ArticleNucleic acids research2013
Transcription factor and chromatin features predict genes associated with eQTLs.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
22 citing papers in PubMed, 35 citations in OpenAlex.
- Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci†.Human molecular genetics · 2015Trial
- Truncated Life History Underlies Rapid Local Adaptation in Island Rattlesnake Venom Expression.Genome biology and evolution · 2026Article
- Analysis of chromatin structure reveals the connection between sQTLs and the splicing of distant genes.Scientific reports · 2025Article
- KansformerEPI: a deep learning framework integrating KAN and transformer for predicting enhancer-promoter interactions.Briefings in bioinformatics · 2025Article
- GATv2EPI: Predicting Enhancer-Promoter Interactions with a Dynamic Graph Attention Network.Genes · 2024Article
- Random Forest model reveals the interaction between N6-methyladenosine modifications and RNA-binding proteins.iScience · 2023Article
- Integrative epigenomic and high-throughput functional enhancer profiling reveals determinants of enhancer heterogeneity in gastric cancer.Genome medicine · 2021Article
- Association of Coding Variants in Hydroxysteroid 17-beta Dehydrogenase 14 (Journal of the American Society of Nephrology : JASN · 2021Article
- Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine.Non-coding RNA · 2021Review
- Long noncoding RNA H19X is a key mediator of TGF-β-driven fibrosis.The Journal of clinical investigation · 2020Article
- Characteristics of functional enrichment and gene expression level of human putative transcriptional target genes.BMC genomics · 2018Article
- McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.Genome biology · 2017Article
- Article
- GeneHancer: genome-wide integration of enhancers and target genes in GeneCards.Database : the journal of biological databases and curation · 2017Article
- Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin.Nature genetics · 2016Article
- eRFSVM: a hybrid classifier to predict enhancers-integrating random forests with support vector machines.Hereditas · 2016Article
- Identifying Cell Type-Specific Transcription Factors by Integrating ChIP-seq and eQTL Data-Application to Monocyte Gene Regulation.Gene regulation and systems biology · 2016Article
- Mechanisms of Evolutionary Innovation Point to Genetic Control Logic as the Key Difference Between Prokaryotes and Eukaryotes.Journal of molecular evolution · 2015Article
- Computational schemes for the prediction and annotation of enhancers from epigenomic assays.Methods (San Diego, Calif.) · 2015Review
- Demystifying the secret mission of enhancers: linking distal regulatory elements to target genes.Critical reviews in biochemistry and molecular biology · 2015Review
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Cell type-specific gene expression in humans involves complex interactions between regulatory factors and DNA at enhancers and promoters. Mapping studies for expression quantitative trait loci (eQTLs), transcription factors (TFs) and chromatin markers have become widely used tools for identifying gene regulatory elements, but prediction of target genes remains a major challenge. Here, we integrate genome-wide data on TF-binding sites, chromatin markers and functional annotations to predict genes associated with human eQTLs. Using the random forest classifier, we found that genomic proximity plus five TF and chromatin features are able to predict >90% of target genes within 1 megabase of eQTLs. Despite being regularly used to map target genes, proximity is not a good indicator of eQTL targets for genes 150 kilobases away, but insulators, TF co-occurrence, open chromatin and functional similarities between TFs and genes are better indicators. Using all six features in the classifier achieved an area under the specificity and sensitivity curve of 0.91, much better compared with at most 0.75 for using any single feature. We hope this study will not only provide validation of eQTL-mapping studies, but also provide insight into the molecular mechanisms explaining how genetic variation can influence gene expression.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.