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Budach, S.* ; Heinig, M. ; Marsico, A.*

Principles of microRNA regulation revealed through modeling microRNA expression quantitative trait loci.

Genetics 203, 1629-1640 (2016)
Publ. Version/Full Text Research data DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Extensive work has been dedicated to study mechanisms of microRNA-mediated gene regulation. However, the transcriptional regulation of microRNAs themselves is far less well understood, due to difficulties determining the transcription start sites of transient primary transcripts. This challenge can be addressed using expression quantitative trait loci (eQTLs) whose regulatory effects represent a natural source of perturbation of cis-regulatory elements. Here we used previously published cis-microRNA-eQTL data for the human GM12878 cell line, promoter predictions and other functional annotations to determine the relationship between functional elements and microRNA regulation. We built a logistic regression model which classifies microRNA/SNP pairs into eQTLs or non-eQTLs with 85% accuracy and shows microRNA-eQTL enrichment for microRNA precursors, promoters, enhancers and transcription factor binding sites and depletion for repressed chromatin. Interestingly, although there is a large overlap between microRNA-eQTLs and mRNA-eQTLs of host genes, 74% of these shared eQTLs affect microRNA and host expression independently. Considering microRNA-only eQTLs we find a significant enrichment for intronic promoters, validating the existence of alternative promoters for intragenic microRNAs. Finally, in line with the GM12878 cell line being derived from B-cells, we find genome-wide association (GWA) variants associated to blood-related traits more likely to be miRNA-eQTLs than random GWA and non-GWA variants, aiding the interpretation of GWA results.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Eqtl ; Microrna ; Promoter ; Regulation ; Variation; Mirna Promoters; Genetic-control; Chromatin; Humans; Rna; Architecture; Identification; Transcriptome; Recognition; Biogenesis
ISSN (print) / ISBN 0016-6731
e-ISSN 0016-6731
Journal Genetics
Quellenangaben Volume: 203, Issue: 4, Pages: 1629-1640 Article Number: , Supplement: ,
Publisher Genetics Society of America
Publishing Place Bethesda
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)