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. 2014 Feb 18;110(4):1088–1100. doi: 10.1038/bjc.2013.769

FGF receptor genes and breast cancer susceptibility: results from the Breast Cancer Association Consortium

D Agarwal 1,2, S Pineda 1, K Michailidou 3, J Herranz 1,4, G Pita 5, L T Moreno 5, M R Alonso 5, J Dennis 3, Q Wang 3, M K Bolla 3, K B Meyer 6, P Menéndez-Rodríguez 7, D Hardisson 8, M Mendiola 9, A González-Neira 5, A Lindblom 10, S Margolin 11, A Swerdlow 12,13, A Ashworth 14, N Orr 14, M Jones 12, K Matsuo 15, H Ito 16, H Iwata 17, N Kondo 17; kConFab Investigators18; Australian Ovarian Cancer Study Group18,19, M Hartman 20, M Hui 21, W Y Lim 21, P T-C Iau 22, E Sawyer 23, I Tomlinson 24,25, M Kerin 26, N Miller 26, D Kang 27,28, J-Y Choi 28, S K Park 27,28, D-Y Noh 29, J L Hopper 30, D F Schmidt 30, E Makalic 30, M C Southey 31, S H Teo 32,33, C H Yip 33, K Sivanandan 32, W-T Tay 34, H Brauch 35,36, T Brüning 37, U Hamann 38; The GENICA Network35,36,37,38,39,40,41, A M Dunning 42, M Shah 42, I L Andrulis 43,44, J A Knight 43,45, G Glendon 46, S Tchatchou 43, M K Schmidt 47, A Broeks 47, E H Rosenberg 47, L J van't Veer 47, P A Fasching 48,49, S P Renner 48, A B Ekici 50, M W Beckmann 48, C-Y Shen 51,52, C-N Hsiung 51, J-C Yu 53, M-F Hou 54,55, W Blot 56, Q Cai 56, A H Wu 57, C-C Tseng 57, D Van Den Berg 57, D O Stram 57, A Cox 58, I W Brock 58, M W R Reed 58, K Muir 59,60, A Lophatananon 60, S Stewart-Brown 60, P Siriwanarangsan 61, W Zheng 62, S Deming-Halverson 62, M J Shrubsole 62, J Long 62, X-O Shu 62, W Lu 63, Y-T Gao 64, B Zhang 62, P Radice 65, P Peterlongo 66, S Manoukian 67, F Mariette 66,68, S Sangrajrang 69, J McKay 70, F J Couch 71, A E Toland 72; TNBCC73, D Yannoukakos 74, O Fletcher 75, N Johnson 75, I dos Santos Silva 76, J Peto 76, F Marme 77,78, B Burwinkel 77,79, P Guénel 80,81, T Truong 80,81, M Sanchez 80,81, C Mulot 82,83, S E Bojesen 84,85, B G Nordestgaard 84,85, H Flyer 86, H Brenner 87,88, A K Dieffenbach 87,88, V Arndt 87, C Stegmaier 89, A Mannermaa 90,91,92, V Kataja 91,93,94, V-M Kosma 90,91,92, J M Hartikainen 90,91,92, D Lambrechts 95, B T Yesilyurt 95, G Floris 96, K Leunen 96, J Chang-Claude 97, A Rudolph 97, P Seibold 97, D Flesch-Janys 98,99, X Wang 71, J E Olson 100, C Vachon 100, K Purrington 100, G G Giles 30,101, G Severi 30,101, L Baglietto 30,101, C A Haiman 57, B E Henderson 57, F Schumacher 57, L Le Marchand 102, J Simard 103, M Dumont 103, M S Goldberg 104,105, F Labrèche 106, R Winqvist 107, K Pylkäs 107, A Jukkola-Vuorinen 108, M Grip 109, P Devilee 110, R A E M Tollenaar 111, C Seynaeve 112, M García-Closas 13,113,114, S J Chanock 113, J Lissowska 115, J D Figueroa 113, K Czene 116, M Eriksson 116, K Humphreys 116, H Darabi 116, M J Hooning 112, M Kriege 112, J M Collée 117, M Tilanus-Linthorst 118, J Li 119, A Jakubowska 120, J Lubinski 120, K Jaworska-Bieniek 120, K Durda 120, H Nevanlinna 121, T A Muranen 121, K Aittomäki 122, C Blomqvist 123, N Bogdanova 124,125, T Dörk 124, P Hall 116, G Chenevix-Trench 19, D F Easton 3,42, P D P Pharoah 3,42, J I Arias-Perez 126, P Zamora 127, J Benítez 1,5, R L Milne 1,30,101,*
PMCID: PMC3929867  PMID: 24548884

Abstract

Background:

Breast cancer is one of the most common malignancies in women. Genome-wide association studies have identified FGFR2 as a breast cancer susceptibility gene. Common variation in other fibroblast growth factor (FGF) receptors might also modify risk. We tested this hypothesis by studying genotyped single-nucleotide polymorphisms (SNPs) and imputed SNPs in FGFR1, FGFR3, FGFR4 and FGFRL1 in the Breast Cancer Association Consortium.

Methods:

Data were combined from 49 studies, including 53 835 cases and 50 156 controls, of which 89 050 (46 450 cases and 42 600 controls) were of European ancestry, 12 893 (6269 cases and 6624 controls) of Asian and 2048 (1116 cases and 932 controls) of African ancestry. Associations with risk of breast cancer, overall and by disease sub-type, were assessed using unconditional logistic regression.

Results:

Little evidence of association with breast cancer risk was observed for SNPs in the FGF receptor genes. The strongest evidence in European women was for rs743682 in FGFR3; the estimated per-allele odds ratio was 1.05 (95% confidence interval=1.02–1.09, P=0.0020), which is substantially lower than that observed for SNPs in FGFR2.

Conclusion:

Our results suggest that common variants in the other FGF receptors are not associated with risk of breast cancer to the degree observed for FGFR2.

