ArticleScientific reports2025
Joint analysis of sQTL and Hi-C reveals spatial proximity between sQTLs and target genes in cancer tissues.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning driven colorectal polyp analysis: a review of detection, classification and segmentation methods.Frontiers in artificial intelligence · 2026Pooled it
- Enhancing tumor T cell antigen prediction by integrating deep protein representations.BMC bioinformatics · 2026Article
- Development and validation of a clinical-adipometric nomogram for predicting tumor response to transarterial chemoembolization in hepatocellular carcinoma.Scientific reports · 2026Article
- A distributed fusion framework for breast cancer recurrence prediction using MapReduce.Scientific reports · 2026Article
- Deep DNA and protein level feature integration for robust clinical variant interpretation using probabilistic gradient boosting.Frontiers in digital health · 2026Article
- Development and expert radiologist validation of a custom pipeline for simplification of oncology radiology reports using large language model.Frontiers in oncology · 2026Article
- CNN-RNN framework for lung cancer classification using CT imaging and GAN-based augmentation.Frontiers in artificial intelligence · 2026Article
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Abstract
Gene expression and regulation with or without alternative splicing are crucial for tissues and cells to function correctly. They have been studied from three almost independent perspectives at the genome level: 1- Recognition of splicing quantitative trait loci (sQTLs), 2- Expression quantitative trait loci (eQTLs) recognition, and 3- Recognition of longer-range physical chromatin interactions between genome segments which model 3D dynamics of cells and tissues. Even though the associations between eQTLs and longer-range chromatin interactions have been previously studied, a similar relationship between sQTLs and chromatin interactions has yet to be analyzed. In this case, examining whether sQTLs control the alternative splicing of their target genes' mRNA via physically interacting genome segments is crucial. We have jointly analyzed high-throughput chromatin conformation capture (Hi-C) and sQTL datasets over eight human cancer tissues. We have discovered a positive association between the number of genes having sQTLs and chromatin interaction frequency. Such a positive association still exists when we also control for eQTLs. Additionally, sQTLs and their target genes generally exist inside identical topologically associating domains (TADs). These findings are observed over the whole set of analyzed cancer types and functional subsets of the sQTL dataset, such as survival-related sQTLs. Furthermore, tissue-specific sQTLs are statistically enriched in tissue-specific frequently interacting regions (FIREs) in 6 out of 8 human cancer tissues (Chronic Myeloid Leukemia, Colon Adenocarcinoma, Acute Myeloid Leukemia, Lung Adenocarcinoma, Prostate Cancer, Sarcoma). Our sQTL and Hi-C datasets have shown the existence of closer spatial distance between sQTLs and their target genes with possible alternative splicing across several different cancer types in humans. Such a closer spatial distance also exists, independent of whether we integrate eQTLs into the analysis. We found that sQTLs regulate alternative splicing through chromatin interactions. Source code of the analysis in this research is available on https://github.com/seferlab/sqtlhic .
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