Integrating transcription factor occupancy with transcriptome-wide association analysis identifies susceptibility genes in human cancers

Jingni He, Wanqing Wen, Alicia Beeghly, Zhishan Chen, Chen Cao, Xiao-Ou Shu, Wei Zheng, Quan Long () and Xingyi Guo ()
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Jingni He: University of Calgary
Wanqing Wen: Vanderbilt University School of Medicine
Alicia Beeghly: Vanderbilt University School of Medicine
Zhishan Chen: Vanderbilt University School of Medicine
Chen Cao: University of Calgary
Xiao-Ou Shu: Vanderbilt University School of Medicine
Wei Zheng: Vanderbilt University School of Medicine
Quan Long: University of Calgary
Xingyi Guo: Vanderbilt University School of Medicine

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract Transcriptome-wide association studies (TWAS) have successfully discovered many putative disease susceptibility genes. However, TWAS may suffer from inaccuracy of gene expression predictions due to inclusion of non-regulatory variants. By integrating prior knowledge of susceptible transcription factor occupied elements, we develop sTF-TWAS and demonstrate that it outperforms existing TWAS approaches in both simulation and real data analyses. Under the sTF-TWAS framework, we build genetic models to predict alternative splicing and gene expression in normal breast, prostate and lung tissues from the Genotype-Tissue Expression project and apply these models to data from large genome-wide association studies (GWAS) conducted among European-ancestry populations. At Bonferroni-corrected P

Date: 2022
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DOI: 10.1038/s41467-022-34888-0

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