Skip to main content
SpringerLink
Log in

Estimating the Absolute Risk of Disease Associated with Identified Mutations

  • Chapter
  • First Online:
Book cover Handbook on Analyzing Human Genetic Data

  • 1804 Accesses

Abstract

For a given mutation status, we define the absolute risk as the chance that disease develops in a defined age interval, given that the person is well at the beginning of the interval. Absolute risk is reduced by competing risks of mortality, that may cause the person to die before the disease of interest develops. We distinguish absolute risk from the pure cumulative risk of disease that is often estimated in the genetic epidemiologic literature, and we concentrate on estimating marginal risks for members selected at random from the population, rather than family specific risks. We review cohort, population-based case–control, case–control family, and kin-cohort designs for estimating absolute and pure cumulative risks associated with a measurable genetic mutation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson P, Borgen O, Gill R, Keiding N (1991) Statistical Models based on counting processes. Springer-Verlag, New York

    Google Scholar 

  2. Begg C (2002) On the use of familial aggregation in population-based case probands for calculating penetrance. J Natl Cancer Inst 94:1221–1226

    PubMed  Google Scholar 

  3. Benichou J, Gail M (1990) Estimates of absolute cause-specific risk in cohort studies. Biometrics 46:813–826

    Article  CAS  PubMed  Google Scholar 

  4. Benichou J, Gail M (1995) Methods of inference for estimates of absolute risk derived from population-based case-control studies. Biometrics 51:182–194

    Article  CAS  PubMed  Google Scholar 

  5. Chatterjee N, Wacholder S (2001) A marginal likelihood approach for estimating penetrance from kin-cohort designs. Biometrics 57:245–252

    Article  CAS  PubMed  Google Scholar 

  6. Chatterjee N, Hartge P, Wacholder S (2003) Adjustment for competing risk in kin-cohort estimation. Genetic Epidemiol 25:303–313

    Article  Google Scholar 

  7. Chatterjee N, Kalaylioglu Z, Shih J, Gail M (2006) Case-control and case-only designs with genotype and family history data: estimating relative risk, residual familial aggregation, and cumulative risk. Biometrics 62:36–48

    Article  PubMed  Google Scholar 

  8. Claus E, Risch N, Thompson W (1991) Genetic analysis of breast cancer in the cancer and steroid hormone study. Am J Human Genetics 48:232–242

    CAS  Google Scholar 

  9. Claus E, Risch N, Thompson W (1994) Autosomal dominant inheritance of early-onset breast cancer. Implications and risk prediction. Cancer 73:643–651

    CAS  Google Scholar 

  10. Clayton DG (1978) A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65:141–151

    Article  Google Scholar 

  11. Cornfield J (1951) A method of estimating comparative rates from clinical data; applications to cancer of the lung, breast, and cervix. J Natl Cancer Inst 11:1269–1275

    CAS  PubMed  Google Scholar 

  12. Ford D, Easton D, Stratton M, Narod S, Goldar D, Devilee P, Bishop D, Weber B, Lenoir G, Chang-Claude J, et al (1998) Genetic heterogeneity and penetrance analysis of the brca1 and brca2 genes in breast cancer families. the breast cancer linkage consortium. Am J Human Genetics 62:676–689

    Article  CAS  Google Scholar 

  13. Gail M, Chatterjee N (2004) Some biases that may affect kin-cohort studies for estimating the risks from identified disease genes. Springer, New York

    Google Scholar 

  14. Gail M, Brinton L, Byar D, Corle D, Green S, Schairer C, Mulvihill J (1989) Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81:1879–1886

    Article  CAS  PubMed  Google Scholar 

  15. Gail M, Constantino J, Bryant J, Croyle R, Freedman L, Helzlsouer K, Vogel V (1999a) Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer. J Natl Cancer Inst 91:1829–1846

    Article  CAS  PubMed  Google Scholar 

  16. Gail M, Pee D, Benichou J, Carroll R (1999b) Designing studies to estimate the penetrance of an identified autosomal dominant mutation: cohort, case-control, and genotyped-proband designs. Genetic Epidemiol 16:15–39

