Guochong Jia, Zhishan Chen, Jie Ping, Qiuyin Cai, Ran Tao, Chao 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, Chen-Yang Shen, Melissa A. Troester, Song Yao, Ying Zheng, Thomas Ahearn, Abenaa M. Brewster, Adeyinka Falusi, Anselm J. M. Hennis, Hidemi Ito, Michiaki Kubo, Eun-Sook Lee, Timothy Makumbi, Paul Ndom, 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, Michelle L. Cote, Christine B. Ambrosone, Dezheng Huo, Bingshan Li, Daehee Kang, Julie R. Palmer, Xiao-Ou Shu, Christopher A. Haiman, Xingyi Guo, Jirong Long, Wei Zheng
{"title":"Refining breast cancer genetic risk and biology through multi-ancestry fine-mapping analyses of 192 risk regions","authors":"Guochong Jia, Zhishan Chen, Jie Ping, Qiuyin Cai, Ran Tao, Chao 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, Chen-Yang Shen, Melissa A. Troester, Song Yao, Ying Zheng, Thomas Ahearn, Abenaa M. Brewster, Adeyinka Falusi, Anselm J. M. Hennis, Hidemi Ito, Michiaki Kubo, Eun-Sook Lee, Timothy Makumbi, Paul Ndom, 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, Michelle L. Cote, Christine B. Ambrosone, Dezheng Huo, Bingshan Li, Daehee Kang, Julie R. Palmer, Xiao-Ou Shu, Christopher A. Haiman, Xingyi Guo, Jirong Long, Wei Zheng","doi":"10.1038/s41588-024-02031-y","DOIUrl":null,"url":null,"abstract":"Genome-wide association studies have identified approximately 200 genetic risk loci for breast cancer, but the causal variants and target genes are mostly unknown. We sought to fine-map all known breast cancer risk loci using genome-wide association study data from 172,737 female breast cancer cases and 242,009 controls of African, Asian and European ancestry. We identified 332 independent association signals for breast cancer risk, including 131 signals not reported previously, and for 50 of them, we narrowed the credible causal variants down to a single variant. Analyses integrating functional genomics data identified 195 putative susceptibility genes, enriched in PI3K/AKT, TNF/NF-κB, p53 and Wnt/β-catenin pathways. Single-cell RNA sequencing or in vitro experiment data provided additional functional evidence for 105 genes. Our study uncovered large numbers of association signals and candidate susceptibility genes for breast cancer, uncovered breast cancer genetics and biology, and supported the value of including multi-ancestry data in fine-mapping analyses. Multi-ancestry fine-mapping of breast cancer susceptibility regions identifies candidate causal variants and prioritizes likely effector genes supported by functional genomic evidence.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 1","pages":"80-87"},"PeriodicalIF":31.7000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature genetics","FirstCategoryId":"99","ListUrlMain":"https://www.nature.com/articles/s41588-024-02031-y","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Genome-wide association studies have identified approximately 200 genetic risk loci for breast cancer, but the causal variants and target genes are mostly unknown. We sought to fine-map all known breast cancer risk loci using genome-wide association study data from 172,737 female breast cancer cases and 242,009 controls of African, Asian and European ancestry. We identified 332 independent association signals for breast cancer risk, including 131 signals not reported previously, and for 50 of them, we narrowed the credible causal variants down to a single variant. Analyses integrating functional genomics data identified 195 putative susceptibility genes, enriched in PI3K/AKT, TNF/NF-κB, p53 and Wnt/β-catenin pathways. Single-cell RNA sequencing or in vitro experiment data provided additional functional evidence for 105 genes. Our study uncovered large numbers of association signals and candidate susceptibility genes for breast cancer, uncovered breast cancer genetics and biology, and supported the value of including multi-ancestry data in fine-mapping analyses. Multi-ancestry fine-mapping of breast cancer susceptibility regions identifies candidate causal variants and prioritizes likely effector genes supported by functional genomic evidence.
期刊介绍:
Nature Genetics publishes the very highest quality research in genetics. It encompasses genetic and functional genomic studies on human and plant traits and on other model organisms. Current emphasis is on the genetic basis for common and complex diseases and on the functional mechanism, architecture and evolution of gene networks, studied by experimental perturbation.
Integrative genetic topics comprise, but are not limited to:
-Genes in the pathology of human disease
-Molecular analysis of simple and complex genetic traits
-Cancer genetics
-Agricultural genomics
-Developmental genetics
-Regulatory variation in gene expression
-Strategies and technologies for extracting function from genomic data
-Pharmacological genomics
-Genome evolution