PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
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 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.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
7.080
1.666
40
49
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
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
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
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
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-503800-001
G-553500-001
DOI 10.7554/eLife.52155
WOS ID WOS:000521140300001
Scopus ID 85082094066
PubMed ID 32149610
Erfassungsdatum 2020-04-17
[➜Korrektur melden]