Article

Genome-Wide Co-Expression Analysis in Multiple Tissues

University of Cape Town, South Africa
PLoS ONE (Impact Factor: 3.53). 12/2008; 3(12):e4033. DOI: 10.1371/journal.pone.0004033
Source: PubMed

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.

0 Bookmarks
 · 
116 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Although gene co-expression domains have been reported in most eukaryotic organisms, data available to date suggested that co-expression rarely concerned more than doublets or triplets of adjacent genes in mammals. Using expression data from hearts of mice from the panel of AxB/BxA recombinant inbred mice, we detected (according to window sizes) 42-53 loci linked to the expression levels of clusters of 3 or more neighboring genes. These loci thus formed "cis-expression QTL clusters", because their position matched that of the genes whose expression was linked to the loci. Compared to matching control regions, genes contained within cis-eQTL clusters showed much higher levels of co-expression. Corresponding regions also showed greater abundance of polymorphic elements corresponding to SINE retrotransposons. The latter showed enrichment for the motifs of binding sites for various transcription factors, with binding sites for the chromatin organizing CCCTC-binding factor showing highest levels of enrichment in polymorphic SINEs. Similar cis-eQTL clusters were also detected when using data obtained with several tissues from BxD recombinant inbred mice. In addition to strengthening the evidence for gene expression domains in mammalian genomes, our data suggest a possible mechanism whereby non-coding polymorphisms could affect the coordinate expression of several neighboring genes.
    G3-Genes Genomes Genetics 03/2013; DOI:10.1534/g3.113.005546 · 2.51 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Studying genetic determinants of intermediate phenotypes is a powerful tool to increase our understanding of genotype-phenotype correlations. Metabolic traits pertinent to the central nervous system (CNS) constitute a potentially informative target for genetic studies of intermediate phenotypes as their genetic underpinnings may elucidate etiological mechanisms. We therefore conducted a genome-wide association study (GWAS) of monoamine metabolite (MM) levels in cerebrospinal fluid (CSF) of 414 human subjects from the general population. In a linear model correcting for covariates, we identified one locus associated with MMs at a genome-wide significant level (standardized β=0.32, P=4.92 × 10(-8)), located 20 kb from SSTR1, a gene involved with brain signal transduction and glutamate receptor signaling. By subsequent whole-genome expression quantitative trait locus (eQTL) analysis, we provide evidence that this variant controls expression of PDE9A (β=0.21; P(unadjusted)=5.6 × 10(-7); P(corrected)=0.014), a gene previously implicated in monoaminergic transmission, major depressive disorder and antidepressant response. A post hoc analysis of loci significantly associated with psychiatric disorders suggested that genetic variation at CSMD1, a schizophrenia susceptibility locus, plays a role in the ratio between dopamine and serotonin metabolites in CSF. The presented DNA and mRNA analyses yielded genome-wide and suggestive associations in biologically plausible genes, two of which encode proteins involved with glutamate receptor functionality. These findings will hopefully contribute to an exploration of the functional impact of the highlighted genes on monoaminergic transmission and neuropsychiatric phenotypes.Molecular Psychiatry advance online publication, 15 January 2013; doi:10.1038/mp.2012.183.
    Molecular Psychiatry 01/2013; DOI:10.1038/mp.2012.183 · 15.15 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Cardiovascular disease (CVD) affects millions of people worldwide and is influenced by numerous factors, including lifestyle and genetics. Expression quantitative trait loci (eQTLs) influence gene expression and are good candidates for CVD risk. Founder-effect pedigrees can provide additional power to map genes associated with disease risk. Therefore, we identified eQTLs in the genetic isolate of Norfolk Island (NI) and tested for associations between these and CVD risk factors. We measured genome-wide transcript levels of blood lymphocytes in 330 individuals and used pedigree-based heritability analysis to identify heritable transcripts. eQTLs were identified by genome-wide association testing of these transcripts. Testing for association between CVD risk factors (i.e., blood lipids, blood pressure, and body fat indices) and eQTLs revealed 1,712 heritable transcripts (p < 0.05) with heritability values ranging from 0.18 to 0.84. From these, we identified 200 cis-acting and 70 trans-acting eQTLs (p < 1.84 × 10(-7)) An eQTL-centric analysis of CVD risk traits revealed multiple associations, including 12 previously associated with CVD-related traits. Trait versus eQTL regression modeling identified four CVD risk candidates (NAAA, PAPSS1, NME1, and PRDX1), all of which have known biological roles in disease. In addition, we implicated several genes previously associated with CVD risk traits, including MTHFR and FN3KRP. We have successfully identified a panel of eQTLs in the NI pedigree and used this to implicate several genes in CVD risk. Future studies are required for further assessing the functional importance of these eQTLs and whether the findings here also relate to outbred populations.
    The American Journal of Human Genetics 12/2013; 93(6):1087-99. DOI:10.1016/j.ajhg.2013.11.004 · 11.20 Impact Factor

Full-text (3 Sources)

Download
23 Downloads
Available from
May 16, 2014