The single-cell eQTLGen consortium
| Autor: | Youssef Idaghdour, Fabian J. Theis, Ahmed Mahfouz, Monique G. P. van der Wijst, Matthias Heinig, Oliver Stegle, DH de Vries, CC Hon, Gosia Trynka, Joseph E. Powell, Lude Franke, Marc Jan Bonder, P. van der Harst, Hilde E. Groot, Martijn C. Nawijn, Chun Jimmie Ye |
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| Přispěvatelé: | Groningen Research Institute for Asthma and COPD (GRIAC), Cardiovascular Centre (CVC), Macromolecular Chemistry & New Polymeric Materials, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL) |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
PREDICTION Computer science DIVERSITY Gene regulatory network Gene Expression Population genetics gene regulatory network 0302 clinical medicine DRIVERS genetics RNA-Seq Biology (General) Genetic risk ASSOCIATIONS RISK General Neuroscience General Medicine Medicine Single-Cell Analysis Functional genomics EXPRESSION Genotype QH301-705.5 Science Quantitative Trait Loci Genomics Computational biology GENE REGULATORY NETWORKS eQTL Polymorphism Single Nucleotide General Biochemistry Genetics and Molecular Biology 03 medical and health sciences genomics Humans Leverage (statistics) Genetic Predisposition to Disease human IDENTIFICATION General Immunology and Microbiology Sequence Analysis RNA Feature Article Eqtl Gene Regulatory Network Genetics Human Pbmc Science Forum Single-cell PBMC Genetics and Genomics single-cell science forum Data resources Genetics Population 030104 developmental biology Expression quantitative trait loci 030217 neurology & neurosurgery |
| Zdroj: | eLife eLife 9:e52155 (2020) eLife, 9:52155. ELIFE SCIENCES PUBLICATIONS LTD eLife, Vol 9 (2020) eLife, 9 |
| ISSN: | 2050-084X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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