Mammalian enhancers and GWASs act proximally and seldom skip active genes
Choudhary, C.; Narita, T.
Show abstract
Enhancers play a critical role in regulating transcription. Nearly 90% of human genetic variants identified in genome-wide association studies (GWAS) are located in distal regions, underscoring the importance of enhancers in human development, diseases, and traits. It is widely suggested that mammalian enhancers frequently skip active genes, and thus, linear proximity is a poor predictor of their targets. A key unresolved question is how often mammalian enhancers skip proximal active genes to specifically target distal genes. Genome-wide enhancer-promoter mapping shows that enhancers frequently bypass active genes, while ultra-deep locus-specific analyses reveal extensive multi-way interactions between enhancers and promoters, forming nested microcompartments. The functional significance of these seemingly contrasting phenomena remains unclear. Here, we compared hundreds of enhancer-target gene pairs identified using enhancer-promoter chromatin contact maps, enhancer-promoter RNA interaction data, and genome-scale CRISPR interference (CRISPRi) perturbations. Our findings reveal limited overlap between active gene-skipping enhancer-gene pairs identified through physical interaction mapping and CRISPRi. Additionally, promoters involved in multi-way enhancer interactions are not co-regulated by shared coactivators. Notably, gene-skipping and non-skipping enhancers identified via CRISPRi differ fundamentally in chromatin features, gene activation strength, false discovery rates, target gene distance, coactivator requirements, and cell-type specificity of target genes. These results suggest that gene-skipping enhancer-promoter interactions observed in chromatin and RNA-based analyses do not reliably predict functional enhancer-gene relationships. We propose that linear enhancer-promoter proximity and coactivator dependency offer a simple, scalable, and cost-effective method for genome-wide prediction of enhancer and GWAS targets, with accuracy comparable to state-of-the-art experimental techniques. While enhancers can skip active genes, such deliberate skipping appears to be the exception rather than the rule.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Recruitment of Homodimeric Proneural Factors by Conserved CAT-CAT E-Boxes Drives Major Epigenetic Reconfiguration in Cortical Neurogenesis 96%
- Regulatory elements can be essential for maintaining broad chromatin organization and cell viability 95%
- A high-resolution map of functional miR-181 response elements in the thymus reveals the role of coding sequence targeting and an alternative seed match 95%
Similar papers in this journal
- Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes 97%
- A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods 96%
- An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced TF binding 96%
Similar papers in this journal
- Chromatin loop dynamics during cellular differentiation are associated with changes to both anchor and internal regulatory features 96%
- The role of insulators and transcription in 3D chromatin organisation of flies 96%
- Functional non-coding SNPs in human endothelial cells fine-map vascular trait associations 96%
Similar papers in this journal
- Putative Looping Factor ZNF143/ZFP143 is an Essential Transcriptional Regulator with No Looping Function 96%
- A comprehensive Schizosaccharomyces pombe atlas of physical transcription factor interactions with proteins and chromatin 96%
- Identification of molecular determinants of gene-specific bursting patterns by high-throughput imaging screens 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.