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Directed Mammalian Gene Regulatory Networks Using Expression and Comparative Genomic Hybridization Microarray Data from Radiation Hybrids

Meiotic mapping of quantitative trait loci regulating expression (eQTLs) has allowed the construction of gene networks. However, the limited mapping resolution of these studies has meant that genotype data are largely ignored, leading to undirected networks that fail to capture regulatory hierarchie...

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Autores principales: Ahn, Sangtae, Wang, Richard T., Park, Christopher C., Lin, Andy, Leahy, Richard M., Lange, Kenneth, Smith, Desmond J.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2690838/
https://www.ncbi.nlm.nih.gov/pubmed/19521529
http://dx.doi.org/10.1371/journal.pcbi.1000407
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author Ahn, Sangtae
Wang, Richard T.
Park, Christopher C.
Lin, Andy
Leahy, Richard M.
Lange, Kenneth
Smith, Desmond J.
author_facet Ahn, Sangtae
Wang, Richard T.
Park, Christopher C.
Lin, Andy
Leahy, Richard M.
Lange, Kenneth
Smith, Desmond J.
author_sort Ahn, Sangtae
collection PubMed
description Meiotic mapping of quantitative trait loci regulating expression (eQTLs) has allowed the construction of gene networks. However, the limited mapping resolution of these studies has meant that genotype data are largely ignored, leading to undirected networks that fail to capture regulatory hierarchies. Here we use high resolution mapping of copy number eQTLs (ceQTLs) in a mouse-hamster radiation hybrid (RH) panel to construct directed genetic networks in the mammalian cell. The RH network covering 20,145 mouse genes had significant overlap with, and similar topological structures to, existing biological networks. Upregulated edges in the RH network had significantly more overlap than downregulated. This suggests repressive relationships between genes are missed by existing approaches, perhaps because the corresponding proteins are not present in the cell at the same time and therefore unlikely to interact. Gene essentiality was positively correlated with connectivity and betweenness centrality in the RH network, strengthening the centrality-lethality principle in mammals. Consistent with their regulatory role, transcription factors had significantly more outgoing edges (regulating) than incoming (regulated) in the RH network, a feature hidden by conventional undirected networks. Directed RH genetic networks thus showed concordance with pre-existing networks while also yielding information inaccessible to current undirected approaches.
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spelling pubmed-26908382009-06-12 Directed Mammalian Gene Regulatory Networks Using Expression and Comparative Genomic Hybridization Microarray Data from Radiation Hybrids Ahn, Sangtae Wang, Richard T. Park, Christopher C. Lin, Andy Leahy, Richard M. Lange, Kenneth Smith, Desmond J. PLoS Comput Biol Research Article Meiotic mapping of quantitative trait loci regulating expression (eQTLs) has allowed the construction of gene networks. However, the limited mapping resolution of these studies has meant that genotype data are largely ignored, leading to undirected networks that fail to capture regulatory hierarchies. Here we use high resolution mapping of copy number eQTLs (ceQTLs) in a mouse-hamster radiation hybrid (RH) panel to construct directed genetic networks in the mammalian cell. The RH network covering 20,145 mouse genes had significant overlap with, and similar topological structures to, existing biological networks. Upregulated edges in the RH network had significantly more overlap than downregulated. This suggests repressive relationships between genes are missed by existing approaches, perhaps because the corresponding proteins are not present in the cell at the same time and therefore unlikely to interact. Gene essentiality was positively correlated with connectivity and betweenness centrality in the RH network, strengthening the centrality-lethality principle in mammals. Consistent with their regulatory role, transcription factors had significantly more outgoing edges (regulating) than incoming (regulated) in the RH network, a feature hidden by conventional undirected networks. Directed RH genetic networks thus showed concordance with pre-existing networks while also yielding information inaccessible to current undirected approaches. Public Library of Science 2009-06-12 /pmc/articles/PMC2690838/ /pubmed/19521529 http://dx.doi.org/10.1371/journal.pcbi.1000407 Text en Ahn et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Ahn, Sangtae
Wang, Richard T.
Park, Christopher C.
Lin, Andy
Leahy, Richard M.
Lange, Kenneth
Smith, Desmond J.
Directed Mammalian Gene Regulatory Networks Using Expression and Comparative Genomic Hybridization Microarray Data from Radiation Hybrids
title Directed Mammalian Gene Regulatory Networks Using Expression and Comparative Genomic Hybridization Microarray Data from Radiation Hybrids
title_full Directed Mammalian Gene Regulatory Networks Using Expression and Comparative Genomic Hybridization Microarray Data from Radiation Hybrids
title_fullStr Directed Mammalian Gene Regulatory Networks Using Expression and Comparative Genomic Hybridization Microarray Data from Radiation Hybrids
title_full_unstemmed Directed Mammalian Gene Regulatory Networks Using Expression and Comparative Genomic Hybridization Microarray Data from Radiation Hybrids
title_short Directed Mammalian Gene Regulatory Networks Using Expression and Comparative Genomic Hybridization Microarray Data from Radiation Hybrids
title_sort directed mammalian gene regulatory networks using expression and comparative genomic hybridization microarray data from radiation hybrids
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2690838/
https://www.ncbi.nlm.nih.gov/pubmed/19521529
http://dx.doi.org/10.1371/journal.pcbi.1000407
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