Single-nucleotide polymorphisms do not substantially improve risk prediction for bre...(he SNP-based model was still less able t...),Health

Date:7/8/2008

he SNP-based model was still less able to accurately discriminate risk than BCRAT. When he added the seven characterized SNPs to BCRAT, the discriminatory accuracy of the model improved modestly. However, the increase was less than would be gained by adding information on breast density, which is also associated with breast cancer risk.

"Experience to date and quantitative arguments indicate that a huge increase in the numbers of case patients with breast cancer and control subjects would be required in genome-wide association studies to find enough SNPs to achieve high discriminatory accuracy," Gail concludes.

In an accompanying editorial, Margaret Pepe, Ph.D., and Holly Janes, Ph.D., of the Fred Hutchinson Cancer Research Center in Seattle note that although Gail's calculations are based on several assumptions, the paper provides a starting point for important discussions about risk models.

The editorialists point out, however, that Gail did not actually calculate the proportion of women that are classified as high risk with the BCRAT model or how many more would be identified by adding SNP information. They also suggest that even if SNPS are not informative in the population as a whole, they may have value in subpopulations.

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Contact: Liz Savage jncimedia@oxfordjournals.org 301-841-1287 Journal of the National Cancer Institute Source:Eurekalert

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