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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]
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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“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]
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“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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Citations
Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Self-citationGene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
Self-citationAn integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Self-citationMapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · cited 1×
“Extended data (1-12), supplementary figures (S1-S13), supplementary tables (S1-S6) are available in the online version of the paper.”
BackgroundRNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · cited 2×
“(5) Transcription factor binding sites (TFBS) based on ENCODE ChIP-seq data in the H1-ESC cell-line.”
BackgroundComprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898
Self-citationHigh-Resolution Profiling of Histone Methylations in the Human Genome
Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Schones, D. E., Wang, Z., Wei, G., Chepelev, I., & Zhao, K. (2007) · Cell · doi:10.1016/j.cell.2007.05.009 · Cited by 6,845
Topological domains in mammalian genomes identified by analysis of chromatin interactions
Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012) · Nature · doi:10.1038/nature11082 · Cited by 6,829
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Items 401–500 of 1,071
Watanabe, K., Jansen, P. R., Savage, J. E., Nandakumar, P., Wang, X., Agee, M., Aslibekyan, S. W., Auton, A., Bell, R. K., Bryc, K., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01124-w · Cited by 240
Self-citationNovel genetic loci underlying human intracranial volume identified through genome-wide association
Adams, H. H. H., Hibar, D. P., Chouraki, V. A., Stein, J. L., Nyquist, P., Rentería, M. E., Trompet, S., Vásquez, A. A., Seshadri, S. S., Desrivières, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4398 · Cited by 221
DNA methylation in childhood asthma: an epigenome-wide meta-analysis
Xu, C., Söderhäll, C., Bustamante, M., Baïz, N., Gruzieva, O., Gehring, U., Mason, D., Chatzi, L., Basterrechea, M., Llop, S., et al. (2018) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(18)30052-3 · Cited by 236
Genetics of rheumatoid arthritis: 2018 status
Okada, Y., Eyre, S., Suzuki, A., Kochi, Y., & Yamamoto, K. (2018) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2018-213678 · Cited by 242
A mammalian methylation array for profiling methylation levels at conserved sequences
Arneson, A., Haghani, A., Thompson, M. J., Pellegrini, M. A., Kwon, S. B., Vu, H., Maciejewski, E., Yao, M., Li, C. Z., Lu, A. T.-H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28355-z · Cited by 224
Self-citationGenetic and epigenetic characteristics of inflammatory bowel disease-associated colorectal cancer
Rajamäki, K., Taira, A., Katainen, R., Välimäki, N., Kuosmanen, A., Plaketti, R.-M., Seppälä, T. T., Ahtiainen, M., Wirta, E.-V., Vartiainen, E., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.04.042 · Cited by 230
Péneau, C., Imbeaud, S., Bella, T. L., Hirsch, T. Z., Caruso, S., Caldéraro, J., Paradis, V., Blanc, J., Letouzé, É., Nault, J., et al. (2021) · Gut · doi:10.1136/gutjnl-2020-323153 · Cited by 234
Marzi, S. J., Leung, S. K., Ribarska, T., Hannon, E., Smith, A. R., Pishva, E., Poschmann, J., Moore, K., Troakes, C., Al-Sarraj, S., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0253-7 · Cited by 232
Common genetic variation influencing human white matter microstructure
Zhao, B., Li, T., Yang, Y., Wang, X., Luo, T., Shan, Y., Zhu, Z., Xiong, D., Hauberg, M. E., Bendl, J. M., et al. (2021) · Science · doi:10.1126/science.abf3736 · Cited by 222
“First, we used regulatory elements across multiple adult and fetal tissues (107).”
BackgroundMethodSelf-citationi-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly
Imrichová, H., Hulselmans, G., Kalender Atak, Z., Potier, D., & Aerts, S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv395 · Cited by 202
“Bigwig files were used for the ranking of the CRRs for tracks from Roadmap Epigenomics Project.”
