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Identification of genetic elements in metabolism by high-throughput mouse phenotypingopen access

Authors
Rozman, JanRathkolb, BirgitOestereicher, Manuela A.Schütt, ChristineRavindranath, Aakash ChavanLeuchtenberger, StefanieCho, Soo Young
Issue Date
Jan-2018
Publisher
Nature Publishing Group
Citation
Nature Communications, v.9, no.1, pp 1 - 16
Pages
16
Indexed
SCI
SCIE
SCOPUS
Journal Title
Nature Communications
Volume
9
Number
1
Start Page
1
End Page
16
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114377
DOI
10.1038/s41467-017-01995-2
ISSN
2041-1723
2041-1723
Abstract
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 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. © 2017 The Author(s).
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Cho, Soo Young
ERICA 과학기술융합대학 (ERICA 의약생명과학과)
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