The Ties That Bind: Mapping the Dynamic Enhancer-Promoter Interactome
2016; Cell Press; Volume: 167; Issue: 5 Linguagem: Inglês
10.1016/j.cell.2016.10.054
ISSN1097-4172
AutoresCailyn H. Spurrell, Diane E. Dickel, Axel Visel,
Tópico(s)Chromosomal and Genetic Variations
ResumoCoupling chromosome conformation capture to molecular enrichment for promoter-containing DNA fragments enables the systematic mapping of interactions between individual distal regulatory sequences and their target genes. In this Minireview, we describe recent progress in the application of this technique and related complementary approaches to gain insight into the lineage- and cell-type-specific dynamics of interactions between regulators and gene promoters. Coupling chromosome conformation capture to molecular enrichment for promoter-containing DNA fragments enables the systematic mapping of interactions between individual distal regulatory sequences and their target genes. In this Minireview, we describe recent progress in the application of this technique and related complementary approaches to gain insight into the lineage- and cell-type-specific dynamics of interactions between regulators and gene promoters. Distal regulatory elements, such as enhancers, play a central role in controlling expression in mammalian genomes. Enhancer sequences act as substrates for binding of tissue-specific transcription factors and drive transcription through physical interaction with gene promoters (Spitz and Furlong, 2012Spitz F. Furlong E.E.M. Nat. Rev. Genet. 2012; 13: 613-626Crossref PubMed Scopus (1168) Google Scholar). Recent chromatin profiling studies reveal the exceptional cell type and temporal specificity of enhancer activity, which exceeds that of other classes of gene regulatory sequences (Ernst and Kellis, 2010Ernst J. Kellis M. Nat. Biotechnol. 2010; 28: 817-825Crossref PubMed Scopus (728) Google Scholar, Nord et al., 2013Nord A.S. Blow M.J. Attanasio C. Akiyama J.A. Holt A. Hosseini R. Phouanenavong S. Plajzer-Frick I. Shoukry M. Afzal V. et al.Cell. 2013; 155: 1521-1531Abstract Full Text Full Text PDF PubMed Scopus (246) Google Scholar). This stunning specificity, alongside advances in sequencing technologies and the increasingly recognized importance of non-coding sequences in human development and disease, has driven large-scale efforts to annotate regulatory elements and gene transcription in the human genome under a wide variety of conditions. The International Human Epigenome Consortium (IHEC) (Bae, 2013Bae J.-B. Genomics Inform. 2013; 11: 7-14Crossref PubMed Google Scholar) connects many of these projects, with the goal of characterizing 1,000 epigenomes from different human cell types at diverse developmental stages and disease states. New studies published in this issue of Cell and in Cell Reports and described in greater detail throughout the following sections of this Minireview build upon IHEC efforts to explore the role of cell-type-specific regulation and begin to address several important challenges in the field (Schmitt et al., 2016Schmitt A.D. Hu M. Jung I. Xu Z. Qiu Y. Tan C.L. Li Y. Lin S. Lin Y. Barr C.L. Ren B. Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.061Abstract Full Text Full Text PDF PubMed Scopus (422) Google Scholar, Javierre et al., 2016Javierre B.M. Burren O.S. Wilder S.P. Kreuzhuber R. Hill S.M. Sewitz S. Cairns J. Wingett S.W. Várnai C. Thiecke M.J. et al.Cell. 2016; 167 (this issue): 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar, Breeze et al., 2016Breeze C.E. Paul D.S. van Dongen J. Butcher L.M. Ambrose J.C. Barrett J.E. Lowe R. Rakyan V.K. Iotchkova V. Frontini F. et al.Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.059Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar, Pellacani et al., 2016Pellacani D. Bilenky M. Kannan N. Heravi-Moussavi A. Knapp D.J.H.F. Gakkhar S. Moksa M. Carles A. Moore R. Mungall A.J. et al.Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.058Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar). In brief, Pellacani et al., 2016Pellacani D. Bilenky M. Kannan N. Heravi-Moussavi A. Knapp D.J.H.F. Gakkhar S. Moksa M. Carles A. Moore R. Mungall A.J. et al.Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.058Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar tackle the question of cell type specificity of enhancers across the individual cell types that make up heterogeneous tissues. The authors use chromatin profiling methods to identify regulatory elements active in the distinct cell populations that comprise mammary tissue. While chromatin profiling is powerful for identifying predicted enhancer sequences, it is limited in its ability to elucidate the gene target(s) of the predicted enhancers. To address this challenge, Javierre et al., 2016Javierre B.M. Burren O.S. Wilder S.P. Kreuzhuber R. Hill S.M. Sewitz S. Cairns J. Wingett S.W. Várnai C. Thiecke M.J. et al.Cell. 2016; 167 (this issue): 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar and Schmitt et al., 2016Schmitt A.D. Hu M. Jung I. Xu Z. Qiu Y. Tan C.L. Li Y. Lin S. Lin Y. Barr C.L. Ren B. Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.061Abstract Full Text Full Text PDF PubMed Scopus (422) Google Scholar use cutting-edge chromosome conformation capture techniques to map enhancer-promoter interactions in a variety of human tissues and primary cell types. Finally, disease-associated variants identified in genome-wide association studies (GWAS) are overwhelmingly non-coding (Altshuler et al., 2010Altshuler D. Lander E. Ambrogio L.A. Nature. 2010; 476: 1061-1073Google Scholar, Visel et al., 2009Visel A. Rubin E.M. Pennacchio L.A. Nature. 2009; 461: 199-205Crossref PubMed Scopus (441) Google Scholar) and are enriched in non-coding loci harboring regulatory functions (Maurano et al., 2012Maurano M.T. Humbert R. Rynes E. Thurman R.E. Haugen E. Wang H. Reynolds A.P. Sandstrom R. Qu H. Brody J. et al.Science. 2012; 337: 1190-1195Crossref PubMed Scopus (2200) Google Scholar), but specific examples of non-coding sequence variants conclusively and mechanistically linked to disease remain limited. The functional genome annotations from the series of new papers (Schmitt et al., 2016Schmitt A.D. Hu M. Jung I. Xu Z. Qiu Y. Tan C.L. Li Y. Lin S. Lin Y. Barr C.L. Ren B. Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.061Abstract Full Text Full Text PDF PubMed Scopus (422) Google Scholar, Javierre et al., 2016Javierre B.M. Burren O.S. Wilder S.P. Kreuzhuber R. Hill S.M. Sewitz S. Cairns J. Wingett S.W. Várnai C. Thiecke M.J. et al.Cell. 2016; 167 (this issue): 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar, Pellacani et al., 2016Pellacani D. Bilenky M. Kannan N. Heravi-Moussavi A. Knapp D.J.H.F. Gakkhar S. Moksa M. Carles A. Moore R. Mungall A.J. et al.Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.058Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar) along with a computational algorithm capable of integrating epigenomic findings described in Breeze et al., 2016Breeze C.E. Paul D.S. van Dongen J. Butcher L.M. Ambrose J.C. Barrett J.E. Lowe R. Rakyan V.K. Iotchkova V. Frontini F. et al.Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.059Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar provide handy tools for addressing the gap between disease-associated non-coding variants and their regulatory gene targets. Using these complementary techniques to explore the regulatory landscape in human tissues and isolated primary cell populations, these studies report insights and resources that will be instrumental in linking variants with causal mechanisms of disease. Histone ChIP-seq has now become a standard method to identify regulatory regions genome-wide (Park, 2009Park P.J. Nat. Rev. Genet. 2009; 10: 669-680Crossref PubMed Scopus (1305) Google Scholar). ChIP-seq combines chromatin immunoprecipitation of modified histones with high-throughput sequencing to identify active enhancers and other regulatory features. While the underlying DNA sequence does not vary between cell types, histone modifications mark regions that are active or repressed in vivo in a tissue-specific manner. When paired with technologies for capturing specific cell types, ChIP-seq can be used to identify differential regulation in cell populations derived from heterogeneous tissue. An elegant example of this approach is provided by Pellacani et al., 2016Pellacani D. Bilenky M. Kannan N. Heravi-Moussavi A. Knapp D.J.H.F. Gakkhar S. Moksa M. Carles A. Moore R. Mungall A.J. et al.Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.058Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar, who generate histone ChIP-seq, DNA methylation, and gene expression data to identify cell-type-specific regulatory elements in primary human mammary tissue. Consistent with previous findings (Gascard et al., 2015Gascard P. Bilenky M. Sigaroudinia M. Zhao J. Li L. Carles A. Delaney A. Tam A. Kamoh B. Cho S. et al.Nat. Commun. 