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The single-cell eQTLGen consortium.
eLife 9:e52155 (2020)
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,
Artikelnummer: e52155
Verlag
eLife Sciences Publications
Verlagsort
Sheraton House, Castle Park, Cambridge, Cb3 0ax, England
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)