warmerdam and J Blom for patient accrual, administering questionnaires and managing clinical information. The LUMC survival data were retrieved from the Leiden hospital-based cancer registry system (ONCDOC) with the help of Dr J Molenaar. PBCS would like to thank Louise Brinton, Mark Sherman, Stephen Chanock, Neonila Szeszenia-Dabrowska, Beata Peplonska, Witold Zatonski, Pei Chao and Michael Stagner. pKARMA would like to thank The Swedish Medical Research Counsel. RBCS would like to thank Petra Bos, Jannet Blom, Ellen Crepin, Elisabeth Huijskens, Annette Heemskerk and the Erasmus MC Family Cancer Clinic. SASBAC would like to thank The Swedish Medical Research Counsel. SBCGS thanks study participants and research staff for their contributions and commitment to the study. SBCS would like to thank Sue Higham, Helen Cramp and Dan Connley. SEARCH would like to thank The SEARCH and EPIC teams. SGBCC would like to thank the participants and research coordinator Kimberley Chua. SKKDKFZS are grateful to all of the patients for their participation and thank the physicians and other hospital staff, scientists, research assistants and study staff who contributed to the patient recruitment, data collection and sample preparation. UKBGS thanks Breakthrough Breast Cancer and the Institute of Cancer Research for support and funding of the Breakthrough Generations Study, and the study participants, study staff, and the doctors, nurses and other healthcare providers and health information sources who have contributed to the study. Consortia members The GENICA network: Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, and University of Tübingen, Germany (Christina Justenhoven, Hiltrud Brauch); Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany (Yon-Dschun Ko, Christian Baisch); Institute of Pathology, University of Bonn, Germany (Hans-Peter Fischer); Molecular Genetics of Breast Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany (Ute Hamann); Institute for Prevention and Occupational Medicine of the German Social Accident Insurance (IPA), Bochum, Germany (Thomas Bruening, Beate Pesch, Sylvia Rabstein, Anne Spickenheuer); and Institute for Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Germany (Volker Harth). kConFab Investigators: David Amor, Lesley Andrews, Yoland Antill, Shane Armitage, Rosemary Balleine, Agnes Bankier, Patti Bastick, John Beilby, Barbara Bennett, Ian Bennett, Anneke Blackburn, Michael Bogwitz, Meagan Brennan, Melissa Brown, Michael Buckley, Matthew Burgess, Jo Burke, Phyllis Butow, Ian Campbell, Alice Christian, Georgia Chenevix-Trench, Christine Clarke, Alison Colley, Dick Cotton, Bronwyn Culling, Margaret Cummings, Sarah-Jane Dawson, Anna DeFazio, Martin Delatycki, Rebecca Dickson, Alexander Dobrovic, Tracy Dudding, Ted Edkins, Stacey Edwards, Gelareh Farshid, Susan Fawcett, Georgina Fenton, Michael Field, James Flanagan, Peter Fong, John Forbes, Stephen Fox, Juliet French, Clara Gaff, Mac Gardner, Mike Gattas, Graham Giles, Grantley Gill, Jack Goldblatt, Sian Greening, Scott Grist, Eric Haan, Marion Harris, Stewart Hart, Nick Hayward, Sue Healey, Louise Heiniger, John Hopper, Clare Hunt, Paul James, Mark Jenkins, Rick Kefford, Alexa Kidd, Belinda Kiely, Judy Kirk, James Kollias, Jessica Koehler, Serguei Kovalenko, Sunil Lakhani, Jennifer Leary, Geoff Lindeman, Lara Lipton, Liz Lobb, Graham Mann, Deborah Marsh, Bettina Meiser, Roger Milne, Gillian Mitchell, Shona O’Connell, Nick Pachter, Briony Patterson, Lester Peters, Kelly Phillips, Melanie Price, Lynne Purser, Tony Reeve, Edwina Rickard, Bridget Robinson, Barney Rudzki, Elizabeth Salisbury, Christobel Saunders, Joe Sambrook, Jodi Saunus, Robyn Sayer, Clare Scott, Elizabeth Scott, Rodney Scott, Adrienne Sexton, Raghwa Sharma, Andrew Shelling, Peter Simpson, Melissa Southey, Amanda Spurdle, Graeme Suthers, Pamela Sykes, Jessica Taylor, Ella Thompson, Heather Thorne, Sharron Townshend, Alison Trainer, Kathy Tucker, Janet Tyler, Jane Visvader, Logan Walker, Paul Waring, Robin Ward, Bev Warner, Rachael Williams, Ingrid Winship, Mary Ann Young (Peter MacCallum Cancer Center, Melbourne, Australia). The Australian Ovarian Cancer Study Group: David D Bowtell, Adele C Green, Georgia Chenevix-Trench, Anna deFazio, Dorota Gertig, Penelope M Webb (Peter MacCallum Cancer Center, Melbourne, Australia).

