title: Genome-wide co-expression analysis in multiple tissues.
abstract: 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.
authors.affiliation: MRC Clinical Sciences Centre, Imperial College, Hammersmith Hospital, London, United Kingdom.
authors.forename: Ian C
authors.initials: IC
authors.lastname: Grieve
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Dickens
authors.forename: Michal
authors.initials: M
authors.lastname: Pravenec
authors.forename: Vladimir
authors.initials: V
authors.lastname: Kren
authors.forename: Norbert
authors.initials: N
authors.lastname: Hubner
authors.forename: Stuart A
authors.initials: SA
authors.lastname: Cook
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Aitman
authors.forename: Enrico
authors.initials: E
authors.lastname: Petretto
authors.forename: Jonathan
authors.initials: J
authors.lastname: Mangion
mesh_terms: D000818:Animals; D015870:Gene Expression; D005786:Gene Expression Regulation; D005809:Genes, Regulator; D008040:Genetic Linkage; D016678:Genome; D005838:Genotype; D018895:Microsatellite Repeats; D008957:Models, Genetic; D008962:Models, Theoretical; D005810:Multigene Family; D040641:Quantitative Trait Loci; D051381:Rats; D014018:Tissue Distribution