Abstract
Meiotic mapping of quantitative trait loci regulating expression (eQTLs) has allowed the construction of gene networks. However, the limited mapping resolution of these studies has meant that genotype data are largely ignored, leading to undirected networks that fail to capture regulatory hierarchies. Here we use high resolution mapping of copy number eQTLs (ceQTLs) in a mouse-hamster radiation hybrid (RH) panel to construct directed genetic networks in the mammalian cell. The RH network covering 20,145 mouse genes had significant overlap with, and similar topological structures to, existing biological networks. Upregulated edges in the RH network had significantly more overlap than downregulated. This suggests repressive relationships between genes are missed by existing approaches, perhaps because the corresponding proteins are not present in the cell at the same time and therefore unlikely to interact. Gene essentiality was positively correlated with connectivity and betweenness centrality in the RH network, strengthening the centrality-lethality principle in mammals. Consistent with their regulatory role, transcription factors had significantly more outgoing edges (regulating) than incoming (regulated) in the RH network, a feature hidden by conventional undirected networks. Directed RH genetic networks thus showed concordance with pre-existing networks while also yielding information inaccessible to current undirected approaches.
Highlights
Interrogating genome-scale datasets is a necessary step to a systems biology of the mammalian cell [1,2]
Transcript abundance and marker dosage were measured by mouse expression arrays and comparative genomic hybridization arrays, respectively
Overlap with Existing Datasets We examined the similarity of our network to existing datasets including protein-protein interactions from HPRD (Human Protein Reference Database) [6], the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway database [10,11,12], Gene Ontology (GO) annotations [25] and a coexpression network obtained from the SymAtlas microarray database of normal mouse tissues [26]
Summary
Interrogating genome-scale datasets is a necessary step to a systems biology of the mammalian cell [1,2]. Genes can be linked by virtue of membership of a common pathway [8,9], an example being the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway [10,11,12] Networks constructed using these various approaches are correlated, with some exceptions. Causality between expression and clinical traits has been inferred from eQTL data using conditional correlation measures [18] and structural model analysis [19,20]. This approach has been restricted to a small subset of markers and traits and cannot be extended to constructing gene networks.
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