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Financial support Part of this work was supported by the European Community’s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). This work was partly supported by the Canadian Institutes of Health Research for the ‘CIHR Team in Familial Risks of Breast Cancer’ program (JS, DFE), and the Ministry of Economic Development, Innovation and Export Trade of Quebec – grant number PSR-SIIRI-701 (JS, DFE, PH). The ABCFS and OFBCR work was supported by the United States National Cancer Institute, National Institutes of Health (NIH) 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), Northern California Cancer Center (U01 CA69417) and University of Melbourne (U01 CA69638). Samples from the NC-BCFR 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 centers in the BCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia) and the Victorian Breast Cancer Research Consortium. JLH is a National Health and Medical Research Council (NHMRC) Australia Fellow and a Victorian Breast Cancer Research Consortium Group Leader. MCS is a NHMRC Senior Research Fellow and a Victorian Breast Cancer Research Consortium Group Leader. The ABCS study was supported by the Dutch Cancer Society (grants NKI 2001-2423 and 2007-3839) and the Dutch National Genomics Initiative. The ACP study is funded by the Breast Cancer Research Trust, UK. The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen. BBCS is funded by Cancer Research UK and Breakthrough Breast Cancer, and acknowledges NHS funding to the NIHR Biomedical Research Centre and the National Cancer Research Network. BCAC is funded by CR-UK (C1287/A10118 and C1287/A12014). Meetings of the BCAC have been funded by the European Union COST programme (BM0606). DFE is a Principal Research Fellow of CR-UK. ES (BIGGS) is supported by NIHR Comprehensive Biomedical Research Centre, Guy’s & St. Thomas’ NHS Foundation Trust in partnership with King’s College London, UK. IT is supported by the Oxford Biomedical Research Centre. The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Center (DKFZ). CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council and Herlev Hospital. CNIO-BCS was supported by the Genome Spain Foundation, the Red Temática de Investigación Cooperativa en Cáncer and grants from the Asociación Española Contra el Cáncer and the Fondo de Investigación Sanitario (PI081583 and PI081120). CTS was supported by the California Breast Cancer Act of 1993, the NIH (grants R01 CA77398 and the Lon V Smith Foundation (LVS39420)) and the California Breast Cancer Research Fund (contract 97-10500). Collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885. The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe). GC-HBOC was supported by Deutsche Krebshilfe (107054), the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Centre (DKFZ). GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0 and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Institute for Prevention and Occupational Medicine of the German Social Accident Insurance (IPA), Bochum, as well as the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany. HEBCS was supported by the Academy of Finland (132473), Helsinki University Central Hospital Research Fund, the Sigrid Juselius Foundation, the Finnish Cancer Society and the Nordic Cancer Union. HERPACC was supported by a Grant-in-Aid for Scientific Research on Priority Areas and on Innovative Area from the Ministry of Education, Science, Sports, Culture and Technology of Japan and by a Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy

for Cancer Control from Ministry Health, Labour and Welfare of Japan. HMBCS was supported by short-term fellowships from the German Academic Exchange Program (NVB) and the Friends of Hannover Medical School (NVB). KBCP was financially supported by the special Government Funding (EVO) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, the Academy of Finland and by the strategic funding of the University of Eastern Finland. kConFab is supported by grants from the National Breast Cancer Foundation, the NHMRC, the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia and the Cancer Foundation of Western Australia. The kConFab Clinical Follow Up Study was funded by the NHMRC (145684, 288704, 454508). Financial support for the AOCS was provided by the United States Army