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Principles of microRNA regulation revealed through modeling microRNA expression quantitative trait loci.
Genetics 203, 1629-1640 (2016)
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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4.644
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Eqtl ; Microrna ; Promoter ; Regulation ; Variation; Mirna Promoters; Genetic-control; Chromatin; Humans; Rna; Architecture; Identification; Transcriptome; Recognition; Biogenesis
Language
english
Publication Year
2016
HGF-reported in Year
2016
ISSN (print) / ISBN
0016-6731
e-ISSN
0016-6731
Journal
Genetics
Quellenangaben
Volume: 203,
Issue: 4,
Pages: 1629-1640
Publisher
Genetics Society of America
Publishing Place
Bethesda
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-553500-001
WOS ID
WOS:000382622700011
Scopus ID
84981510501
PubMed ID
27260304
Erfassungsdatum
2016-06-06