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Peer-reviewedCC BYNature

An encyclopedia of human enhancer–gene regulatory interactions

  • Andreas R. Gschwind1,2
  • Kristy S. Mualim2,3
  • Alireza Karbalayghareh4
  • Maya U. Sheth1,5,2
  • Kushal K. Dey4,6
  • Evelyn Jagoda7,8
  • Ramil Nurtdinov9
  • Wang Xi10,11
  • Anthony S. Tan1,2
  • James Galante1,2
  • Hank Jones1,2
  • X. Rosa Ma1,2
34 more authors
  • David Yao2
  • Dulguun Amgalan1,2
  • Judhajeet Ray7,8
  • Chad J. Munger1,2
  • Joseph Nasser12,13
  • Žiga Avsec14
  • Benjamin T. James12,15
  • Muhammad S. Shamim16
  • Neva C. Durand7
  • Suhas S.P. Rao17,18,19
  • Ragini Mahajan20,21
  • Benjamin R. Doughty2
  • Kalina Andreeva2
  • Jacob C. Ulirsch22,12
  • Kaili Fan23,24
  • Elizabeth M. Perez12
  • Tri C. Nguyen1,2
  • David R. Kelley25
  • Hilary K. Finucane26,27,28
  • Jill E. Moore24
  • Zhiping Weng24
  • M Kellis12,15
  • Michael Cory Bassik2
  • Berk Ustun29,30
  • Alkes L. Price6
  • M Beer10,11
  • Roderic Guigó9
  • John A. Stamatoyannopoulos31,32
  • Erez Lieberman Aiden20,17,21
  • William James Greenleaf33,2
  • Christina S. Leslie34
  • Lars Michael Steinmetz2,35,36
  • Anshul Bharat Kundaje37,2
  • J Engreitz38,39,40
40 affiliations
  1. 1Basic Sciences and Engineering Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford, CA, USA
  2. 2Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
  3. 3Department of Plant Biology, Carnegie Institute of Science, Stanford, CA, USA
  4. 4Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  5. 5Department of Bioengineering, Stanford University, Stanford, CA, USA
  6. 6Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
  7. 7Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  8. 8The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  9. 9Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
  10. 10Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
  11. 11McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
  12. 12Broad Institute of MIT and Harvard, Cambridge, MA, USA
  13. 13Department of Systems Biology, Harvard Medical School, Boston, MA, USA
  14. 14Google DeepMind, London, UK
  15. 15Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
  16. 16Department of Pathology, Mass General Brigham, Boston, MA, USA
  17. 17Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX, USA
  18. 18Department of Medicine, University of California San Francisco, San Francisco, CA, USA
  19. 19Department of Structural Biology, Stanford University, Stanford, CA, USA
  20. 20Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
  21. 21Department of Biosciences, Rice University, Houston, TX, USA
  22. 22Artificial Intelligence Laboratory, Illumina Inc., San Diego, CA, USA
  23. 23Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
  24. 24Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
  25. 25Calico Life Sciences LLC, South San Francisco, CA, USA
  26. 26Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
  27. 27Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
  28. 28Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  29. 29Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA
  30. 30Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA, USA
  31. 31Altius Institute for Biomedical Sciences, Seattle, WA, USA
  32. 32Clinical Research Division, Fred Hutch Cancer Center, Seattle, WA, USA
  33. 33Department of Applied Physics, Stanford University, Stanford, CA, USA
  34. 34Memorial Sloan Kettering Cancer Center, New York, NY, USA
  35. 35European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
  36. 36Stanford Genome Technology Center, Palo Alto, CA, USA
  37. 37Department of Computer Science, Stanford University, Stanford, CA, USA
  38. 38Basic Sciences and Engineering Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford, CA, USA. [email protected]
  39. 39Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. [email protected]
  40. 40Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. [email protected]
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Abstract

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the effect of human genetic variation on disease 1–6 . Here we create and evaluate a resource of more than 92 million enhancer–gene regulatory interactions across 1,458 biosamples covering 369 cell types and tissues, by integrating predictive models, chromatin states, three-dimensional 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 10,356 element–gene pairs measured in CRISPR perturbation experiments, more than 30,000 fine-mapped expression quantitative trait loci and 569 fine-mapped genome-wide association study (GWAS) variants linked to a probable causal gene. Using this framework, we develop ENCODE-rE2G, a predictive model achieving state-of-the-art performance across several prediction tasks, demonstrating that iterative perturbations and supervised machine learning can build increasingly accurate predictive models of enhancer regulation. Using ENCODE-rE2G, we build an encyclopedia of enhancer–gene regulatory interactions in the human genome, revealing global properties of enhancer networks, identifying differences in regulatory complexity across genes and improving analyses linking noncoding variants to target genes and cell types for common complex diseases. By interpreting the model, we find that beyond enhancer activity and three-dimensional enhancer–promoter contacts, additional features that guide enhancer–promoter communication include promoter class and enhancer–enhancer synergy. 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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Cite

