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<p>Genome-wide association studies (GWAS) have identified over 200 genetic risk loci for breast cancer, yet their target genes remain largely unknown. We conduct multi-ancestry transcriptome-wide association studies (TWAS) to discover potential breast cancer susceptibility genes. We develop ancestry-specific genetic models to predict levels of gene expression, alternative splicing, and 3’ UTR alternative polyadenylation using genomic and transcriptomic data from 652 normal female tissue samples and apply these models to GWAS data of 178,534 cases and 248,300 controls for association analyses. We identify 290 genes associated with breast cancer risk, including 103 previously unreported and 46 not located at known GWAS loci, and 39 genes show distinct associations with breast cancer risk by estrogen-receptor status. Single-cell RNA sequencing and in vitro experiment data provide additional functional evidence for 169 genes. These genes are enriched in pathways implicated in breast carcinogenesis. Our study uncovers insights into breast cancer genetics and biology.</p>

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Multi-ancestry transcriptome-wide association studies uncover insights into breast cancer genetics and biology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月30日

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  • Jie Ping,
  • Guochong Jia,
  • Qiuyin Cai,
  • Xingyi Guo,
  • Jifeng Wang,
  • Ran Tao,
  • Bingshan Li,
  • Joshua A. Bauer,
  • Yuhan Xie,
  • Stefan Ambs,
  • Mollie E. Barnard,
  • Yu Chen,
  • Ji-Yeob Choi,
  • Yu-Tang Gao,
  • Montserrat Garcia-Closas,
  • Jian Gu,
  • Jennifer J. Hu,
  • Motoki Iwasaki,
  • Esther M. John,
  • Sun-Seog Kweon,
  • Christopher I. Li,
  • Koichi Matsuda,
  • Keitaro Matsuo,
  • Katherine L. Nathanson,
  • Barbara Nemesure,
  • Olufunmilayo I. Olopade,
  • Tuya Pal,
  • Sue K. Park,
  • Boyoung Park,
  • Michael F. Press,
  • Maureen Sanderson,
  • Dale P. Sandler,
  • Song Yao,
  • Ying Zheng,
  • Thomas Ahearn,
  • Abenaa M. Brewster,
  • Adeyinka Falusi,
  • Anselm J. M. Hennis,
  • Hidemi Ito,
  • Michiaki Kubo,
  • Eun-Sook Lee,
  • Timothy Makumbi,
  • Berthe S. E. Mapoko,
  • Dong-Young Noh,
  • Katie M. O’Brien,
  • Oladosu Ojengbede,
  • Andrew F. Olshan,
  • Min-Ho Park,
  • Sonya Reid,
  • Taiki Yamaji,
  • Gary Zirpoli,
  • Ebonee N. Butler,
  • Maosheng Huang,
  • Siew-Kee Low,
  • John Obafunwa,
  • Clarice R. Weinberg,
  • Haoyu Zhang,
  • Hongyu Zhao,
  • Christine B. Ambrosone,
  • Michelle L. Cote,
  • Dezheng Huo,
  • Christopher A. Haiman,
  • Daehee Kang,
  • Julie R. Palmer,
  • Melissa A. Troester,
  • Xiao-Ou Shu,
  • Jirong Long,
  • Wei Zheng

摘要

Genome-wide association studies (GWAS) have identified over 200 genetic risk loci for breast cancer, yet their target genes remain largely unknown. We conduct multi-ancestry transcriptome-wide association studies (TWAS) to discover potential breast cancer susceptibility genes. We develop ancestry-specific genetic models to predict levels of gene expression, alternative splicing, and 3’ UTR alternative polyadenylation using genomic and transcriptomic data from 652 normal female tissue samples and apply these models to GWAS data of 178,534 cases and 248,300 controls for association analyses. We identify 290 genes associated with breast cancer risk, including 103 previously unreported and 46 not located at known GWAS loci, and 39 genes show distinct associations with breast cancer risk by estrogen-receptor status. Single-cell RNA sequencing and in vitro experiment data provide additional functional evidence for 169 genes. These genes are enriched in pathways implicated in breast carcinogenesis. Our study uncovers insights into breast cancer genetics and biology.