title: Meta-analysis of inter-species liver co-expression networks elucidates traits associated with common human diseases.
abstract: Co-expression networks are routinely used to study human diseases like obesity and diabetes. Systematic comparison of these networks between species has the potential to elucidate common mechanisms that are conserved between human and rodent species, as well as those that are species-specific characterizing evolutionary plasticity. We developed a semi-parametric meta-analysis approach for combining gene-gene co-expression relationships across expression profile datasets from multiple species. The simulation results showed that the semi-parametric method is robust against noise. When applied to human, mouse, and rat liver co-expression networks, our method out-performed existing methods in identifying gene pairs with coherent biological functions. We identified a network conserved across species that highlighted cell-cell signaling, cell-adhesion and sterol biosynthesis as main biological processes represented in genome-wide association study candidate gene sets for blood lipid levels. We further developed a heterogeneity statistic to test for network differences among multiple datasets, and demonstrated that genes with species-specific interactions tend to be under positive selection throughout evolution. Finally, we identified a human-specific sub-network regulated by RXRG, which has been validated to play a different role in hyperlipidemia and Type 2 diabetes between human and mouse. Taken together, our approach represents a novel step forward in integrating gene co-expression networks from multiple large scale datasets to leverage not only common information but also differences that are dataset-specific.
authors.affiliation: Department of Genetics, Rosetta Inpharmatics, Seattle, Washington, USA.
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.forename: Manikandan
authors.initials: M
authors.lastname: Narayanan
authors.forename: Hua
authors.initials: H
authors.lastname: Zhong
authors.forename: Martin
authors.initials: M
authors.lastname: Tompa
authors.forename: Eric E
authors.initials: EE
authors.lastname: Schadt
authors.forename: Jun
authors.initials: J
authors.lastname: Zhu
mesh_terms: D000818:Animals; D003198:Computer Simulation; D003920:Diabetes Mellitus / Q000378:metabolism; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D050356:Lipid Metabolism; D008099:Liver / Q000378:metabolism / Q000502:physiology*; D051379:Mice; D008822:Mice, Transgenic; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D051381:Rats; D012044:Regression Analysis; D015398:Signal Transduction; D013045:Species Specificity; D049490:Systems Biology / Q000379:methods*