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Background There is only a limited understanding of the relation between copy number and expression for mammalian genes. We fine mapped cis and trans regulatory loci due to copy number change for essentially all genes using a human-hamster radiation hybrid (RH) panel. These loci are called copy number expression quantitative trait loci (ceQTLs). Results Unexpected findings from a previous study of a mouse-hamster RH panel were replicated. These findings included decreased expression as a result of increased copy number for 30% of genes and an attenuated relationship between expression and copy number on the X chromosome suggesting an Xist independent form of dosage compensation. In a separate glioblastoma dataset, we found conservation of genes in which dosage was negatively correlated with gene expression. These genes were enriched in signaling and receptor activities. The observation of attenuated X-linked gene expression in response to increased gene number was also replicated in the glioblastoma dataset. Of 523 gene deserts of size > 600 kb in the human RH panel, 325 contained trans ceQTLs with -log 10 P > 4.1. Recently discovered genes, ultra conserved regions, noncoding RNAs and microRNAs explained only a small fraction of the results, suggesting a substantial portion of gene deserts harbor as yet unidentified functional elements. Conclusion Radiation hybrids are a useful tool for high resolution mapping of cis and trans loci capable of affecting gene expression due to copy number change. Analysis of two independent radiation hybrid panels show agreement in their findings and may serve as a discovery source for novel regulatory loci in noncoding regions of the genome.

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

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.

Background Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution. Results A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6 , Ube2a and Usp33 , suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK , and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes. Conclusion Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior.

Integrating quantitative proteomic and transcriptomic datasets promises valuable insights in unraveling the molecular mechanisms of the brain. We concentrate on recent studies using mass spectrometry and microarray data to investigate transcript and protein abundance in normal and diseased neural tissues. Highlighted are dual spatial maps of these molecules obtained using voxelation of the mouse brain. We demonstrate that the relationship between transcript and protein levels displays a specific anatomical distribution, with greatest fidelity in midline structures and the hypothalamus. Genes are also identified that have strong correlations between mRNA and protein abundance. In addition, transcriptomic and proteomic analysis of mouse models of Parkinson's disease are discussed.

We mapped regulatory loci for nearly all protein-coding genes in mammals using comparative genomic hybridization and expression array measurements from a panel of mouse–hamster radiation hybrid cell lines. The large number of breaks in the mouse chromosomes and the dense genotyping of the panel allowed extremely sharp mapping of loci. As the regulatory loci result from extra gene dosage, we call them copy number expression quantitative trait loci, or ceQTLs. The −2log 10 P support interval for the ceQTLs was <150 kb, containing an average of <2–3 genes. We identified 29,769 trans ceQTLs with −log 10 P > 4, including 13 hotspots each regulating >100 genes in trans . Further, this work identifies 2,761 trans ceQTLs harboring no known genes, and provides evidence for a mode of gene expression autoregulation specific to the X chromosome.

Mutations that inactivate the retinoblastoma (Rb) pathway are common in human tumors. Such mutations promote tumor growth by deregulating the G1 cell cycle checkpoint. However, uncontrolled cell cycle progression can also produce new liabilities for cell survival. To uncover such liabilities in Rb mutant cells, we performed a clonal screen in the Drosophila eye to identify second-site mutations that eliminate Rbf− cells, but allow Rbf+ cells to survive. Here we report the identification of a mutation in a novel highly conserved peptidyl prolyl isomerase (PPIase) that selectively eliminates Rbf− cells from the Drosophila eye.

Nature Genetics 40, 421–429 (2008); published online 23 March 2008; corrected after print 28 April 2008 In the version of this article initially published, the scale was inadvertently omitted from the bottom left portion of Figure 4. This error has been corrected in the PDF version of the article.

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.