Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
O Stegle
Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
Department of Pathology and Medical Biology, GRIAC Research Institute, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
CJ Ye
Institute for Human Genetics, Bakar Computational Health Sciences Institute, Bakar ImmunoX Initiative, Department of Medicine, Department of Bioengineering and Therapeutic Sciences, Department of Epidemiology and Biostatistics, Chan Zuckerberg Biohub, University of California San Francisco, San Francisco, United States
J Powell
Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute, UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, Australia
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany; Department of Mathematics, Technical University of Munich, Garching bei München, Germany
Leiden Computational Biology Center, Leiden University Medical Center, Leiden, Netherlands; Delft Bioinformatics Lab, Delft University of Technology, Delft, Netherlands
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany; Department of Informatics, Technical University of Munich, Garching bei München, Germany
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.