BibTeXRIS
BibTeX
@article{gschwind2026encyclopedia,
  title = {{An encyclopedia of human enhancer–gene regulatory interactions}},
  author = {Andreas R. Gschwind and Kristy S. Mualim and Alireza Karbalayghareh and Maya U. Sheth and Kushal K. Dey and Evelyn Jagoda and Ramil Nurtdinov and Wang Xi and Anthony S. Tan and James Galante and Hank Jones and X. Rosa Ma and David Yao and Dulguun Amgalan and Judhajeet Ray and Chad J. Munger and Joseph Nasser and Žiga Avsec and Benjamin T. James and Muhammad S. Shamim and Neva C. Durand and Suhas S.P. Rao and Ragini Mahajan and Benjamin R. Doughty and Kalina Andreeva and Jacob C. Ulirsch and Kaili Fan and Elizabeth M. Perez and Tri C. Nguyen and David R. Kelley and Hilary K. Finucane and Jill E. Moore and Zhiping Weng and M Kellis and Michael Cory Bassik and Berk Ustun and Alkes L. Price and M Beer and Roderic Guigó and John A. Stamatoyannopoulos and Erez Lieberman Aiden and William James Greenleaf and Christina S. Leslie and Lars Michael Steinmetz and Anshul Bharat Kundaje and J Engreitz},
  journal = {Nature},
  year = {2026},
  month = jul,
  doi = {10.1038/s41586-026-10781-4},
  url = {https://doi.org/10.1038/s41586-026-10781-4},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - An encyclopedia of human enhancer–gene regulatory interactions
AU  - Andreas R. Gschwind
AU  - Kristy S. Mualim
AU  - Alireza Karbalayghareh
AU  - Maya U. Sheth
AU  - Kushal K. Dey
AU  - Evelyn Jagoda
AU  - Ramil Nurtdinov
AU  - Wang Xi
AU  - Anthony S. Tan
AU  - James Galante
AU  - Hank Jones
AU  - X. Rosa Ma
AU  - David Yao
AU  - Dulguun Amgalan
AU  - Judhajeet Ray
AU  - Chad J. Munger
AU  - Joseph Nasser
AU  - Žiga Avsec
AU  - Benjamin T. James
AU  - Muhammad S. Shamim
AU  - Neva C. Durand
AU  - Suhas S.P. Rao
AU  - Ragini Mahajan
AU  - Benjamin R. Doughty
AU  - Kalina Andreeva
AU  - Jacob C. Ulirsch
AU  - Kaili Fan
AU  - Elizabeth M. Perez
AU  - Tri C. Nguyen
AU  - David R. Kelley
AU  - Hilary K. Finucane
AU  - Jill E. Moore
AU  - Zhiping Weng
AU  - M Kellis
AU  - Michael Cory Bassik
AU  - Berk Ustun
AU  - Alkes L. Price
AU  - M Beer
AU  - Roderic Guigó
AU  - John A. Stamatoyannopoulos
AU  - Erez Lieberman Aiden
AU  - William James Greenleaf
AU  - Christina S. Leslie
AU  - Lars Michael Steinmetz
AU  - Anshul Bharat Kundaje
AU  - J Engreitz
PY  - 2026
DA  - 2026/07/15
JO  - Nature
DO  - 10.1038/s41586-026-10781-4
UR  - https://doi.org/10.1038/s41586-026-10781-4
AB  - Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the effect of human genetic variation on disease 1–6 . Here we create and evaluate a resource of more than 92 million enhancer–gene regulatory interactions across 1,458 biosamples covering 369 cell types and tissues, by integrating predictive models, chromatin states, three-dimensional 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 10,356 element–gene pairs measured in CRISPR perturbation experiments, more than 30,000 fine-mapped expression quantitative trait loci and 569 fine-mapped genome-wide association study (GWAS) variants linked to a probable causal gene. Using this framework, we develop ENCODE-rE2G, a predictive model achieving state-of-the-art performance across several prediction tasks, demonstrating that iterative perturbations and supervised machine learning can build increasingly accurate predictive models of enhancer regulation. Using ENCODE-rE2G, we build an encyclopedia of enhancer–gene regulatory interactions in the human genome, revealing global properties of enhancer networks, identifying differences in regulatory complexity across genes and improving analyses linking noncoding variants to target genes and cell types for common complex diseases. By interpreting the model, we find that beyond enhancer activity and three-dimensional enhancer–promoter contacts, additional features that guide enhancer–promoter communication include promoter class and enhancer–enhancer synergy. 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.
SN  - 0028-0836
SN  - 1476-4687
ER  -