Performance of common genetic variants in breast-cancer risk models

Sholom Wacholder, Patricia Hartge, Ross Prentice, Montserrat Garcia-Closas, Heather Spencer Feigelson, W. Ryan Diver, Michael J. Thun, David G. Cox, Susan E. Hankinson, Peter Kraft, Bernard Rosner, Christine D. Berg, Louise A. Brinton, Jolanta Lissowska, Mark E. Sherman, Rowan Chlebowski, Charles Kooperberg, Rebecca D. Jackson, Dennis W. Buckman, Peter HuiRuth Pfeiffer, Kevin B. Jacobs, Gilles D. Thomas, Robert N. Hoover, Mitchell H. Gail, Stephen J. Chanock, David J. Hunter

Research output: Contribution to journalArticle

Abstract

BACKGROUND: Genomewide association studies have identified multiple genetic variants associated with breast cancer. The extent to which these variants add to existing risk-assessment models is unknown. METHODS: We used information on traditional risk factors and 10 common genetic variants associated with breast cancer in 5590 case subjects and 5998 control subjects, 50 to 79 years of age, from four U.S. cohort studies and one case-control study from Poland to fit models of the absolute risk of breast cancer. With the use of receiver-operating-characteristic curve analysis, we calculated the area under the curve (AUC) as a measure of discrimination. By definition, random classification of case and control subjects provides an AUC of 50%; perfect classification provides an AUC of 100%. We calculated the fraction of case subjects in quintiles of estimated absolute risk after the addition of genetic variants to the traditional risk model. RESULTS: The AUC for a risk model with age, study and entry year, and four traditional risk factors was 58.0%; with the addition of 10 genetic variants, the AUC was 61.8%. About half the case subjects (47.2%) were in the same quintile of risk as in a model without genetic variants; 32.5% were in a higher quintile, and 20.4% were in a lower quintile. CONCLUSIONS: The inclusion of newly discovered genetic factors modestly improved the performance of risk models for breast cancer. The level of predicted breast-cancer risk among most women changed little after the addition of currently available genetic information.

Original languageEnglish (US)
Pages (from-to)986-993
Number of pages8
JournalNew England Journal of Medicine
Volume362
Issue number11
DOIs
StatePublished - Mar 18 2010
Externally publishedYes

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Breast Neoplasms
Area Under Curve
Genetic Models
Poland
ROC Curve
Case-Control Studies
Cohort Studies

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Wacholder, S., Hartge, P., Prentice, R., Garcia-Closas, M., Feigelson, H. S., Diver, W. R., ... Hunter, D. J. (2010). Performance of common genetic variants in breast-cancer risk models. New England Journal of Medicine, 362(11), 986-993. https://doi.org/10.1056/NEJMoa0907727

Performance of common genetic variants in breast-cancer risk models. / Wacholder, Sholom; Hartge, Patricia; Prentice, Ross; Garcia-Closas, Montserrat; Feigelson, Heather Spencer; Diver, W. Ryan; Thun, Michael J.; Cox, David G.; Hankinson, Susan E.; Kraft, Peter; Rosner, Bernard; Berg, Christine D.; Brinton, Louise A.; Lissowska, Jolanta; Sherman, Mark E.; Chlebowski, Rowan; Kooperberg, Charles; Jackson, Rebecca D.; Buckman, Dennis W.; Hui, Peter; Pfeiffer, Ruth; Jacobs, Kevin B.; Thomas, Gilles D.; Hoover, Robert N.; Gail, Mitchell H.; Chanock, Stephen J.; Hunter, David J.

In: New England Journal of Medicine, Vol. 362, No. 11, 18.03.2010, p. 986-993.

