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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)
Verlagsversion DOI
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Eqtl ; Gene Regulatory Network ; Genetics ; Genomics ; Human ; Pbmc ; Science Forum ; Single-cell; Gene Regulatory Networks; Expression; Risk; Identification; Associations; Prediction; Diversity; Drivers
ISSN (print) / ISBN 2050-084X
e-ISSN 2050-084X
Zeitschrift eLife
Quellenangaben Band: 9, Heft: , Seiten: , Artikelnummer: e52155 Supplement: ,
Verlag eLife Sciences Publications
Verlagsort Sheraton House, Castle Park, Cambridge, Cb3 0ax, England
Begutachtungsstatus Peer reviewed