Journal article
Variance of gene expression identifies altered network constraints in neurological disease
JC Mar, NA Matigian, A Mackay-Sim, GD Mellick, CM Sue, PA Silburn, JJ McGrath, J Quackenbush, CA Wells
Plos Genetics | PUBLIC LIBRARY SCIENCE | Published : 2011
Open access
Abstract
Gene expression analysis has become a ubiquitous tool for studying a wide range of human diseases. In a typical analysis we compare distinct phenotypic groups and attempt to identify genes that are, on average, significantly different between them. Here we describe an innovative approach to the analysis of gene expression data, one that identifies differences in expression variance between groups as an informative metric of the group phenotype. We find that genes with different expression variance profiles are not randomly distributed across cell signaling networks. Genes with low-expression variance, or higher constraint, are significantly more connected to other network members and tend to..
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Awarded by U.S. National Library of Medicine
Funding Acknowledgements
JQ and JCM are supported by a grant from the US National Human Genome Research Institute (P50-HG004233). JQ is also supported, in part, by a Jackson Memorial Visiting Fellowship from Griffith University and a Harvard Club of Australia Foundation Fellowship, and from the US National Library of Medicine (R01-LM010129). CAW is supported by the Australian Research Council International linkage scheme (LX0882502), the National Health and Medical Research Council Australia (CDA fellowship 481945), and grants from the Australian Stem Cell Centre. The National Centre for Adult Stem Cell Research is supported by a grant from the Australian Department of Health and Ageing to AM-S. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.