Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk

Yaohua Yang (), Yaxin Chen, Shuai Xu, Xingyi Guo, Guochong Jia, Jie Ping, Xiang Shu, Tianying Zhao, Fangcheng Yuan, Gang Wang, Yufang Xie, Hang Ci, Hongmo Liu, Yawen Qi, Yongjun Liu, Dan Liu, Weimin Li, Fei Ye, Xiao-Ou Shu, Wei Zheng, Li Li, Qiuyin Cai () and Jirong Long ()
Additional contact information
Yaohua Yang: University of Virginia
Yaxin Chen: Sichuan University
Shuai Xu: Vanderbilt University Medical Center
Xingyi Guo: Vanderbilt University Medical Center
Guochong Jia: Vanderbilt University Medical Center
Jie Ping: Vanderbilt University Medical Center
Xiang Shu: Memorial Sloan Kettering Cancer Center
Tianying Zhao: Vanderbilt University Medical Center
Fangcheng Yuan: Vanderbilt University Medical Center
Gang Wang: Sichuan University
Yufang Xie: Sichuan University
Hang Ci: Sichuan University
Hongmo Liu: Sichuan University
Yawen Qi: Sichuan University
Yongjun Liu: University of Washington Medical Center
Dan Liu: Sichuan University
Weimin Li: Sichuan University
Fei Ye: Vanderbilt University Medical Center
Xiao-Ou Shu: Vanderbilt University Medical Center
Wei Zheng: Vanderbilt University Medical Center
Li Li: University of Virginia
Qiuyin Cai: Vanderbilt University Medical Center
Jirong Long: Vanderbilt University Medical Center

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P

Date: 2024
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DOI: 10.1038/s41467-024-50404-y

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