The single-cell eQTLGen consortium

Author:

van der Wijst MGP1ORCID,de Vries DH1,Groot HE2ORCID,Trynka G34ORCID,Hon CC5,Bonder MJ67ORCID,Stegle O67,Nawijn MC8ORCID,Idaghdour Y9ORCID,van der Harst P2ORCID,Ye CJ10,Powell J11,Theis FJ1213ORCID,Mahfouz A1415ORCID,Heinig M1216ORCID,Franke L1ORCID

Affiliation:

1. Department of Genetics, Oncode Institute, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

2. Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

3. Wellcome Sanger Institute, Hinxton, United Kingdom

4. Open Targets, Hinxton, United Kingdom

5. RIKEN Center for Integrative Medical Sciences, Yokahama, Japan

6. Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany

7. Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

8. Department of Pathology and Medical Biology, GRIAC Research Institute, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

9. Program in Biology, Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

10. 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

11. Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute, UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, Australia

12. Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany

13. Department of Mathematics, Technical University of Munich, Garching bei München, Germany

14. Leiden Computational Biology Center, Leiden University Medical Center, Leiden, Netherlands

15. Delft Bioinformatics Lab, Delft University of Technology, Delft, Netherlands

16. Department of Informatics, Technical University of Munich, Garching bei München, Germany

Abstract

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.

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

European Research Council

Oncode Institute

National Health and Medical Research Council

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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