Genetic and Cellular Architecture of Breast Cancer Risk in Multi-Ancestry Studies of 159,297 Cases and 212,102 Controls.
Authors
Li JL, Zanti M, Williams J, Jahagirdar O, Jia G, Turcan A, Hu Q, Brandenburg JT, Yan L, Ho WK, Li J, Miranda JP, Godbole D, Dias JA, Zhang X, Dorling L, Chen WC, Boddicker N, Wang Y, Martin A, ...show more Zhang YD, Dennis J, John EM, Torres-Mejia G, Kushi L, Weitzel J, Neuhausen SL, Carvajal-Carmona L, Haiman C, Ziv E, Fejerman L, Zheng W, Huo D, Easton D, Chanock SJ, Chatterjee N, Kraft P, Garcia-Closas M, Wong WSW, Michailidou K, Zhu Q, Zhang MJ, Dutta D, Ahearn TU, Zhang H
Affiliations
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Semenyih, Selangor, Malaysia.
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore.
- Department of Nutrition, Diabetes and Metabolism, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Department of Statistics & Actuarial Science, School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China.
- Departments of Epidemiology & Population Health and of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA.
- Instituto Nacional de Salud Pública, Cuernavaca, Mexico.
- UC Davis Genome Center, University of California, Davis, Davis, CA, USA.
- Division of Precision Prevention, University of Kansas Comprehensive Cancer Center, Kansas City, KS, USA.
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA.
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California at Davis, Davis, CA, USA.
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Department of Public Health Sciences, University of California Davis, Davis, CA, USA.
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
- Cancer Epidemiology and Prevention Research Unit, The Institute of Cancer Research and Imperial College London, London, UK.
Abstract
Breast cancer genome-wide association studies (GWAS) have identified over 200 independent genome-wide significant susceptibility markers. However, most studies have focused on one or two ancestral groups. We examined breast cancer genetic architecture using GWAS summary statistics from African (AFR), East Asian (EAS), European (EUR) and Hispanic/Latina (H/L) samples, totaling 159,297 cases and 212,102 controls, comprising the largest multi-ancestry study of breast cancer to date. The logit-scale heritability of breast cancer ranged from h 2 = 0.47 (SE = 0.07) in EAS to AFR h 2 = 0.61 (SE = 0.10), with no significant differences across ancestries (p=0.63). The estimated number of susceptibility markers in a sparse normal-mixture effects 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). Common variants in regulatory elements were enriched for genetic association across samples. By integrating the GWAS summary statistics with the Tabula Sapiens scRNA-seq atlas, we identified ancestry-shared associations between breast cancer and specific cell types, including innate immune cells, secretory epithelial cells and stromal cells. Collectively, these results support a largely shared polygenic architecture of breast cancer across ancestries, with consistent enrichment of common regulatory variants and convergent cellular signatures identified through single-cell analyses.
Publication Details
PubMed ID
40909829
Digital Object Identifier
10.1101/2025.08.20.25334075
Publication
medRxiv. 2025 Nov 7