MethodNon-invasive detection of human cardiomyocyte death using methylation patterns of circulating DNA
Zemmour, H., Planer, D., Magenheim, J., Moss, J., Neiman, D., Gilon, D., Korach, A., Gläser, B., Shemer, R., Landesberg, G., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03961-y · Cited by 230
Novel technologies and emerging biomarkers for personalized cancer immunotherapy
Yuan, J. D., Hegde, P. S., Clynes, R., Foukas, P. G., Harari, A., Kleen, T. O., Kvistborg, P., Maccalli, C., Maecker, H. T., Page, D. B., et al. (2016) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-016-0107-3 · Cited by 235
Co-activation of super-enhancer-driven CCAT1 by TP63 and SOX2 promotes squamous cancer progression
Jiang, Y. Y., Jiang, Y.-Y., Xie, J., Mayakonda, A., Hazawa, M., Chen, L., Xiao, J.-F., Li, C., Huang, M., Ding, L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06081-9 · Cited by 227
Smeland, O. B., Bahrami, S., Frei, O., Shadrin, A. A., O’Connell, K. S., Savage, J. E., Watanabe, K., Krull, F., Bettella, F., Steen, N. E., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0332-x · Cited by 231
Sequence determinants of human gene regulatory elements
Sahu, B., Hartonen, T., Pihlajamaa, P., Wei, B., Dave, K., Zhu, F., Kaasinen, E., Lidschreiber, K., Lidschreiber, M., Daub, C. O., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-01009-4 · Cited by 198
A systematic evaluation of the design and context dependencies of massively parallel reporter assays
Klein, J. C., Agarwal, V., Inoue, F., Keith, A., Martin, B., Kircher, M., Ahituv, N., & Shendure, J. (2020) · Nature Methods · doi:10.1038/s41592-020-0965-y · Cited by 226
Integrative Analyses of Human Reprogramming Reveal Dynamic Nature of Induced Pluripotency
Cacchiarelli, D., Trapnell, C., Ziller, M. J., Soumillon, M., Cesana, M., Karnik, R., Donaghey, J., Smith, Z. D., Ratanasirintrawoot, S., Zhang, X., et al. (2015) · Cell · doi:10.1016/j.cell.2015.06.016 · Cited by 240
Self-citationPaternally inherited cis-regulatory structural variants are associated with autism
Brandler, W. M., Antaki, D., Gujral, M., Kleiber, M. L., Whitney, J., Maile, M. S., Hong, O., Chapman, T. R., Tan, S., Tandon, P., et al. (2018) · Science · doi:10.1126/science.aan2261 · Cited by 209
Chen, K., Wei, Z. C., Zhang, Q., Wu, X., Rong, R., Lu, Z.-L., Su, J., de Magalhães, J. P., Rigden, D. J., & Meng, J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz074 · Cited by 227
Recent progress in genetic and epigenetic research on type 2 diabetes
Kwak, S. H., & Park, K. S. (2016) · Experimental & Molecular Medicine · doi:10.1038/emm.2016.7 · Cited by 228
Functional Dissection of the Enhancer Repertoire in Human Embryonic Stem Cells
Barakat, T. S., Halbritter, F., Zhang, M., Rendeiro, A. F., Perenthaler, E., Bock, C., & Chambers, I. (2018) · Cell stem cell · doi:10.1016/j.stem.2018.06.014 · Cited by 227
Self-citationSchork, A. J., Won, H., Appadurai, V., Nudel, R., Gandal, M., Delaneau, O., Revsbech Christiansen, M., Hougaard, D. M., Bækved-Hansen, M., Bybjerg-Grauholm, J., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0320-0 · Cited by 223
Cao, Q., Anyansi, C., Hu, X. S., Xu, L., Xiong, L., Tang, W., Mok, M. T. S., Cheng, C., Fan, X., Gerstein, M., et al. (2017) · Nature Genetics · doi:10.1038/ng.3950 · Cited by 220
Huan, T., Joehanes, R., Song, C., Peng, F., Guo, Y., Mendelson, M., Yao, C., Liu, C., Ma, J., Richard, M. A., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12228-z · Cited by 224
Influence of donor age on induced pluripotent stem cells
Lo Sardo, V., Ferguson, W., Erikson, G. A., Topol, E. J., Baldwin, K. K., & Torkamani, A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3749 · Cited by 223
Doorenweerd, N., Mahfouz, A. E., van Putten, M., Kaliyaperumal, R., Hoen, P. A. C. ’., Hendriksen, J. G. M., Aartsma-Rus, A. M., Verschuuren, J. J. G. M., Niks, E. H., Reinders, M. J. T., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-12981-5 · Cited by 235
Lu, Q., Li, B., Ou, D., Erlendsdottir, M., Powles, R. L., Jiang, T., Hu, Y., Chang, D., Jin, C., Dai, W., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.11.001 · Cited by 220
DeepC: predicting 3D genome folding using megabase-scale transfer learning
Schweßinger, R., Gosden, M. E., Downes, D. J., Brown, R. C., Oudelaar, A. M., Telenius, J. M., Teh, Y. W., Lunter, G. A., & Hughes, J. R. (2020) · Nature Methods · doi:10.1038/s41592-020-0960-3 · Cited by 223
“In a first phase of training the initial convolutional module learns to predict a compendium of chromatin features such as open chromatin regions and CTCF binding sites across cell types 12,18,19 .”
