Performance of common genetic variants in breast-cancer risk models

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.

Figure 1. Five Models of Breast-Cancer Risk

Data are from a hypothetical population comprising the five study populations. The straight line indicates random classification. The proportions on the curve run from highest to lowest. For example, in Panel A, the inclusive model indicates that 34% of women who had breast cancer were among the 20% of women at highest risk. Panel B shows results limited to women in the lowest 20% of estimated risk, according to the inclusive model. The inset shows where this lowest 20% would be located in Panel A. Panel C shows results limited to women in the highest 20% of estimated risk according to the inclusive model. The inset shows where this highest 20% would be located in Panel A.