Research output: Contribution to journalArticle

Wacholder, S, Hartge, P, Prentice, R, Garcia-Closas, M, Feigelson, HS, Diver, WR, Thun, MJ, Cox, DG, Hankinson, SE, Kraft, P, Rosner, B, Berg, CD, Brinton, LA, Lissowska, J, Sherman, ME, Chlebowski, R, Kooperberg, C, Jackson, RD, Buckman, DW, Hui, P, Pfeiffer, R, Jacobs, KB, Thomas, GD, Hoover, RN, Gail, MH, Chanock, SJ & Hunter, DJ 2010, 'Performance of common genetic variants in breast-cancer risk models', New England Journal of Medicine, vol. 362, no. 11, pp. 986-993. https://doi.org/10.1056/NEJMoa0907727
Wacholder S, Hartge P, Prentice R, Garcia-Closas M, Feigelson HS, Diver WR et al. Performance of common genetic variants in breast-cancer risk models. New England Journal of Medicine. 2010 Mar 18;362(11):986-993. https://doi.org/10.1056/NEJMoa0907727
Wacholder, Sholom ; Hartge, Patricia ; Prentice, Ross ; Garcia-Closas, Montserrat ; Feigelson, Heather Spencer ; Diver, W. Ryan ; Thun, Michael J. ; Cox, David G. ; Hankinson, Susan E. ; Kraft, Peter ; Rosner, Bernard ; Berg, Christine D. ; Brinton, Louise A. ; Lissowska, Jolanta ; Sherman, Mark E. ; Chlebowski, Rowan ; Kooperberg, Charles ; Jackson, Rebecca D. ; Buckman, Dennis W. ; Hui, Peter ; Pfeiffer, Ruth ; Jacobs, Kevin B. ; Thomas, Gilles D. ; Hoover, Robert N. ; Gail, Mitchell H. ; Chanock, Stephen J. ; Hunter, David J. / Performance of common genetic variants in breast-cancer risk models. In: New England Journal of Medicine. 2010 ; Vol. 362, No. 11. pp. 986-993.
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T1 - Performance of common genetic variants in breast-cancer risk models

AU - Wacholder, Sholom

AU - Hartge, Patricia

AU - Prentice, Ross

AU - Garcia-Closas, Montserrat

AU - Feigelson, Heather Spencer

AU - Diver, W. Ryan

AU - Thun, Michael J.

AU - Cox, David G.

AU - Hankinson, Susan E.

AU - Kraft, Peter

AU - Rosner, Bernard

AU - Berg, Christine D.

AU - Brinton, Louise A.

AU - Lissowska, Jolanta

AU - Sherman, Mark E.

AU - Chlebowski, Rowan

AU - Kooperberg, Charles

AU - Jackson, Rebecca D.

AU - Buckman, Dennis W.

AU - Hui, Peter

AU - Pfeiffer, Ruth

AU - Jacobs, Kevin B.

AU - Thomas, Gilles D.

AU - Hoover, Robert N.

AU - Gail, Mitchell H.

AU - Chanock, Stephen J.

AU - Hunter, David J.

PY - 2010/3/18

Y1 - 2010/3/18

N2 - BACKGROUND: Genomewide association studies have identified multiple genetic variants associated with breast cancer. The extent to which these variants add to existing risk-assessment models is unknown. METHODS: We used information on traditional risk factors and 10 common genetic variants associated with breast cancer in 5590 case subjects and 5998 control subjects, 50 to 79 years of age, from four U.S. cohort studies and one case-control study from Poland to fit models of the absolute risk of breast cancer. With the use of receiver-operating-characteristic curve analysis, we calculated the area under the curve (AUC) as a measure of discrimination. By definition, random classification of case and control subjects provides an AUC of 50%; perfect classification provides an AUC of 100%. We calculated the fraction of case subjects in quintiles of estimated absolute risk after the addition of genetic variants to the traditional risk model. RESULTS: The AUC for a risk model with age, study and entry year, and four traditional risk factors was 58.0%; with the addition of 10 genetic variants, the AUC was 61.8%. About half the case subjects (47.2%) were in the same quintile of risk as in a model without genetic variants; 32.5% were in a higher quintile, and 20.4% were in a lower quintile. CONCLUSIONS: The inclusion of newly discovered genetic factors modestly improved the performance of risk models for breast cancer. The level of predicted breast-cancer risk among most women changed little after the addition of currently available genetic information.

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