BackgroundHan, Y., Jia, Q., Jahani, P. S., Hurrell, B. P., Pan, C., Huang, P., Gukasyan, J., Woodward, N. C., Eskin, E., Gilliland, F. D., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15649-3 · Cited by 222
Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis
Fiorito, G., the BIOS Consortium, the Lifepath consortium, McCrory, C., Robinson, O., Carmeli, C., Rosales, C. O., Zhang, Y., Colicino, E., Dugué, P.-A., et al. (2019) · Aging · doi:10.18632/aging.101900 · Cited by 219
“The genomic locations of SEMs were annotated by merging the Illumina information on the chromosomal position of each probe with ENCODE/NIH Roadmap ChIP-Seq data for chromatin states and transcription factor binding sites (TFBS) in hESC [42-44].”
MethodDynamic genetic regulation of gene expression during cellular differentiation
Strober, B. J., Elorbany, R., Rhodes, K., Krishnan, N., Tayeb, K., Battle, A., & Gilad, Y. (2019) · Science · doi:10.1126/science.aaw0040 · Cited by 219
Pan, Z., Yao, Y., Yin, H. H., Cai, Z., Wang, Y., Bai, L., Kern, C., Halstead, M. M., Chanthavixay, G., Trakooljul, N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26153-7 · Cited by 214
Stevelink, R., Campbell, C., Chen, S., Abou-Khalil, B. W., Adesoji, O. M., Afawi, Z., Amadori, E., Anderson, A., Anderson, J., Andrade, D. M., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01485-w · Cited by 242
Mutant-IDH1-dependent chromatin state reprogramming, reversibility, and persistence
Turcan, Ş., Makarov, V., Taranda, J., Wang, Y., Fabius, A. W. M., Wu, W., Zheng, Y., El-Amine, N., Haddock, S., Nanjangud, G. J., et al. (2017) · Nature Genetics · doi:10.1038/s41588-017-0001-z · Cited by 221
Butcher, D. T., Cytrynbaum, C., Turinsky, A. L., Siu, M. T., Inbar-Feigenberg, M., Mendoza-Londono, R., Chitayat, D., Walker, S., Machado, J., Caluseriu, O., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.04.004 · Cited by 200
Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling
Mack, S. C., Pajtler, K. W., Chávez, L., Okonechnikov, K., Bertrand, K. C., Wang, X., Erkek, S., Alexander Joel Federation, Song, A., Lee, C., et al. (2017) · Nature · doi:10.1038/nature25169 · Cited by 217
Self-citationCollaborators, N., Wu, L., kConFab/AOCS Investigators, Shi, W., Long, J., Guo, X., Michailidou, K., Beesley, J., Bolla, M. K., Shu, X., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0132-x · Cited by 208
Causality-enriched epigenetic age uncouples damage and adaptation
Ying, K., Liu, H., Tarkhov, A. E., Sadler, M. C., Lu, A. T.-H., Moqri, M., Horvath, S., Kutalik, Z., Shen, X., & Gladyshev, V. N. (2024) · Nature Aging · doi:10.1038/s43587-023-00557-0 · Cited by 222
“…for aging-GIP1 are significantly more evolutionarily conserved compared to noncausal CpGs, based on both functional genomic conservation scores (Learning Evidence of Conservation from Integrated Functional genomic annotations) and the phastCons/phyloP scores across 100 vertebrate genomes 24 (Fig.”
Crossing epigenetic frontiers: the intersection of novel histone modifications and diseases
Yao, W., Hu, X., & Wang, X. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01918-w · Cited by 257
“43 Over the past fi ve years, a multitude of epigenetic drugs targeting DNA methyltransferases, HDACs, HMTs, and BET proteins have been tested in clinical trials, investigating their ef fi cacy in treating various diseases, both as monotherapies and in combination therapies.”