Keywords: breast cancer, SNP, FGF receptors, susceptibility, disease subtypes

Breast cancer is a complex disease, with multiple genetic and environmental factors involved in its etiology. Rare mutations in the DNA repair genes BRCA1 and BRCA2 confer a high lifetime risk of breast cancer (Antoniou et al, 2003) and are routinely screened for in women with a strong family history of the disease. Studies focused on other DNA repair genes have led to the discovery that rare coding variants in CHEK2, ATM, BRIP1 and PALB2 (Swift et al, 1987; Meijers-Heijboer et al, 2002; Seal et al, 2006; Rahman et al, 2007) are associated with moderately increased breast cancer risk. However, few, if any, candidate-gene- or pathway-based association studies have identified convincing associations with breast cancer risk for common genetic variants (The Breast Cancer Association Consortium, 2006). In contrast, empirical genome-wide association studies (GWAS) have proven to be a successful approach to identify common variants associated with small increases in risk, with more than 70 identified in this way to date (Easton et al, 2007; Hunter et al, 2007; Stacey et al, 2007, 2008; Ahmed et al, 2009; Thomas et al, 2009; Zheng et al, 2009; Antoniou et al, 2010; Turnbull et al, 2010; Cai et al, 2011; Fletcher et al, 2011; Haiman et al, 2011; Ghoussaini et al, 2012; Siddiq et al, 2012; Bojesen et al, 2013; Garcia-Closas et al, 2013; Michailidou et al, 2013). For the great majority of these associations, the causal variant(s), and even the causal gene, are unknown; thus, the identification of novel candidate genetic susceptibility pathways through this approach is not straightforward.

An intronic variant in the FGFR2 gene was one of the first single-nucleotide polymorphisms (SNPs) identified by GWAS as tagging a breast cancer susceptibility locus (Easton et al, 2007; Hunter et al, 2007). It is now well-established that the minor allele of this SNP is associated with increased risk of breast cancer, particularly estrogen receptor (ER)-positive disease (Garcia-Closas et al, 2008). Fine-mapping of the region has suggested that at least one causal variant is located in intron 2 of FGFR2 (Easton et al, 2007; Udler et al, 2009), and functional studies have proposed that rs2981578 affects FGFR2 expression (Meyer et al, 2008; Udler et al, 2009; Huijts et al, 2011). These findings strongly suggest that FGFR2 is a breast cancer susceptibility gene.

FGFR2 is a fibroblast growth factor (FGF) receptor gene; the amino-acid sequence of the protein it encodes is highly conserved across all FGF receptors. The other FGF receptor genes and other genes acting downstream of them in the FGF pathway may also be implicated in the development of breast cancer, although associations with disease risk have not been assessed comprehensively by a study with adequate sample size to detect odds ratios (ORs) of the magnitude observed for SNPs in FGFR2.

We hypothesised that common variants in other genes in the FGF pathway, and in the other FGF receptor genes in particular, might also confer increased breast cancer risk. The primary aim of our investigation was to comprehensively assess associations between breast cancer risk and common variation in the FGF receptor genes FGFR1, FGFR3, FGFR4 and FGFRL1 by genotyping selected tag-SNPs in the Breast Cancer Association Consortium (BCAC). A secondary objective was to assess common variants in other genes in the FGF pathway based on a two-stage design.

Materials and methods

Participants

Study participants were women from 49 studies participating in BCAC: 38 from populations of predominantly European ancestry, 9 of Asian women and 2 of African–American women (Table 1 and Supplementary Table 1). The majority were population-based or hospital-based case–control studies, but some studies selected subjects based on age or oversampled for cases with a family history or bilateral disease. Cases and controls from the CNIO-BCS were also studied in a previous assessment of selected genes in the FGF pathway. All study participants gave informed consent and each study was approved by the corresponding local ethics committee.

Table 1. Number of cases and controls included, by study.

