Genome-wide co-expression analysis in multiple tissues

Expression quantitative trait loci (eQTLs) represent genetic control points of gene expression, and can be categorized as cis- and trans-acting, reflecting local and distant regulation of gene expression respectively. Although there is evidence of co-regulation within clusters of trans-eQTLs, the extent of co-expression patterns and their relationship with the genotypes at eQTLs are not fully understood. We have mapped thousands of cis- and trans-eQTLs in four tissues (fat, kidney, adrenal and left ventricle) in a large panel of rat recombinant inbred (RI) strains. Here we investigate the genome-wide correlation structure in expression levels of eQTL transcripts and underlying genotypes to elucidate the nature of co-regulation within cis- and trans-eQTL datasets. Across the four tissues, we consistently found statistically significant correlations of cis-regulated gene expression to be rare (<0.9% of all pairs tested). Most (>80%) of the observed significant correlations of cis-regulated gene expression are explained by correlation of the underlying genotypes. In comparison, co-expression of trans-regulated gene expression is more common, with significant correlation ranging from 2.9%-14.9% of all pairs of trans-eQTL transcripts. We observed a total of 81 trans-eQTL clusters (hot-spots), defined as consisting of > or =10 eQTLs linked to a common region, with very high levels of correlation between trans-regulated transcripts (77.2-90.2%). Moreover, functional analysis of large trans-eQTL clusters (> or =30 eQTLs) revealed significant functional enrichment among genes comprising 80% of the large clusters. The results of this genome-wide co-expression study show the effects of the eQTL genotypes on the observed patterns of correlation, and suggest that functional relatedness between genes underlying trans-eQTLs is reflected in the degree of co-expression observed in trans-eQTL clusters. Our results demonstrate the power of an integrative, systematic approach to the analysis of a large gene expression dataset to uncover underlying structure, and inform future eQTL studies

A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk

Combined analyses of gene networks and DNA sequence variation can provide new insights into the aetiology of common diseases that may not be apparent from genome-wide association studies alone. Recent advances in rat genomics are facilitating systems-genetics approaches. Here we report the use of integrated genome-wide approaches across seven rat tissues to identify gene networks and the loci underlying their regulation. We defined an interferon regulatory factor 7 (IRF7)-driven inflammatory network (IDIN) enriched for viral response genes, which represents a molecular biomarker for macrophages and which was regulated in multiple tissues by a locus on rat chromosome 15q25. We show that Epstein-Barr virus induced gene 2 (Ebi2, also known as Gpr183), which lies at this locus and controls B lymphocyte migration, is expressed in macrophages and regulates the IDIN. The human orthologous locus on chromosome 13q32 controlled the human equivalent of the IDIN, which was conserved in monocytes. IDIN genes were more likely to associate with susceptibility to type 1 diabetes (T1D)-a macrophage-associated autoimmune disease-than randomly selected immune response genes (P = 8.85 x 10(-6)). The human locus controlling the IDIN was associated with the risk of T1D at single nucleotide polymorphism rs9585056 (P = 7.0 x 10(-10); odds ratio, 1.15), which was one of five single nucleotide polymorphisms in this region associated with EBI2 (GPR183) expression. These data implicate IRF7 network genes and their regulatory locus in the pathogenesis of T1D

Mouse Chromosome 3

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46995/1/335_2004_Article_BF00648421.pd

Mouse Chromosome 11

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46996/1/335_2004_Article_BF00648429.pd

The molecular genetics of type 1 (insulin-dependent) diabetes mellitus

Available from British Library Document Supply Centre- DSC:D173809 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Genetics of coronary heart disease

Genes and Common Diseases359-37

Integrated gene expression profiling and linkage analysis in the rat

The combined application of genome-wide expression profiling from microarray experiments with genetic linkage analysis enables the mapping of expression quantitative trait loci (eQTLs) which are primary control points for gene expression across the genome. This approach allows for the dissection of primary and secondary genetic determinants of gene expression. The cis-acting eQTLs in practice are easier to investigate than the trans-regulated eQTLs because they are under simpler genetic control and are likely to be due to sequence variants within the gene itself or its neighboring regulatory elements. These genes are therefore candidates both for variation in gene expression and for contributions to whole-body phenotypes, particularly when these are located within known and relevant physiologic QTLs. Multiple trans-acting eQTLs tend to cluster to the same genetic location, implying shared regulatory control mechanisms that may be amenable to network analysis to identify gene clusters within the same metabolic pathway. Such clusters may ultimately underlie development of individual complex, whole-body phenotypes. The combined expression and linkage approach has been applied successfully in several mammalian species, including the rat which has specific features that demonstrate its value as a model for studying complex traits

eQTL Explorer: integrated mining of combined genetic linkage and expression experiments

The development of computational resources to visualize and explore data from combined genome-wide expression and linkage studies is essential for the development of testable hypotheses. eQTL Explorer stores expression profiles, linkage data and information from external sources in a relational database and enables simultaneous visualization and intuitive interpretation of the combined data via a Java graphical interface. eQTL Explorer provides a new and powerful tool to interrogate these very large and complex datasets

Integrated genomic approaches to identification of candidate genes underlying metabolic and cardiovascular phenotypes in the spontaneously hypertensive rat

The Spontaneously Hypertensive rat (SHR) is a widely used rodent model of hypertension and metabolic syndrome. Previously we identified thousands of cis-regulated expression quantitative trait loci (eQTLs) across multiple tissues using a panel of rat recombinant inbred (RI) strains derived from Brown Norway (BN) and SHR progenitors. These cis-eQTLs represent potential susceptibility loci underlying physiological and pathophysiological traits manifested in SHR. We have prioritised 60 cis-eQTLs, and confirmed differential expression between the parental strains by quantitative PCR (qPCR) in 43 (72%) of the eQTL transcripts. Quantitative trait transcript (QTT) analysis in the RI strains showed highly significant correlation between cis-eQTL transcript abundance and clinically relevant traits such as systolic blood pressure and blood glucose, with the physical location of a subset co-localising with "physiological" QTLs (pQTLs) for these same traits. These co-localising correlated cis-eQTLs (c3-eQTLs) are highly attractive as primary susceptibility loci for the co-localising pQTLs. Furthermore, sequence analysis of the c3-eQTL genes identified SNPs that are predicted to affect transcription factor binding affinity, splicing and protein function. These SNPs, which potentially alter transcript abundance and stability, represent strong candidate factors underlying not just eQTL expression phenotypes, but also the correlated metabolic and physiological traits. In conclusion, by integration of genomic sequence, eQTL and QTT datasets we have identified several genes that are strong positional candidates for pathophysiological traits observed in the SHR strain. These findings provide a basis for the functional testing and ultimate elucidation of the molecular basis of these metabolic and cardiovascular phenotypes