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Integrative analysis of 111 reference human epigenomes
Open accessAbstract
The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease. This study describes the integrative analysis of 111 reference human epigenomes, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression; the results annotate candidate regulatory elements in diverse tissues and cell types, their candidate regulators, and the set of human traits for which they show genetic variant enrichment, providing a resource for interpreting the molecular basis of human disease. The goal of the NIH Roadmap Epigenomics Consortium was to generate a reference collection of human epigenomes for primary cells and tissues. This study describes the integrative analysis of 111 reference human epigenomes, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. The results show that disease and trait-associated genetic variants are enriched in predicted tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits and providing a resource for interpreting the molecular basis of human disease.
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“The techniques described for processing the ChIP-seq and ATAC-seq reads are based on ENCODE/Roadmap guidelines with a few modifications (Gjoneska et al., 2015; Landt et al., 2012; Roadmap Epigenomics et al., 2015).”
From Loss of epigenetic information as a cause of mammalian aging (2023) · cites this paper as Roadmap Epigenomics et al., 2015
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Similar to the results above, genic enhancers (average RPKM, EnhG1: 0.391 and EnhG2: 0.392) and transcribed regions (average RPKM, Tx: 0.378 and TxWk: 0.367) are hotspots for oxidative DNA damage (Fig. 2b).”
From Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability (2025) · cites this paper as [49]
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“Within cell-specific enhancers, DNA sequences provide information about the identities of key LDTFs and the activity states of signaling pathways that control the functions of SDTFs (31, 33–35).”
From An environment-dependent transcriptional network specifies human microglia identity (2017) · cites this paper as [34]
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“aSNPs are generally depleted from inactive chromatin states (states not associated with gene expression as defined by [34]) as well as promoters and transcription start sites, but enriched within transcribed and subsets of enhancer elements (Fig 1; full results are available at S5 Table).”
From Denisovan introgression has shaped the immune system of present-day Papuans (2022) · cites this paper as [34]
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“We specifically analysed all of our variants across multiple cell types and functional chromatin states aiming to account for the strong dependency on the cells’ chromatin landscapes [34] when assessing the potential activities of introgressed alleles.”
From Denisovan introgression has shaped the immune system of present-day Papuans (2022) · cites this paper as [34]
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“as well as DNase-seq and ChIP–seq from ENCODE and the Epigenomics Roadmap31,99 and pseudo-bulk single-cell ATAC–seq data from CATlas100,101, which focuses the model towards distal regulatory elements.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [99]
In the citing paper
“Second, MAGMA does not take into account tissue-specific regulatory relationships, whereas disease risk SNPs are enriched in regulatory elements of the disease-relevant tissue5,6.”
From A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles (2020) · cites this paper as [5]
In the citing paper
“In addition, ChIP-seq experiments involving histone modifications have been used to understand the general role of these epigenomic changes on gene expression [147, 148].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [147]
In the citing paper
“Except for genetic profile being identical across tissues and cell types, all other omic profiles (e.g. transcriptome, epigenome, proteome and metabolome) vary across tissues and cell types.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Roadmap Epigenomics et al., 2015
In the citing paper
“On the contrary, consortia studies have demonstrated distinct patterns of omic profiles across tissues and cell types (Bernstein et al., 2012; Roadmap Epigenomics et al., 2015).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Roadmap Epigenomics et al., 2015
In the citing paper
“The national institute of health Roadmap Epigenomics Consortium generated 111 human reference epigenomes for primary cells and tissues, the largest collection so far (Roadmap Epigenomics et al., 2015).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Roadmap Epigenomics et al., 2015
In the citing paper
“For example, the ENCODE and Roadmap Epigenomics projects have generated maps of histone modifications, transcription-factor binding, chromatin accessibility, three-dimensional genome structure and other regulatory annotations across hundreds of cell types and tissues107,108.”
From A brief history of human disease genetics (2020) · cites this paper as [108]
In the citing paper
“the Genotype-Tissue Expression (GTEx) project has associated many noncoding variants with expression quantitative-trait loci (eQTLs)”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [9]
In the citing paper
“the ENCODE and Roadmap Epigenomics (Roadmap) projects have identified noncoding regions acting as enhancers and promoters (8–10).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [9]
In the citing paper
“This search has been facilitated by large-scale annotation projects such as the Roadmap Epigenomics Project [180], the Blueprint Project [181], and PsychENCODE [182]. These projects aim to generate, and make publicly available, high-quality reference annotation maps across a wide variety of human cell and tissue types.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [180]
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“This search has been facilitated by large-scale annotation projects such as the Roadmap Epigenomics Project (Roadmap Epigenomics, Consortium, et al., 2015), the Blueprint Project (Fernandez et al., 2016), and PsychENCODE (Psych, Encode Consortium, et al., 2015).”
