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dc.contributor.authorFulco, Charles P.
dc.contributor.authorNasser, Joseph
dc.contributor.authorJones, Thouis R.
dc.contributor.authorMunson, Glen
dc.contributor.authorBergman, Drew T.
dc.contributor.authorSubramanian, Vidya
dc.contributor.authorGrossman, Sharon Rachel
dc.contributor.authorAnyoha, Rockwell
dc.contributor.authorDoughty, Benjamin R.
dc.contributor.authorPatwardhan, Tejal A.
dc.contributor.authorNguyen, Tung Hoang
dc.contributor.authorKane, Michael A.
dc.contributor.authorPerez, Elizabeth M.
dc.contributor.authorDurand, Neva C.
dc.contributor.authorLareau, Caleb A.
dc.contributor.authorStamenova, Elena K.
dc.contributor.authorAiden, Erez Lieberman
dc.contributor.authorLander, Eric Steven
dc.contributor.authorEngreitz, Jesse Michael
dc.date.accessioned2021-02-23T16:44:41Z
dc.date.available2021-02-23T16:44:41Z
dc.date.issued2019-11
dc.date.submitted2019-01
dc.identifier.issn1061-4036
dc.identifier.issn1546-1718
dc.identifier.urihttps://hdl.handle.net/1721.1/129976
dc.description.abstractEnhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases1–4. Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer–gene connections across cell types5,6. We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test >3,500 potential enhancer–gene connections for 30 genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer–gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome.en_US
dc.description.sponsorshipNHGRI (Grant 1K99HG009917-01)
dc.description.sponsorshipNational Institute of General Medical Sciences (Grant T32GM007753)
dc.description.sponsorshipNSF (Award PHY1427654)
dc.description.sponsorshipWelch Foundation (Grant Q-1866)
dc.description.sponsorshipUSDA (Grant 2017-05741)
dc.description.sponsorshipNIH (Grant U01HL130010 and Award UM1HG009375)
dc.language.isoen
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionofhttp://dx.doi.org/10.1038/s41588-019-0538-0en_US
dc.rightsArticle 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.sourcePMCen_US
dc.titleActivity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbationsen_US
dc.typeArticleen_US
dc.identifier.citationFulco, Charles P. et al. "Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations." Nature Genetics 51, 12 (November 2019): 1664–1669 © 2019 The Author(s)
dc.contributor.departmentBroad Institute of MIT and Harvarden_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.relation.journalNature Geneticsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-06-19T17:10:23Z
dspace.date.submission2020-06-19T17:10:26Z
mit.journal.volume51en_US
mit.journal.issue12en_US
mit.licensePUBLISHER_POLICY
mit.metadata.statusComplete


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