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Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases
| dc.contributor.author | Marbach, Daniel | |
| dc.contributor.author | Lamparter, David | |
| dc.contributor.author | Quon, Gerald | |
| dc.contributor.author | Kellis, Manolis | |
| dc.contributor.author | Kutalik, Zoltán | |
| dc.contributor.author | Bergmann, Sven | |
| dc.date.accessioned | 2017-08-31T17:40:05Z | |
| dc.date.available | 2017-08-31T17:40:05Z | |
| dc.date.issued | 2016-03 | |
| dc.date.submitted | 2015-07 | |
| dc.identifier.issn | 1548-7091 | |
| dc.identifier.issn | 1548-7105 | |
| dc.identifier.uri | http://hdl.handle.net/1721.1/111077 | |
| dc.description.abstract | Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type– and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity among transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) showed that disease-associated genetic variants—including variants that do not reach genome-wide significance—often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissues | en_US |
| dc.language.iso | en_US | |
| dc.publisher | Nature Publishing Group | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.1038/nmeth.3799 | en_US |
| dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
| dc.source | PMC | en_US |
| dc.title | Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Marbach, Daniel et al. “Tissue-Specific Regulatory Circuits Reveal Variable Modular Perturbations Across Complex Diseases.” Nature Methods 13, 4 (March 2016): 366–370 © 2016 Nature America, Inc | en_US |
| dc.contributor.department | Broad Institute of MIT and Harvard | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
| dc.contributor.mitauthor | Quon, Gerald | |
| dc.contributor.mitauthor | Kellis, Manolis | |
| dc.relation.journal | Nature Methods | en_US |
| dc.eprint.version | Author's final manuscript | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dspace.orderedauthors | Marbach, Daniel; Lamparter, David; Quon, Gerald; Kellis, Manolis; Kutalik, Zoltán; Bergmann, Sven | en_US |
| dspace.embargo.terms | N | en_US |
| dc.identifier.orcid | https://orcid.org/0000-0002-1716-0153 | |
| mit.license | PUBLISHER_POLICY | en_US |
