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

IMPC Consortium

doi:10.1038/s41467-017-01995-2

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

IMPC Consortium

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

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.

Original language	English
Pages (from-to)	1-16
Number of pages	16
Journal	Nature Communications
Volume	9
Issue number	1
Early online date	18 Jan 2018
DOIs	https://doi.org/10.1038/s41467-017-01995-2
Publication status	E-pub ahead of print - 18 Jan 2018
Externally published	Yes

Keywords / Materials (for Non-textual outputs)

Animals
Area Under Curve
Basal Metabolism/genetics
Blood Glucose/metabolism
Body Weight/genetics
Diabetes Mellitus, Type 2/genetics
Gene Regulatory Networks
Genome-Wide Association Study
High-Throughput Screening Assays
Humans
Metabolic Diseases/genetics
Mice
Mice, Knockout
Obesity/genetics
Oxygen Consumption/genetics
Phenotype
Triglycerides/metabolism

Access to Document

10.1038/s41467-017-01995-2

s41467-017-01995-2Final published version, 2.34 MBLicence: Creative Commons: Attribution (CC-BY)

Cite this

@article{035878d2c29e41dba2d77f83500a36ae,

title = "Identification of genetic elements in metabolism by high-throughput mouse phenotyping",

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.",

keywords = "Animals, Area Under Curve, Basal Metabolism/genetics, Blood Glucose/metabolism, Body Weight/genetics, Diabetes Mellitus, Type 2/genetics, Gene Regulatory Networks, Genome-Wide Association Study, High-Throughput Screening Assays, Humans, Metabolic Diseases/genetics, Mice, Mice, Knockout, Obesity/genetics, Oxygen Consumption/genetics, Phenotype, Triglycerides/metabolism",

author = "{IMPC Consortium} and Jan Rozman and Birgit Rathkolb and Oestereicher, {Manuela A} and Christine Sch{\"u}tt and Ravindranath, {Aakash Chavan} and Stefanie Leuchtenberger and Sapna Sharma and Martin Kistler and Monja Willersh{\"a}user and Robert Brommage and Meehan, {Terrence F} and Jeremy Mason and Hamed Haselimashhadi and Tertius Hough and Ann-Marie Mallon and Sara Wells and Luis Santos and Lelliott, {Christopher J} and White, {Jacqueline K} and Tania Sorg and Marie-France Champy and Bower, {Lynette R} and Reynolds, {Corey L} and Flenniken, {Ann M} and Murray, {Stephen A} and Nutter, {Lauryl M J} and Svenson, {Karen L} and David West and Tocchini-Valentini, {Glauco P} and Beaudet, {Arthur L} and Fatima Bosch and Braun, {Robert B} and Dobbie, {Michael S} and Xiang Gao and Yann Herault and Ala Moshiri and Moore, {Bret A} and {Kent Lloyd}, {K C} and Colin McKerlie and Hiroshi Masuya and Nobuhiko Tanaka and Paul Flicek and Parkinson, {Helen E} and Radislav Sedlacek and Seong, {Je Kyung} and Wang, {Chi-Kuang Leo} and Mark Moore and Brown, {Steve D} and Tsch{\"o}p, {Matthias H} and Wolfgang Wurst and Martin Klingenspor and Eckhard Wolf and Johannes Beckers and Fausto Machicao and Andreas Peter and Harald Staiger and Hans-Ulrich H{\"a}ring and Harald Grallert and Monica Campillos and Holger Maier and Helmut Fuchs and Valerie Gailus-Durner and Thomas Werner and {Hrabe de Angelis}, Martin",

year = "2018",

month = jan,

day = "18",

doi = "10.1038/s41467-017-01995-2",

language = "English",

volume = "9",

pages = "1--16",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

}

TY - JOUR

T1 - Identification of genetic elements in metabolism by high-throughput mouse phenotyping

AU - IMPC Consortium

AU - Rozman, Jan

AU - Rathkolb, Birgit

AU - Oestereicher, Manuela A

AU - Schütt, Christine

AU - Ravindranath, Aakash Chavan

AU - Leuchtenberger, Stefanie

AU - Sharma, Sapna

AU - Kistler, Martin

AU - Willershäuser, Monja

AU - Brommage, Robert

AU - Meehan, Terrence F

AU - Mason, Jeremy

AU - Haselimashhadi, Hamed

AU - Hough, Tertius

AU - Mallon, Ann-Marie

AU - Wells, Sara

AU - Santos, Luis

AU - Lelliott, Christopher J

AU - White, Jacqueline K

AU - Sorg, Tania

AU - Champy, Marie-France

AU - Bower, Lynette R

AU - Reynolds, Corey L

AU - Flenniken, Ann M

AU - Murray, Stephen A

AU - Nutter, Lauryl M J

AU - Svenson, Karen L

AU - West, David

AU - Tocchini-Valentini, Glauco P

AU - Beaudet, Arthur L

AU - Bosch, Fatima

AU - Braun, Robert B

AU - Dobbie, Michael S

AU - Gao, Xiang

AU - Herault, Yann

AU - Moshiri, Ala

AU - Moore, Bret A

AU - Kent Lloyd, K C

AU - McKerlie, Colin

AU - Masuya, Hiroshi

AU - Tanaka, Nobuhiko

AU - Flicek, Paul

AU - Parkinson, Helen E

AU - Sedlacek, Radislav

AU - Seong, Je Kyung

AU - Wang, Chi-Kuang Leo

AU - Moore, Mark

AU - Brown, Steve D

AU - Tschöp, Matthias H

AU - Wurst, Wolfgang

AU - Klingenspor, Martin

AU - Wolf, Eckhard

AU - Beckers, Johannes

AU - Machicao, Fausto

AU - Peter, Andreas

AU - Staiger, Harald

AU - Häring, Hans-Ulrich

AU - Grallert, Harald

AU - Campillos, Monica

AU - Maier, Holger

AU - Fuchs, Helmut

AU - Gailus-Durner, Valerie

AU - Werner, Thomas

AU - Hrabe de Angelis, Martin

PY - 2018/1/18

Y1 - 2018/1/18

N2 - 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.

AB - 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.

KW - Animals

KW - Area Under Curve

KW - Basal Metabolism/genetics

KW - Blood Glucose/metabolism

KW - Body Weight/genetics

KW - Diabetes Mellitus, Type 2/genetics

KW - Gene Regulatory Networks

KW - Genome-Wide Association Study

KW - High-Throughput Screening Assays

KW - Humans

KW - Metabolic Diseases/genetics

KW - Mice

KW - Mice, Knockout

KW - Obesity/genetics

KW - Oxygen Consumption/genetics

KW - Phenotype

KW - Triglycerides/metabolism

U2 - 10.1038/s41467-017-01995-2

DO - 10.1038/s41467-017-01995-2

M3 - Article

C2 - 29348434

SN - 2041-1723

VL - 9

SP - 1

EP - 16

JO - Nature Communications

JF - Nature Communications

IS - 1

ER -

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

Abstract / Description of output

Keywords / Materials (for Non-textual outputs)

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