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Identification of genetic elements in metabolism by high-throughput mouse phenotyping

dc.contributor.authorRozman, Jan
dc.contributor.authorRathkolb, Birgit
dc.contributor.authorOestereicher, Manuela A
dc.contributor.authorSchutt, Christine
dc.contributor.authorRavindranath, Aakash Chavan
dc.contributor.authorLeuchtenberger, Stefanie
dc.contributor.authorSharma, Sapna
dc.contributor.authorKistler, Martin
dc.contributor.authorWillershauser, Monja
dc.contributor.authorBrommage, Robert
dc.contributor.authorMeehan, Terrence F
dc.contributor.authorDobbie, Michael
dc.date.accessioned2019-04-21T11:08:02Z
dc.date.available2019-04-21T11:08:02Z
dc.date.issued2018
dc.date.updated2019-03-12T07:33:49Z
dc.description.abstractMetabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn2041-1723en_AU
dc.identifier.urihttp://hdl.handle.net/1885/160571
dc.language.isoen_AUen_AU
dc.publisherMacmillan Publishers Ltden_AU
dc.rightsThe Author/sen_AU
dc.rights.licenseThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/.
dc.rights.urihttp://creativecommons.org/ licenses/by/4.0/
dc.sourceNature Communicationsen_AU
dc.titleIdentification of genetic elements in metabolism by high-throughput mouse phenotypingen_AU
dc.typeJournal articleen_AU
dcterms.accessRightsOpen Accessen_AU
local.contributor.affiliationRozman, Jan, German Research Center for Environmental Healthen_AU
local.contributor.affiliationRathkolb, Birgit, German Research Center for Environmental Healthen_AU
local.contributor.affiliationOestereicher, Manuela A, German Research Center for Environmental Healthen_AU
local.contributor.affiliationSchutt, Christine, German Research Center for Environmental Healthen_AU
local.contributor.affiliationRavindranath, Aakash Chavan, German Center for Diabetes Research (DZD)en_AU
local.contributor.affiliationLeuchtenberger, Stefanie, German Research Center for Environmental Healthen_AU
local.contributor.affiliationSharma, Sapna, German Center for Diabetes Research (DZD)en_AU
local.contributor.affiliationKistler, Martin, German Research Center for Environmental Healthen_AU
local.contributor.affiliationWillershauser, Monja, Technical University of Munichen_AU
local.contributor.affiliationBrommage, Robert, German Research Center for Environmental Healthen_AU
local.contributor.affiliationMeehan, Terrence F, European Bioinformatics Instituteen_AU
local.contributor.affiliationDobbie, Michael, College of Health and Medicine, ANUen_AU
local.contributor.authoruidDobbie, Michael, u4384816en_AU
local.description.notesImported from ARIESen_AU
local.identifier.absfor060408 - Genomicsen_AU
local.identifier.absfor110107 - Metabolic Medicineen_AU
local.identifier.absseo970111 - Expanding Knowledge in the Medical and Health Sciencesen_AU
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciencesen_AU
local.identifier.ariespublicationu4485658xPUB2311en_AU
local.identifier.citationvolume9en_AU
local.identifier.doi10.1038/s41467-017-01995-2en_AU
local.identifier.thomsonID000422745800023
local.type.statusPublished Versionen_AU

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