Performance of Polygenic Risk Scores for Breast Cancer in African Populations: A Systematic Review and Meta-Analysis

Background Genetic susceptibility is a major contributor to breast cancer risk. Beyond the analysis of individual susceptibility genes, genome-wide association studies (GWAS) have enabled the development of polygenic risk scores (PRS) based on single nucleotide variants (SNVs). However, most existing PRS have been developed using predominantly European populations, with limited representation of individuals of African ancestry. This systematic review and meta-analysis aimed to evaluate the discriminative performance of breast cancer PRS in African populations using the area under the receiver operating characteristic curve (AUC). Methods A systematic literature search was conducted through October 5, 2025, in four electronic databases: PubMed , Web of Science , Embase , and Scopus . The methodological quality of the included studies was assessed using the QUADAS-2 tool. The study protocol was prospectively registered in PROSPERO (CRD420251230937 ). Pooled estimates of the area under the receiver operating characteristic curve (AUC) were calculated using a random-effects model with 95% confidence intervals in Stata 17. Subgroup analyses were performed in R to investigate potential sources of heterogeneity, including the SNP composition of PRS models and the ancestry of the populations used for PRS development. Results Overall, PRS models demonstrated modest predictive performance for breast cancer risk in women of African ancestry, with a pooled AUC of 0.57 (95% CI: 0.56–0.59; I² = 85.46%). The European-derived PRS313 achieved the highest discriminatory performance in African populations (pooled AUC = 0.59, 95% CI: 0.56–0.62; I² = 61.87%). Subgroup analyses showed that recalibrated and multi-ancestry PRS outperformed African-derived PRS, highlighting the importance of accounting for genetic ancestry in PRS development and application. Comparative analysis of SNP composition revealed a progressive convergence toward a shared set of variants among recently developed PRS models. Conclusion PRS developed using Caucasian populations perform less well when applied to African populations, although they remain the best option available to date. Recalibrating a specific model is essential to improve its accuracy and clinical utility. Ultimately, greater representation of Africans in genomic studies will enable the creation of specific PRSs that will perform better.