Dataset Information


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.


PROVIDER: S-EPMC2603584 | BioStudies | 2008-01-01

REPOSITORIES: biostudies

Similar Datasets

2018-01-01 | S-EPMC5753226 | BioStudies
2015-01-01 | S-EPMC4512630 | BioStudies
2014-01-01 | S-EPMC4256471 | BioStudies
2014-01-01 | S-EPMC3989189 | BioStudies
2020-01-01 | S-EPMC7470823 | BioStudies
2014-01-01 | S-EPMC4089026 | BioStudies
2019-01-01 | S-EPMC6915738 | BioStudies
2017-01-01 | S-EPMC5264466 | BioStudies
2011-01-01 | S-EPMC3217321 | BioStudies
2009-01-01 | S-EPMC2789871 | BioStudies