2015; 6: 6351Crossref PubMed Scopus (44) Google Scholar), their results show widespread differences among the different cell types isolated from this heterogeneous tissue and relative to previous results from immortalized mammary cell lines. The biological relevance of these observations is reinforced by the findings that differential enhancer utilization in mammary cell types is consistent with cell-specific gene expression and that cell-type-specific enhancers are enriched for unique transcription factor binding sites. This view of enhancer activity mirrors results from previous chromatin profiling studies, and these data allow the authors to derive insights into the cells that make up a complex tissue. While ChIP-seq can identify differential activity of regulatory elements across tissues and cell types, it does not provide evidence that formally links individual distal regulatory elements to their respective target genes. Tools based on chromosome conformation capture (3C) enable the identification of genomic regions that can be far apart in the linear genome sequence but are proximate in three-dimensional (3D) space within the nucleus. Hi-C, one variant of 3C, identifies these distal yet interacting partners on a global genomic scale by digesting cross-linked chromatin and ligating physically interacting fragments together (Lieberman-Aiden et al., 2009Lieberman-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.Science. 2009; 326: 289-293Crossref PubMed Scopus (4868) Google Scholar). The resulting libraries are sequenced without further molecular enrichment for marks associated with any particular functional class of genomic elements, thereby creating a largely unbiased genome-wide map of chromatin architecture. The high complexity of these libraries requires deep sequencing to identify statistically significant interactions. Thus, the approach was initially used to identify megabase-scale topologically associated domains (TADs) of chromosome organization (Dixon et al., 2012Dixon J.R. Selvaraj S. Yue F. Kim A. Li Y. Shen Y. Hu M. Liu J.S. Ren B. Nature. 2012; 485: 376-380Crossref PubMed Scopus (3955) Google Scholar). This high-level architecture tends to be conserved across cell types and mammalian species, but initial datasets yielded limited insight into intra-TAD interactions. Efforts to create higher-resolution maps require an order of magnitude more sequencing but are able to provide kilobase resolution views of such interactions (Rao et al., 2014Rao S.S.P. Huntley M.H. Durand N.C. Stamenova E.K. Bochkov I.D. Robinson J.T. Sanborn A.L. Machol I. Omer A.D. Lander E.S. Aiden E.L. Cell. 2014; 159: 1665-1680Abstract Full Text Full Text PDF PubMed Scopus (3598) Google Scholar). A new paper by Schmitt et al. reports Hi-C analysis of 14 primary human tissues and describes computational methods to identify new features of genomic architecture. The authors designed an algorithm to normalize sequencing depth variation across tissues, which allows them to identify both TADs and cell-specific interactions. Consistent with the results from previous cell-based studies, the authors observed that TAD structure is stable across different human tissues. Beyond the resolution of TADs, however, high-resolution chromatin loops have been described to partition the genome into smaller domains within the TAD structure (Rao et al., 2014Rao S.S.P. Huntley M.H. Durand N.C. Stamenova E.K. Bochkov I.D. Robinson J.T. Sanborn A.L. Machol I. Omer A.D. Lander E.S. Aiden E.L. Cell. 2014; 159: 1665-1680Abstract Full Text Full Text PDF PubMed Scopus (3598) Google Scholar). Reinforcing these previous observations, a subset of the interactions reported by Schmitt et al. represents a distinct set of sub-TAD regulatory networks. The chromatin interactions within TADs show a remarkable degree of tissue specificity; ∼40% of interactions are unique to one tissue type. These tissue-specific interaction regions tend to be located near genes with tissue-specific expression, and they are enriched for marks of active enhancers. These findings can begin to be used to directly link genes with some of their non-coding regulatory elements, and they further demonstrate the diverse regulatory landscape across human tissues. A second paper, by Javierre et al., 2016Javierre B.M. Burren O.S. Wilder S.P. Kreuzhuber R. Hill S.M. Sewitz S. Cairns J. Wingett S.W. Várnai C. Thiecke M.J. et al.Cell. 2016; 167 (this issue): 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar, defines even more specific chromatin interaction architecture using a variant of Hi-C that employs biotinylated RNA baits to enrich for interactions involving promoter sequences (Schoenfelder et al., 2015Schoenfelder S. Furlan-Magaril M. Mifsud B. Tavares-Cadete F. Sugar R. Javierre B.