The structure of a gene co-expression network reveals biological functions underlying eQTLs.

What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incompl...

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Autores principales:	Nathalie Villa-Vialaneix, Laurence Liaubet, Thibault Laurent, Pierre Cherel, Adrien Gamot, Magali SanCristobal
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ee6743e3d6ee4c59bee0e8e3409c590c
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spelling	oai:doaj.org-article:ee6743e3d6ee4c59bee0e8e3409c590c2021-11-18T07:50:31ZThe structure of a gene co-expression network reveals biological functions underlying eQTLs.1932-620310.1371/journal.pone.0060045https://doaj.org/article/ee6743e3d6ee4c59bee0e8e3409c590c2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23577081/?tool=EBIhttps://doaj.org/toc/1932-6203What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.Nathalie Villa-VialaneixLaurence LiaubetThibault LaurentPierre CherelAdrien GamotMagali SanCristobalPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 4, p e60045 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nathalie Villa-Vialaneix Laurence Liaubet Thibault Laurent Pierre Cherel Adrien Gamot Magali SanCristobal The structure of a gene co-expression network reveals biological functions underlying eQTLs.
description	What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.
format	article
author	Nathalie Villa-Vialaneix Laurence Liaubet Thibault Laurent Pierre Cherel Adrien Gamot Magali SanCristobal
author_facet	Nathalie Villa-Vialaneix Laurence Liaubet Thibault Laurent Pierre Cherel Adrien Gamot Magali SanCristobal
author_sort	Nathalie Villa-Vialaneix
title	The structure of a gene co-expression network reveals biological functions underlying eQTLs.
title_short	The structure of a gene co-expression network reveals biological functions underlying eQTLs.
title_full	The structure of a gene co-expression network reveals biological functions underlying eQTLs.
title_fullStr	The structure of a gene co-expression network reveals biological functions underlying eQTLs.
title_full_unstemmed	The structure of a gene co-expression network reveals biological functions underlying eQTLs.
title_sort	structure of a gene co-expression network reveals biological functions underlying eqtls.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/ee6743e3d6ee4c59bee0e8e3409c590c
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The structure of a gene co-expression network reveals biological functions underlying eQTLs.

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