BackgroundSelf-citationRewiring of human neurodevelopmental gene regulatory programs by human accelerated regions
Girskis, K. M., Stergachis, A. B., DeGennaro, E. M., Doan, R. N., Qian, X., Johnson, M. B., Wang, P. P., Sejourne, G. M., Nagy, M. A., Pollina, E. A., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.005 · Cited by 193
Genetic determinants of co-accessible chromatin regions in activated T cells across humans
Gate, R. E., Cheng, C. S., Aiden, A. P., Siba, A., Tabaka, M., Lituiev, D., Machol, I., Gordon, M. G., Subramaniam, M., Shamim, M., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0156-2 · Cited by 181
Tebani, A., Afonso, C., Marret, S., & Bekri, S. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17091555 · Cited by 228
Wu, Y., Murray, G. K., Byrne, E. M., Sidorenko, J., Visscher, P. M., & Wray, N. R. (2021) · Nature Communications · doi:10.1038/s41467-021-21280-7 · Cited by 220
Self-citationSouroullas, G. P., Jeck, W. R., Parker, J. S., Simon, J. M., Liu, J.-Y., Paulk, J., Xiong, J., Clark, K. S., Fedoriw, Y., Qi, J., et al. (2016) · Nature Medicine · doi:10.1038/nm.4092 · Cited by 212
HiCPlotter integrates genomic data with interaction matrices
Akdemir, K. C., & Chin, L. (2015) · Genome biology · doi:10.1186/s13059-015-0767-1 · Cited by 216
Integrative analysis of reference epigenomes in 20 rice varieties
Zhao, L., Xie, L., Zhang, Q., Ouyang, W., Deng, L., Guan, P., Ma, M., Li, Y., Zhang, Y., Xiao, Q., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16457-5 · Cited by 206
Liu, H., Doke, T., Guo, D., Sheng, X., Ma, Z., Park, J., Vy, H. M. T., Nadkarni, G. N., Abedini, A., Miao, Z., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01097-w · Cited by 216
Epigenetic ageing clocks: statistical methods and emerging computational challenges
Teschendorff, A. E., & Horvath, S. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-024-00807-w · Cited by 226
“…of non-epigenetic ageing clocks 182,183 , could help realize these goals, for instance, by incorporating the spatial genomic embedding of DNAm values in the context of prior epigenetic data (for example, chromatin states) from resources such as the International Human Epigenome Consortium 179,184 .”
Link, V. M., Duttke, S. H., Chun, H. B., Holtman, I. R., Westin, E., Hoeksema, M. A., Abe, Y., Skola, D., Romanoski, C. E., Tao, J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.04.018 · Cited by 182
Self-citationGenetic regulatory signatures underlying islet gene expression and type 2 diabetes
Varshney, A., Scott, L. J., Welch, R., Erdos, M. R., Chines, P. S., Narisu, N., Albanus, R. D., Orchard, P., Wolford, B. N., Kursawe, R., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621192114 · Cited by 198
Human sex reversal is caused by duplication or deletion of core enhancers upstream of SOX9
Croft, B., Ohnesorg, T., Hewitt, J. K., Bowles, J., Quinn, A., Tan, J., Corbin, V., Pelosi, E., van den Bergen, J., Sreenivasan, R., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07784-9 · Cited by 216
Haller, F., Bieg, M., Will, R. D., Körner, C., Weichenhan, D., Bott, A., Ishaque, N., Lutsik, P., Moskalev, E. A., Mueller, S. K., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08069-x · Cited by 209
“Correlation of enhancer activity determined in the dual luciferase reporter assay for ten different genomic fragments (upper panel) with chromatin marks determined by ChIP-seq analyzes in parotid salivary gland tissue (middle panel) and putative enhancer regions based on genome wide chromatin segmentation (lower panel…”
A large data resource of genomic copy number variation across neurodevelopmental disorders
