A Compendium of Promoter-Centered Long-Range Chromatin Interactions in the Human Genome

By Inkyung Jung, Anthony D. Schmitt, Yarui Diao, Andrew J. Lee, Tristin Liu, Dongchan Yang, Catherine Tan, Junghyun Eom, Marilynn Chan, Sora Chee and 8 more. Nature Genetics, 2019

319 citations recorded by the source corpus.

A large number of putative cis-regulatory sequences have been annotated in the human genome, but the genes they control remain poorly defined. To bridge this gap, we generate maps of long-range chromatin interactions centered on 18,943 well-annotated promoters for protein-coding genes in 27 human cell/tissue types. We use this information to infer the target genes of 70,329 candidate regulatory elements and suggest potential regulatory function for 27,325 noncoding sequence variants associated with 2,117 physiological traits and diseases. Integrative analysis of these promoter-centered interactome maps reveals widespread enhancer-like promoters involved in gene regulation and common molecular pathways underlying distinct groups of human traits and diseases. Mapping long-range chromatin interactions in 27 human cell/tissue types identifies candidate target genes of 70,329 putative regulatory elements. Further analysis suggests potential regulatory function for 27,325 noncoding variants associated with 2,117 traits and diseases.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/a9d0ae2cd68c2d53dea4c15d2af1fefc9248e7c2

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Citations in the corpus

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases (2020, background): “…variants with predicted enhancer–gene connections from (i) correlating enhancer and promoter activity (E-P correlation) 15,16 , (ii) three-dimensional loops from promoter capture Hi-C (PCHi-C) 12–14 and (iii) Activity-by-Contact (ABC) 11 maps to identify genes regulated by fine-mapped variants.”
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding (2022, background): “…that in parallel to the coarse-scale global model (for example, the whole-atlas integration model), finer-scale regulatory inference could be conducted by training dedicated models on cells from a single tissue, potentially with spatiotemporal-specific prior knowledge incorporated as well 67 .”