Conceived and designed the experiments: MD FJC GCT DFE MJD ACA DMA KO. Performed the experiments: TK TG JV JMK CG AVS LM CK JM SH OMS DSL SM MGV HS ML MF GEMO Study Collaborators FBLH MAR JMC NH KEPvR HEBON Study Collaborators MP WR SN LVL SVB TC MdlH HN KA CL IB AA OTJ RBB PD OIO SLN XW ZSF PP SM MB AV PR CMP SN GR FL AF IA GG HO AET MM ED EF YL AB MB SJR SMD KLN TR ABS XC HH kConFab Collaboration EMJ JLH SSB MBD MCS MBT NT TVOH FCN MG PLM AO MD RA JB JNW JG UH SP MC CO DF RP DGE FL RE LI LW JE JB AKG RKS BW SE NA DG KO. Analyzed the data: MMG TK TG KM BG RJK KO. Contributed reagents/materials/analysis tools: TK JV JMK CG AVS LM CK JM SH OMS DSL SM MGV HS ML MF GEMO Study Collaborators FBLH MAR JMC NH KEPvR HEBON Study Collaborators MP WR SN LVL SVB TC MdlH HN KA CL IB AA OTJ RBB PD OIO SLN XW ZSF PP SM MB AV PR CMP SN GR FL AF IA GG HO AET MM ED EF YL AB MB SJR SMD KLN TR ABS XC HH kConFab Collaboration EMJ JLH SSB MBD MCS MBT NT TVOH FCN MG PLM AO MD RA JB JNW JG UH SP MC CO DF RP DGE FL RE LI LW JE JB AKG RKS BW SE NA DG MD BG RJK FJC GCT DFE MJD ACA DMA KO. Wrote the paper: MMG TG JV JMK KM KO.
Membership of each consortium is provided in the Acknowledgements.
The authors have declared that no competing interests exist.
The considerable uncertainty regarding cancer risks associated with inherited mutations of
The risk of breast cancer associated with
After more than a decade of clinical testing for mutations of
In stage 1, genotype data were available for 899 young (<40 years) affected and 804 older (>40 years) unaffected carriers of European ancestry after quality control filtering and removal of ethnic outliers (
Gene | SNP | Chr. | Stage 1 | Stage 2 | Stage 1 and 2 Combined | |||||||
N (Controls/Cases) | p-value |
HR (95% CI) |
N (Controls/Cases) | HR (95% CI) |
p-value |
N (Controls/Cases) | MAF | HR (95% CI) |
p-value |
|||
rs2981575 | 10 | 794/892 | 6.0×10−6 | 1.30 (1.16–1.45) | 1,222/1,263 | 1.26 (1.11–1.43) | 4.4×10−4 | 2,016/2,155 | 0.42 | 1.28 (1.18–1.39) | 1.2×10−8 | |
rs3803662 | 16 | 804/899 | 5.8×10−3 | 1.19 (1.05–1.34) | 1,222/1,263 | 1.22 (1.07–1.39) | 2.8×10−3 | 2,026/2,162 | 0.29 | 1.20 (1.10–1.31) | 4.9×10−5 | |
rs16917302 | 10 | 804/898 | 1.8×10−5 | 0.67 (0.56–0.80) | 1,222/1,264 | 0.85 (0.70–1.04) | 0.14 | 2,026/2,162 | 0.11 | 0.75 (0.66–0.86) | 3.8×10−5 | |
rs311499 | 20 | 792/882 | 3.5×10−5 | 0.60 (0.47–0.78) | 1,209/1,255 | 0.84 (0.67–1.06) | 0.13 | 2,001/2,137 | 0.07 | 0.72 (0.61–0.85) | 6.6×10−5 |
p-value was calculated based on the 1-degree of freedom score test statistic stratified by country of study and 6174delT (c.5946delT) mutation status, and modified to allow for the non-independence among related individuals.