    Article  CAS  Google Scholar 

  17. Gail M, Pee D, Carroll R (1999c) Kin-cohort designs for gene characterization. J Natl Cancer Inst Monogr 26:55–60

    CAS  PubMed  Google Scholar 

  18. Gail M, Pee D, Carroll R (2001) Effects of violations of assumptions on likelihood methods for estimating the penetrance of an autosomal dominant mutation from kin-cohort studies. J Stat Plan Infer 96:167–177

    Article  Google Scholar 

  19. Genest C, Mackay R (1986) The joy of copulas: bivariate distributions with given marginals. Am Stat 40:280–283

    Article  Google Scholar 

  20. Hooper J, Southey M, Dite G, Jolley D, Giles G, McGredie M, Venter DED (1990) Population-based estimate of the average age-specific cumulative risk of breast cancer for a defined set of protein-truncating mutations in brca1 and brca2. Australian breast cancer family study. Am J Human Genetics 8:813–826

    Google Scholar 

  21. Hsu L, Prentice R, Zhao L, Fan J (1999) On dependence estimation using correlated failure time data from case-control family studies. Biometrika 86:743–753

    Article  Google Scholar 

  22. Langholz B, Goldstein L (1996) Estimation of absolute risk from nested case-control data. Biometrics 53:767–774

    Article  Google Scholar 

  23. Li H (1998) Analysis of age of onset data from case-control family studies. Biometrics 54:1030–1039

    Article  CAS  PubMed  Google Scholar 

  24. Liang K, Zeger S, Qaqish B (1992) Multivariate regression-analyses for categorical-data. J Royal Stat Soc Ser B 54:3–40

    Google Scholar 

  25. Moore D, Chatterjee N, Pee D, Gail M (2001) Pseudo-likelihood estimates of the cumulative risk of an autosomal dominant disease from a kin-cohort study. Genetic Epidemioly 20: 210–227

    Article  CAS  Google Scholar 

  26. Oakes D (1989) Bivariate survival models induced by frailties. J Am Stat Assoc 84:487–493

    Article  Google Scholar 

  27. Prentice R, Pyke R (1979) Logistic disease incidence models and case-control studies. Biometrika 66:403–411

    Article  Google Scholar 

  28. Prentice R, Kalbfleisch J, Jr AP, Flournoy N, Farewell V, Breslow N (1978) The analysis of failure times in the presence of competing risks. Biometrics 34:541–554

    Article  CAS  PubMed  Google Scholar 

  29. Self S, Prentice R (1988) Asymptotic distribution theory and efficiency results for case-cohort studies. Ann Statistics 16:64–81

    Article  Google Scholar 

  30. Shih J, Chatterjee N (2002) Analysis of survival data from case-control family studies. Biometrics 54:1115–1128

    Article  Google Scholar 

  31. Struewing J, Hartge P, Wacholder S, Baker S, Berlin M, McAdams M, Timmerman M, Brody L, Tucker M (1997) The risk of cancer associated with specific mutations of brca1 and brca2 among ashkenazi jews. New Engl J Med 336:1401–1408

    Article  CAS  PubMed  Google Scholar 

  32. Wacholder S, Hartge P, Struewing J, Pee D, McAdams M, Brody L, Tucker M (1998) The kin-cohort study for estimating penetrance. Am J Epidemiol 148:623–630

    Article  CAS  PubMed  Google Scholar 

  33. Whittemore A (1995) Logistic regression of family data from case-control studies. Biometrika 82:57–67

    Article  Google Scholar 

  34. Zhao L, Hsu L, Holte S, Chen Y, Quiaoit F, Prentice R (1998) Combined association and aggregation analysis of data from case-control family studies. Biometrika 85:299–315

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute NIH, DHHS, Rockville, MD, 20852, USA

    Mitchell H. Gail & Nilanjan Chatterjee

Authors
  1. Mitchell H. Gail
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nilanjan Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Gail, M.H., Chatterjee, N. (2009). Estimating the Absolute Risk of Disease Associated with Identified Mutations. In: Handbook on Analyzing Human Genetic Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69264-5_10

Download citation

Publish with us

Policies and ethics

Search

Navigation