-M. Nagano T. Katsman Y. Sakthidevi M. Wingett S.W. et al.Genome Res. 2015; 25: 582-597Crossref PubMed Scopus (275) Google Scholar). This promoter capture Hi-C (PCHi-C) technology results in libraries with far lower complexity than standard Hi-C, greatly reducing the amount of sequencing required and resulting in high-resolution maps showing interactions between promoters and other loci. Javierre et al. applied this method to 17 primary human cell types from the hematopoietic lineage to further characterize the types of loci that interact with promoters and to understand how long-range interactions between promoters and other loci evolve during cell differentiation. The observed interactions anchored on promoters span a median distance of ∼300 kb, and the distal interacting partners do not always link to the closest gene by linear distance. Consistent with the Schmitt et al., 2016Schmitt A.D. Hu M. Jung I. Xu Z. Qiu Y. Tan C.L. Li Y. Lin S. Lin Y. Barr C.L. Ren B. Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.061Abstract Full Text Full Text PDF PubMed Scopus (422) Google Scholar study, these distal regions identified as interacting with promoters are enriched for chromatin marks associated with active enhancers. Javierre et al., 2016Javierre B.M. Burren O.S. Wilder S.P. Kreuzhuber R. Hill S.M. Sewitz S. Cairns J. Wingett S.W. Várnai C. Thiecke M.J. et al.Cell. 2016; 167 (this issue): 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar further investigate the biological role of promoter-interacting regions by comparing them to previously reported expression quantitative trait loci (eQTLs). Expression QTLs are identified by measuring gene expression in a population of cells and linking expression differences to alleles of a sequence variant (Cookson et al., 2009Cookson W. Liang L. Abecasis G. Moffatt M. Lathrop M. Nat. Rev. Genet. 2009; 10: 184-194Crossref PubMed Scopus (612) Google Scholar). Using published eQTL data from several cell types, the authors observe an enrichment for eQTLs in the promoter-interacting regions from the same cell types. In particular, distal regions are enriched for eQTLs that associate with the same interacting gene. This result supports that promoter-interacting regions have a functional regulatory role and that variation within promoter-interacting regions can be connected to potential gene targets. One important finding from Javierre et al., 2016Javierre B.M. Burren O.S. Wilder S.P. Kreuzhuber R. Hill S.M. Sewitz S. Cairns J. Wingett S.W. Várnai C. Thiecke M.J. et al.Cell. 2016; 167 (this issue): 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar is that, in the hematopoietic lineage, chromatin architecture is highly dynamic and lineage-specific interactions delineate the myeloid and lymphoid regulatory landscape. The regulatory complexities of the promoter-interacting regions are schematically outlined in Figure 1. The first column is an example of an invariant interaction between a single promoter and multiple enhancers across all cell types. While invariant interactions are abundant, many interactions vary by cell type. Clustering the promoter-enhancer interactions shows a general divergence between interactions found in the myeloid and lymphoid lineages. Schematic examples of myeloid- and lymphoid-specific interactions are represented in columns 2 and 3 of Figure 1. These interactions are invariant within each lineage but divergent between the two cell lineages. Column 4 shows a CD4+ T cell-specific interaction, representative of cell-type-specific interactions, which were also observed in other individual cell types examined. Surprisingly, ∼80% of promoters had lineage- or cell-type-specific interactions. Further showing the complexity of the regulatory network, in cells of the myeloid and lymphoid lineages the same promoter may be regulated through different enhancer interactions (column 5), and one enhancer can interact with different promoters in a lineage-specific manner (column 6). Javierre et al., 2016Javierre B.M. Burren O.S. Wilder S.P. Kreuzhuber R. Hill S.M. Sewitz S. Cairns J. Wingett S.W. Várnai C. Thiecke M.J. et al.Cell. 2016; 167 (this issue): 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar cluster these highly specific interactions to create a detailed lineage tree of all 17 hematopoietic cell types that recapitulates the known relationships between different cell populations. Consistent with this, promoter-associated enhancers are predicted to be active in a manner that mirrors the cell type specificity of expression of the interacting gene. The authors combined their chromatin interaction data with enhancer annotations and clustered genes according to enhancer specificity for each cell type. This analysis identifies sets of genes that are dynamically regulated in different cell types across the hematopoietic tree. The