Per allele hazard ratios (HR) (i.e., multiplicative model) were estimated on the log scale, assuming independence of age, using the retrospective likelihood. All analyses were stratified by country of residence and 6174delT (c.5946delT) mutation status, and used calendar-year- and cohort-specific breast cancer incidence rates for
The region also includes other possible genes including
Using the stage 1 data, we also performed a GSEA as implemented in MAGENTA
In the combined stage 1 and stage 2 results, four independent SNPs (pairwise
The color of the dots indicates linkage disequilibrium (LD; based on r2 values) in the CEU population (as per scale). Triangle plots below represent LD from actual data of
We also examined the association of both high-frequency CNPs and low-frequency CNVs to case-control status using the stage 1 data. After performing standard quality control measures including a minor allele frequency (MAF) threshold of 5%, we identified 191 polymorphisms with reliable genotypes. No associations were found between CNVs and the phenotype; there was no inflation or deflation of the test statistic, and the best p-value was
Because of the prior evidence of significant LD extent around the 6174delT (c.5946delT) founder mutation in the Ashkenazi Jewish population
In this GWAS of
rs16917302 is located on chromosome 10, in the zinc finger protein 365 gene (
There was some evidence that the HR associated with rs311499 may change with age. We also observed that the stage 1 HR for this SNPs was larger in magnitude compared to the stage 2 HR, consistent with a winner's curse effect
Mutations in known genes (
While we took great efforts to collect all of the possible known
As expected, we observed associations with some of the major common genetic variants seen in genome-wide scans of breast cancer in a non-
Replication in larger datasets will be necessary to precisely estimate the magnitude of the associations of suspected loci identified from our study, candidate gene analysis
All carriers were recruited to studies (
Stage 1 | Stage 2 | |||||||
Affected (n = 1,156) | Unaffected (n = 1,038) | Affected (n = 1,524) | Unaffected (n = 1,508) | |||||
Factor | N | % | N | % | N | % | N | % |
<40 | 763 | 66.7 | 11 | 1.1 | 368 | 23.7 | 1007 | 66.0 |
40–44 | 308 | 26.9 | 230 | 22.2 | 225 | 14.5 | 119 | 7.8 |
45–49 | 72 | 6.3 | 232 | 22.4 | 334 | 21.5 | 131 | 8.6 |
50–54 | 1 | 0.1 | 176 | 17.0 | 286 | 18.4 | 90 | 5.9 |
55–59 | 0 | 0.0 | 138 | 13.3 | 164 | 10.5 | 73 | 4.8 |
60+ | 0 | 0.0 | 248 | 24.0 | 178 | 11.4 | 105 | 6.9 |
Unknown | 125 | 10.9 | 80 | 7.7 | 329 | 21.2 | 293 | 19.2 |
Caucasian | 873 | 76.3 | 723 | 69.9 | 1037 | 66.7 | 1036 | 67.9 |
Ashkenazi Jewish | 146 | 12.8 | 232 | 22.4 | 189 | 12.2 | 196 | 12.9 |