Medical Research and Materiel Command (DAMD17-01-1-0729), the Cancer Council of Tasmania and Cancer Foundation of Western Australia and the NHMRC (199600). GC-T and P Webb are supported by the NHMRC. LAABC is supported by grants (1RB-0287, 3PB-0102, 5PB-0018, 10PB-0098) from the California Breast Cancer Research Program. Incident breast cancer cases were collected by the USC Cancer Surveillance Program (CSP), which is supported under subcontract by the California Department of Health. CSP is also part of the National Cancer Institute’s Division of Cancer Prevention and Control Surveillance, Epidemiology, and End Results Program, under contract number N01CN25403. LMBC is supported by the ‘Stichting tegen Kanker’ (232-2008 and 196-2010). DL is supported by the KULPFV/10/016SymBioSysII. The MARIE study was supported by the Deutsche Krebshilfe e.V. (70-2892-BR I), the Hamburg Cancer Society, the German Cancer Research Center and the genotype work in part by the Federal Ministry of Education and Research (BMBF) Germany (01KH0402). MBCSG was funded by grants from Italian Association for Cancer Research (AIRC, IG 8713), and by Italian citizens who allocated the 5 × 1,000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale dei Tumori, according to Italian laws (INT-Institutional strategic projects ‘5x1000’). MCBCS was supported by the NIH grants CA116167 and CA128978, an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), the Breast Cancer Research Foundation, and a generous gift from the David F and Margaret T Grohne Family Foundation and the Ting Tsung and Wei Fong Chao Foundation. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. MCCS was further supported by Australian NHMRC grants 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. MEC was support by NIH grants CA63464, CA54281, CA098758 and CA132839. The work of MTLGEBCS was supported by the Quebec Breast Cancer Foundation, the Canadian Institutes of Health Research for the ‘CIHR Team in Familial Risks of Breast Cancer’ program (grant number CRN-87521) and the Ministry of Economic Development, Innovation and Export Trade (grant number PSR-SIIRI-701). MYBRCA is funded by research grants from the Malaysian Ministry of Science, Technology and Innovation (MOSTI), Malaysian Ministry of Higher Education (UM.C/HlR/MOHE/06) and Cancer Research Initiatives Foundation (CARIF). Additional controls were recruited by the Singapore Eye Research Institute, which was supported by a grant from the Biomedical Research Council, Singapore (BMRC08/1/35/19/550) and the National Medical Research Council, Singapore (NMRC/CG/SERI/2010). NBCS was supported by grants from the Norwegian Research council, 155218/ V40, 175240/S10 to A-LB-D, FUGE-NFR 181600/V11 to VNK and a Swizz Bridge Award to A-LB-. NBHS was supported by NIH grant R01CA100374. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. OBCS was supported by research grants from the Finnish Cancer Foundation, the Academy of Finland, the University of Oulu, and the Oulu University Hospital. The ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The pKARMA study was supported by Märit and Hans Rausings Initiative Against Breast Cancer. RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). The SASBAC study was supported by funding from the Agency for Science, Technology and Research of Singapore (A*STAR), the US NIH and the Susan G Komen Breast Cancer Foundation. SBCGS was supported primarily by NIH grants R01CA64277, R01CA148667, and R37CA70867. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. SBCS was supported by Yorkshire Cancer Research S295, S299, S305PA. SCCS is supported by a grant from the

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National Institutes of Health (R01 CA092447). Data on SCCS cancer cases used in this publication were provided by the Alabama Statewide Cancer Registry; Kentucky Cancer Registry, Lexington, KY; Tennessee Department of Health, Office of Cancer Surveillance; Florida Cancer Data System; North Carolina Central Cancer Registry, North Carolina Division of Public Health; Georgia Comprehensive Cancer Registry; Louisiana Tumor Registry; Mississippi Cancer Registry; South Carolina Central Cancer Registry; Virginia Department of Health, Virginia Cancer Registry; and Arkansas Department of Health, Cancer Registry, Little Rock. