Since individual common genetic variants associated with breast cancer have too small of an impact to contribute to risk, researchers aimed to determine whether a combination of 76 genetic variants could help to better predict risk when combined with factors such as breast density. Researchers conducted three epidemiologic studies using logistic regression models to determine whether a combination of 76 SNPs, called the polygenic risk score (PRS), was a statistically significant risk factor independent of Breast Imaging Reporting and Data System (BI-RADS) breast density, which categorizes breasts into four categories of almost entirely fat, scattered fibroglandular densities, heterogeneously dense, extremely dense. After incorporating the data from 1643 case patients and 2397 control patients, researchers found that BI-RADS breast density and the PRS function independently in contribution of significant risk factor. Both are important risk factors for breast cancer prediction (Vachon et al.,
Since individual common genetic variants associated with breast cancer have too small of an impact to contribute to risk, researchers aimed to determine whether a combination of 76 genetic variants could help to better predict risk when combined with factors such as breast density. Researchers conducted three epidemiologic studies using logistic regression models to determine whether a combination of 76 SNPs, called the polygenic risk score (PRS), was a statistically significant risk factor independent of Breast Imaging Reporting and Data System (BI-RADS) breast density, which categorizes breasts into four categories of almost entirely fat, scattered fibroglandular densities, heterogeneously dense, extremely dense. After incorporating the data from 1643 case patients and 2397 control patients, researchers found that BI-RADS breast density and the PRS function independently in contribution of significant risk factor. Both are important risk factors for breast cancer prediction (Vachon et al.,