Study Country Controls Cases ER+ ER−
White European women
Australian Breast Cancer Family Studya (ABCFS) Australia 551 790 456 261
Amsterdam Breast Cancer Study (ABCS) Netherlands 1429 1325 420 153
Bavarian Breast Cancer Cases and Controls (BBCC) Germany 458 564 460 83
British Breast Cancer Study (BBCS) UK 1397 1554 507 114
Breast Cancer In Galway Genetic Study (BIGGS) Ireland 719 836 495 154
Breast Cancer Study of the University Clinic Heidelberg (BSUCH) Germany 954 852 499 154
CECILE Breast Cancer Study (CECILE) France 999 1019 797 144
Copenhagen General Population Study (CGPS) Denmark 4086 2901 1919 357
Spanish National Cancer Centre Breast Cancer Study (CNIO-BCS) Spain 876 902 242 88
California Teachers Study (CTS) USA 71 68 0 17
ESTHER Breast Cancer Study (ESTHER) Germany 502 478 304 98
Gene–Environment Interaction and Breast Cancer in Germany (GENICA) Germany 427 465 328 119
Helsinki Breast Cancer Study (HEBCS) Finland 1234 1664 1295 237
Hannover-Minsk Breast Cancer Study (HMBCS) Belarus 130 690 37 0
Karolinska Breast Cancer Study (KARBAC) Sweden 662 722 338 63
Kuopio Breast Cancer Project (KBCP) Finland 251 445 304 97
kConFab/Australian Ovarian Cancer Study (kConFab/AOCS) Australia 897 613 162 59
Leuven Multidisciplinary Breast Centre (LMBC) Belgium 1388 2671 2071 379
Mammary Carcinoma Risk Factor Investigation (MARIE) Germany 1778 1818 1349 399
Milan Breast Cancer Study Group (MBCSG) Italy 400 488 149 42
Mayo Clinic Breast Cancer Study (MCBCS) USA 1931 1862 1486 295
Melbourne Collaborative Cohort Study (MCCS) Australia 511 614 352 119
Multi-ethnic Cohort (MEC) USA 741 731 415 87
Montreal Gene–Environment Breast Cancer Study (MTLGEBCS) Canada 436 489 421 64
Norwegian Breast Cancer Study (NBCS) Norway 70 22 0 22
Oulu Breast Cancer Study (OBCS) Finland 414 507 407 100
Ontario Familial Breast Cancer Registryb (OFBCR) Canada 511 1175 630 268
Leiden University Medical Centre Breast Cancer Study (ORIGO) Netherlands 327 357 211 70
NCI Polish Breast Cancer Study (PBCS) Poland 424 519 519 0
Karolinska Mammography Project for Risk Prediction of Breast Cancer (pKARMA) Sweden 5537 5434 3672 702
Rotterdam Breast Cancer Study (RBCS) Netherlands 699 664 368 131
Singapore and Sweden Breast Cancer Study (SASBAC) Sweden 1378 1163 663 144
Sheffield Breast Cancer Study (SBCS) UK 848 843 377 105
Studies of Epidemiology and Risk factors in Cancer Heredity (SEARCH) UK 8069 9347 5160 1181
Städtisches Klinikum Karlsruhe Deutsches Krebsforschungszentrum Study (SKKDKFZS) Germany 29 136 0 136
IHCC-Szczecin Breast Cancer Study (SZBCS) Poland 315 365 165 60
Triple Negative Breast Cancer Consortium Study (TNBCC) Various 542 881 0 881
UK Breakthrough Generations Study (UKBGS)
 UK
 470
 476
 96
 22
Asian women
Asian Cancer Project (ACP) Thailand 636 423 92 53
Hospital-based Epidemiologic Research Program at Aichi Cancer Center (HERPACC) Japan 1376 694 395 139
Los Angeles County Asian-American Breast Cancer Case–Control (LAABC) USA 990 812 528 138
Malaysian Breast Cancer Genetic Study (MYBRCA) Malaysia 610 770 422 291
Shanghai Breast Cancer Genetic Study (SBCGS) China 892 848 510 276
Seoul Breast Cancer Study (SEBCS) South Korea 1129 1162 657 375
Singapore Breast Cancer Cohort (SGBCC) Singapore 502 533 272 108
IARC-Thai Breast Cancer (TBCS) Thailand 253 138 26 26
Taiwanese Breast Cancer Study (TWBCS)
 Taiwan
 236
 889
 460
 204
African
Southern Community Cohort Study (SCCS) USA 680 679 0 0
Nashville Breast Health Study (NBHS) USA 252 437 199 222
           
Total   50156 53835 30635 9120
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Abbreviations: ER−=estrogen receptor-negative cases; ER+=estrogen receptor-positive cases.

a

Australian site of the Breast Cancer Family Registry.

b

Ontario site of the Breast Cancer Family Registry.

Gene and SNP selection

Ingenuity Pathways Analysis and selected publications (Eswarakumar et al, 2005; Presta et al, 2005; Chen & Forough, 2006; Schwertfeger, 2009) were used to identify genes reported to be involved downstream of the FGF genes in the FGF pathway, particularly those related to angiogenesis. A total of 39 genes, including the FGF receptors FGFR1 (located at 8p11.22), FGFR2 (10q26.13), FGFR3 (4p16.3), FGFR4 (5q35.2) and FGFRL1 (4p16.3), was selected for tagging. Single-nucleotide polymorphisms with minor allele frequency (MAF) >5% in the coding and non-coding regions, and within 5 kb upstream and 5 kb downstream of each gene, were identified using HapMap CEU genotype data and dbSNP 128 as reference. The minimum number of tag-SNPs were then selected among all identified SNP using Haploview (Barrett et al, 2005) based on the following criteria: r2>0.8 and Illumina assay score >0.60. A total of 384 SNPs tagging 39 genes was genotyped in the CNIO-BCS, 324 of which were successfully genotyped (Supplementary Table 2). The 31 SNPs tagging genes FGFR1, FGFR3, FGFR4 and FGFRL1 were all genotyped in BCAC, along with a further 26 of the 324 tag-SNPs. The latter group comprised SNPs selected based on evidence of association with breast cancer under a log-additive model in the Stage 1 CNIO-BCS. Single-nucleotide polymorphisms in FGFR2 were not considered, as all were included as part of a separate fine-mapping study (Meyer et al, submitted). Results from Stage 1 are summarised in Supplementary Table 2.

Genotyping

Genotyping of the 57 SNPs in the BCAC samples was conducted using a custom Illumina Infinium array (iCOGS) in four centers, as part of a multi-consortia collaboration (the Collaborative Oncological Gene–Environment Study, COGS) as described previously (Michailidou et al, 2013). Genotypes were called using Illumina's proprietary GenCall algorithm.

For the genotyping of the 384 SNPs in the Stage 1 CNIO-BCS, genomic DNA was isolated from peripheral blood lymphocytes using automatic DNA extraction (MagNA Pure, Roche Diagnostics, Indianapolis, IN, USA) according to the manufacturer's recommended protocols. This DNA was quantified using Picogreen (Invitrogen, Life Technologies, Grand Island, NY, USA) and for each sample a final quantity of 250 ng was extracted and used for GoldenGate genotyping with VeraCode Technology (Illumina Inc., San Diego, CA, USA). Samples were arranged on 25 96-well plates containing one negative control and at least one study sample in duplicate. Three Centre d'Etude du Polymorphisme Humain (CEPH) trios were used as internal intra- and inter-plate duplicates and to check for Mendelian segregation errors. DNA was extracted, quantified, plated and genotyped at the Spanish National Genotyping Centre (CeGen), Madrid, Spain. All genotypes were determined for each SNP and each plate using manual clustering. Single-nucleotide polymorphisms with call rate <90% were excluded, as were samples with no-calls for more than 20% of included SNPs.