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the Mississippi Cancer Registry, which participates in the National Program of Cancer Registries of the CDC. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry. SEARCH is funded by programme grants from Cancer Research UK (C490/A10124 and C8197/ A10123) and NIH grant 5U01CA098216-07. SEBCS was supported by the Korea Health 21 R&D Project (AO30001), Ministry of Health and Welfare, Republic of Korea. SGBCC is funded by the National Medical Research Council start-up Grant and Centre Grant (NMRC/CG/NCIS /2010). Additional controls were recruited by the Singapore Consortium of Cohort Studies-Multi-ethnic cohort (SCCS-MEC), which was funded by the Biomedical Research Council, grant number: 05/1/21/ 19/425. SKKDKFZS is supported by the DKFZ, Heidelberg, Germany. KJ-B (SZBCS) is a fellow of International PhD program, Postgraduate School of Molecular Medicine, Warsaw Medical University, supported by the Polish Foundation of Science. TBCS was funded by The National Cancer Institute, Thailand. TNBCC was supported by an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), the Breast Cancer Research Foundation, a generous gift from the David F and Margaret T Grohne Family Foundation and the Ting Tsung and Wei Fong Chao Foundation; The Stefanie Spielman Breast Cancer Fund and the OSU Comprehensive Cancer Center; the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program ‘Education and Lifelong Learning’ of the National Strategic Reference Framework (NSRF) – Research Funding Program of the General Secretariat for Research & Technology: ARISTEIA. TWBCS is supported by the Taiwan Biobank project of the Institute of Biomedical Sciences, Academia Sinica, Taiwan. UKBGS is funded by Breakthrough Breast Cancer and the Institute of Cancer Research (ICR). ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. Author details Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK. 2Division of Breast Cancer Research, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK. 3Non-communicable Disease Epidemiology Department, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK. 4Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Belmont, Sutton, Surrey SM2 5NG, UK. 5The Academic Department of Biochemistry, The Royal Marsden Hospital, Fulham Road, London SW3 6JJ, UK. 6Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, 1-100 Gratton Street, Parkville, Melbourne, Victoria 3010, Australia. 7Genetic Epidemiology Department, Department of Pathology, The University of Melbourne, 1-100 Gratton Street, Parkville, Melbourne, Victoria 3010, Australia. 8Division of Molecular Pathology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands. 9Sanquin, Radboud Universiteit Nijmegen, 6525 GA, Nijmegen, The Netherlands. 10 Warwick Medical School, University of Warwick, Coventry CV4 7AJ, UK. 11 University Breast Center, Department of Gynecology and Obstetrics, University Hospital Erlangen, Postfach 2306, D-91012 Erlangen, Germany. 12 David Geffen School of Medicine, Department of Medicine, Division of Hematology and Oncology, University of California, 10833 Le Conte Avenue, Los Angeles, CA 90095, USA. 13Institute of Human Genetics, Friedrich Alexander University Erlangen- Nuremberg, Schlossplatz 4, 91054 Erlangen, Germany. 14Division of Cancer Studies, NIHR Comprehensive Biomedical Research Centre, Guy’s & St. Thomas’ NHS Foundation Trust in partnership with King’s College London, Guy’s Hospital, Great Maze Pond, London SE1 9RT, UK. 15Welcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. 16Oxford Biomedical Research Centre, University of Oxford, The Churchill Hospital, Old Road, Headington OX3 7LE Oxford UK. 17Surgery, Clinical Science Institute, Galway University Hospital
1

