Global Similarity and Local Variance in Human Gene Coexpression Networks

For the study presented here, we performed a comparative analysis of whole-genome gene expression variation in 210 unrelated HapMap individuals to assess the extent of expression divergence between 4 human populations and to explore the connection between the variation of gene expression and function. We used the GEO series GSE6536 to compare changes in expression of 47,294 human transcripts between four human populations. Gene expression patterns were resolved into gene coexpression networks and the topological properties of these networks were compared. The interrogation of coexpression networks allows for the use of a well-developed set of analytical and conceptual tools and provides an opportunity for the simultaneous comparison of variation at different levels of systemic organization, i.e., global vs. local network properties. The results of this comparison indicate that human co-expression networks are indistinguishable in terms of their global properties but show divergence at the local level.

[1]  D. di Bernardo,et al.  How to infer gene networks from expression profiles , 2007, Molecular systems biology.

[2]  Michael Griffin,et al.  Gene co-expression network topology provides a framework for molecular characterization of cellular state , 2004, Bioinform..

[3]  Eugene V Koonin,et al.  Evolutionary significance of gene expression divergence. , 2005, Gene.

[4]  R. Redon,et al.  Relative Impact of Nucleotide and Copy Number Variation on Gene Expression Phenotypes , 2007, Science.

[5]  S. Pääbo,et al.  A Neutral Model of Transcriptome Evolution , 2004, PLoS biology.

[6]  Toshihiro Tanaka The International HapMap Project , 2003, Nature.

[7]  Shoshana J. Wodak,et al.  GenePro: a cytoscape plug-in for advanced visualization and analysis of interaction networks , 2006, Bioinform..

[8]  I. Yanai,et al.  Incongruent expression profiles between human and mouse orthologous genes suggest widespread neutral evolution of transcription control. , 2004, Omics : a journal of integrative biology.

[9]  Mark Gerstein,et al.  The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics , 2007, PLoS Comput. Biol..

[10]  Jun Dong,et al.  Geometric Interpretation of Gene Coexpression Network Analysis , 2008, PLoS Comput. Biol..

[11]  E. Wingender,et al.  Topology of mammalian transcription networks. , 2005, Genome informatics. International Conference on Genome Informatics.

[12]  J. Scott Brockenbrough,et al.  Computational Genomics: Theory and Application , 2005 .

[13]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[14]  Olivier Bodenreider,et al.  Global similarity and local divergence in human and mouse gene co-expression networks , 2006, BMC Evolutionary Biology.