ArticleNucleic acids research2024
PancanQTLv2.0: a comprehensive resource for expression quantitative trait loci across human cancers.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Functional characterization of the 9q34.13 locus identifies RAPGEF1 as a candidate gene modulating risk for melanoma and nevi via RAS activation.American journal of human genetics · 2026Article
- Expression quantitative trait methylation across multiple cancer types with functional and therapeutic characterization using Onco-eQTM.NAR genomics and bioinformatics · 2026Article
- EnhancerDB 2.0: a comprehensive cross-species enhancer atlas.Molecular biology and evolution · 2026Article
- eQTL analysis: A bridge from genome to mechanism.Genes & diseases · 2026Review
- Functional characterization of the 9q34.13 locus identifiesbioRxiv : the preprint server for biology · 2026Article
- Integrative genomic profiling identifies MLPH as a candidate gene in prostate cancer.Frontiers in medicine · 2026Article
- Genome-wide association study identifies common variants associated with breast cancer in South African Black women.Nature communications · 2025Article
- Deciphering genetic regulation at single-cell resolution in gastric cancer.Cell genomics · 2025Article
- Pharmacogenomics Tools for Precision Public Health and Lessons for Low- and Middle-Income Countries: A Scoping Review.Pharmacogenomics and personalized medicine · 2025Review
- Epigenome-augmented eQTL-hotspots reveal genome-wide transcriptional programs in 36 human tissues.Briefings in bioinformatics · 2024Article
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Authors and funding
10 authors.
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Abstract
Expression quantitative trait locus (eQTL) analysis is a powerful tool used to investigate genetic variations in complex diseases, including cancer. We previously developed a comprehensive database, PancanQTL, to characterize cancer eQTLs using The Cancer Genome Atlas (TCGA) dataset, and linked eQTLs with patient survival and GWAS risk variants. Here, we present an updated version, PancanQTLv2.0 (https://hanlaboratory.com/PancanQTLv2/), with advancements in fine-mapping causal variants for eQTLs, updating eQTLs overlapping with GWAS linkage disequilibrium regions and identifying eQTLs associated with drug response and immune infiltration. Through fine-mapping analysis, we identified 58 747 fine-mapped eQTLs credible sets, providing mechanic insights of gene regulation in cancer. We further integrated the latest GWAS Catalog and identified a total of 84 592 135 linkage associations between eQTLs and the existing GWAS loci, which represents a remarkable ∼50-fold increase compared to the previous version. Additionally, PancanQTLv2.0 uncovered 659516 associations between eQTLs and drug response and identified 146948 associations between eQTLs and immune cell abundance, providing potentially clinical utility of eQTLs in cancer therapy. PancanQTLv2.0 expanded the resources available for investigating gene expression regulation in human cancers, leading to advancements in cancer research and precision oncology.
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