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The single-cell eQTLGen consortium
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 c...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7077978/ https://www.ncbi.nlm.nih.gov/pubmed/32149610 http://dx.doi.org/10.7554/eLife.52155 |
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author | van der Wijst, MGP de Vries, DH Groot, HE Trynka, G Hon, CC Bonder, MJ Stegle, O Nawijn, MC Idaghdour, Y van der Harst, P Ye, CJ Powell, J Theis, FJ Mahfouz, A Heinig, M Franke, L |
author_facet | van der Wijst, MGP de Vries, DH Groot, HE Trynka, G Hon, CC Bonder, MJ Stegle, O Nawijn, MC Idaghdour, Y van der Harst, P Ye, CJ Powell, J Theis, FJ Mahfouz, A Heinig, M Franke, L |
author_sort | van der Wijst, MGP |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7077978 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-70779782020-03-19 The single-cell eQTLGen consortium van der Wijst, MGP de Vries, DH Groot, HE Trynka, G Hon, CC Bonder, MJ Stegle, O Nawijn, MC Idaghdour, Y van der Harst, P Ye, CJ Powell, J Theis, FJ Mahfouz, A Heinig, M Franke, L eLife Genetics and Genomics 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. eLife Sciences Publications, Ltd 2020-03-09 /pmc/articles/PMC7077978/ /pubmed/32149610 http://dx.doi.org/10.7554/eLife.52155 Text en © 2020, van der Wijst et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Genetics and Genomics van der Wijst, MGP de Vries, DH Groot, HE Trynka, G Hon, CC Bonder, MJ Stegle, O Nawijn, MC Idaghdour, Y van der Harst, P Ye, CJ Powell, J Theis, FJ Mahfouz, A Heinig, M Franke, L The single-cell eQTLGen consortium |
title | The single-cell eQTLGen consortium |
title_full | The single-cell eQTLGen consortium |
title_fullStr | The single-cell eQTLGen consortium |
title_full_unstemmed | The single-cell eQTLGen consortium |
title_short | The single-cell eQTLGen consortium |
title_sort | single-cell eqtlgen consortium |
topic | Genetics and Genomics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7077978/ https://www.ncbi.nlm.nih.gov/pubmed/32149610 http://dx.doi.org/10.7554/eLife.52155 |
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