Carrier | 161 | 14.1 | 271 | 26.2 | 233 | 15.0 | 239 | 15.7 |
Non-carrier | 983 | 85.9 | 764 | 73.8 | 1322 | 85.0 | 1286 | 84.3 |
Australia | 109 | 9.5 | 82 | 7.9 | 149 | 9.6 | 180 | 11.8 |
Canada | 98 | 8.6 | 172 | 16.6 | 55 | 3.5 | 82 | 5.4 |
Denmark | 0 | 0.0 | 0 | 0.0 | 43 | 2.8 | 32 | 2.1 |
France | 52 | 4.5 | 25 | 2.4 | 172 | 11.1 | 50 | 3.3 |
Finland | 27 | 2.4 | 27 | 2.6 | 32 | 2.1 | 27 | 1.8 |
Germany | 68 | 5.9 | 31 | 3.0 | 116 | 7.5 | 54 | 3.5 |
Iceland | 25 | 2.2 | 9 | 0.9 | 81 | 5.2 | 6 | 0.4 |
Israel | 49 | 4.3 | 87 | 8.4 | 77 | 5.0 | 86 | 5.6 |
Italy | 110 | 9.6 | 44 | 4.3 | 98 | 6.3 | 62 | 4.1 |
Spain | 107 | 9.4 | 71 | 6.9 | 99 | 6.4 | 136 | 8.9 |
Sweden | 13 | 1.1 | 13 | 1.3 | 11 | 0.7 | 15 | 1.0 |
The Netherlands | 15 | 1.3 | 26 | 2.5 | 117 | 7.5 | 201 | 13.2 |
United Kingdom | 181 | 15.8 | 179 | 17.3 | 125 | 8.0 | 168 | 11.0 |
USA | 290 | 25.4 | 290 | 28.0 | 380 | 24.3 | 426 | 27.9 |
BCFR-Australia | 19 | 1.7 | 5 | 0.5 | 12 | 0.8 | 10 | 0.7 |
BCFR-NCCC | 12 | 1.0 | 1 | 0.1 | 5 | 0.3 | 2 | 0.1 |
BCFR-Ontario | 29 | 2.5 | 28 | 2.7 | 16 | 1.0 | 17 | 1.1 |
BCFR-UT | 18 | 1.6 | 18 | 1.7 | 11 | 0.7 | 47 | 3.1 |
BCFR-FCCC | 2 | 0.2 | 1 | 0.1 | 14 | 0.9 | 10 | 0.7 |
BCFR-NY | 4 | 0.3 | 5 | 0.5 | 26 | 1.7 | 16 | 1.0 |
BIDMC | 10 | 0.9 | 20 | 1.9 | 7 | 0.5 | 12 | 0.8 |
CBCS | 0 | 0.0 | 0 | 0.0 | 43 | 2.8 | 32 | 2.1 |
CGB_NCI | 7 | 0.6 | 15 | 1.4 | 14 | 0.9 | 43 | 2.8 |
CNIO | 49 | 4.3 | 33 | 3.2 | 40 | 2.5 | 56 | 3.7 |
COH | 30 | 2.6 | 13 | 1.3 | 21 | 1.4 | 16 | 1.0 |
DFCI | 14 | 1.2 | 22 | 2.1 | 10 | 0.6 | 24 | 1.6 |
DKFZ | 7 | 0.6 | 5 | 0.5 | 8 | 0.5 | 7 | 0.5 |
EMBRACE | 178 | 15.6 | 173 | 16.7 | 123 | 7.9 | 161 | 10.6 |
FCCC | 14 | 1.2 | 10 | 1.0 | 12 | 0.8 | 9 | 0.6 |
GC-HBOC | 61 | 5.3 | 26 | 2.5 | 108 | 6.9 | 47 | 3.1 |
GEMO | 52 | 4.5 | 25 | 2.4 | 172 | 11.0 | 50 | 3.3 |
GOG | 64 | 5.6 | 51 | 4.9 | 57 | 3.7 | 91 | 6.0 |
HCSC | 27 | 2.4 | 20 | 1.9 | 34 | 2.2 | 35 | 2.3 |
HEBON | 10 | 0.9 | 17 | 1.6 | 103 | 6.6 | 172 | 11.3 |
HEBCS | 27 | 2.4 | 27 | 2.6 | 32 | 2.1 | 27 | 1.8 |
ICO | 31 | 2.7 | 18 | 1.7 | 25 | 1.6 | 45 | 3.0 |
ILUH | 26 | 2.3 | 9 | 0.9 | 81 | 5.2 | 6 | 0.4 |
IOVHBOCS | 19 | 1.7 | 7 | 0.7 | 44 | 2.8 | 20 | 1.3 |
kConFab | 88 | 7.6 | 77 | 7.3 | 137 | 8.7 | 168 | 11.0 |
LUMC | 5 | 0.4 | 9 | 0.9 | 14 | 0.9 | 29 | 1.9 |
MAGIC-UC | 2 | 0.2 | 2 | 0.2 | 0 | 0.0 | 3 | 0.2 |
MAGIC-UCI | 6 | 0.5 | 9 | 0.9 | 21 | 1.4 | 22 | 1.4 |
MAYO | 5 | 0.4 | 14 | 1.4 | 51 | 3.3 | 24 | 1.6 |
MBCSG | 91 | 8.0 | 37 | 3.6 | 54 | 3.5 | 42 | 2.8 |
MSKCC | 51 | 4.5 | 61 | 5.9 | 52 | 3.3 | 47 | 3.1 |
NICC | 28 | 2.4 | 60 | 5.8 | 46 | 3.0 | 67 | 4.4 |
OCGN | 62 | 5.4 | 60 | 5.8 | 35 | 2.2 | 36 | 2.4 |
OSU CCG | 11 | 1.0 | 8 | 0.8 | 9 | 0.6 | 8 | 0.5 |