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“The FLAIR ‘–trust ends’ option was enabled for long reads and a BED file of CAGE peak data (41) was input to only retain isoforms with 5′ ends within 100nt of known transcriptional start sites.”
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A genomic atlas of systemic interindividual epigenetic variation in humans
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“Analysis of ChromHMM features across 111 reference human epigenomes (derived from the NIH Epigenome Roadmap project) [23] shows that, relative to control regions, CoRSIVs are enriched in quiescent regions and those associated with repressive polycomb marks and depleted in heterochromatic regions, active promoters, and…”
Self-citationplyranges: a grammar of genomic data transformation
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Somatic mutation distributions in cancer genomes vary with three-dimensional chromatin structure
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Self-citationGenomic language models: opportunities and challenges
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“A type of data that may be fashioned into a set of tasks broadly informative of models’ adaptability to genome interpretation are functional genomics data (such as those from the ENCODE [127] or Roadmap Epigenomics [128] projects), which may be used to annotate genomic regions and variants.”
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Hotspots of aberrant enhancer activity punctuate the colorectal cancer epigenome
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Single-Cell Epigenomics and Functional Fine-Mapping of Atherosclerosis GWAS Loci
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Epigenetic supersimilarity of monozygotic twin pairs
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“To explore the association between RT subgroups and early developmental processes, we correlated transcriptome profiles of the subgroups to various progenitor cell types (Kundaje et al., 2015; Chun et al., 2016; Prescott et al., 2015).”
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Nested epistasis enhancer networks for robust genome regulation
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Self-citationMammalian evolution of human cis-regulatory elements and transcription factor binding sites
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Self-citationTransposable elements in mammalian chromatin organization
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Self-citationGenetic determinants of polycystic ovary syndrome: Progress and future directions
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Metadata
- Authors
- Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., Ziller, M. J., Amin, V., Whitaker, J. W., Schultz, M. D., Ward, L. D., Sarkar, A., Quon, G., Sandstrom, R. S., Eaton, M. L., Wu, Y.-C., Pfenning, A. R., Wang, X., Claussnitzer, M., Liu, Y., Coarfa, C., Harris, R. A., Shoresh, N., Epstein, C. B., Gjoneska, E., Leung, D., Xie, W., Hawkins, R. D., Lister, R., Hong, C., Gascard, P., Mungall, A. J., Moore, R., Chuah, E., Tam, A., Canfield, T. K., Hansen, R. S., Kaul, R., Sabo, P. J., Bansal, M. S., Carles, A., Dixon, J. R., Farh, K.-H., Feizi, S., Karlic, R., Kim, A.-R., Kulkarni, A., Li, D., Lowdon, R., Elliott, G., Mercer, T. R., Neph, S. J., Onuchic, V., Polak, P., Rajagopal, N., Ray, P., Sallari, R. C., Siebenthall, K. T., Sinnott-Armstrong, N. A., Stevens, M., Thurman, R. E., Wu, J., Zhang, B., Zhou, X., Beaudet, A. E., Boyer, L. A., De Jager, P. L., Farnham, P. J., Fisher, S. J., Haussler, D., Jones, S. J. M., Li, W., Marra, M. A., McManus, M. T., Sunyaev, S., Thomson, J. A., Tlsty, T. D., Tsai, L.-H., Wang, W., Waterland, R. A., Zhang, M. Q., Chadwick, L. H., Bernstein, B. E., Costello, J. F., Ecker, J. R., Hirst, M., Meissner, A., Milosavljevic, A., Ren, B., Stamatoyannopoulos, J. A., Wang, T., & Kellis, M.
- Affiliations
- Broad Institute (US)
- BC Cancer Agency (CA)
- Baylor College of Medicine (US)
- University of California San Diego (US)
- Salk Institute for Biological Studies (US)
- University of Washington (US)
- University of California, San Francisco (US)
- The University of Texas at Dallas (US)
- Washington University in St. Louis (US)
- The University of Queensland (AU)
- Cold Spring Harbor Laboratory (US)
- Massachusetts Institute of Technology (US)
- University of Southern California (US)
- University of California, Santa Cruz (US)
- University of Wisconsin–Madison (US)
- National Institute of Environmental Health Sciences (US)
- Corresponding author
- Kellis, Manolis
- ORCID
- Kundaje, Anshul
- Meuleman, Wouter
- Ernst, Jason
- Zhang, Zhizhuo
- Wang, Jianrong
- Ziller, Michael J.
