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Polygenic risk; Prediction; Breast cancer Riesgo poligénico; Predicción; Cáncer de mama Risc poligènic; Predicció; Càncer de mama Background: The multifactorial risk prediction model BOADICEA enables identification of women at higher or lower risk of developing breast cancer. BOADICEA models genetic susceptibility in terms of the effects of rare variants in breast cancer susceptibility genes and a polygenic component, decomposed into an unmeasured and a measured component - the polygenic risk score (PRS). The current version was developed using a 313 SNP PRS. Here, we evaluated approaches to incorporating this PRS and alternative PRS in BOADICEA. Methods: The mean, SD, and proportion of the overall polygenic component explained by the PRS (α2) need to be estimated. α was estimated using logistic regression, where the age-specific log-OR is constrained to be a function of the age-dependent polygenic relative risk in BOADICEA; and using a retrospective likelihood (RL) approach that models, in addition, the unmeasured polygenic component. Results: Parameters were computed for 11 PRS, including 6 variations of the 313 SNP PRS used in clinical trials and implementation studies. The logistic regression approach underestimates α⁠, as compared with the RL estimates. The RL α estimates were very close to those obtained by assuming proportionality to the OR per 1 SD, with the constant of proportionality estimated using the 313 SNP PRS. Small variations in the SNPs included in the PRS can lead to large differences in the mean. Conclusions: BOADICEA can be readily adapted to different PRS in a manner that maintains consistency of the model. This work has been supported by grants from Cancer Research UK (PPRPGM-Nov20\100002); the European Union's Horizon 2020 Research and Innovation Programme under grant agreement numbers 633784 (B-CAST) and 634935 (BRIDGES); the PERSPECTIVE I&I project which is funded by the Government of Canada through Genome Canada (#13529) and the Canadian Institutes of Health Research (#155865), the Ministère de l’Économie et de l'Innovation du Québec through Genome Québec, the Quebec Breast Cancer Foundation, the CHU de Quebec Foundation and the Ontario Research Fund; and by the NIHR Cambridge Biomedical Research Centre (BRC-1215–20014). BCAC is funded by the European Union's Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and the PERSPECTIVE I&I project. Additional funding for BCAC is provided via the Confluence project which is funded with intramural funds from the NCI Intramural Research Program, NIH. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer Research UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. MT was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215–20014) and Cancer Research UK C22770/A31523 (International Alliance for Cancer Early Detection programme). The PRISMA study has been funded by Instituto de Salud Carlos III through the project " PI19/01195″ (Co-funded by European Regional Development Fund "A way to make Europe") and it received the institutional support of CERCA Program (Generalitat de Catalunya). The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.