Performance of common genetic variants in breast-cancer risk models.

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.

A new statistic and its power to infer membership in a genome-wide association study using genotype frequencies.

Aggregate results from genome-wide association studies (GWAS), such as genotype frequencies for cases and controls, were until recently often made available on public websites because they were thought to disclose negligible information concerning an individual's participation in a study. Homer et al. recently suggested that a method for forensic detection of an individual's contribution to an admixed DNA sample could be applied to aggregate GWAS data. Using a likelihood-based statistical framework, we developed an improved statistic that uses genotype frequencies and individual genotypes to infer whether a specific individual or any close relatives participated in the GWAS and, if so, what the participant's phenotype status is. Our statistic compares the logarithm of genotype frequencies, in contrast to that of Homer et al., which is based on differences in either SNP probe intensity or allele frequencies. We derive the theoretical power of our test statistics and explore the empirical performance in scenarios with varying numbers of randomly chosen or top-associated SNPs.