and National University of Ireland, University Road, Galway, Ireland. 18 Department of Obstetrics and Gynecology, University of Heidelberg, Vosstrasse 9, 69115 Heidelberg, Germany. 19Unit Molecular Epidemiology C080, German Cancer Research Center, DKFZ, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. 20National Center for Tumor Diseases, University of Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany. 21 Inserm (National Institute of Health and Medical Research), CESP (Center for Research in Epidemiology and Population Health), U1018, Environmental Epidemiology of Cancer, 101 rue de Tolbiac, Villejuif, 75654 Paris, France. 22 University Paris-Sud, UMRS 1018, 101 rue de Tolbiac, Villejuif, 75654 Paris, France. 23Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, Herlev Rinvej 75, 2730 Herlev, Copenhagen, Denmark. 24Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev Rinvej 75, 2730 Herlev, Copenhagen, Denmark. 25Department of Breast Surgery, Herlev Hospital, Copenhagen University Hospital, Herlev Rinvej 75, 2730 Herlev, Copenhagen, Denmark. 26 Genetic and Molecular Epidemiology Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Calle de Melchor Fernandez Almagro, 3, 28029 Madrid, Spain. 27Servicio de Oncología Médica, Hospital Universitario La Paz, Paseo de la Castellana, 261, 28046 Madrid, Spain. 28Servicio de Cirugía General y Especialidades, Hospital Monte Naranco, Avda. Dres. Fernández Vega, 107 Oviedo, Spain. 29Human Genotyping-CEGEN Unit, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Calle de Melchor Fernandez Almagro, 3, 28029 Madrid, Spain. 30Centro de Investigación en Red de Enfermedades Raras (CIBERER), Calle de Melchor Fernandez Almagro, 3, 28029 Madrid, Spain. 31Division of Cancer Etiology, Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, USA. 32 Department of Epidemiology, School of Medicine, 224 Irvine Hall, University of California Irvine, Irvine, California 92697-7550, USA. 33Cancer Prevention Institute of California, 2201 Walnut Avenue, Suite 300, Fremont, California 95438, USA. 34Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 280, 69121 Heidelberg, Germany. 35German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, 69121 Heidelberg, Germany. 36Clinic of Gynecology and Obstetrics, Division of Tumor Genetics, Klinikum rechts der Isar, Technical University Munich, Ismaninger Strasse 22, D-81675 Munich, Germany. 37Institute of Human Genetics, University of Heidelberg, Im Neuenheimer Feld 366, 69121 Heidelberg, Germany. 38Division of Molecular Gyneco-Oncology, Department of Gynaecology and Obstetrics, Center of Molecular Medicine Cologne (CMMC), University Hospital of Cologne, ZMMK-Forschungsgebäude, Robert-Koch-Strasse 21, 50931 Cologne, Germany. 39Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Robert Bosch Stiftung GmbH, Heidehofstrasse 31, 70184 Stuttgart, Germany. 40University of Tübingen, Geschwister-Scholl-Platz, 72074 Tübingen, Germany. 41Department of Internal Medicine, Evangelische Kliniken Bonn GGmbH, Johanniter Krankenhaus, 53113 Bonn, Germany. 42Department of Obstetrics and Gynecology, Helsinki University Central Hospital, University of Helsinki, P.O. Box 140Haartmaninkatu 2, FIN-00029 Helsinki, Finland. 43Department of Clinical Genetics, Helsinki University Central Hospital, P.O. Box 140Haartmaninkatu 2, FIN-00029 Helsinki, Finland. 44Department of Oncology, Helsinki University Central Hospital, P.O. Box 140Haartmaninkatu 2, FIN-00029 Helsinki, Finland. 45Division of Epidemiology and Prevention, Aichi Cancer Center Research Institute, 1-1Kanokoden, Chikusa-ku, Nagoya 464-8681, Japan. 46Department of Obstetrics and Gynaecology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany. 47 Department of Radiation Oncology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany. 48N.N. Alexandrov Research Institute of Oncology and Medical Radiology, 223040, p. Lesnoy, Minsk, Belarus. 49Department of Molecular Medicine and Surgery, Karolinska Institutet, Solnavägen 1, 171 77 Solna, Stockholm, Sweden. 50School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211 Kuopio, Finland. 51Biocenter Kuopio, Cancer Center of Eastern Finland, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211 Kuopio, Finland. 52Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, P.O. Box 100, FI-70029 Kuopio, Finland. 53Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, P.O. Box 100, FI-70029 Kuopio, Finland. 54Department of Genetics, Queensland Institute of Medical Research, 300 Herston Rd, Herston, Brisbane Queensland 4006, Australia. 55Department of Preventive Medicine, Keck School of Medicine,

