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PreprintCited by 119Genomics and Chromatin DynamicsGenetic Associations and EpidemiologyGene Regulatory Network Analysis

An encyclopedia of enhancer-gene regulatory interactions in the human genome

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Gschwind, Andreas R., Mualim, Kristy S., Karbalayghareh, Alireza, Sheth, Maya U., Dey, Kushal K., Jagoda, Evelyn, Nurtdinov, Ramil N., Xi, Wang +33 more·2023·bioRxiv (Cold Spring Harbor Laboratory)
DOI 10.1101/2023.11.09.563812PMC PMC10680627
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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 1–6 . 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 large-scale genetic perturbations generated by the ENCODE Consortium 7 . We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 element-gene 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 enhancer-promoter 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.

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  • 99% (465 of 471) of validated enhancer-gene pairs fall within 1 Mb.

    “For instance, 99% (465 of 471) of validated enhancer–gene pairs fall within 1 Mb (ref. 12).”

    From Advancing regulatory variant effect prediction with AlphaGenome (2026) · cites this paper as [12]

    The Genome-wide maps of regulatory interactions section (page unavailable) says most predicted enhancer–gene interactions span short distances and that this is consistent with CRISPR perturbation results. The Supervised classification section reports 472 CRISPR-positive pairs, but our copy does not give the claimed count of 465 of 471 or establish that 99% of validated pairs fall within 1 Mb. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Enhancer-gene linking performance (ENCODE-rE2G CRISPRi dataset) was evaluated zero-shot stratified by enhancer-to-TSS distance and supervised with AlphaGenome input gradient score integrated into ENCODE-rE2G extended and ENCODE-rE2G models.

    “Enhancer–gene linking performance (ENCODE–rE2G CRISPRi dataset12). Zero-shot evaluation: performance (auPRC) comparison stratified by enhancer-to-TSS distance. Supervised evaluation: AlphaGenome input gradient score integrated into ENCODE–rE2G extended and ENCODE–rE2G models.”

    From Advancing regulatory variant effect prediction with AlphaGenome (2026) · cites this paper as [12]

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References 57 · Cited by 119

Research works printed but not linked: 44

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Authors
Gschwind, A. R., Mualim, K. S., Karbalayghareh, A., Sheth, M. U., Dey, K. K., Jagoda, E., Nurtdinov, R. N., Xi, W., Tan, A. S., Jones, H., Ma, X. R., Yao, D., Nasser, J., Avsec, Ž., James, B. T., Shamim, M. S., Durand, N. C., Rao, S. S. P., Mahajan, R., Doughty, B. R., Andreeva, K., Ulirsch, J. C., Fan, K., Perez, E. M., Nguyen, T. C., Kelley, D. R., Finucane, H. K., Moore, J. E., Weng, Z., Kellis, M., Bassik, M. C., Price, A. L., Beer, M. A., Guigó, R., Stamatoyannopoulos, J. A., Aiden, E. L., Greenleaf, W. J., Leslie, C. S., Steinmetz, L. M., Kundaje, A., & Engreitz, J. M.
Affiliations
  • Lucile Packard Children's Hospital (US)
  • Carnegie Institution for Science (US)
  • Memorial Sloan Kettering Cancer Center (US)
  • Broad Institute (US)
  • Barcelona Institute of Science and Technology (ES)
  • Stanford Medicine (US)
  • Google DeepMind (United Kingdom) (GB)
  • Baylor College of Medicine (US)
  • University of California, San Francisco (US)
  • Harvard University (US)
  • Calico Life Sciences (US)
  • University of Massachusetts Chan Medical School (US)
  • Johns Hopkins University (US)
  • Fred Hutch Cancer Center (US)
Corresponding author
Engreitz, Jesse M.
ORCID
Venue
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Published
2023-11-13
DOI
10.1101/2023.11.09.563812
PMC
PMC10680627
OpenAlex
W4388648559
Cited by
119
References
57
Citations by year
2026: 37 · 2025: 47 · 2024: 33 · 2023: 1 · 2012: 1
Type
posted content
Language
en
Open access
Licence
cc-by-nd
Locations
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Funders
  • National Science Foundation (DGE-165651 8, 1656518, 1745302, U24HG009397)
  • Silicon Valley Community Foundation
  • Ministerio de Economía y Competitividad (BIO2011-26205)
  • Novo Nordisk (NNF21SA0072102)
  • Novo Nordisk Fonden (NNF21SA0072102)
  • National Institutes of Health
  • National Human Genome Research Institute (K99HG009917, R35HG011324, U41HG006992, UM1HG009436, U01HG009395, U01HG009431, U01HG009380, R00HG009917, U24HG009397, U24HG009446, U01HG012069, R01HG012367, R00HG012203, T32HG000044, R01HG011664, U01HG012103, UM1HG011972, U01HG012009)
  • National Cancer Institute (P30CA008748)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
enhancers, enhancer-promoter interactions, prediction model, CRISPR perturbation, GWAS variants, enhancer networks, non-coding variants, gene expression regulation, chromatin state, cell type specificity

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