correlation between cell-type-specific enhancer activity and gene expression supports a functional role for these interactions in regulating cell fate and differentiation. Elucidating the mechanistic role of non-coding sequence variation in human disease remains an unmet challenge. Tissue- and cell-type-specific annotations of regulatory elements generated by ChIP-seq are now widely available through the work of the IHEC members and individual investigators. These efforts represent an important first step in bridging this gap, and work is now being done to integrate these diverse maps together into high-confidence enhancer annotations to identify which disease-associated variants are most likely to impact gene regulatory sequences (Dickel et al., 2016Dickel D.E. Barozzi I. Zhu Y. Fukuda-Yuzawa Y. Osterwalder M. Mannion B.J. May D. Spurrell C.H. Plajzer-Frick I. Pickle C.S. et al.Nat. Commun. 2016; 7: 12923Crossref PubMed Scopus (56) Google Scholar). Chromosome conformation capture techniques complement these datasets by linking tissue-specific enhancers with candidate gene targets, and such approaches are increasingly being used to interpret non-coding disease-associated variation (Martin et al., 2015Martin P. McGovern A. Orozco G. Duffus K. Yarwood A. Schoenfelder S. Cooper N.J. Barton A. Wallace C. Fraser P. et al.Nat. Commun. 2015; 6: 10069Crossref PubMed Scopus (122) Google Scholar, Won et al., 2016Won H. de la Torre-Ubieta L. Stein J.L. Parikshak N.N. Huang J. Opland C.K. Gandal M.J. Sutton G.J. Hormozdiari F. Lu D. et al.Nature. 2016; 538: 523-527Crossref PubMed Scopus (299) Google Scholar). Most studies thus far have focused on one specific cell type or tissue to prioritize GWAS variants. In contrast, Javierre et al., 2016Javierre B.M. Burren O.S. Wilder S.P. Kreuzhuber R. Hill S.M. Sewitz S. Cairns J. Wingett S.W. Várnai C. Thiecke M.J. et al.Cell. 2016; 167 (this issue): 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar and Schmitt et al., 2016Schmitt A.D. Hu M. Jung I. Xu Z. Qiu Y. Tan C.L. Li Y. Lin S. Lin Y. Barr C.L. Ren B. Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.061Abstract Full Text Full Text PDF PubMed Scopus (422) Google Scholar analyze genome interactions across many tissue types or cell populations, further facilitating the prioritization of regulatory candidates. The papers show that lineage- and cell-type-specific regulatory regions are enriched for genetic variation from association studies of phenotypes with similar cell specificity. Javierre et al., 2016Javierre B.M. Burren O.S. Wilder S.P. Kreuzhuber R. Hill S.M. Sewitz S. Cairns J. Wingett S.W. Várnai C. Thiecke M.J. et al.Cell. 2016; 167 (this issue): 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar also use lineage-specific interactions elucidated by PCHi-C to create a prioritized list of genes that may be implicated in disease through interactions with disease-associated non-coding regions identified by GWAS. One type of interaction diagrammed in Figure 1 is "lineage-specific promoter interactions." Hypothetically, the presence of a phenotype-associated variant in an enhancer that interacts with two promoters in a relevant cell lineage would prioritize these genes over other nearby candidates, thereby helping to narrow down the list of genes whose misregulation might underlie the phenotype. Javierre et al., 2016Javierre B.M. Burren O.S. Wilder S.P. Kreuzhuber R. Hill S.M. Sewitz S. Cairns J. Wingett S.W. Várnai C. Thiecke M.J. et al.Cell. 2016; 167 (this issue): 1369-1384Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar outline how this strategy based on PCHi-C data can be used to complement eQTL-based approaches, which require variants to have detectable effects on gene expression in order to link a regulatory sequence to a target gene (Guo et al., 2015Guo H. Fortune M.D. Burren O.S. Schofield E. Todd J.A. Wallace C. Hum. Mol. Genet. 2015; 24: 3305-3313Crossref PubMed Scopus (83) Google Scholar). Their results highlight the strength of using physical interaction data to link disease-relevant genes and enhancers. Complementary to GWAS, epigenome-wide association studies (EWAS) identify changes in the epigenome that are associated with disease susceptibility. For example, previous EWAS studies have found associations between specific changes in DNA methylation and phenotypic status (Liu et al., 2013Liu Y. Aryee M.J. Padyukov L. Fallin M.D. Hesselberg E. Runarsson A. Reinius L. Acevedo N. Taub M. Ronninger M. et al.Nat. Biotechnol. 2013; 31: 142-147Crossref PubMed Scopus (685) Google Scholar). Building upon the success of the FORGE software (Dunham et al., 2014Dunham I. Kulesha E. Iotchkova V. Morganella S. Birney E. bioRxiv. 