Letter | Published:

Genome-wide association studies identify loci associated with age at menarche and age at natural menopause

Nature Genetics volume 41, pages 724728 (2009) | Download Citation

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Abstract

Age at menarche and age at natural menopause are associated with causes of substantial morbidity and mortality such as breast cancer and cardiovascular disease. We conducted a joint analysis of two genome-wide association studies of these two traits in a total of 17,438 women from the Nurses' Health Study (NHS, N = 2,287) and the Women's Genome Health Study (WGHS, N = 15,151). For age at menarche, we identified ten associated SNPs (P = 1 × 10−7–3 × 10−13) clustered at 6q21 (in or near the gene LIN28B) and 9q31.2 (in an intergenic region). For age at natural menopause, we identified 13 associated SNPs (P = 1 × 10−7–1 × 10−21) clustered at 20p12.3 (in the gene MCM8), 19q13.42 (in or near the gene BRSK1), 5q35.2 (in or near genes UIMC1 and HK3) and 6p24.2 (in the gene SYCP2L). These newly identified loci might expand understanding of the biological pathways regulating these two traits.

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References

  1. 1.

    Reproductive factors in breast cancer epidemiology. Acta Oncol. 31, 187–194 (1992).

  2. 2.

    , & Obesity, endogenous hormones, and endometrial cancer risk: a synthetic review. Cancer Epidemiol. Biomarkers Prev. 11, 1531–1543 (2002).

  3. 3.

    et al. Menstrual and reproductive factors and fracture risk: the Leisure World Cohort Study. J. Womens Health (Larchmt) 14, 808–819 (2005).

  4. 4.

    et al. Relationships of age at menarche and menopause, and reproductive year with mortality from cardiovascular disease in Japanese postmenopausal women: the JACC study. J. Epidemiol. 16, 177–184 (2006).

  5. 5.

    & The variability of female reproductive ageing. Hum. Reprod. Update 8, 141–154 (2002).

  6. 6.

    , , & Estimating genetic influences on the age-at-menarche: a survival analysis approach. Am. J. Med. Genet. 39, 148–154 (1991).

  7. 7.

    Genetic basis of human female pelvic morphology: a twin study. Am. J. Phys. Anthropol. 117, 327–333 (2002).

  8. 8.

    , & Genes control the cessation of a woman's reproductive life: a twin study of hysterectomy and age at menopause. J. Clin. Endocrinol. Metab. 83, 1875–1880 (1998).

  9. 9.

    et al. The role of genetic factors in age at natural menopause. Hum. Reprod. 16, 2014–2018 (2001).

  10. 10.

    , , , & Heritability of age at natural menopause in the Framingham Heart Study. J. Clin. Endocrinol. Metab. 90, 3427–3430 (2005).

  11. 11.

    et al. Replicating genotype-phenotype associations. Nature 447, 655–660 (2007).

  12. 12.

    et al. Genomewide linkage scan for quantitative trait loci underlying variation in age at menarche. J. Clin. Endocrinol. Metab. 91, 1009–1014 (2006).

  13. 13.

    et al. Weight-adjusted genome scan analysis for mapping quantitative trait Loci for menarchal age. J. Clin. Endocrinol. Metab. 91, 3534–3537 (2006).

  14. 14.

    et al. Chromosomal regions 22q13 and 3p25 may harbor quantitative trait loci influencing both age at menarche and bone mineral density. Hum. Genet. 123, 419–427 (2008).

  15. 15.

    et al. A genome-wide linkage scan for age-at-menarche in three populations of European descent. J. Clin. Endocrinol. Metab. 93, 3965–3970 (2008).

  16. 16.

    et al. Linkage analysis of extremely discordant and concordant sibling pairs identifies quantitative trait loci influencing variation in human menopausal age. Am. J. Hum. Genet. 74, 444–453 (2004).

  17. 17.

    , , & Genome-wide linkage analysis to age at natural menopause in a community-based sample: the Framingham Heart Study. Fertil. Steril. 84, 1674–1679 (2005).

  18. 18.

    et al. Association study of the oestrogen signalling pathway genes in relation to age at natural menopause. J. Genet. 86, 269–276 (2007).

  19. 19.

    , , & Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat. Genet. 38, 209–213 (2006).

