Threshold-Based Overlap of Breast Cancer High-Risk Classification Using Family History, Polygenic Risk Scores, and Traditional Risk Models in 180,398 Women

Simple Summary Breast cancer is influenced by both inherited genetic factors and lifestyle or personal factors such as age, family history, and reproductive history. Scientists have developed tools to estimate a woman’s risk of developing breast cancer. One type of tool, called a polygenic risk score, uses many small genetic variations to estimate risk, while another, the Gail model, uses personal and family medical information. We studied how well these tools predict breast cancer risk in women of European and Asian backgrounds. Our research included more than 180,000 women and compared performance across age groups and cancer types. We found that genetic scores were especially useful in younger women and in women of Asian background, while the Gail model worked better in older women of European background. However, both tools showed some inaccuracy when comparing predicted and observed risks. Overall, combining genetic information with traditional risk factors could improve how doctors identify women at higher risk for breast cancer, leading to more personalized screening and prevention strategies across different populations. Abstract Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.