Statistical methods

For each SNP, we estimated ORs and 95% confidence intervals (CIs) using unconditional logistic regression. For the analysis of BCAC data, we considered per-allele and co-dominant models using common-allele homozygotes as reference and including study and ethnicity-specific principal components as covariates, as previously described (Michailidou et al, 2013). Departure from the Hardy–Weinberg equilibrium (HWE) was tested for in controls from individual studies using the genhwi module in STATA 11.2 (College Station, TX, USA). A study-stratified χ2 test (1df) was applied across studies (Haldane, 1954; Robertson & Hill, 1984). Between-study heterogeneity in ORs was assessed for each of the three broad racial groups using the metan command in STATA to meta-analyse study-specific per-allele log-OR estimates and generate I2 statistics; values greater than 50% were considered notable (Higgins & Thompson, 2002). Odds ratios specific to disease subtypes defined by ER, PR and HER2 status (positive and negative) were estimated separately for each ethnic subgroup using polytomous logistic regression with control status as the reference outcome. Differences in ORs by disease subtypes were assessed using a likelihood ratio test (LRT). All statistical tests were two-sided.

The effective number of independent SNPs (VeffLi) was estimated using the method described by Li & Ji (2005). This method was applied via the web-interface matSpDlite (http://gump.qimr.edu.au/general/daleN/matSpDlite/), based on the observed correlations between SNPs (Nyholt, 2004). VeffLi was then used to calculate a Bonferroni-corrected significance threshold (α*). Power calculations were carried out using Quanto v1.2.4 (http://hydra.usc.edu/gxe/).

Single-nucleotide polymorphism imputation

The genotypes of untyped SNPs were imputed based on data from the March 2012 release of the 1000 genomes project using IMPUTE v2.2. These were converted to allele doses using the impute2mach function in the GenABEL library in R (Aulchenko et al, 2007) and analysed under a per-allele model. Imputed SNPs with an estimated MAF <5% were excluded, as were SNPs with an imputation r2<80%.

Results

All SNPs in the present analysis had overall call rates >95%. Very strong evidence of departure from HWE was observed for rs34869253 for one study (pKarma, P=3.3 × 10−21), which was excluded from the subsequent analyses of that SNP. After quality control, there were data available for 53 835 cases and 50 156 controls from BCAC, including 89 050 European women (46 450 cases and 42 600 controls), 12 893 Asian (6269 cases and 6624 controls) and 2048 African–American women (1116 cases and 932 controls) (Table 1).

Results from the analysis of the 31 tag-SNPs in FGFR genes for white Europeans are summarised in Table 2. No strong evidence of association was observed, although one SNP (rs743682) in FGFR3 (MAF=9%) was marginally significant after correction for multiple testing based on a VeffLi of 23 (per-allele OR=1.05, 95%CI=1.02–1.09, P=0.0020, α*=0.0022). All SNPs with an associated P-value <0.05 were intronic, with the exception of rs1966265, which is a missense variant in FGFR4. However, PolyPhen (http://genetics.bwh.harvard.edu/pph2/) predicts this amino acid change to be benign, with a score of 0.000. On the basis of ENCODE data, no SNP with an associated P-value <0.05 was located in a region involved or predicted to be involved in epigenetic regulation, nor at, or within 2 kb of, a CpG island. For European women, we did not observe any evidence of between-study heterogeneity for any SNPs (I2⩽19% P⩾0.15) and little evidence of differential associations by disease subtypes defined by ER (P⩾0.036), PR (P⩾0.084) or HER2 status (P⩾0.019).

Table 2. Summary results for SNPs in FGF receptor genes for white European women.

 
  
  
 OR (95%CI)
  
 OR (95%CI)
  