SMC | 21 | 1.8 | 27 | 2.6 | 31 | 2.0 | 19 | 1.2 |
SWE-BRCA | 13 | 1.1 | 13 | 1.3 | 11 | 0.7 | 15 | 1.0 |
UCSF | 10 | 0.9 | 6 | 0.6 | 12 | 0.8 | 8 | 0.5 |
UKGRFOCR | 2 | 0.2 | 6 | 0.6 | 2 | 0.1 | 7 | 0.5 |
UPENN | 33 | 2.9 | 13 | 1.3 | 58 | 3.7 | 46 | 3.0 |
WCRI | 6 | 0.5 | 84 | 8.1 | 4 | 0.3 | 29 | 1.9 |
A total of 6,272
All eligible DNA samples provided by participating centers were subjected to a rigorous quality control assessment, including measures of overall DNA quality and quantity. A total of 1,156 young (≤50 years) affected women and 1,038 unaffected women with high quality DNA samples were selected (
Prior to the genome-wide scan, we genotyped five SNPs previously genotyped by the CIMBA study centers as a pre-filter for sample identification. Thirty-one samples (
The genotyping for the stage 1 GWAS was performed on 2,163 eligible carriers using the Affymetrix 6.0 GeneChip array that included 906,622 SNPs (
The DNA samples and genotyping calls for both phases of stage 1 were filtered through a series of data quality control parameters using the Birdseed module of the Birdsuite software developed at Broad Institute
SNPs were also filtered using Birdseed and were removed if monomorphic or >10% missing (n = 38,962), genotype call rates <95% (n = 50,810), minor allele frequencies <1% (n = 104,792), departures from Hardy-Weinberg Equilibrium (p<10−6; n = 1,090), differential missingness with respect to phenotype (p<10−3; n = 275), and differential missingness with respect to nearby SNPs (p<10−10; n = 22,065). A total of 6,212 SNPs had different missingness patterns in phase 1 compared to phase 2, and were excluded. Since we found that significant missingness correlated to SNPs mapping to longer fragments of Affymetrix 6.0 digestion products, we also removed the SNPs on fragments longer than 1000bp (n = 85,990).
With the remaining 1,805 carriers and 596,426 SNPs, an iterative process proceeded to drop all individuals with low call rates (<95%), high autosomal heterozygosity rates (false discovery rate <0.1%), and high identity by descent scores (≥0.95) and to drop all SNPs with minor allele frequencies <1% and SNP call rates <95% until the final run contained individuals above the individual and SNP filter thresholds (n = 1,747 samples and 592,566 SNPs). A more stringent HWE filter (p<10−7) was then applied and 403 additional SNPs were dropped. Nine individuals with missing mutation descriptions were removed.
Finally, principal components analysis was used to identify the ethnic outliers (
Where directly genotyped data were not available, probabilities were imputed with Beagle.3.0.2 (using the default parameters) using CEU+TSI samples on HapMap3 release2 B36 as the reference panel (410 chromosomes, 1.4 M SNPs).
