Identification of cis- and trans-regulatory variation modulating microRNA expression levels in human fibroblasts - PubMed (original) (raw)
doi: 10.1101/gr.109371.110. Epub 2010 Dec 8.
Samuel Deutsch, Audrey Letourneau, Eugenia Migliavacca, Stephen B Montgomery, Antigone S Dimas, Charles E Vejnar, Homa Attar, Maryline Gagnebin, Corinne Gehrig, Emilie Falconnet, Yann Dupré, Emmanouil T Dermitzakis, Stylianos E Antonarakis
Affiliations
- PMID: 21147911
- PMCID: PMC3012927
- DOI: 10.1101/gr.109371.110
Identification of cis- and trans-regulatory variation modulating microRNA expression levels in human fibroblasts
Christelle Borel et al. Genome Res. 2011 Jan.
Abstract
MicroRNAs (miRNAs) are regulatory noncoding RNAs that affect the production of a significant fraction of human mRNAs via post-transcriptional regulation. Interindividual variation of the miRNA expression levels is likely to influence the expression of miRNA target genes and may therefore contribute to phenotypic differences in humans, including susceptibility to common disorders. The extent to which miRNA levels are genetically controlled is largely unknown. In this report, we assayed the expression levels of miRNAs in primary fibroblasts from 180 European newborns of the GenCord project and performed association analysis to identify eQTLs (expression quantitative traits loci). We detected robust expression for 121 miRNAs out of 365 interrogated. We have identified significant cis- (10%) and trans- (11%) eQTLs. Furthermore, we detected one genomic locus (rs1522653) that influences the expression levels of five miRNAs, thus unraveling a novel mechanism for coregulation of miRNA expression.
Figures
Figure 1.
Examples of _cis_-eQTLs for miR-100 (A), miR-320 (B), and miR-218-1 (C). The panels show the distribution of −log10 _P_-values for SNPs across a 1-Mb region surrounding the miRNA (“0” position). The highest significant −log10 _P_-values are shown as red dots. Also shown are the mapping of RefSeq genes in blue and miRNAs in red. The boxplots depict the relationship between miRNA relative expression levels (log2) and genotypes for the most significant SNPs. Boxplots are divided by median values.
Figure 2.
Example of _trans_- eQTLs for miR-134. (A) Manhattan plot displays −log10 _P_-values of a GWAS for miR-134 expression variation. Each chromosome is depicted as different shades of blue. Chromosome Y and mitochondrial genotypes have not been included in this study. Red dots indicate SNPs with the highest significant _P_-values after Bonferroni correction for multiple testing (see Table 1). Those two eQTLs, rs2824791 on chromosome 21 and rs7533447 on chromosome 3, are detailed in panels B and C, respectively. (B,C, left panels) The location of associated SNPs, as well as RefSeq transcripts, conservation, and LD information (LOD scores for CEU population). (B,C, right panels) Boxplots for miRNA expression for different genotypic groups.
Figure 3.
Master miRNA _trans_-eQTLs. Plot shows SNPs associated with the expression variation of multiple miRNAs (using a threshold of an unadjusted _P_-value < 10−6 per association) (see Supplemental Table S2). Each circle represents a single SNP. Only SNPs with at least one association below the P < 10−6 threshold are shown. One SNP (rs1522653) is significantly associated with the expression of five miRNAs (*, permutated _P_-value of 0.005). The identities and unadjusted _P_-values for these miRNAs are shown.
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