SNP Alleles MAF Aa aa per-a-allele P ER− ER+ P-het
FGFR1
rs10958704 AG 0.40 0.98 (0.95–1.01) 0.98 (0.94–1.02) 0.99 (0.97–1.01) 0.18 0.99 (0.96–1.03) 0.99 (0.97–1.02) 0.91
rs17182141 AG 0.06 1.05 (1.00–1.09) 0.95 (0.75–1.22) 1.04 (1.00–1.08) 0.057 1.08 (1.00–1.17) 1.04 (0.99–1.09) 0.30
rs2288696 GA 0.21 1.02 (0.99–1.05) 1.07 (1.00–1.14) 1.03 (1.00–1.05) 0.023 1.05 (1.01–1.10) 1.03 (1.00–1.06) 0.35
rs2411256 GA 0.24 1.02 (0.99–1.05) 1.01 (0.95–1.07) 1.01 (0.99–1.03) 0.36 1.00 (0.95–1.04) 1.01 (0.99–1.04) 0.44
rs2978076 GA 0.08 0.99 (0.96–1.03) 1.22 (1.04–1.44) 1.01 (0.98–1.05) 0.53 0.99 (0.92–1.06) 1.02 (0.98–1.06) 0.37
rs2978083 GA 0.05 0.99 (0.96–1.03) 1.22 (1.04–1.44) 1.01 (0.98–1.05) 0.53 0.97 (0.89–1.06) 1.03 (0.97–1.08) 0.27
rs3758102 GA 0.26 1.01 (0.98–1.04) 1.02 (0.96–1.07) 1.01 (0.99–1.03) 0.35 1.01 (0.97–1.05) 1.01 (0.98–1.04) 0.95
rs3925 GA 0.23 1.01 (0.98–1.04) 1.00 (0.95–1.07) 1.01 (0.99–1.03) 0.51 0.99 (0.95–1.04) 1.01 (0.99–1.04) 0.39
rs4733930 GA 0.42 1.00 (0.97–1.03) 1.04 (1.00–1.08) 1.02 (1.00–1.04) 0.11 1.03 (0.99–1.07) 1.02 (1.00–1.04) 0.67
rs4733946 CA 0.08 1.00 (0.97–1.03) 1.04 (1.00–1.08) 1.02 (1.00–1.04) 0.11 1.01 (0.95–1.08) 1.04 (1.00–1.09) 0.39
rs6474354 GA 0.21 0.98 (0.95–1.01) 0.99 (0.92–1.05) 0.98 (0.96–1.01) 0.18 0.96 (0.92–1.01) 0.98 (0.96–1.01) 0.37
rs6996321 GA 0.39 1.01 (0.98–1.04) 1.00 (0.96–1.04) 1.00 (0.98–1.02) 0.95 1.00 (0.97–1.04) 0.99 (0.97–1.02) 0.54
rs6983315 GA 0.44 1.01 (0.97–1.04) 0.98 (0.94–1.02) 0.99 (0.97–1.01) 0.39 0.97 (0.93–1.00) 0.99 (0.97–1.02) 0.13
rs7012413
 GA
 0.30
 1.00 (0.97–1.02)
 0.99 (0.95–1.04)
 1.00 (0.98–1.02)
 0.69
 1.00 (0.97–1.04)
 1.00 (0.98–1.02)
 0.82
FGFR3
rs12502543 GA 0.10 1.04 (1.01–1.08) 1.10 (0.96–1.25) 1.04 (1.01–1.08) 0.0076 0.99 (0.93–1.05) 1.06 (1.02–1.10) 0.036
rs2234909 AG 0.14 0.99 (0.95–1.02) 0.97 (0.88–1.07) 0.99 (0.96–1.01) 0.29 0.99 (0.94–1.04) 0.98 (0.95–1.02) 0.77
rs3135848 AG 0.28 1.02 (0.99–1.04) 1.02 (0.96–1.07) 1.01 (0.99–1.03) 0.31 1.00 (0.96–1.04) 1.01 (0.99–1.04) 0.55
rs743682 GA 0.09 1.05 (1.01–1.09) 1.16 (1.00–1.34) 1.05 (1.02–1.09) 0.0020 1.01 (0.95–1.08) 1.06 (1.02–1.10) 0.24
rs746779
 GA
 0.18
 0.99 (0.96–1.02)
 0.98 (0.90–1.06)
 0.99 (0.96–1.01)
 0.29
 1.00 (0.95–1.05)
 0.98 (0.95–1.01)
 0.48
FGFR4
rs1076891 GA 0.06 1.03 (0.99–1.08) 0.99 (0.81–1.22) 1.03 (0.99–1.07) 0.14 1.06 (0.98–1.14) 1.01 (0.97–1.06) 0.25
rs1966265 GA 0.23 0.97 (0.94–1.00) 0.93 (0.88–0.99) 0.97 (0.95–0.99) 0.0060 0.98 (0.94–1.03) 0.97 (0.95–1.00) 0.54
rs2456173 GA 0.21 1.00 (0.97–1.03) 0.99 (0.92–1.05) 0.99 (0.97–1.02) 0.66 0.98 (0.94–1.02) 1.00 (0.98–1.03) 0.34
rs376618 AG 0.24 1.00 (0.97–1.03) 0.96 (0.91–1.02) 0.99 (0.97–1.01) 0.33 0.97 (0.93–1.01) 0.99 (0.97–1.02) 0.29
rs641101 GA 0.31 1.01 (0.98–1.04) 0.99 (0.94–1.03) 1.00 (0.98–1.02) 0.98 0.99 (0.95–1.03) 1.00 (0.98–1.02) 0.56
rs6556301
 CA
 0.36
 0.99 (0.97–1.02)
 0.96 (0.92–1.00)
 0.98 (0.97–1.00)
 0.13
 0.99 (0.95–1.02)
 0.98 (0.96–1.01)
 0.84
FGFRL1
rs34869253 AG 0.43 1.00 (0.97–1.04) 1.00 (0.96–1.04) 1.00 (0.98–1.02) 0.96 0.98 (0.94–1.01) 0.99 (0.97–1.01) 0.52
rs3755955 GA 0.16 1.00 (0.97–1.03) 1.02 (0.94–1.11) 1.00 (0.98–1.03) 0.82 1.00 (0.95–1.05) 1.00 (0.97–1.03) 0.83
rs4505759 GA 0.30 0.99 (0.96–1.02) 0.98 (0.93–1.03) 0.99 (0.97–1.00) 0.38 1.00 (0.96–1.04) 0.99 (0.97–1.02) 0.78
rs4647932 GA 0.06 1.04 (0.99–1.08) 0.98 (0.80–1.20) 1.03 (0.99–1.07) 0.14 1.06 (0.98–1.14) 1.02 (0.97–1.06) 0.31
rs6855233 AG 0.29 0.99 (0.97–1.02) 1.03 (0.98–1.08) 1.01 (0.98–1.03) 0.62 0.98 (0.94–1.02) 1.00 (0.98–1.03) 0.31
rs748651 AG 0.48 1.00 (0.97–1.03) 1.02 (0.98–1.06) 1.01 (0.99–1.03) 0.31 1.03 (0.99–1.07) 1.01 (0.98–1.03) 0.22
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Abbreviations: SNP=single-nucleotide polymorphism; FGF=fibroblast growth factor; OR=odds ratio where A is the common allele, a is the rare allele and both Aa and aa are compared with AA genotypes; CI=confidence interval; MAF=minor allele frequency; P=P-value for the per-a-allele model; ER−=results (per a-allele) for risk of estrogen receptor-negative disease; ER+=results (per a-allele) for risk of estrogen receptor-positive disease; P-het=P-value for heterogeneity by disease sub-type defined by estrogen receptor status.

We similarly observed little evidence of association with overall breast cancer risk in Asian and African–American women (Supplementary Tables 3 and 4, respectively). Nevertheless, a consistent result was observed for Europeans and Asians for rs1966265 in FGFR4. The estimated OR per risk (G) allele was 1.03 (95%CI=1.01–1.05; P=0.0060) for European women and 1.08 (95%CI=1.03–1.14; P=0.0036) for Asian women. There was no evidence of heterogeneity by race for any of the 31 SNPs in FGF receptors (I2=18% P=0.14).

