$Background:$ Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes $BRCA1$ or $BRCA2$. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates.

$Methods:$ We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through populationbased GWAS: for BC (overall, estrogen receptor [ER]–positive, and ER-negative) and for OC. Using data from 15 252 female $BRCA1$ and 8211 $BRCA2$ carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS.

$Results:$ The PRS for ER-negative BC displayed the strongest association with BC risk in $BRCA1$ carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, $P$ = 8.2 $\times$ 10$\mathrm{<mrow\; data-mjx-texclass="ORD">-53</mrow>}$). In $BRCA2$ carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, $P$ = 7.2 $\times$ 10$\mathrm{<mrow\; data-mjx-texclass="ORD">-20</mrow>}$). The OC PRS was strongly associated with OC risk for both $BRCA1$ and $BRCA2$ carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom AR deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for $BRCA2$ carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS.

$Conclusions:$ BC and OC PRS are predictive of cancer risk in $BRCA1$ and $BRCA2$ carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management.