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University of Southern California, 1975 Zonal Ave, Los Angeles, CA 90033, USA. 56Laboratory for Translational Genetics, Department of Oncology, University of Leuven, Oude Markt 13 - bus 5005, 3000 Leuven, Belgium. 57 Vesalius Research Center, VIB, Herestraat 49, box 912, Onderwijs & Navorsing 4, Building 404-24, 3000 Leuven, Belgium. 58Multidisciplinary Breast Center, University Hospital Gasthuisberg, Herestraat 49, 3000 Leuven, Belgium. 59Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. 60 Department of Cancer Epidemiology/Clinical Cancer Registry, University Clinic Hamburg-Eppendorf, Martinistrasse 52, D - 20246 Hamburg, Germany. 61 Institute for Medical Biometrics and Epidemiology, University Clinic Hamburg-Eppendorf, Martinistrasse 52, D - 20246 Hamburg, Germany. 62Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Via Venezian 1, 20133 Milan, Italy. 63IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, Via Adamello 16, 20139 Milan, Italy. 64Division of Cancer Prevention and Genetics, Istituto Europeo di Oncologia (IEO), Via Giuseppe Ripamonti 435, 20141 Milan, Italy. 65Cogentech Cancer Genetic Test Laboratory, IFOM-IEO Campus, Via Adamello16, 20139 Milan, Italy. 66 Department of Laboratory Medicine and Pathology, Division of Experimental Pathology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. 67Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. 68Cancer Epidemiology Centre, The Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria 3004, Australia. 69Department of Medicine, McGill University and Division of Clinical Epidemiology, McGill University Health Centre, Royal Victoria Hospital, 687 Pine Avenue West, Montréal, Québec H3A 1A1, Canada. 70Department of Social and Preventive Medicine and Department of Environmental and Occupational Health at Work, University of Montréal, Marguerite d'Youville Pavilion, 2375 Côte Ste-Catherine, Suite 4095, Montréal, Québec H3T 1A8, Canada. 71Cancer Genomics Laboratory, Centre Hospitalier Universitaire de Québec Research Center and Laval University, 2325 Rue de l'Université, Québec City, Québec G1V 0A6, Canada. 72Breast Cancer Research Unit, University of Malaya Cancer Research Institute, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia. 73Cancer Research Initiatives Foundation, Sime Darby Medical Centre Subang Jaya, 1, Jalan SS 12 / 1A, 47500 Subang Jaya, Selangor Darul Ehsan, Malaysia. 74Singapore Eye Research Institute, National University of Singapore, Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore 168751, Singapore. 75 Department of Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, N-0310 Oslo, Norway. 76Faculty of Medicine (Faculty Division Ahus), University of Oslo, Sogn Arena, Klaus Torgårds vei 3, 2. etg, 0372 Oslo, Norway. 77Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 1161 21st Ave S # T1217, Nashville, TN 37232, USA. 78Laboratory of Cancer Genetics and Tumor Biology, Department of Clinical Chemistry and Biocenter Oulu, University of Oulu, Oulu University Hospital, Kajaanintie 50, 90220 Oulu, Finland. 79 Department of Oncology, Oulu University Hospital, University of Oulu, Kajaanintie 50, 90220 Oulu, Finland. 80Department of Surgery, Oulu University Hospital, University of Oulu, Kajaanintie 50, 90220 Oulu, Finland. 81Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 982 - 600 University Avenue, Toronto, Ontario M5G 1X5, Canada. 82Department of Molecular Genetics, University of Toronto, Medical Science Building, Room 4386, 1 King's College Cir, Toronto, Ontario M5S 1A8, Canada. 83Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, 6th floor, 155 College St, Toronto, Ontario M5T 3M7, Canada. 84Ontario Cancer Genetics Network, 620 University Avenue, Toronto, Ontario M5G 2L7, Canada. 85 Department of Laboratory Medicine and Pathobiology, University of Toronto, Medical Sciences Building, 6th Floor, 1 King's College Cir, Toronto, Ontario M5S 1A8, Canada. 86University Health Network, R. Fraser Elliott Building, 1st Floor, 190 Elizabeth St., Toronto, Ontario M5G 2C4, Canada. 87 Department of Human Genetics & Department of Pathology, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands. 88Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, USA. 89 Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center & Institute of Oncology, Roentena 5, 02-781 Warsaw, Poland. 90Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solnavägen 1, Stockholm 17177, Sweden. 91Department of Medical Oncology, Family Cancer Clinic, Erasmus University Medical Center, Groene

