Polygenic risk scores for prediction of breast cancer risk in women of African ancestry: a cross-ancestry approach
- PMID: 35554533
- PMCID: PMC9476624
- DOI: 10.1093/hmg/ddac102
Polygenic risk scores for prediction of breast cancer risk in women of African ancestry: a cross-ancestry approach
Abstract
Polygenic risk scores (PRSs) are useful for predicting breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remains relatively low. We aim to develop optimal PRSs for the prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in AA women. The AA dataset comprised 9235 cases and 10 184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. We built PRSs using individual-level AA data by a forward stepwise logistic regression and then developed joint PRSs that combined (1) the PRSs built in the AA training dataset and (2) a 313-variant PRS previously developed in women of European ancestry. PRSs were evaluated in the AA validation set. For overall breast cancer, the odds ratio per standard deviation of the joint PRS in the validation set was 1.34 [95% confidence interval (CI): 1.27-1.42] with the area under receiver operating characteristic curve (AUC) of 0.581. Compared with women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 1.98-fold increased risk (95% CI: 1.63-2.39). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.608 and 0.576, respectively. Compared with existing methods, the proposed joint PRSs can improve prediction of breast cancer risk in AA women.
© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Figures
References
-
- Zhang, H., Ahearn, T.U., Lecarpentier, J., Barnes, D., Beesley, J., Qi, G., Jiang, X. O’Mara, T.A. Zhao, N. Bolla, M.K. et al. (2020) Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. Nat. Genet., 52(6), 572–581. - PMC - PubMed
-
- Amos, C.I., Dennis, J., Wang, Z., Byun, J., Schumacher, F.R., Gayther, S.A., Casey, G. Hunter, D.J. Sellers. T.A. Gruber, S.B. Dunning, A.M. et al. (2017) The OncoArray Consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol. Biomark. Prev., 26(1), 126–135. - PMC - PubMed
Publication types
MeSH terms
Substances
Grants and funding
- U01 CA069417/CA/NCI NIH HHS/United States
- P30 ES010126/ES/NIEHS NIH HHS/United States
- R01 CA089085/CA/NCI NIH HHS/United States
- R01 CA142996/CA/NCI NIH HHS/United States
- Z01 ES044005/ImNIH/Intramural NIH HHS/United States
- P01 CA151135/CA/NCI NIH HHS/United States
- U01 CA261339/CA/NCI NIH HHS/United States
- UM1 CA164974/CA/NCI NIH HHS/United States
- T32 GM140935/GM/NIGMS NIH HHS/United States
- 10118/CRUK_/Cancer Research UK/United Kingdom
- R01 CA100598/CA/NCI NIH HHS/United States
- U01 CA164974/CA/NCI NIH HHS/United States
- R01 CA098663/CA/NCI NIH HHS/United States
- P50 CA058223/CA/NCI NIH HHS/United States
- R01 CA100374/CA/NCI NIH HHS/United States
- R01 CA228198/CA/NCI NIH HHS/United States
- P20 CA233307/CA/NCI NIH HHS/United States
- R01 CA058420/CA/NCI NIH HHS/United States
- R01 CA242929/CA/NCI NIH HHS/United States
- K05 CA136967/CA/NCI NIH HHS/United States
- R01 CA063464/CA/NCI NIH HHS/United States
- U01 CA164973/CA/NCI NIH HHS/United States
- R01 CA228357/CA/NCI NIH HHS/United States
- R37 CA054281/CA/NCI NIH HHS/United States
- R01 CA202981/CA/NCI NIH HHS/United States
- R03 CA227357/CA/NCI NIH HHS/United States
LinkOut - more resources
Full Text Sources
Medical
