Gene coexpression network analysis for family studies based on a meta-analytic approach

For a better understanding of the biological mechanisms involved in complex traits or diseases, networks are often useful tools in genetic studies: coexpression networks based on pairwise correlations between genes are commonly used. In case of a family-based design, it can be problematic when there...

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Main Authors:	Tissier, Renaud, Uh, Hae-Won, van den Akker, Erik, Balliu, Brunilda, Tsonaka, Spyridoula, Houwing-Duistermaat, Jeanine
Format:	Online
Language:	English
Published:	BioMed Central 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133496/

id	pubmed-5133496
recordtype	oai_dc
spelling	pubmed-51334962016-12-15 Gene coexpression network analysis for family studies based on a meta-analytic approach Tissier, Renaud Uh, Hae-Won van den Akker, Erik Balliu, Brunilda Tsonaka, Spyridoula Houwing-Duistermaat, Jeanine Proceedings For a better understanding of the biological mechanisms involved in complex traits or diseases, networks are often useful tools in genetic studies: coexpression networks based on pairwise correlations between genes are commonly used. In case of a family-based design, it can be problematic when there is a large between-family variation in expression levels. We propose here a gene coexpression network analysis for family studies. We build a coexpression network for each family and then combine the results. We applied our approach to data provided for analysis in the Genetic Analysis Workshop 19 and compared it to 2 naïve approaches—ignoring correlations among the expressions and decorrelating the gene expression by using the residuals of a mixed model—and a single-probe analysis. Our approach seemed to better deal with heterogeneity with regard to the naïve approaches. The naïve approaches did not provide any significant results, while our approach detected genes via indirect effects. It also detected more genes than the single-probe analysis. BioMed Central 2016-10-18 /pmc/articles/PMC5133496/ /pubmed/27980622 http://dx.doi.org/10.1186/s12919-016-0016-y Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Tissier, Renaud Uh, Hae-Won van den Akker, Erik Balliu, Brunilda Tsonaka, Spyridoula Houwing-Duistermaat, Jeanine
spellingShingle	Tissier, Renaud Uh, Hae-Won van den Akker, Erik Balliu, Brunilda Tsonaka, Spyridoula Houwing-Duistermaat, Jeanine Gene coexpression network analysis for family studies based on a meta-analytic approach
author_facet	Tissier, Renaud Uh, Hae-Won van den Akker, Erik Balliu, Brunilda Tsonaka, Spyridoula Houwing-Duistermaat, Jeanine
author_sort	Tissier, Renaud
title	Gene coexpression network analysis for family studies based on a meta-analytic approach
title_short	Gene coexpression network analysis for family studies based on a meta-analytic approach
title_full	Gene coexpression network analysis for family studies based on a meta-analytic approach
title_fullStr	Gene coexpression network analysis for family studies based on a meta-analytic approach
title_full_unstemmed	Gene coexpression network analysis for family studies based on a meta-analytic approach
title_sort	gene coexpression network analysis for family studies based on a meta-analytic approach
description	For a better understanding of the biological mechanisms involved in complex traits or diseases, networks are often useful tools in genetic studies: coexpression networks based on pairwise correlations between genes are commonly used. In case of a family-based design, it can be problematic when there is a large between-family variation in expression levels. We propose here a gene coexpression network analysis for family studies. We build a coexpression network for each family and then combine the results. We applied our approach to data provided for analysis in the Genetic Analysis Workshop 19 and compared it to 2 naïve approaches—ignoring correlations among the expressions and decorrelating the gene expression by using the residuals of a mixed model—and a single-probe analysis. Our approach seemed to better deal with heterogeneity with regard to the naïve approaches. The naïve approaches did not provide any significant results, while our approach detected genes via indirect effects. It also detected more genes than the single-probe analysis.
publisher	BioMed Central
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133496/
_version_	1613752489879797760

Gene coexpression network analysis for family studies based on a meta-analytic approach

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