We conducted a genome-wide association study (GWAS) of breast cancer by genotyping 528,173 SNPs in 1,145 postmenopausal women of European ancestry with invasive breast cancer and 1,142 controls. We identified four SNPs in intron 2 of FGFR2 (which encodes a receptor tyrosine kinase and is amplified or overexpressed in some breast cancers) that were highly associated with breast cancer and confirmed this association in 1,776 affected individuals and 2,072 controls from three additional studies. Across the four studies, the association with all four SNPs was highly statistically significant (Ptrend for the most strongly associated SNP (rs1219648) = 1.1 × 10−10; population attributable risk = 16%). Four SNPs at other loci most strongly associated with breast cancer in the initial GWAS were not associated in the replication studies. Our summary results from the GWAS are available online in a form that should speed the identification of additional risk loci.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    et al. Polygenic susceptibility to breast cancer and implications for prevention. Nat. Genet. 31, 33–36 (2002).

  2. 2.

    et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am. J. Hum. Genet. 72, 1117–1130 (2003).

  3. 3.

    et al. Population BRCA1 and BRCA2 mutation frequencies and cancer penetrances: a kin-cohort study in Ontario, Canada. J. Natl. Cancer Inst. 98, 1694–1706 (2006).

  4. 4.

    & Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6, 95–108 (2005).

  5. 5.

    et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007).

  6. 6.

    et al. A genome-wide association study identifies multiple breast cancer susceptibility loci. Nature, advance online publication 27 May 2007 (doi:10.1038/nature05887).

  7. 7.

    et al. Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat. Genet. 39, 645–649 (2007).

  8. 8.

    et al. Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat. Genet. 39, 631–637 (2007).

  9. 9.

    , , & A prospective study of plasma prolactin concentrations and risk of premenopausal and postmenopausal breast cancer. J. Clin. Oncol. 25, 1482–1488 (2007).

  10. 10.

    & Fibroblast growth factor signaling in tumorigenesis. Cytokine Growth Factor Rev. 16, 179–186 (2005).

  11. 11.

    & Mapping trait loci by use of inferred ancestral recombination graphs. Am. J. Hum. Genet. 79, 910–922 (2006).

  12. 12.

    , , , & Circulating insulin and c-peptide levels and risk of breast cancer among predominately premenopausal women. Cancer Epidemiol. Biomarkers Prev. 16, 161–164 (2007).

  13. 13.

    et al. Etiologic and early marker studies in the prostate, lung, colorectal and ovarian (PLCO) cancer screening trial. Control. Clin. Trials 21, 349S–355S (2000).

  14. 14.

    , , , & Association of polymorphisms in the paraoxonase 1 gene with breast cancer incidence in the CPS-II Nutrition Cohort. Cancer Epidemiol. Biomarkers Prev. 15, 1226–1228 (2006).

  15. 15.

    , , , & Estimating the population attributable risk for multiple risk factors using case-control data. Am. J. Epidemiol. 122, 904–914 (1985).

  16. 16.

    & Differential signal transduction of alternatively spliced FGFR2 variants expressed in human mammary epithelial cells. J. Cell. Physiol. 210, 720–731 (2007).

  17. 17.

    et al. What constitutes replication of a genotype-phenotype association? Summary of an NCI-NHGRI working group. Nature (in the press).

  18. 18.

    , , & Optimal two-stage genotyping designs for genome-wide association scans. Genet. Epidemiol. 30, 356–368 (2006).

  19. 19.

    , & Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).

  20. 20.

    , , , & Accounting for haplotype uncertainty in matched association studies: a comparison of simple and flexible techniques. Genet. Epidemiol. 28, 261–272 (2005).

  21. 21.

    International HapMap Consortium. A haplotype map of the human genome. Nature 437, 1299–1320 (2005).

  22. 22.

    et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

  23. 23.

    , & Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

Download references


We thank B. Egan, L. Egan, H. Judge Ellis, H. Ranu and P. Soule for assistance, and we thank the participants in the Nurses' Health Studies. We thank P. Prorok (Division of Cancer Prevention, National Cancer Institute); the Screening Center investigators and staff of PLCO; T. Riley, C. Williams and staff (Information Management Services, Inc.); B. O'Brien and staff (Westat, Inc.) and B. Kopp, T. Sheehy and staff (SAIC-Frederick). We acknowledge the study participants for their contributions in making this study possible. We thank C. Lichtman for data management and the participants on the CPS-II. We thank M. Minichiello for providing the Margarita program and for discussions. We acknowledge D. Easton and colleagues for sharing prepublication results. The Nurses' Health Studies are supported by US NIH grants CA65725, CA87969, CA49449, CA67262, CA50385 and 5UO1CA098233. The ACS study is supported by UO1 CA098710. The PLCO study is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS.