Hilledijk 301, 3075EA, Rotterdam, The Netherlands. 92Department of Medical Oncology, Josephine Nefkens Institute, Erasmus University Medical Center, Groene Hilledijk 301, 3075 EA, Rotterdam, The Netherlands. 93Department of Clinical Genetics, Family Cancer Clinic, Erasmus University Medical Center, Groene Hilledijk 301, 3075 EA, Rotterdam, The Netherlands. 94Human Genetics Division, Genome Institute of Singapore, 60 Biopolis St, Singapore 138672, Singapore. 95Institute for Cancer Studies, Department of Oncology, CRUK/YCR Sheffield Cancer Research Centre, University of Sheffield, 385a Glossop Road, Sheffield S10 2HQ, UK. 96Academic Unit of Surgical Oncology, Department of Oncology, CRUK/YCR Sheffield Cancer Research Centre, University of Sheffield, 385a Glossop Road, Sheffield S10 2HQ, UK. 97 Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield S10 2HQ, UK. 98International Epidemiology Institute, 1455 Research Blvd, Rockville, MD 20850, USA. 99 Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. 100Channing Division of Network Medicine, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA. 101Dana-Farber/Harvard Cancer Center, 450 Brookline Ave, Boston, MA 02215, USA. 102Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, UK. 103Seoul National University College of Medicine, Yongeon-103 Daehangno, Jongno-gu, Seoul 110-799, Korea. 104Department of Preventive Medicine, Seoul National University College of Medicine, Yongeon-103 Daehangno, Jongno-gu, Seoul 110-799, Korea. 105Department of Biomedical Science, Seoul National University Graduate School, Yongeon-103 Daehangno, Jongno-gu, Seoul 110-799, Korea. 106Cancer Research Institute, Seoul National University, Yongeon-103 Daehangno, Jongno-gu, Seoul 110-799, Korea. 107Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, 1E, Kent Ridge Road, Singapore 119228, Singapore. 108National University Health System, 1E, Kent Ridge Road, Singapore 119228, Singapore. 109 Saw Swee Hock School of Public Health, National University of Singapore, MD3, 16 Medical Drive, Singapore 117597, Singapore. 110Division of General Surgery, National University Health System, 1E, Kent Ridge Road, Singapore 119228, Singapore. 111Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. 112Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. 113Center for Primary Health Care Research, University of Lund, Paradisgatan 5, SE-221 00 Lund, Malmö, Sweden. 114Institute of Pathology, Städtisches Klinikum Karlsruhe, Moltkestrasse 90, 76133 Karlsruhe, Germany. 115 Frauenklinik der Stadtklinik Baden-Baden, Balger Strasse 50, 76532 Baden-Württemberg, Germany. 116Department of Genetics and Pathology, Pomeranian Medical University, Rybacka 1, 70-204 Szczecin, Poland. 117 Postgraduate School of Molecular Medicine, Warsaw Medical University, Żwirki i Wigury 61, 02-091 Warsaw, Poland. 118National Cancer Institute, 268/ 1 Rama VI Road, Rajathevi, Bangkok 10400, Thailand. 119International Agency for Research on Cancer, 150 Cours Albert Thomas, 69372 Lyon, CEDEX 08, France. 120Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, 410 W. 10th Avenue, Columbus, OH 43210, USA. 121Molecular Diagnostics Laboratory, IRRP, National Centre for Scientific Research “Demokritos”, Aghia Paraskevi Attikis 153 10, Athens, Greece. 122College of Public Health, China Medical University, No.91, Hsueh-Shih Road, Taichung 40402, Taiwan. 123 Institute of Biomedical Sciences, Academia Sinica, 2 Academia Road, Nankang, Taipei 115, Taiwan. 124Department of Surgery, Tri-Service General Hospital, No.325, Sec.2 Chenggong RoadNeihu District, Taipei City 114, Taiwan. 125Department of Surgery, National Taiwan University Hospital, No.1, Changde StreetZhongzheng District, Taipei City 10048, Taiwan. 126Cancer Center, Kaohsiung Medical University Chung-Ho Memorial Hospital, No.100, Tzyou 1st Road, Kaohsiung 807, Taiwan. 127Department of Surgery, Kaohsiung Medical University Chung-Ho Memorial Hospital, No.100, Tzyou 1st Road, Kaohsiung 807, Taiwan. 128McGill University and Génome Québec Innovation Centre, 740, Dr. Penfield Avenue, Room 7104, Montréal, Québec H3A 0G1, Canada. 129Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, UK.

Received: 4 July 2013 Accepted: 24 April 2014 Published: 26 May 2014

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doi:10.1186/bcr3662 Cite this article as: Johnson et al.: Genetic variation at CYP3A is associated with age at menarche and breast cancer risk: a case-control study. Breast Cancer Research 2014 16:R51.

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