The primary SNP selection strategy was based on the results of the kinship-adjusted score test of 592,163 GWAS genotyped SNPs. From stage 1, a total of 79 top independent regions (
Samples were excluded for call rates ≤95% (n = 476), duplication in stage 2 (identity by state (IBS)∼1.0; n = 43), duplication in stage 1 and 2 (IBS; n = 25), lack of complete phenotype data (n = 1), and insufficient country-specific numbers (n = 1;
Carriers were censored at the first breast or ovarian cancer or bilateral prophylactic mastectomy, whichever occurred first. Carriers who developed any cancer were censored at time of bilateral prophylactic mastectomy if it occurred more than a year prior to the cancer diagnosis (to avoid censoring at bilateral mastectomies related to diagnosis in which rounded ages were used). The remaining carriers were censored at the age of last observation. This was defined either by the age/date at interview or age at follow-up depending on the information provided by the participating center. Carriers censored at diagnosis of breast cancer were considered cases in the analysis. Mutation carriers censored at ovarian cancer diagnosis were considered unaffected. Carriers with a censoring/last follow-up age older than age 80 were censored at age 80 because there are no reliable cancer incidence rates for
Analyses, based on 1,703
To estimate the magnitude of the associations, the effect of each SNP was modelled either as a per allele hazard ratio (HR) (i.e., multiplicative model) or as separate HRs for heterozygotes and homozygotes, and these were estimated on the log scale. The HRs were assumed to be independent of age (i.e. we used a Cox proportional-hazards model). For the most significant novel associations this assumption was verified by adding a genotype-by-age interaction term to the model to fit models in which the HR changed with age. The retrospective likelihood was implemented in the pedigree-analysis software MENDEL
We also examined the association of both high-frequency and low-frequency copy number variants (CNV) to the age of diagnosis of breast cancer as a dichotomous trait using the stage 1 data
We looked for evidence of excess sharing across the genome and the
We tested whether 59 genes known to regulate or interact with
Data filtering of stage 1
(0.57 MB TIF)
Quantile-quantile plot comparing expected distribution of chi-square values and observed chi-square values from a genome-wide scan of breast cancer cases and unaffected
(0.31 MB TIF)
Manhattan plot of p-values by chromosomal position from a genome-wide scan of breast cancer cases and unaffected
(0.51 MB TIF)
Quantile-quantile plot comparing expected distribution of p-values and observed p-values of association of common copy number polymorphisms (CNPs) from a genome-wide scan of breast cancer cases and unaffected
(0.32 MB TIF)
Principal components analysis, including all eligible (after filtering)
(2.39 MB TIF)
Data filtering of stage 2
(0.35 MB TIF)
List of 59
(0.13 MB DOC)
Ranked results for the 85 SNPs successfully genotyped in stage 2,
(0.13 MB DOC)
HEBON, kConFab, and GEMO were listed authors. The GEMO study is represented by the named authors: Olga M. Sinilnikova, Dominique Stoppa-Lyonnet, Sylvie Mazoyer, Marion Gauthier-Villars, Hagay Sobol, Michel Longy, and Marc Frenay. We wish to thank all the GEMO collaborating groups for their contribution to this study. GEMO Collaborating Centers are: Coordinating Centres, Unité Mixte de Génétique Constitutionnelle des Cancers Fréquents, Centre Hospitalier Universitaire de Lyon/Centre Léon Bérard, & UMR5201 CNRS, Université de Lyon, Lyon: Olga Sinilnikova, Laure Barjhoux, Sophie Giraud, Mélanie Léone, Sylvie Mazoyer; and INSERM U509, Service de Génétique Oncologique, Institut Curie, Paris: Dominique Stoppa-Lyonnet, Marion Gauthier-Villars, Claude Houdayer, Virginie Moncoutier, Muriel Belotti, Antoine