The single-cell eQTLGen consortium
- University of Groningen, University Medical Center Groningen, Netherlands
- Wellcome Sanger Institute, United Kingdom
- RIKEN Center for Integrative Medical Sciences, Japan
- European Molecular Biology Laboratory, European Bioinformatics Institute, United Kingdom
- DKFZ, Germany
- New York University Abu Dhabi, United Arab Emirates
- University of California, San Francisco, United States
- Garvan Institute, Australia
- Helmholtz Zentrum München, Germany
- Leiden University Medical Center, Netherlands
- Institute of Computational Biology, Helmholtz Zentrum München, Technical University of Munich, 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.
Data availability
Not applicable
Article and author information
Author details
Monique GP van der Wijst
Funding
Dutch Research Council (NWO-Veni 192.029)
- Monique GP van der Wijst
Dutch Research Council (ZonMW-VIDI 917.14.374)
- Lude Franke
European Research Council (ERC Starting grant Immrisk 637640)
- Lude Franke
Oncode Institute
- Lude Franke
National Health and Medical Research Council (Investigator grant 1175781)
- Joseph Powell
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Helena Pérez Valle, eLife, United Kingdom
Publication history
- Received: September 24, 2019
- Accepted: March 3, 2020
- Accepted Manuscript published: March 9, 2020 (version 1)
- Version of Record published: March 17, 2020 (version 2)
Copyright
© 2020, van der Wijst et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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The single-cell eQTLGen consortium
eLife 9:e52155.
https://doi.org/10.7554/eLife.52155
Further reading
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South Asian women are at increased risk of developing gestational diabetes mellitus (GDM). Few studies have investigated the genetic contributions to GDM risk. We investigated the association of a type 2 diabetes (T2D) polygenic risk score (PRS), on its own, and with GDM risk factors, on GDM-related traits using data from two birth cohorts in which South Asian women were enrolled during pregnancy. 837 and 4372 pregnant South Asian women from the SouTh Asian BiRth CohorT (START) and Born in Bradford (BiB) cohort studies underwent a 75-g glucose tolerance test. PRSs were derived using genome-wide association study results from an independent multi-ethnic study (~18% South Asians). Associations with fasting plasma glucose (FPG); 2 hr post-load glucose (2hG); area under the curve glucose; and GDM were tested using linear and logistic regressions. The population attributable fraction (PAF) of the PRS was calculated. Every 1 SD increase in the PRS was associated with a 0.085 mmol/L increase in FPG ([95% confidence interval, CI=0.07–0.10], p=2.85×10−20); 0.21 mmol/L increase in 2hG ([95% CI=0.16–0.26], p=5.49×10−16); and a 45% increase in the risk of GDM ([95% CI=32–60%], p=2.27×10−14), independent of parental history of diabetes and other GDM risk factors. PRS tertile 3 accounted for 12.5% of the population’s GDM alone, and 21.7% when combined with family history. A few weak PRS and GDM risk factors interactions modulating FPG and GDM were observed. Taken together, these results show that a T2D PRS and family history of diabetes are strongly and independently associated with multiple GDM-related traits in women of South Asian descent, an effect that could be modulated by other environmental factors.
