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van der Wijst, M.* ; de Vries, D.H.* ; Groot, H.E.* ; Trynka, G.* ; Hon, C.C.* ; Bonder, M.J.* ; Stegle, O.* ; Nawijn, M.C.* ; Idaghdour, Y.* ; van der Harst, P.* ; Ye, C.J.* ; Powell, J.* ; Theis, F.J. ; Mahfouz, A.* ; Heinig, M. ; Franke, L.*

The single-cell eQTLGen consortium.

eLife 9:e52155 (2020)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Eqtl ; Gene Regulatory Network ; Genetics ; Genomics ; Human ; Pbmc ; Science Forum ; Single-cell; Gene Regulatory Networks; Expression; Risk; Identification; Associations; Prediction; Diversity; Drivers
Language english
Publication Year 2020
HGF-reported in Year 2020
ISSN (print) / ISBN 2050-084X
e-ISSN 2050-084X
Journal eLife
Quellenangaben Volume: 9, Issue: , Pages: , Article Number: e52155 Supplement: ,
Publisher eLife Sciences Publications
Publishing Place Sheraton House, Castle Park, Cambridge, Cb3 0ax, England
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-503800-001
G-553500-001
DOI 10.7554/eLife.52155
WOS ID WOS:000521140300001
Scopus ID 85082094066
PubMed ID 32149610
Erfassungsdatum 2020-04-17
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