The SNPs genotyped were estimated to capture a variable proportion of the common variation in the four genes considered, as described in the 1000 genomes project; at r2⩾0.80, this coverage was 75% for FGFR1, 77% for FGFR3, 66% for FGFR4 and 17% for FGFRL1. This coverage was dramatically improved with the inclusion of imputed common SNPs (with imputation r2>0.80) to 95%, 93%, 97% and 84% for FGFR1, FGFR3, FGFR4 and FGFRL1, respectively. No stronger evidence of association was observed for any imputed SNPs (Supplementary Tables 5–8).

Finally, we observed little evidence of association for any of the 26 SNPs in other genes in the FGF pathway, selected based on results from Stage 1 (Supplementary Table 9). The results were consistent across the three ethnic groups considered and for disease subtypes defined by ER, PR and HER2 expression.

It is noteworthy that strong association signals were observed in the Stage 1 Spanish study for selected tag-SNPs rs10736303 (MAF=0.49; per-allele OR=1.37, 95% CI=1.21–1.55, P=2.8 × 10−7), and rs2981582 (MAF=0.40; per-allele OR=1.35, 95% CI=1.19–1.53, P=8.3 × 10−7), both in FGFR2.

Discussion

In this multicentre case–control study, we comprehensively assessed common variation in the FGF receptor genes FGFR1, FGFR3, FGFR4 and FGFRL1 in 53 835 cases and 50 156 controls and found little evidence of association with risk of breast cancer. This is the largest study we know of assessing a family of genes via a candidate approach based on the findings from GWAS.

A non-trivial issue in analyses of this kind is the establishment of a statistical significance threshold that adequately controls the proportion of false-positive findings. As permutation-testing was not feasible due to the sample size and number of dummy variables required to adjust for study, we dealt with the issue of non-independence of multiple tests by estimating that the 31 tag-SNPs represented an effective number of 23 independent variables, and applying a Bonferroni correction accordingly. The association of one SNP (rs743682) in FGFR3 for European women was found to be statistically significant on this basis. However, the P-value threshold applied is somewhat questionable in the context of the total of more than 70 000 SNPs nominated for genotyping by BCAC and the total 210 000 genotyped on the iCOGS array. Thus, the current result is far from genome-wide statistical significance and certainly requires independent replication. In any case, the per-allele ORs for FGFR3_rs743682 (1.05, 95% CI=1.02–1.09) and FGFR4_rs1966265 (1.03, 95% CI=1.01–1.05) appear to be substantially lower than that for rs2981582 in FGFR2 (1.26, 95% CI=1.23–1.30) (Easton et al, 2007).

We estimated that for common SNPs (MAF >0.05) associated with overall breast cancer risk in European women, we had greater than 99% power to detect at genome-wide statistical significance (P<5 × 10−8) a per-allele OR as low as 1.23 (the lower 95% confidence limit for the OR for FGFR2_rs2981582). For a per-allele OR as low as 1.05 and for SNPs with MAF of 0.10, 0.20 and 0.30, the estimated power was 1%, 10% and 24%, respectively. That is, our study provides strong evidence that common variation in FGFR1, FGFR3, FGFR4 and FGFRL1 are not associated with breast cancer risk to the degree observed for SNPs in FGFR2, although associations of smaller magnitude may exist.

The hypothesis underlying our study was based on the identification of a functional SNP in intron 2 of FGFR2 associated with breast cancer susceptibility (Easton et al, 2007; Meyer et al, 2008; Udler et al, 2009; Huijts et al, 2011). A recent study has subsequently identified three independent risk signals within FGFR2, and uncovered likely causal variants and functional mechanisms behind them (Meyer et al, 2013). Although an association between these SNPs and expression of FGFR2 has not been established, these results provide strong evidence that FGFR2 is the target gene, and it therefore seems plausible that other FGF receptors or genes acting in the FGF pathway might also be implicated in breast cancer risk. However, we find little evidence that this is the case for the receptors, at least not to the extent observed for common variants in FGFR2. Admittedly, the degree to which common variation in the FGF receptor genes was tagged by the genotyped SNPs was good for FGFR1, FGFR3 and FGFR4 and poor for FGFRL1, but substantial improvement was afforded by imputation. Nevertheless, it is possible that common variation not captured by the genotyped or imputed SNPs may be associated with breast cancer risk. It is also possible that these genes may be implicated in disease susceptibility via regulatory mechanisms involving variants outside the chromosomal boundaries defined for each gene considered. That said, few studies have assessed common variation in candidate genes to this extent, in terms of both gene coverage and sample size.

The power of our study was much lower for Asian and African–American women; however, our primary focus on European women is consistent with our hypothesis, based on the previous finding in FGFR2 in this population. Our study was also limited by the power and gene coverage of the stage 1 component which assessed tag-SNPs in the selected genes of the FGF pathway. Therefore, no conclusions can be drawn about the potential implication of common variation in these genes in breast cancer susceptibility. Nevertheless, we checked the chromosomal locations of the 76 established risk-associated loci (http://www.nature.com/icogs/primer/shared-susceptibility-loci-for-breast-prostate-and-ovarian-cancers/) and found that none were located within 10 kb of any of the 39 genes considered, with the exception of the FGFR2 locus.

In conclusion, in this, possibly the largest candidate-gene association study carried out to date, we have observed little evidence of association between common variation in the FGFR1, FGFR3, FGFR4 and FGFRL1 genes and risk of breast cancer. Our results suggest that common variants in these FGF receptors are not associated with risk of breast cancer to the degree observed for FGFR2.

