Improved breast cancer risk prediction using chromosomal-scale length variation

In conclusion, we found that this method of computing genetic risk scores for breast cancer is a substantial improvement over SNP-based polygenic risk scores. In addition, we compared models trained on populations of only White women and only Black women. We found that the models trained only on White women performed better than models trained only on Black women when tested on only White women. We did not see a significant difference between the two models when tested on only Black women.

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Publication Details

Journal
Human Genomics
Published
2025-06-11
DOI
https://doi.org/10.1186/s40246-025-00776-z
Citations
1
Primary Topic
Genomic variations and chromosomal abnormalities
Type
article
Field-Weighted Citation Impact
1.82

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article

Improved breast cancer risk prediction using chromosomal-scale length variation

James P. Brody, Yasaman Fatapour
1 citations
Human Genomics
Genomic variations and chromosomal abnormalities
1.82
article

Improved breast cancer risk prediction using chromosomal-scale length variation

James P. Brody, Yasaman Fatapour
article en
1 citations

Abstract

In conclusion, we found that this method of computing genetic risk scores for breast cancer is a substantial improvement over SNP-based polygenic risk scores. In addition, we compared models trained on populations of only White women and only Black women. We found that the models trained only on White women performed better than models trained only on Black women when tested on only White women. We did not see a significant difference between the two models when tested on only Black women.

Human GenomicsVol. 19(1)
University of California, Irvine (US), Irvine University (US)
National Institutes of Health
Openalex Percentile: Top 15%
Genomic variations and chromosomal abnormalities
1.82
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