The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Performance of Common Genetic Variants in Breast-Cancer Risk Models
Open accessAbstract
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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Pencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493
Gail, M. H., Brinton, L. A., Byar, D. P., Corle, D. K., Green, S. B., Schairer, C., & Mulvihill, J. J. (1989) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/81.24.1879 · Cited by 3,334
Self-citationHuman papillomavirus and cervical cancer
Schiffman, M., Castle, P. E., Jeronimo, J., Rodriguez, A. C., & Wacholder, S. (2007) · The Lancet · doi:10.1016/s0140-6736(07)61416-0 · Cited by 2,985
Self-citationGenome-wide association study identifies novel breast cancer susceptibility loci
Easton, D. F., Pooley, K. A., Dunning, A. M., Pharoah, P. D. P., Thompson, D., Ballinger, D. G., Struewing, J. P., Morrison, J., Field, H., Luben, R., et al. (2007) · Nature · doi:10.1038/nature05887 · Cited by 2,319
Self-citationScreening for breast cancer: U.S. Preventive Services Task Force recommendation statement
Siu, A. L., & on behalf of the U.S. Preventive Services Task Force (2016) · Annals of Internal Medicine · doi:10.7326/m15-2886 · Cited by 2,094
Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement
U.S. Preventive Services Task Force* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-10-200911170-00008 · Cited by 1,686
Hunter, D. J., Kraft, P., Jacobs, K. B., Cox, D. G., Yeager, M., Hankinson, S. E., Wacholder, S., Wang, Z., Welch, R., Hutchinson, A., et al. (2007) · Nature Genetics · doi:10.1038/ng2075 · Cited by 1,481
Self-citationGreenland, P. (2008) · Statistics in Medicine · doi:10.1002/sim.2976 · Cited by 1,300
Clinical risk factors, DNA variants, and the development of type 2 diabetes
Lyssenko, V., Jonsson, A., Almgren, P., Pulizzi, N., Isomaa, B., Tuomi, T., Berglund, G., Altshuler, D., Nilsson, P., & Groop, L. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0801869 · Cited by 938
One Degree of Freedom for Non-Additivity
Tukey, J. W. (1949) · Biometrics · doi:10.2307/3001938 · Cited by 910
Cited by
Power and predictive accuracy of polygenic risk scores
Dudbridge, F. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003348 · Cited by 1,509
“There the more relevant issue is the extent to which genetics improves established models [34].”
BackgroundPark, J.-H., Wacholder, S., Gail, M. H., Peters, U., Jacobs, K. B., Chanock, S. J., & Chatterjee, N. (2010) · Nature Genetics · doi:10.1038/ng.610 · Cited by 678
Self-citationMayers, J. R., Wu, C., Clish, C. B., Kraft, P., Torrence, M. E., Fiske, B. P., Yuan, C., Bao, Y., Townsend, M. K., Tworoger, S. S., et al. (2014) · Nature Medicine · doi:10.1038/nm.3686 · Cited by 649
Self-citationLeening, M. J. G., Vedder, M. M., Witteman, J. C. M., Pencina, M. J., & Steyerberg, E. W. (2014) · Annals of Internal Medicine · doi:10.7326/m13-1522 · Cited by 594
Chua, W., Charles, K. A., Baracos, V. E., & Clarke, S. J. (2011) · British Journal of Cancer · doi:10.1038/bjc.2011.100 · Cited by 468
Metadata
- Authors
- Wacholder, S., Hartge, P., Prentice, R., Garcia-Closas, M., Feigelson, H. S., Diver, W. R., Thun, M. J., Cox, D. G., Hankinson, S. E., Kraft, P., Rosner, B., Berg, C. D., Brinton, L. A., Lissowska, J., Sherman, M. E., Chlebowski, R., Kooperberg, C., Jackson, R. D., Buckman, D. W., Hui, P., Pfeiffer, R., Jacobs, K. B., Thomas, G. D., Hoover, R. N., Gail, M. H., Chanock, S. J., & Hunter, D. J.
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2010-03-18
- Volume
- 362
- Issue
- 11
- Pages
- 986-993
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W2099356722
- Cited by
- 411
- References
- 31
- FWCI
- 35.54
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Wacholder, S., Hartge, P., Prentice, R., Garcia-Closas, M., Feigelson, H. S., Diver, W. R., Thun, M. J., Cox, D. G., Hankinson, S. E., Kraft, P., Rosner, B., Berg, C. D., Brinton, L. A., Lissowska, J., Sherman, M. E., Chlebowski, R., Kooperberg, C., Jackson, R. D., Buckman, D. W., . . . Hunter, D. J. (2010). Performance of Common Genetic Variants in Breast-Cancer Risk Models. New England Journal of Medicine. https://doi.org/10.1056/nejmoa0907727
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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