James L Li, Maria Zanti, Jacob Williams, Om Jahagirdar, Guochong Jia, Alistair Turcan, Qiang Hu, Jean-Tristan Brandenburg, Li Yan, Weang-Kee Ho, Jingmei Li, José Patricio Miranda, Devika Godbole, Julie-Alexia Dias, Xiaomeng Zhang, Leila Dorling, Wenlong Carl Chen, Nicholas Boddicker, Ying Wang, Alicia Martin, Yan Dora Zhang, Joe Dennis, Esther M John, Gabriela Torres-Mejia, Lawrence H Kushi, Jeffrey Weitzel, Susan L Neuhausen, Luis Carvajal-Carmona, Christopher Haiman, Elad Ziv, Laura Fejerman, Wei Zheng, Dezheng Huo, Douglas Easton, Stephen J Chanock, Nilanjan Chatterjee, Peter Kraft, Montserrat Garcia-Closas, Wendy S W Wong, Kyriaki Michailidou, Qianqian Zhu, Martin Jinye Zhang, Diptavo Dutta, Thomas U Ahearn, Haoyu Zhang
These results indicate substantial cross-ancestry sharing of breast cancer polygenic architecture, highlight a consistent contribution of regulatory variation, and identify convergent cellular contexts that motivate functional follow-up and inform expectations for the transferability and attainable performance of common-variant risk prediction across populations.
BACKGROUND: Breast cancer genome-wide association studies (GWAS) have identified more than 200 susceptibility loci, but most studies are dominated by European and East Asian populations.
METHODS: We analyzed breast cancer GWAS summary statistics from African (AFR), East Asian (EAS), European (EUR), and Hispanic/Latina (H/L) samples (159,297 cases and 212,102 controls). We estimated logit-scale SNP-based heritability, polygenicity, and cross-ancestry genetic correlation, partitioned heritability across functional annotations, and integrated GWAS results with the Tabula Sapiens single-cell atlas using scDRS+.
RESULTS: The logit-scale heritability of breast cancer ranged from h2=0.47 (SE = 0.07) in EAS to AFR h2=0.61 (SE = 0.10), with no significant differences across ancestries (p = 0.63). The model-implied number of non-null susceptibility SNPs in the sparse normal-mixture effect-size model also varied from 4,446 (SE = 3,100) in EAS to 8,308 (SE = 2,751) in AFR, but differences were not significant across ancestries (p = 0.55). Cross-sample genetic correlations varied, with the strongest correlation between EUR and EAS (ρ=0.79, SE = 0.08) and weakest between AFR and H/L (ρ=0.26, SE = 0.24). Regulatory annotations were enriched for breast cancer heritability across samples. Integration with single-cell expression profiles implicated ancestry-shared associations with innate immune, secretory epithelial, and stromal cell types.
CONCLUSION: These results indicate substantial cross-ancestry sharing of breast cancer polygenic architecture, highlight a consistent contribution of regulatory variation, and identify convergent cellular contexts that motivate functional follow-up and inform expectations for the transferability and attainable performance of common-variant risk prediction across populations.