New Genetic Tools Improve Breast Cancer Risk Prediction for African American Women

Existing risk prediction tools often fail to accurately assess risk in this group, contributing to later diagnoses and poorer outcomes. Greater genetic diversity and higher rates of aggressive tumor subtypes further complicate early detection. Researchers have now developed improved genetic risk models that significantly enhance breast cancer risk prediction in women of African ancestry.

In the study led by the University of Chicago Medicine (Chicago, IL, USA) researchers used genetic data from more than 36,000 women to develop new polygenic risk score (PRS) models specifically for women of African ancestry. The models were built using data from the African Ancestry Breast Cancer Genetics Consortium, which includes participants from the U.S., the Caribbean, and Sub-Saharan Africa.

Polygenic risk scores estimate cancer risk by analyzing multiple single-nucleotide polymorphisms (SNPs) across the genome. Because African ancestry populations have greater genetic diversity, models trained primarily on European data often miss key risk signals. The researchers created separate PRS models for overall breast cancer, estrogen receptor–positive cancer, estrogen receptor–negative cancer, and triple-negative breast cancer, ensuring more precise risk stratification.

The new models showed significantly improved predictive performance, with area under the curve (AUC) values ranging from 0.61 to 0.64, compared with 0.56 to 0.58 for earlier models. The research, published in Nature Genetics, also demonstrated that simplified models using fewer genetic markers maintained similar accuracy. Validation across multiple independent datasets confirmed the robustness of the approach.

The findings suggest that women identified as high risk could benefit from earlier and more frequent screening, potentially starting in their early 30s. Combining PRS results with family history further strengthened risk prediction, identifying individuals with lifetime breast cancer risks exceeding 50%. Researchers emphasize that future work will refine these models for broader African populations and support more equitable cancer prevention strategies.

“With improved risk prediction, doctors can start screening earlier for women at higher risk, tailor care based on a woman’s specific risk profile and catch the cancers sooner,” said Professor Dezheng Huo, PhD, senior author of the study.

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