de Pauw. Institut Gustave Roussy, Villejuif: Brigitte Bressac-de-Paillerets, Audrey Remenieras, Véronique Byrde, Olivier Caron, Gilbert Lenoir. Centre Jean Perrin, Clermont–Ferrand: Yves-Jean Bignon, Nancy Uhrhammer. Centre Léon Bérard, Lyon: Christine Lasset, Valérie Bonadona. Centre François Baclesse, Caen: Agnès Hardouin, Pascaline Berthet. Institut Paoli Calmettes, Marseille: Hagay Sobol, Violaine Bourdon, Tetsuro Noguchi, François Eisinger. Groupe Hospitalier Pitié-Salpétrière, Paris: Florence Coulet, Chrystelle Colas, Florent Soubrier. CHU de Arnaud-de-Villeneuve, Montpellier: Isabelle Coupier. Centre Oscar Lambret, Lille: Jean-Philippe Peyrat, Joëlle Fournier, Françoise Révillion, Philippe Vennin, Claude Adenis. Centre René Huguenin, St Cloud: Etienne Rouleau, Rosette Lidereau, Liliane Demange, Catherine Nogues. Centre Paul Strauss, strasbourg: Danièle Muller, Jean-Pierre Fricker. Institut Bergonié, Bordeaux: Michel Longy, Nicolas Sevenet. Institut Claudius Regaud, toulouse: Christine Toulas, Rosine Guimbaud, Laurence Gladieff, Viviane Feillel. CHU de Grenoble: Dominique Leroux, Hélène Dreyfus, Christine Rebischung. CHU de Dijon: Cécile Cassini, Laurence Faivre. CHU de St-Etienne: Fabienne Prieur. Hôtel Dieu Centre Hospitalier, Chambéry: Sandra Fert Ferrer. Centre Antoine Lacassagne, Nice: Marc Frénay. CHU de Limoges: Laurence Vénat-Bouvet. Creighton University, Omaha, USA: Henry T. Lynch. The HEBON study is represented by the named authors: Frans B. L. Hogervorst, Matti A. Rookus, J. Margriet Collée, Nicoline Hoogerbrugge, and Kees E.P. van Roozendaal. The HEBON is compromised of: Coordinating center: Netherlands Cancer Institute, Amsterdam: Frans B. L. Hogervorst, Senno Verhoef, Martijn Verheus, Laura J. van 't Veer, Flora E. van Leeuwen, Matti A. Rookus; Erasmus Medical Center, Rotterdam: Margriet Collée, Ans M.W. van den Ouweland, Agnes Jager, Maartje J. Hooning, Madeleine M.A. Tilanus-Linthorst, Caroline Seynaeve; Leiden University Medical Center, Leiden: Christi J. van Asperen, Juul T. Wijnen, Maaike P. Vreeswijk, Rob A. Tollenaar, Peter Devilee; Radboud University Nijmegen Medical Center, Nijmegen: Marjolijn J. Ligtenberg, Nicoline Hoogerbrugge; University Medical Center Utrecht, Utrecht: Margreet G. Ausems, Rob B. van der Luijt; Amsterdam Medical Center: Cora M. Aalfs, Theo A. van Os; VU University Medical Center, Amsterdam: Johan J.P. Gille, Quinten Waisfisz, Hanne Meijers-Heijboer; University Hospital Maastricht, Maastricht: Encarna B. Gomez-Garcia, Cees E. van Roozendaal, Marinus J. Blok; University Medical Center Groningen University: Jan C. Oosterwijk, Annemarie H van der Hout, Marian J. Mourits; The Netherlands Foundation for the detection of hereditary tumours, Leiden, the Netherlands: Hans F. Vasen. For the kConFab study, Amanda B. Spurdle and Georgia Chenevix-Trench take responsibility for the manuscript on behalf of the kConFab members (listed at
The LUMC study would like to thank Hans Vasen, Inge van Leeuwen, and Hanne Meijers for patient accrual. For the CNIO study, the authors would like to thank R.M. Alonso, G. Pita and R.M. Milne for their assistance. We thank IBGM, Universidad de Valladolid and Consejería de Sanidad Junta de Castilla y León. For the SWE-BRCA, the authors would like to also acknowledge the contribution of Per Karlsson, Margareta Nordling, Annika Bergman and Zakaria Einbeigi, Gothenburg, Sahlgrenska University Hospital; Marie Stenmark-Askmalm and Sigrun Liedgren Linköping University Hospital; Åke Borg, Niklas Loman, Håkan Olsson, Ulf Kristoffersson, Helena Jernström, Katja Harbst and Karin Henriksson, Lund University Hospital; Annika Lindblom, Brita Arver, Anna von Wachenfeldt, Annelie Liljegren, Gisela Barbany-Bustinza and Johanna Rantala, Stockholm, Karolinska University Hospital; Beatrice Melin, Henrik Grönberg, Eva-Lena Stattin and Monica Emanuelsson, Umeå University Hospital; Hans