The single-cell eQTLGen consortium

Mgp van der Wijst; D H de Vries; H E Groot; G Trynka; C C Hon; M J Bonder; O Stegle; M C Nawijn; Y Idaghdour; P van der Harst; C J Ye; J Powell; F J Theis; A Mahfouz; M Heinig; L Franke

doi:10.7554/eLife.52155

The single-cell eQTLGen consortium

Mgp van der Wijst, D H de Vries, H E Groot, G Trynka, C C Hon, M J Bonder, O Stegle, M C Nawijn, Y Idaghdour, P van der Harst, C J Ye, J Powell, F J Theis, A Mahfouz, M Heinig, L Franke

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Article number	e52155
Journal	eLife
Volume	9
DOIs	https://doi.org/10.7554/eLife.52155
Publication status	Published - 9 Mar 2020
Externally published	Yes

Keywords

Gene Expression
Gene Regulatory Networks
Genetic Predisposition to Disease
Genetics, Population
Genotype
Humans
Polymorphism, Single Nucleotide
Quantitative Trait Loci
RNA-Seq
Sequence Analysis, RNA
Single-Cell Analysis

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10.7554/eLife.52155

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title = "The single-cell eQTLGen consortium",

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.",

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author = "{van der Wijst}, Mgp and {de Vries}, {D H} and Groot, {H E} and G Trynka and Hon, {C C} and Bonder, {M J} and O Stegle and Nawijn, {M C} and Y Idaghdour and {van der Harst}, P and Ye, {C J} and J Powell and Theis, {F J} and A Mahfouz and M Heinig and L Franke",

note = "Publisher Copyright: {\textcopyright} van der Wijst et al.",

year = "2020",

month = mar,

day = "9",

doi = "10.7554/eLife.52155",

language = "English",

volume = "9",

journal = "eLife",

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AU - van der Wijst, Mgp

AU - de Vries, D H

AU - Groot, H E

AU - Trynka, G

AU - Hon, C C

AU - Bonder, M J

AU - Stegle, O

AU - Nawijn, M C

AU - Idaghdour, Y

AU - van der Harst, P

AU - Ye, C J

AU - Powell, J

AU - Theis, F J

AU - Mahfouz, A

AU - Heinig, M

AU - Franke, L

N1 - Publisher Copyright: © van der Wijst et al.

PY - 2020/3/9

Y1 - 2020/3/9

N2 - 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.

AB - 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.

KW - Gene Expression

KW - Gene Regulatory Networks

KW - Genetic Predisposition to Disease

KW - Genetics, Population

KW - Genotype

KW - Humans

KW - Polymorphism, Single Nucleotide

KW - Quantitative Trait Loci

KW - RNA-Seq

KW - Sequence Analysis, RNA

KW - Single-Cell Analysis

U2 - 10.7554/eLife.52155

DO - 10.7554/eLife.52155

M3 - Article

C2 - 32149610

SN - 2050-084X

VL - 9

JO - eLife

JF - eLife

M1 - e52155

ER -

The single-cell eQTLGen consortium

Abstract

Keywords

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