  20. 20.

    , & Selective blockade of microRNA processing by Lin28. Science 320, 97–100 (2008).

  21. 21.

    et al. Colocalization of MCM8 and MCM7 with proteins involved in distinct aspects of DNA replication. Microsc. Res. Tech. 71, 288–297 (2008).

  22. 22.

    et al. SAD: a presynaptic kinase associated with synaptic vesicles and the active zone cytomatrix that regulates neurotransmitter release. Neuron 50, 261–275 (2006).

  23. 23.

    , , & The SAD-1 kinase regulates presynaptic vesicle clustering and axon termination. Neuron 29, 115–129 (2001).

  24. 24.

    et al. The ubiquitin-interacting motif containing protein RAP80 interacts with BRCA1 and functions in DNA damage repair response. Cancer Res. 67, 6647–6656 (2007).

  25. 25.

    et al. Hexokinase III, cyclin A and galectin-3 are overexpressed in malignant follicular thyroid nodules. Clin. Endocrinol. 68, 252–257 (2008).

  26. 26.

    et al. Mouse SYCP2 is required for synaptonemal complex assembly and chromosomal synapsis during male meiosis. J. Cell Biol. 173, 497–507 (2006).

  27. 27.

    et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat. Genet. 39, 870–874 (2007).

  28. 28.

    et al. Loci related to metabolic-syndrome pathways including LEPR,HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women's Genome Health Study. Am. J. Hum. Genet. 82, 1185–1192 (2008).

  29. 29.

    et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

  30. 30.

    & Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539–1558 (2002).

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Acknowledgements

We thank J. Miletich and A. Parker as well as the technical staff at Amgen for their collaboration and scientific support in performing the genotyping for the WGHS. The NHS GWAS was performed as part of the Cancer Genetic Markers of Susceptibility initiative of the NCI. We particularly acknowledge the contributions of R. Hoover, A. Hutchinson, K. Jacobs and G. Thomas. We thank J. Chen for discussion of gene functions. We thank H. Ranu and P. Soule for assistance. The WGHS is supported by HL 043851 and HL69757 from the National Heart, Lung, and Blood Institute and CA 047988 from the National Cancer Institute, the Donald W. Reynolds Foundation and the Fondation Leducq, with collaborative scientific support and funding for genotyping provided by Amgen. The NHS is supported by CA 40356 and U01-CA98233 from the National Cancer Institute. We acknowledge the study participants in the NHS and the WGHS for their contribution in making this study possible.

Author information

Affiliations

  1. Program in Molecular and Genetic Epidemiology, Boston, Massachusetts, USA.

    • Chunyan He
    • , Peter Kraft
    • , Constance Chen
    •  & David J Hunter
  2. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.

    • Chunyan He
    • , Peter Kraft
    • , Constance Chen
    • , Julie E Buring
    • , Susan E Hankinson
    • , Paul M Ridker
    •  & David J Hunter
  3. Donald W. Reynolds Center for Cardiovascular Research, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

    • Julie E Buring
    • , Guillaume Paré
    • , Paul M Ridker
    •  & Daniel I Chasman
  4. Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

    • Julie E Buring
    • , Guillaume Paré
    • , Paul M Ridker
    •  & Daniel I Chasman
  5. Center for Cardiovascular Disease Prevention, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

    • Guillaume Paré
    • , Paul M Ridker
    •  & Daniel I Chasman
  6. Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

    • Susan E Hankinson
    •  & David J Hunter
  7. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA.

    • Stephen J Chanock
    •  & David J Hunter
  8. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

    • David J Hunter

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Contributions

C.H., C.C., P.K. and D.I.C. performed the primary GWAS analyses in each study. C.H., C.C. and P.K. performed the joint analysis and contributed to the graphics supporting the figures. C.H., D.J.H. and D.I.C. wrote the manuscript with inputs from the other authors, especially S.J.C., P.K. and P.M.R. P.K., S.E.H. and D.J.H. are investigators of the NHS. J.E.B., G.P., P.M.R. and D.I.C. are investigators of the WGHS. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chunyan He.

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    Supplementary Text and Figures

    Supplementary Tables 1–4, Supplementary Figures 1 and 2 and Supplementary Methods

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DOI

https://doi.org/10.1038/ng.385

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