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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.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
Altmetric
4.644
1.245
13
12
Anmerkungen
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Eqtl ; Microrna ; Promoter ; Regulation ; Variation; Mirna Promoters; Genetic-control; Chromatin; Humans; Rna; Architecture; Identification; Transcriptome; Recognition; Biogenesis
Sprache
englisch
Veröffentlichungsjahr
2016
HGF-Berichtsjahr
2016
ISSN (print) / ISBN
0016-6731
e-ISSN
0016-6731
Zeitschrift
Genetics
Quellenangaben
Band: 203,
Heft: 4,
Seiten: 1629-1640
Verlag
Genetics Society of America
Verlagsort
Bethesda
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)
POF Topic(s)
30205 - Bioengineering and Digital Health
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-553500-001
WOS ID
WOS:000382622700011
Scopus ID
84981510501
PubMed ID
27260304
Erfassungsdatum
2016-06-06