RapamycinFungusV1, Main, Exploration, bibRecord, 000466

Temporal genetic association and temporal genetic causality methods for dissecting complex networks.

Identifieur interne : 000466 ( Main/Exploration ); précédent : 000465; suivant : 000467

Temporal genetic association and temporal genetic causality methods for dissecting complex networks.

Auteurs : Luan Lin [États-Unis] ; Quan Chen [États-Unis] ; Jeanne P. Hirsch [États-Unis] ; Seungyeul Yoo [États-Unis] ; Kayee Yeung [États-Unis] ; Roger E. Bumgarner [États-Unis] ; Zhidong Tu [États-Unis] ; Eric E. Schadt [États-Unis] ; Jun Zhu [États-Unis]

Source :

Nature communications [ 2041-1723 ] ; 2018.

RBID : pubmed:30266904

Descripteurs français

English descriptors

KwdEn :
- Algorithms (MeSH), Bayes Theorem (MeSH), Computational Biology (methods), Gene Expression Profiling (methods), Gene Regulatory Networks (genetics), Genetic Association Studies (methods), Multivariate Analysis (MeSH), Quantitative Trait Loci (genetics), Saccharomyces cerevisiae (genetics), Saccharomyces cerevisiae Proteins (genetics), Time Factors (MeSH).
MESH :
- chemical , genetics : Saccharomyces cerevisiae Proteins.
- genetics : Gene Regulatory Networks, Quantitative Trait Loci, Saccharomyces cerevisiae.
- methods : Computational Biology, Gene Expression Profiling, Genetic Association Studies.
- Algorithms, Bayes Theorem, Multivariate Analysis, Time Factors.

Abstract

A large amount of panomic data has been generated in populations for understanding causal relationships in complex biological systems. Both genetic and temporal models can be used to establish causal relationships among molecular, cellular, or phenotypical traits, but with limitations. To fully utilize high-dimension temporal and genetic data, we develop a multivariate polynomial temporal genetic association (MPTGA) approach for detecting temporal genetic loci (teQTLs) of quantitative traits monitored over time in a population and a temporal genetic causality test (TGCT) for inferring causal relationships between traits linked to the locus. We apply MPTGA and TGCT to simulated data sets and a yeast F2 population in response to rapamycin, and demonstrate increased power to detect teQTLs. We identify a teQTL hotspot locus interacting with rapamycin treatment, infer putative causal regulators of the teQTL hotspot, and experimentally validate RRD1 as the causal regulator for this teQTL hotspot.

DOI: 10.1038/s41467-018-06203-3
PubMed: 30266904
PubMed Central: PMC6162292

Affiliations:

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Le document en format XML

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<term>Gene Expression Profiling (methods)</term>
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<term>Réseaux de régulation génique (génétique)</term>
<term>Saccharomyces cerevisiae (génétique)</term>
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<front><div type="abstract" xml:lang="en">A large amount of panomic data has been generated in populations for understanding causal relationships in complex biological systems. Both genetic and temporal models can be used to establish causal relationships among molecular, cellular, or phenotypical traits, but with limitations. To fully utilize high-dimension temporal and genetic data, we develop a multivariate polynomial temporal genetic association (MPTGA) approach for detecting temporal genetic loci (teQTLs) of quantitative traits monitored over time in a population and a temporal genetic causality test (TGCT) for inferring causal relationships between traits linked to the locus. We apply MPTGA and TGCT to simulated data sets and a yeast F2 population in response to rapamycin, and demonstrate increased power to detect teQTLs. We identify a teQTL hotspot locus interacting with rapamycin treatment, infer putative causal regulators of the teQTL hotspot, and experimentally validate RRD1 as the causal regulator for this teQTL hotspot.</div>
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<DateCompleted><Year>2019</Year>
<Month>01</Month>
<Day>07</Day>
</DateCompleted>
<DateRevised><Year>2019</Year>
<Month>09</Month>
<Day>28</Day>
</DateRevised>
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<JournalIssue CitedMedium="Internet"><Volume>9</Volume>
<Issue>1</Issue>
<PubDate><Year>2018</Year>
<Month>09</Month>
<Day>28</Day>
</PubDate>
</JournalIssue>
<Title>Nature communications</Title>
<ISOAbbreviation>Nat Commun</ISOAbbreviation>
</Journal>
<ArticleTitle>Temporal genetic association and temporal genetic causality methods for dissecting complex networks.</ArticleTitle>
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</Pagination>
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<Abstract><AbstractText>A large amount of panomic data has been generated in populations for understanding causal relationships in complex biological systems. Both genetic and temporal models can be used to establish causal relationships among molecular, cellular, or phenotypical traits, but with limitations. To fully utilize high-dimension temporal and genetic data, we develop a multivariate polynomial temporal genetic association (MPTGA) approach for detecting temporal genetic loci (teQTLs) of quantitative traits monitored over time in a population and a temporal genetic causality test (TGCT) for inferring causal relationships between traits linked to the locus. We apply MPTGA and TGCT to simulated data sets and a yeast F2 population in response to rapamycin, and demonstrate increased power to detect teQTLs. We identify a teQTL hotspot locus interacting with rapamycin treatment, infer putative causal regulators of the teQTL hotspot, and experimentally validate RRD1 as the causal regulator for this teQTL hotspot.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lin</LastName>
<ForeName>Luan</ForeName>
<Initials>L</Initials>
<AffiliationInfo><Affiliation>Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Chen</LastName>
<ForeName>Quan</ForeName>
<Initials>Q</Initials>
<AffiliationInfo><Affiliation>Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
</AffiliationInfo>
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<ForeName>Jeanne P</ForeName>
<Initials>JP</Initials>
<AffiliationInfo><Affiliation>Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Yoo</LastName>
<ForeName>Seungyeul</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
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<ForeName>Kayee</ForeName>
<Initials>K</Initials>
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<AffiliationInfo><Affiliation>Department of Microbiology, University of Washington, Box 358070, Seattle, WA, 98195, USA.</Affiliation>
</AffiliationInfo>
</Author>
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<ForeName>Roger E</ForeName>
<Initials>RE</Initials>
<AffiliationInfo><Affiliation>Department of Microbiology, University of Washington, Box 358070, Seattle, WA, 98195, USA.</Affiliation>
</AffiliationInfo>
</Author>
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<ForeName>Zhidong</ForeName>
<Initials>Z</Initials>
<AffiliationInfo><Affiliation>Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
</AffiliationInfo>
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<ForeName>Eric E</ForeName>
<Initials>EE</Initials>
<AffiliationInfo><Affiliation>Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Zhu</LastName>
<ForeName>Jun</ForeName>
<Initials>J</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0003-0834-8178</Identifier>
<AffiliationInfo><Affiliation>Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. jun.zhu@mssm.edu.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. jun.zhu@mssm.edu.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA. jun.zhu@mssm.edu.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
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<Acronym>HG</Acronym>
<Agency>NHGRI NIH HHS</Agency>
<Country>United States</Country>
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<Agency>U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)</Agency>
<Country>International</Country>
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<Acronym>AG</Acronym>
<Agency>NIA NIH HHS</Agency>
<Country>United States</Country>
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<Grant><GrantID>AI118610</GrantID>
<Agency>U.S. Department of Health and Human Services (U.S. Department of Health & Human Services)</Agency>
<Country>International</Country>
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<Grant><GrantID>AG046170</GrantID>
<Agency>U.S. Department of Health and Human Services (U.S. Department of Health & Human Services)</Agency>
<Country>International</Country>
</Grant>
<Grant><GrantID>U19 AI118610</GrantID>
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<MeshHeading><DescriptorName UI="D001499" MajorTopicYN="N">Bayes Theorem</DescriptorName>
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	Serveur d'exploration sur la rapamycine et les champignons
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Serveur d'exploration sur la rapamycine et les champignons

Temporal genetic association and temporal genetic causality methods for dissecting complex networks.

Temporal genetic association and temporal genetic causality methods for dissecting complex networks.

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Abstract

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Pour générer des pages wiki