Author information


  1. Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.

    • David J Hunter
    • , David G Cox
    • , Susan E Hankinson
    •  & Walter C Willett
  2. Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.

    • David J Hunter
    • , Peter Kraft
    •  & David G Cox
  3. Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.

    • David J Hunter
  4. Division of Cancer Epidemiology and Genetics, National Cancer Institute (NCI), US National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Bethesda, Maryland 20892, USA.

    • David J Hunter
    • , Meredith Yeager
    • , Sholom Wacholder
    • , Zhaoming Wang
    • , Robert Welch
    • , Amy Hutchinson
    • , Junwen Wang
    • , Kai Yu
    • , Nilanjan Chatterjee
    • , Regina G Ziegler
    • , Richard B Hayes
    • , Margaret Tucker
    • , Joseph F Fraumeni Jr
    • , Robert N Hoover
    • , Gilles Thomas
    •  & Stephen J Chanock
  5. Bioinformed Consulting Services, Gaithersburg, Maryland 20877, USA.

    • Kevin B Jacobs
  6. SAIC-Frederick, NCI-FCRDC, Frederick, Maryland 21702, USA.

    • Meredith Yeager
    • , Zhaoming Wang
    • , Robert Welch
    • , Amy Hutchinson
    •  & Junwen Wang
  7. Pediatric Oncology Branch, Center for Cancer Research, NCI, NIH, DHHS, Bethesda, Maryland 20892, USA.

    • Nick Orr
    •  & Stephen J Chanock
  8. Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts 02115, USA.

    • Walter C Willett
  9. Washington University School of Medicine, St. Louis, Missouri 63130, USA.

    • Graham A Colditz
  10. Division of Cancer Prevention, NCI, NIH, DHHS, Bethesda, Maryland 20892, USA.

    • Christine D Berg
  11. Department of Internal Medicine, University of Utah, Salt Lake City, Utah 84112, USA.

    • Saundra S Buys
  12. The Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin 54449, USA.

    • Catherine A McCarty
  13. Department of Epidemiology and Surveillance Research, American Cancer Society, Atlanta, Georgia 30329, USA.

    • Heather Spencer Feigelson
    • , Eugenia E Calle
    •  & Michael J Thun
  14. Office of Cancer Genomics, NCI, NIH, DHHS, Bethesda, Maryland 20892, USA.

    • Daniela S Gerhard


  1. Search for David J Hunter in:

  2. Search for Peter Kraft in:

  3. Search for Kevin B Jacobs in:

  4. Search for David G Cox in:

  5. Search for Meredith Yeager in:

  6. Search for Susan E Hankinson in:

  7. Search for Sholom Wacholder in:

  8. Search for Zhaoming Wang in:

  9. Search for Robert Welch in:

  10. Search for Amy Hutchinson in:

  11. Search for Junwen Wang in:

  12. Search for Kai Yu in:

  13. Search for Nilanjan Chatterjee in:

  14. Search for Nick Orr in:

  15. Search for Walter C Willett in:

  16. Search for Graham A Colditz in:

  17. Search for Regina G Ziegler in:

  18. Search for Christine D Berg in:

  19. Search for Saundra S Buys in:

  20. Search for Catherine A McCarty in:

  21. Search for Heather Spencer Feigelson in:

  22. Search for Eugenia E Calle in:

  23. Search for Michael J Thun in:

  24. Search for Richard B Hayes in:

  25. Search for Margaret Tucker in:

  26. Search for Daniela S Gerhard in:

  27. Search for Joseph F Fraumeni in:

  28. Search for Robert N Hoover in:

  29. Search for Gilles Thomas in:

  30. Search for Stephen J Chanock in:

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to David J Hunter.

Supplementary information

PDF files

  1. 1.

    Supplementary Fig. 1

    Distribution of the admixture vector of each NHS participant as determined by STRUCTURE.

  2. 2.

    Supplementary Fig. 2

    Probability-probability plot of the uncorrected P values (blue dots) compared with the expected uniform distribution (green line), where magenta dots show the P values corrected for age in 5-year intervals, an indicator for recent hormone use, and three eigenvectors controlling for population stratification.

  3. 3.

    Supplementary Fig. 3

    ARG analysis with 81 SNPs after 106 permutations.

  4. 4.

    Supplementary Table 1

    Identification of protective and at-risk haplotypes for the FGFR2 susceptibility locus.

  5. 5.

    Supplementary Table 2

    Evidence for association between the six most significant SNPs in the NHS genome-wide association scan, the three replication studies, and the pooled scan and replication data.

  6. 6.

    Supplementary Methods

About this article

Publication history






Further reading