Single-nucleotide polymorphisms do not substantially improve risk prediction for bre...( Recently identified genetic markers ...),Health

Recently identified genetic markers, called single nucleotide polymorphisms (SNPs), that are associated with a small but statistically significant increase in the risk of breast cancer do not appear to substantially improve the accuracy of existing models that use clinical factors to predict an individual's risk, according to a study in the July 8 online issue of the Journal of the National Cancer Institute.

Risk prediction models provide estimates of the risk of disease to assist in clinical management of individual patients. A woman at high risk of breast cancer might benefit from a preventative intervention, whereas the side effects of the intervention might outweigh the benefits in a woman at lower risk of breast cancer. An ideal risk model would provide much higher risk estimates for women who eventually develop breast cancer than for women who do not, a feature called discriminatory accuracy. Researchers have traditionally used clinical information, such as age and family history, to build risk models, but other characteristics can be incorporated into models. Scientists recently identified seven SNPs that are associated with a moderate increase in the risk of breast cancer.

In the current study, Mitchell Gail, M.D., Ph.D., of the National Cancer Institute in Bethesda, Md., compared the discriminatory accuracy of these seven SNPs to an established risk model, called the Breast Cancer Risk Assessment Tool (BCRAT, frequently called the Gail model). BCRAT uses a woman's age, age at menarche and at birth of first child, family history of breast cancer, and breast biopsy results to predict her risk of disease. He also tested whether the addition of the SNPs to the existing model could improve its accuracy.

Gail found that the seven SNPs had less discriminatory accuracy than the BCRAT model. In fact, when he doubled the number of SNPs in the model, by adding seven hypothetical SNPs with similar characteristics, he found that t
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