Acknowledgments

We thank all the individuals who took part in these studies and all the researchers, study staff, clinicians and other health-care providers, technicians and administrative staff who have enabled this work to be carried out. In particular, we thank: Andrew Berchuck (OCAC), Rosalind A Eeles, Ali Amin Al Olama, Zsofia Kote-Jarai, Sara Benlloch (PRACTICAL), Antonis Antoniou, Lesley McGuffog, Ken Offit (CIMBA), Andrew Lee, and Ed Dicks, Craig Luccarini and the staff of the Centre for Genetic Epidemiology Laboratory, Daniel C Tessier, Francois Bacot, Daniel Vincent, Sylvie LaBoissière, Frederic Robidoux and the staff of the McGill University and Génome Québec Innovation Centre, Sune F Nielsen, and the staff of the Copenhagen DNA laboratory, Julie M Cunningham, Sharon A Windebank, Christopher A Hilker, Jeffrey Meyer and the staff of Mayo Clinic Genotyping Core Facility, Maggie Angelakos, Judi Maskiell, Sten Cornelissen, Ellen van der Schoot, Emiel Rutgers, Senno Verhoef, Frans Hogervorst, the Thai Ministry of Public Health (MOPH), Dr Prat Boonyawongviroj (former Permanent Secretary of MOPH), Dr Pornthep Siriwanarungsan (Department Director-General of Disease Control), Alexander Hein, Michael Schrauder, Matthias Rübner, Sonja Oeser, Silke Landrith, Eileen Williams, Elaine Ryder-Mills, Kara Sargus, Niall McInerney, Gabrielle Colleran, Andrew Rowan, Angela Jones, Christ of Sohn, Andeas Schneeweiß, Peter Bugert, the Danish Breast Cancer Group, Ana Osorio, Alicia Barroso, Victoria Fernández, Maika González-Neira, Núria Malats, Francisco Real, Belén Herráez, Núria Álvarez, Esther Díaz, María Miguel-Martin, the CTS Steering Committee (including: Leslie Bernstein, James Lacey, Sophia Wang, Huiyan Ma, Yani Lu and Jessica Clague DeHart at the Beckman Research Institute of the City of Hope; Dennis Deapen, Rich Pinder, Eunjung Lee and Fred Schumacher at the University of Southern California; Pam Horn-Ross, Peggy Reynolds and David Nelson at the Cancer Prevention Institute of California; and Hannah Park at the University of California Irvine), Hartwig Ziegler, Sonja Wolf, Volker Hermann, The GENICA network (Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, and University of Tübingen, Germany; (HB, Wing-Yee Lo, Christina Justenhoven), Department of Internal Medicine, Evangelische Kliniken Bonn GmbH, 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 (UH), Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Germany (TB, Beate Pesch, Sylvia Rabstein, Anne Lotz), Institute of Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Germany (Volker Harth)), Tuomas Heikkinen, Irja Erkkilä, Kirsimari Aaltonen, Karl von Smitten, Natalia Antonenkova, Peter Hillemanns, Hans Christiansen, Eija Myöhänen, Helena Kemiläinen, Heather Thorne, Eveline Niedermayr, the AOCS Management Group (D Bowtell, G Chenevix-Trench, A deFazio, D Gertig, A Green, P Webb), the ACS Management Group (A Green, P Parsons, N Hayward, P Webb and D Whiteman), the LAABC data collection team, especially Annie Fung and June Yashiki, Gilian Peuteman, Dominiek Smeets, Thomas Van Brussel, Kathleen Corthouts, Nadia Obi, Judith Heinz, Sabine Behrens, Ursula Eilber, Muhabbet Celik, Til Olchers, Bernad Peissel, Giulietta Scuvera, Daniela Zaffaroni, Bernardo Bonanni, Monica Barile, Irene Feroce, the personnel of the Cogentech Cancer Genetic Test Laboratory, The Mayo Clinic Breast Cancer Patient Registry, Martine Tranchant, Marie-France Valois, Annie Turgeon, Lea Heguy, Phuah Sze Yee, Peter Kang, Kang In Nee, Shivaani Mariapun, Yoon Sook-Yee, Daphne Lee, Teh Yew Ching, Nur Aishah Mohd Taib, Meeri Otsukka, Kari Mononen, Teresa Selander, Nayana Weerasooriya, OFBCR staff, E Krol-Warmerdam, J Molenaar, J Blom, Louise Brinton, Neonila Szeszenia-Dabrowska, Beata Peplonska, Witold Zatonski, Pei Chao, Michael Stagner, Petra Bos, Jannet Blom, Ellen Crepin, Anja Nieuwlaat, Annette Heemskerk, the Erasmus MC Family Cancer Clinic, Sue Higham, Simon Cross, Helen Cramp, Dan Connley, The Eastern Cancer Registration and Information Centre, the SEARCH and EPIC teams, Craig Luccarini, Don Conroy, Caroline Baynes, Kimberley Chua, the Ohio State University Human Genetics Sample Bank and Robert Pilarski. 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; Arkansas Department of Health, Cancer Registry. BCAC is funded by Cancer Research UK (C1287/A10118, C1287/A12014) and by the European Community's Seventh Framework Programme under grant agreement number 223175 (HEALTH-F2-2009-223175) (COGS). Meetings of the BCAC have been funded by the European Union COST programme (BM0606). Genotyping of the iCOGS array was funded by the European Union (HEALTH-F2-2009-223175), Cancer Research UK (C1287/A10710), the Canadian Institutes of Health Research for the ‘CIHR Team in Familial Risks of Breast Cancer' programme, and the Ministry of Economic Development, Innovation and Export Trade of Quebec (PSR-SIIRI-701). Additional support for the iCOGS infrastructure was provided by the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 – the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The ABCFS and OFBCR work was supported by grant UM1 CA164920 from the National Cancer Institute (USA). 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 Breast Cancer Family Registry (BCFR) nor does mention of trade names, commercial products or organizations imply endorsement by the US Government or the BCFR. The 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) Senior Principal Research Fellow and MCS is a NHMRC Senior Research Fellow. The OFBCR work was also supported by the Canadian Institutes of Health Research ‘CIHR Team in Familial Risks of Breast Cancer' program. The ABCS was funded by the Dutch Cancer Society Grant no. NKI2007-3839 and NKI2009-4363. The ACP study is funded by the Breast Cancer Research Trust, UK. The work of the BBCC was partly funded by ELAN-Programme of the University Hospital of Erlangen. The 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 (NCRN). ES is supported by NIHR Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust in partnership with King's College London, United Kingdom. 