Ehrencrona, Richard Rosenquist Brandell and Niklas Dahl, Uppsala University Hospital. Douglas F. Easton is the PI of the EMBRACE study. EMBRACE Collaborating Centers are: Coordinating Centre, Cambridge: Susan Peock, Margaret Cook, Clare Oliver, Debra Frost. North of Scotland Regional Genetics Service, Aberdeen: Helen Gregory, Zosia Miedzybrodzka. Northern Ireland Regional Genetics Service, Belfast: Patrick J. Morrison, Lisa Jeffers. West Midlands Regional Clinical Genetics Service, Birmingham: Trevor Cole, Carole McKeown, Kai-Ren Ong, Laura Boyes. South Western Regional Genetics Service, Bristol: Alan Donaldson. East Anglian Regional Genetics Service, Cambridge: Joan Paterson. All Wales Medical Genetics Services, Cardiff: Alexandra Murray, Mark T. Rogers, Emma McCann. St James's Hospital, Dublin & National Centre for Medical Genetics, Dublin: M. John Kennedy, David Barton. South East of Scotland Regional Genetics Service, Edinburgh: Mary Porteous. Peninsula Clinical Genetics Service. Exeter: Carole Brewer, Emma Kivuva, Anne Searle, Selina Goodman. West of Scotland Regional Genetics Service, Glasgow: Rosemarie Davidson, Victoria Murday, Nicola Bradshaw, Lesley Snadden, Mark Longmuir, Catherine Watt, Sarah Gibson. South East Thames Regional Genetics Service, Guys Hospital London: Louise Izatt, Gabriella Pichert, Chris Jacobs, Caroline Langman. North West Thames Regional Genetics Service, Kennedy-Galton Centre, Harrow: Huw Dorkins. Leicestershire Clinical Genetics Service, Leicester: Julian Barwell. Yorkshire Regional Genetics Service, Leeds: Carol Chu, Tim Bishop, Julie Miller. Merseyside & Cheshire Clinical Genetics Service. Liverpool: Ian Ellis, Catherine Houghton. Manchester Regional Genetics Service, Manchester: D Gareth Evans, Fiona Lalloo, Felicity Holt. North East Thames Regional Genetics Service, North East Thames: Lucy Side, Alison Male, Cheryl Berlin. Nottingham Centre for Medical Genetics, Nottingham: Carol Gardiner. Northern Clinical Genetics Service, Newcastle: Fiona Douglas, Oonagh Claber. Oxford Regional Genetics Service, Oxford: Lisa Walker, Diane McLeod, Dorothy Halliday, Sarah Durrell, Barbara Stayner. The Institute of Cancer Research and Royal Marsden NHS Foundation Trust: Ros Eeles, Susan Shanley, Nazneen Rahman, Richard Houlston, Elizabeth Bancroft, Lucia D'Mello, Elizabeth Page, Audrey Ardern-Jones, Kelly Kohut, Jennifer Wiggins. Elena Castro, Anita Mitra, Lisa Robertson. North Trent Clinical Genetics Service, Sheffield: Jackie Cook, Oliver Quarrell, Cathryn Bardsley. South Essex Cancer Research Network, Southend: Anne Robinson. South West Thames Regional Genetics Service, London: Shirley Hodgson, Sheila Goff, Glen Brice, Lizzie Winchester. Wessex Clinical Genetics Service. Princess Anne Hospital, Southampton: Diana Eccles, Anneke Lucassen, Gillian Crawford, Emma Tyler, Donna McBride. HEBCS thanks Dr. Carl Blomqvist and Tuomas Heikkinen for their help with the patient data and samples. Fox Chase Cancer Center (FCCC) thanks Ms. JoEllen Weaver and Mr. John Malick for expert technical assistance. The UCSF study would like to acknowledge Ms. Salina Chan for her database management assistance. CONsorzio Studi Italiani Tumori Ereditari Alla Mammella, CONSIT TEAM acknowledges Marco Pierotti, Bernard Peissel, Daniela Zaffaroni and Carla B. Ripamonti of the Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Bernardo Bonanni of the Istituto Europeo di Oncologia, Milan, Italy and Loris Bernard of the Cogentech, Consortium for Genomic Technologies, Milan, Italy. CBCS would like to thank Bent Ejlerten, Mette K. Andersen, Susanne Kjaergaard and Anne-Marie Gerdes for clinical data. The authors wish to thank Nichole Hansen for help in manuscript preparation. The authors also acknowledge support of the Robert and Kate Niehaus Clinical Cancer Genetics Initiative at MSKCC.
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.