Zarrei, M., Burton, C. L., Engchuan, W., Young, E. J., Higginbotham, E. J., MacDonald, J. R., Trost, B., Chan, A. J. S., Walker, S., Lamoureux, S., et al. (2019) · npj Genomic Medicine · doi:10.1038/s41525-019-0098-3 · Cited by 187
Self-citationInterrogation of human hematopoiesis at single-cell and single-variant resolution
Ulirsch, J. C., Lareau, C. A., Bao, E. L., Ludwig, L. S., Guo, M. H., Benner, C., Satpathy, A. T., Kartha, V. K., Salem, R. M., Hirschhorn, J. N., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0362-6 · Cited by 208
Epigenome Editing: State of the Art, Concepts, and Perspectives
Kungulovski, G., & Jeltsch, A. (2015) · Trends in Genetics · doi:10.1016/j.tig.2015.12.001 · Cited by 212
Warrier, V., Kwong, A. S. F., Luo, M., Dalvie, S., Croft, J., Sallis, H. M., Baldwin, J., Munafò, M. R., Nievergelt, C. M., Grant, A. J., et al. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30569-1 · Cited by 224
Cell-type deconvolution in epigenome-wide association studies: a review and recommendations
Teschendorff, A. E., & Zheng, S. (2017) · Epigenomics · doi:10.2217/epi-2016-0153 · Cited by 206
Iotchkova, V., Ritchie, G. R. S., Geihs, M., Morganella, S., Min, J. L., Walter, K., Timpson, N. J., Dunham, I., Birney, E., & Soranzo, N. (2019) · Nature Genetics · doi:10.1038/s41588-018-0322-6 · Cited by 216
Guo, W., Zhu, P., Pellegrini, M., Zhang, M. Q., Wang, X., & Ni, Z. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx595 · Cited by 198
Self-citationLandscape of cohesin-mediated chromatin loops in the human genome
Grubert, F., Srivas, R., Spacek, D. V., Kasowski, M. M., Ruiz-Velasco, M., Sinnott-Armstrong, N., Greenside, P., Narasimha, A. M., Liu, Q., Geller, B. S., et al. (2020) · Nature · doi:10.1038/s41586-020-2151-x · Cited by 203
Self-citationGWAS of epigenetic aging rates in blood reveals a critical role for TERT
Lu, A. T.-H., Xue, L., Salfati, E., Chen, B. H., Ferrucci, L., Levy, D. A., Joehanes, R., Murabito, J. M., Kiel, D. P., Tsai, P.-C., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02697-5 · Cited by 208
Epigenetic editing of the Dlg4/PSD95 gene improves cognition in aged and Alzheimer’s disease mice
Bustos, F. J., Ampuero, E., Jury, N., Aguilar, R., Falahi, F., Toledo, J. A., Ahumada, J., Lata, J., Cubillos, P., Henríquez, B., et al. (2017) · Brain · doi:10.1093/brain/awx272 · Cited by 203
Liu, X., Xu, J., Rosenthal, S., Zhang, L., McCubbin, R., Meshgin, N., Shang, L., Koyama, Y., Ma, H., Sharma, S., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.01.027 · Cited by 208
Small, K. S., Todorčević, M., Civelek, M., El-Sayed Moustafa, J. S., Wang, X., Simon, M. M., Fernandez-Tajes, J., Mahajan, A., Horikoshi, M., Hugill, A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0088-x · Cited by 168
Self-citationVujkovic, M., Ramdas, S., Lorenz, K. M., Guo, X., Darlay, R., Cordell, H. J., He, J., Gindin, Y., Chung, C., Myers, R. P., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01078-z · Cited by 205
Epigenetic restriction of extraembryonic lineages mirrors the somatic transition to cancer
Smith, Z. D., Shi, J., Gu, H., Donaghey, J., Clement, K., Cacchiarelli, D., Gnirke, A., Michor, F., & Meissner, A. (2017) · Nature · doi:10.1038/nature23891 · Cited by 201
Self-citationShah, N., Wang, P., Wongvipat, J., Karthaus, W. R., Abida, W. M., Armenia, J., Rockowitz, S., Drier, Y., Bernstein, B., Long, H. W., et al. (2017) · eLife · doi:10.7554/elife.27861 · Cited by 207
Self-citationKöttgen, A., Gall, É. C.-L., Halbritter, J., Kiryluk, K., Mallett, A. J., Parekh, R. S., Rasouly, H. M., Sampson, M. G., Tin, A., Antignac, C., et al. (2022) · Kidney International · doi:10.1016/j.kint.2022.03.019 · Cited by 217
“In this regard, participants of this KDIGO Controversies Conference did not reach consensus regarding renaming of traditional disease terms, such as Alport Syndrome.”
