Validated personalized risk prediction models incorporating genetic (cancer susceptibility gene [CSG] and polygenic risk score [PRS]) and nongenetic (family history [FH]/epidemiologic/reproductive/hormonal profile/mammographic density) factors are available and increasingly used to identify women at elevated breast cancer (BC) risk.1,2,3,4 Genetic testing for pathogenic variants (PVs) in BC CSGs has expanded from BRCA1/BRCA2/PALB2 to incorporate moderate penetrance genes, including ATM/CHEK2/RAD51C/RAD51D. The gene discussed is RAD51D; the disease is breast cancer.