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). The CECILE study was funded by the Fondation de France, the French National Institute of Cancer (INCa), The National League against Cancer, the National Agency for Environmental and Occupational Health and Food Safety (ANSES), the National Agency for Research (ANR) and the Association for Research against Cancer (ARC). The CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council and Herlev Hospital. The 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 (PI11/00923 and PI081120). The Human Genotyping-CEGEN Unit, CNIO is supported by the Instituto de Salud Carlos III. DA was supported by a Fellowship from the Michael Manzella Foundation (MMF) and was a participant in the CNIO Summer Training Program. The CTS was initially supported by the California Breast Cancer Act of 1993 and the California Breast Cancer Research Fund (contract 97-10500) and is currently funded through the National Institutes of Health (R01 CA77398). Collection of cancer incidence data 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. HAC receives support from the Lon V Smith Foundation (LVS39420). 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). The 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, Institute of the Ruhr University Bochum (IPA) as well as the Department of Internal Medicine, Evangelische Kliniken Bonn GmbH, Johanniter Krankenhaus Bonn, Germany. The HEBCS was supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (132473), the Finnish Cancer Society, The Nordic Cancer Union and the Sigrid Juselius Foundation. The HERPACC was supported by a Grant-in-Aid for Scientific Research on Priority Areas from the Ministry of Education, Science, Sports, Culture and Technology of Japan, by a Grant-in-Aid for the Third-Term Comprehensive 10-Year Strategy for Cancer Control from Ministry Health, Labour and Welfare of Japan, by a research grant from Takeda Science Foundation, by Health and Labour Sciences Research Grants for Research on Applying Health Technology from Ministry Health, Labour and Welfare of Japan and by National Cancer Center Research and Development Fund. The HMBCS was supported by short-term fellowships from the German Academic Exchange Program (to NB) and the Friends of Hannover Medical School (to NB)—Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the Stockholm Cancer Foundation and the Swedish Cancer Society. The 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 and 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). GCT and PW are supported by the NHMRC. LAABC is supported by grants (1RB-0287, 3PB-0102, 5PB-0018 and 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. The 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). The MARIE study was supported by the Deutsche Krebshilfe e.V. (70-2892-BR I), the Federal Ministry of Education and Research (BMBF) Germany (01KH0402), the Hamburg Cancer Society and the German Cancer Research Center (DKFZ). MBCSG is supported by grants from the Italian Association for Cancer Research (AIRC) and by funds from the Italian citizens who allocated a 5 out of 1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale dei Tumori, according to Italian laws (INT-Institutional strategic projects “5 × 1000” ). The MCBCS was supported by the NIH grants (CA122340 and CA128978) and a 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. The MCCS was further supported by Australian NHMRC grants 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. The MEC was supported 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 (grant CRN-87521) and the Ministry of Economic Development, Innovation and Export Trade (grant 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 (BMRC08/1/35/19<tel:08/1/35/19>/550), Singapore and the National medical Research Council, Singapore (NMRC/CG/SERI/2010). The NBCS was supported by grants from the Norwegian Research council (155218/V40, 175240/S10 to ALBD, FUGE-NFR 181600/V11 to VNK and a Swizz Bridge Award to ALBD). The NBHS was supported by NIH grant R01CA100374. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The OBCS was supported by research grants from the Finnish Cancer Foundation, the Sigrid Juselius 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). The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. pKARMA is a combination of the KARMA and LIBRO-1 studies. KARMA was supported by Märit and Hans Rausings Initiative against Breast Cancer. KARMA and LIBRO-1 were supported the Cancer Risk Prediction Center (CRisP; www.crispcenter.org), a Linnaeus Centre (Contract ID 70867902) financed by the Swedish Research Council. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). SASBAC was supported by funding from the Agency for Science, Technology and Research of Singapore (A*STAR), the US National Institute of Health (NIH) and the Susan G Komen Breast Cancer Foundation. KC was financed by the Swedish Cancer Society (5128-B07-01PAF). The 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. The SBCS was supported by Yorkshire Cancer Research S305PA, S299 and S295. Funding for the SCCS was provided by NIH grant R01 CA092447. 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 (NPCR) of the Centers for Disease Control and Prevention (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 a programme grant from Cancer Research UK (C490/A10124) and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. SGBCC is funded by the National Medical Research Council start-up Grant and Centre Grant (NMRC/CG/NCIS/2010). The recruitment of controls by the Singapore Consortium of Cohort Studies-Multi-ethnic cohort (SCCS-MEC) was funded by the Biomedical Research Council (grant number: 05/1/21/19/425). SKKDKFZS is supported by the DKFZ. Katarzyna Jaworska is a fellow of International PhD program, Postgraduate School of Molecular Medicine, Warsaw Medical University, supported by the Polish Foundation of Science. The TNBCC was supported by the NIH grant (CA128978), the Breast Cancer Research Foundation, Komen Foundation for the Cure, the Ohio State University Comprehensive Cancer Center, the Stefanie Spielman fund for Breast Cancer Research and a generous gift from the David F and Margaret T Grohne Family Foundation and the Ting Tsung and Wei Fong Chao Foundation. Part of the TNBCC (DEMOKRITOS) has been co-financed by 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. The TWBCS is supported by the Institute of Biomedical Sciences, Academia Sinica and the National Science Council, Taiwan. The UKBGS is funded by Breakthrough Breast Cancer and the Institute of Cancer Research (ICR). ICR acknowledges NHS funding to the NIHR Biomedical Research Centre.

The authors declare no conflict of interest.

Footnotes

Supplementary Information accompanies this paper on British Journal of Cancer website (http://www.nature.com/bjc)

This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported License.

Supplementary Material

References

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