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

Rozman, Jan; Rathkolb, Birgit; Oestereicher, Manuela A; Schutt, Christine; Ravindranath, Aakash Chavan; Leuchtenberger, Stefanie; Sharma, Sapna; Kistler, Martin; Willershauser, Monja; Brommage, Robert; Meehan, Terrence F; Dobbie, Michael

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Metabolic 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...[Show more]

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.identifier.issn2041-1723
dc.identifier.urihttp://hdl.handle.net/1885/160571
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.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherMacmillan Publishers Ltd
dc.rightsThe Author/s
dc.rights.urihttp://creativecommons.org/ licenses/by/4.0/
dc.sourceNature Communications
dc.titleIdentification of genetic elements in metabolism by high-throughput mouse phenotyping
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume9
dc.date.issued2018
local.identifier.absfor060408 - Genomics
local.identifier.absfor110107 - Metabolic Medicine
local.identifier.ariespublicationu4485658xPUB2311
local.type.statusPublished Version
local.contributor.affiliationRozman, Jan, German Research Center for Environmental Health
local.contributor.affiliationRathkolb, Birgit, German Research Center for Environmental Health
local.contributor.affiliationOestereicher, Manuela A, German Research Center for Environmental Health
local.contributor.affiliationSchutt, Christine, German Research Center for Environmental Health
local.contributor.affiliationRavindranath, Aakash Chavan, German Center for Diabetes Research (DZD)
local.contributor.affiliationLeuchtenberger, Stefanie, German Research Center for Environmental Health
local.contributor.affiliationSharma, Sapna, German Center for Diabetes Research (DZD)
local.contributor.affiliationKistler, Martin, German Research Center for Environmental Health
local.contributor.affiliationWillershauser, Monja, Technical University of Munich
local.contributor.affiliationBrommage, Robert, German Research Center for Environmental Health
local.contributor.affiliationMeehan, Terrence F, European Bioinformatics Institute
local.contributor.affiliationDobbie, Michael, College of Health and Medicine, ANU
local.identifier.doi10.1038/s41467-017-01995-2
local.identifier.absseo970111 - Expanding Knowledge in the Medical and Health Sciences
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciences
dc.date.updated2019-03-12T07:33:49Z
local.identifier.thomsonID000422745800023
dcterms.accessRightsOpen Access
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/.
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