2014; https://doi.org/10.1101/013045Crossref Google Scholar), which intersects GWAS results with maps of DNase-hypersensitive sites to determine which disease-associated variants fall into regulatory sequences, a new paper (Breeze et al., 2016Breeze C.E. Paul D.S. van Dongen J. Butcher L.M. Ambrose J.C. Barrett J.E. Lowe R. Rakyan V.K. Iotchkova V. Frontini F. et al.Cell Rep. 2016; 17 (Published online November 15, 2016)https://doi.org/10.1016/j.celrep.2016.10.059Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar) describes eFORGE, software designed to perform similar analyses for EWAS results. The new tool maps regions of differential methylation that have been implicated in disease through EWAS to regulatory regions genome-wide. Thus, eFORGE identifies potential mechanistic links between cell-type-specific distal regulation and epigenome-wide association studies, information that could aid in the development of disease treatments. The compelling new studies presented here use epigenomic data to assess the regulatory architecture across an impressive range of primary human cells and tissues. Their findings emphasize the cell type specificity of regulatory interactions and the dynamic nature of regulatory networks, and this information will be valuable for the interpretation of human disease findings. While this Minireview focused on assessing non-coding variants from GWAS, cell-type-specific interactions can also be used to interpret rare non-coding variation from whole-genome sequencing studies (Weedon et al., 2014Weedon M.N. Cebola I. Patch A.-M. Flanagan S.E. De Franco E. Caswell R. Rodríguez-Seguí S.A. Shaw-Smith C. Cho C.H.-H. Lango Allen H. et al.International Pancreatic Agenesis ConsortiumNat. Genet. 2014; 46: 61-64Crossref PubMed Scopus (187) Google Scholar), a technology that is being adopted with increasing frequency for human disease studies. The computational and experimental resources from these epigenomic studies will be valuable for understanding chromatin structure, as well as for facing the considerable challenge of linking non-coding variation with cell-specific mechanisms of disease. This work was supported by National Institutes of Health grants R01HG003988, U54HG006997, U01DE024427, R24HL123879, and UM1HL098166. Research conducted at the E.O. Lawrence Berkeley National Laboratory was performed under Department of Energy Contract DE-AC02-05CH11231, University of California. eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic DataBreeze et al.Cell ReportsNovember 15, 2016In BriefBreeze et al. develop a tool for the analysis and interpretation of EWAS data. The eFORGE tool identifies cell type-specific, disease-relevant signals in heterogeneous EWAS data and can also identify cell-composition effects. Explore consortium data at the Cell Press IHEC webportal at http://www.cell.com/consortium/IHEC . Full-Text PDF Open AccessLineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene PromotersJavierre et al.CellNovember 17, 2016In BriefThis study deploys a promoter capture Hi-C approach in 17 primary blood cell types to match collaborating regulatory regions and identify genes regulated by noncoding disease-associated variants. Explore this and other papers at the Cell Press IHEC webportal at http://www.cell.com/consortium/IHEC . Full-Text PDF Open AccessA Compendium of Chromatin Contact Maps Reveals Spatially Active Regions in the Human GenomeSchmitt et al.Cell ReportsNovember 15, 2016In BriefSchmitt et al. analyze Hi-C maps in 21 human cell lines and primary tissues and uncover a class of genome organizational features termed FIREs. FIREs are local interaction hotspots, highly tissue-specific, and correspond to active enhancers. We discuss the implications of our findings for the study of gene regulation and disease. Explore the Cell Press IHEC web portal at http://www.cell.com/consortium/IHEC . Full-Text PDF Open AccessAnalysis of Normal Human Mammary Epigenomes Reveals Cell-Specific Active Enhancer States and Associated Transcription Factor NetworksPellacani et al.Cell ReportsNovember 15, 2016In BriefPellacani et al. present comprehensive histone and DNA modification profiles for four cell types in normal human breast tissue and three immortalized human mammary epithelial cell lines. Analysis of activated enhancers place luminal progenitors in between bipotent progenitor-containing basal cells and nonproliferative luminal cells. Explore consortium data at the Cell Press IHEC webportal at http://www.cell.com/consortium/IHEC . Full-Text PDF Open Access
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