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An encyclopedia of enhancer-gene regulatory interactions in the human genome.
- Source :
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BioRxiv : the preprint server for biology [bioRxiv] 2023 Nov 13. Date of Electronic Publication: 2023 Nov 13. - Publication Year :
- 2023
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Abstract
- Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease <superscript>1-6</superscript> . Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and largescale genetic perturbations generated by the ENCODE Consortium <superscript>7</superscript> . We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 elementgene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancerpromoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.<br />Competing Interests: Conflict of Interest Statement Z.A. is employed by Google DeepMind. J.C.U. is an employee of Illumina, Inc. D.R.K. is employed by Calico Life Sciences LLC. Z.W. co-founded Rgenta Therapeutics, and she serves as a scientific advisor for the company and is a member of its board. W.J.G. is an inventor on IP licensed by 10x Genomics. A.Kundaje is on the scientific advisory board of PatchBio, SerImmune and OpenTargets, was a consultant with Illumina, and owns shares in DeepGenomics, ImmunAI and Freenome. J.M.E. is a consultant and equity holder in Martingale Labs, Inc. and has received materials from 10x Genomics unrelated to this study.
Details
- Language :
- English
- Database :
- MEDLINE
- Journal :
- BioRxiv : the preprint server for biology
- Accession number :
- 38014075
- Full Text :
- https://doi.org/10.1101/2023.11.09.563812