ArticleNature communications2022
Integrating transcription factor occupancy with transcriptome-wide association analysis identifies susceptibility genes in human cancers.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes.American journal of human genetics · 2023Pooled it
- Deconvolution-based cell-type specific DNA methylation-wide and transcriptome-wide association studies identify risk CpG sites and genes associated with colorectal cancer risk.medRxiv : the preprint server for health sciences · 2026Article
- Multi-ancestry transcriptome-wide association studies uncover insights into breast cancer genetics and biology.Nature communications · 2026Article
- Tissue-specific transfer learning improves functional variant and therapeutic target discoveries in breast and prostate cancer.PLoS genetics · 2026Article
- MOKA: a pipeline for multiomics bridged SNP-set kernel association test.G3 (Bethesda, Md.) · 2026Article
- Mixed-model and transcriptome-wide association analyses identify transcription factors and genes associated with colorectal cancer susceptibility.Nature communications · 2026Article
- Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention.American journal of human genetics · 2026Article
- ASTWAS: modeling alternative polyadenylation and SNP effects in kernel-driven TWAS reveal novel genetic associations for complex traits.Briefings in bioinformatics · 2026Article
- Learning image derived PDE-phenotypes from fMRI data.Brain informatics · 2025Article
- A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Loci and Genes for Lung Cancer Risk.Cancer medicine · 2025Article
- Multi-ancestry genome-wide and transcriptome-wide association analyses identified new risk loci and genes for inflammatory bowel disease.medRxiv : the preprint server for health sciences · 2025Article
- Transcriptome-wide association study of alternative polyadenylation identifies susceptibility genes in non-small cell lung cancer.Oncogene · 2025Article
- A Multi-omic study integrating plasma protein, multiple tissues, and single-cell identifies RNASET2 as a key gene for lung cancer.Discover oncology · 2025Article
- Enhancing disease risk gene discovery by integrating transcription factor-linked trans-variants into transcriptome-wide association analyses.Nucleic acids research · 2025Article
- Multiome-wide Association Studies: Novel Approaches for Understanding Diseases.Genomics, proteomics & bioinformatics · 2024Review
- Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases.Computational and structural biotechnology journal · 2024Article
- A proteome-wide association study identifies putative causal proteins for breast cancer risk.British journal of cancer · 2024Article
- COFFEE: consensus single cell-type specific inference for gene regulatory networks.Briefings in bioinformatics · 2024Article
- Leveraging Random Effects in Cistrome-Wide Association Studies for Decoding the Genetic Determinants of Prostate Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes.Cancer research · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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 < 0.05, we identify 354 putative susceptibility genes for these cancers, including 189 previously unreported in GWAS loci and 45 in loci unreported by GWAS. These findings provide additional insight into the genetic susceptibility of human cancers. Additionally, we show the generalizability of the sTF-TWAS on non-cancer diseases.
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