- Whitaker, John W.
- Ward, Lucas D.
- Sarkar, Abhishek
- Quon, Gerald
- Sandstrom, Richard S.
- Eaton, Matthew L.
- Wu, Yi-Chieh
- Pfenning, Andreas R.
- Wang, Xinchen
- Claussnitzer, Melina
- Liu, Yaping
- Coarfa, Cristian
- Harris, R. Alan
- Epstein, Charles B.
- Gjoneska, Elizabeta
- Leung, Danny
- Xie, Wei
- Hawkins, R. David
- Lister, Ryan
- Gascard, Philippe
- Mungall, Andrew J.
- Tam, Angela
- Kaul, Rajinder
- Sabo, Peter J.
- Bansal, Mukul S.
- Dixon, Jesse R.
- Feizi, Soheil
- Kim, Ah-Ram
- Kulkarni, Ashwinikumar
- Li, Daofeng
- Lowdon, Rebecca
- Elliott, GiNell
- Mercer, Tim R.
- Neph, Shane J.
- Onuchic, Vitor
- Polak, Paz
- Ray, Pradipta
- Sallari, Richard C.
- Wu, Jie
- Zhang, Bo
- Zhou, Xin
- Boyer, Laurie A.
- Farnham, Peggy J.
- Fisher, Susan J.
- Haussler, David
- Jones, Steven J. M.
- Li, Wei
- Marra, Marco A.
- McManus, Michael T.
- Sunyaev, Shamil
- Thomson, James A.
- Tlsty, Thea D.
- Tsai, Li-Huei
- Waterland, Robert A.
- Zhang, Michael Q.
- Chadwick, Lisa H.
- Bernstein, Bradley E.
- Costello, Joseph F.
- Ecker, Joseph R.
- Hirst, Martin
- Meissner, Alexander
- Ren, Bing
- Stamatoyannopoulos, John A.
- Wang, Ting
- Kellis, Manolis
- Venue
- Nature
- Published
- 2015-02-18
- Volume
- 518
- Issue
- 7539
- Pages
- 317-330
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4530010
- OpenAlex
- W2076154138
- Cited by
- 6,368
- References
- 124
- Citations by year
- 2026: 329 · 2025: 429 · 2024: 495 · 2023: 564 · 2022: 589 · 2021: 676 · 2020: 678 · 2019: 644 · 2018: 628 · 2017: 560 · 2016: 517 · 2015: 246
- FWCI
- 280.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature (publishedVersion, cc-by-nc-sa)
- UWA Profiles and Research Repository (UWA) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- eScholarship (California Digital Library) (submittedVersion)
- PMC
- Funders
- National Science Foundation (1254200, CAREER)
- Howard Hughes Medical Institute
- Massachusetts General Hospital
- Washington University in St. Louis
- National Natural Science Foundation of China (91019016)
- Tsinghua University (100084)
- Tsinghua National Laboratory for Information Science and Technology
- National Institutes of Health
- National Institute on Aging (R01AG017917, U01AG046152, R01AG015819, P30AG010161, RF1AG015819, RF1AG036042)
- National Heart, Lung, and Blood Institute (R00HL119617, K99HL119617, F32HL110473)
- National Institute of Mental Health (P50MH096890)
- National Institute on Drug Abuse (U01DA025956, R25DA027995, P01DA008227)
- National Human Genome Research Institute (R01HG007175, R01HG007354, RC1HG005334, R01HG004037, U54HG007990)
- National Institute on Deafness and Other Communication Disorders
- National Institute of General Medical Sciences (T32GM007266, T32GM081739, T32GM007198)
- National Institute of Diabetes and Digestive and Kidney Diseases (U54DK106829)
- National Institute of Neurological Disorders and Stroke (R01NS078839)
- National Institute of Environmental Health Sciences (U01ES017166, R01ES024992, U01ES017156, ES017166, R01ES024984, U01ES017155, T32ES007032, U01ES017154)
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD092419, R24HD000836)
- Common Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- human epigenome, histone modifications, DNA accessibility, DNA methylation, RNA expression, regulatory elements, disease-associated variants, gene expression regulation, cell differentiation
- MeSH terms
- Base Sequence, Cells, Cultured, Chromatin, Chromosomes, Human, DNA, Enhancer Elements, Genetic, Histones, Humans, Organ Specificity, Reference Values, RNA, Genetic Variation, Genome, Human, Cell Lineage, DNA Methylation, Epigenesis, Genetic, Genome-Wide Association Study, Epigenomics, Datasets as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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