BackgroundEpigenomic dissection of Alzheimer’s disease pinpoints causal variants and reveals epigenome erosion
Xiong, X., James, B. T., Boix, C. A., Park, Y. P., Galani, K., Victor, M. B., Sun, N., Hou, L., Ho, L.-L., Mantero, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.040 · Cited by 205
The chromatin accessibility signature of human immune aging stems from CD8+ T cells
Ucar, D., Márquez, E. J., Chung, C.-H., Marches, R., Rossi, R. J., Uyar, A., Wu, T.-C., George, J., Stitzel, M. L., Palucka, A. K., et al. (2017) · Journal of Experimental Medicine · doi:10.1084/jem.20170416 · Cited by 200
Romero, I. G., Pavlovic, B. J., Hernando-Herraez, I., Zhou, X., Ward, M. C., Banovich, N. E., Kagan, C. L., Burnett, J. E., Huang, C. H., Mitrano, A., et al. (2015) · eLife · doi:10.7554/elife.07103 · Cited by 197
Self-citationKiryluk, K., Sánchez, E., Zhou, S., Zanoni, F., Liu, L., Mladkova, N., Khan, A., Marasà, M., Zhang, J. Y., Balderes, O., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01422-x · Cited by 212
Inferring regulatory element landscapes and transcription factor networks from cancer methylomes
Yao, L., Shen, H., Laird, P. W., Farnham, P., & Berman, B. P. (2015) · Genome biology · doi:10.1186/s13059-015-0668-3 · Cited by 193
Self-citationCarlberg, C. (2019) · Nutrients · doi:10.3390/nu11030676 · Cited by 211
“org), collected huge amounts of data on the basal state of more than hundred human cell lines [8] as well as on about the same number of primary human tissues and cell types [9], respectively.”
BackgroundWang, X., He, L., Goggin, S. M., Saadat, A., Wang, L., Sinnott-Armstrong, N., Claussnitzer, M., & Kellis, M. (2018) · Nature Communications · doi:10.1038/s41467-018-07746-1 · Cited by 159
Self-citationChromatin accessibility profiling methods
Minnoye, L., Marinov, G. K., Krausgruber, T., Pan, L., Marand, A. P., Secchia, S., Greenleaf, W. J., Furlong, E. E. M., Zhao, K., Schmitz, R. J., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00008-9 · Cited by 206
Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies
Kichaev, G., & Paşaniuc, B. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.06.007 · Cited by 188
Takata, A., Matsumoto, N., & Kato, T. (2017) · Nature Communications · doi:10.1038/ncomms14519 · Cited by 192
snakePipes: facilitating flexible, scalable and integrative epigenomic analysis
Bhardwaj, V., Heyne, S., Sikora, K., Rabbani, L., Rauer, M., Kilpert, F., Richter, A. S., Ryan, D. P., & Manke, T. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz436 · Cited by 195
Coregulation of tandem duplicate genes slows evolution of subfunctionalization in mammals
Lan, X., & Pritchard, J. K. (2016) · Science · doi:10.1126/science.aad8411 · Cited by 194
The reference epigenome and regulatory chromatin landscape of chronic lymphocytic leukemia
Beekman, R., Chapaprieta, V., Russiñol, N., Vilarrasa‐Blasi, R., Verdaguer-Dot, N., Martens, J. H. A., Duran‐Ferrer, M., Kulis, M., Serra, F., Javierre, B. M., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0028-4 · Cited by 196
Slieker, R. C., Relton, C. L., Gaunt, T. R., Slagboom, P. E., & Heijmans, B. T. (2018) · Epigenetics & Chromatin · doi:10.1186/s13072-018-0191-3 · Cited by 207
Cedernaes, J., Osler, M. E., Voisin, S., Broman, J.-E., Vogel, H., Dickson, S. L., Zierath, J. R., Schiöth, H. B., & Benedict, C. (2015) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2015-2284 · Cited by 188
Romanoski, C. E., Glass, C. K., Stunnenberg, H. G., Wilson, L., & Almouzni, G. (2015) · Nature · doi:10.1038/518314a · Cited by 192
Epigenetics of Atherosclerosis: Emerging Mechanisms and Methods
Khyzha, N., Alizada, A., Wilson, M. D., & Fish, J. E. (2017) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2017.02.004 · Cited by 196
Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits
Tachmazidou, I., Süveges, D., Min, J. L., Ritchie, G. R. S., Steinberg, J., Walter, K., Iotchkova, V., Schwartzentruber, J., Huang, J., Memari, Y., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.04.014 · Cited by 192
Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia
Bryois, J., Garrett, M. E., Song, L., Safi, A., Giusti-Rodriguez, P., Johnson, G. D., Shieh, A. W., Buil, A., Fullard, J. F., Roussos, P., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05379-y · Cited by 167
“ENCODE14 and the Roadmap Epigenomics Mapping Consortium15 provided considerable human functional genomic data and insights into genomic function.”
Backgroundvel Szic, K. S., Declerck, K., Vidaković, M., & Vanden Berghe, W. (2015) · Clinical Epigenetics · doi:10.1186/s13148-015-0068-2 · Cited by 192
Efficient Integrative Multi-SNP Association Analysis via Deterministic Approximation of Posteriors
Wen, X., Lee, Y., Luca, F., & Piqué-Regi, R. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.03.029 · Cited by 179
Histone H3 lysine 4 monomethylation modulates long-range chromatin interactions at enhancers
Yan, J., Chen, S.-A. A., Local, A., Liu, T., Qiu, Y., Lee, A. Y., Jung, I., Preißl, S., Rivera, C. M., Wang, C., et al. (2018) · Cell Research · doi:10.1038/cr.2018.1 · Cited by 198
Self-citationYamagishi, M., Hori, M., Fujikawa, D., Ohsugi, T., Honma, D., Adachi, N., Katano, H., Hishima, T., Kobayashi, S., Nakano, K., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.083 · Cited by 191
How to rule the nucleus: divide et impera
Solovei, I. V., Thanisch, K., & Feodorova, Y. N. (2016) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2016.02.014 · Cited by 198
Alteration of Epigenetic Regulation by Long Noncoding RNAs in Cancer
Morlando, M., & Fatica, A. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19020570 · Cited by 197
Cui, X., Nie, J., Ku, J., Dougherty, U., West-Szymanski, D. C., Collin, F., Ellison, C. K., Sieh, L., Ning, Y., Deng, Z., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20001-w · Cited by 196
Self-citationAn atlas of chromatin accessibility in the adult human brainPreprint
Fullard, J. F., Hauberg, M. E., Bendl, J. M., Egervári, G., Cirnaru, M., Reach, S. M., Motl, J., Ehrlich, M. E., Hurd, Y. L., & Roussos, P. (2018) · Genome Research · doi:10.1101/gr.232488.117 · Cited by 183
Malaria Genomic Epidemiology Network, Band, G., Le, S. Q., Clarke, G. M., Kivinen, K., Hubbart, C., Jeffreys, A. E., Rowlands, K., Leffler, E. M., Jallow, M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13480-z · Cited by 182
Blood lipids influence DNA methylation in circulating cells
Dekkers, K. F., van Iterson, M., Slieker, R. C., Moed, M. H., Bonder, M. J., van Galen, M., Mei, H., Zhernakova, D. V., van den Berg, L. H., Deelen, J., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1000-6 · Cited by 190
Wang, A., Chiou, J., Poirion, O., Buchanan, J., Valdez, M. J., Verheyden, J. M., Hou, X., Kudtarkar, P., Narendra, S., Newsome, J. M., et al. (2020) · eLife · doi:10.7554/elife.62522 · Cited by 207
“Cell-type-specific expression profiles are largely established by distal CREs such as enhancers (ENCODE Project Consortium, 2012; Moore et al., 2020; Kundaje et al., 2015).”
BackgroundCircadian Entrainment Triggers Maturation of Human In Vitro Islets
Alvarez-Dominguez, J. R., Donaghey, J., Rasouli, N., Kenty, J. H. R., Helman, A., Charlton, J., Straubhaar, J. R., Meissner, A., & Melton, D. A. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2019.11.011 · Cited by 189
Self-citationA Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci
Hoffmann, T. J., Choquet, H., Yin, J., Banda, Y., Kvale, M. N., Glymour, M. M., Schaefer, C. A., RISCH, N. J., & Jorgenson, E. (2018) · Genetics · doi:10.1534/genetics.118.301479 · Cited by 181
“Out of the 30 SNPs, 10 had a significant GTEx eQTL (Table S10) in a wide range of tissues.”
MethodInfluential
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
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