Department of Genetics, Oncode Institute, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
Elife. 2020 Mar 9;9:e52155. doi: 10.7554/eLife.52155.
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.
近年来,功能基因组学方法将遗传信息与批量 RNA 测序数据相结合,通过所谓的表达数量性状基因座 (eQTL) 分析,确定了与疾病相关的遗传风险因素的下游表达效应。单细胞 RNA 测序为在不同细胞类型和动态过程中绘制 eQTL 提供了巨大的机会,而使用批量方法则掩盖了许多机会。现在,通量的快速增加和每个细胞的成本降低使得这项技术能够应用于大规模的群体遗传学研究。为了充分利用这些新兴的数据资源,我们成立了单细胞 eQTLGen 联盟 (sc-eQTLGen),旨在精确定位导致疾病的遗传变异影响基因表达的细胞环境。在这里,我们概述了 sc-eQTLGen 联盟的目标、方法和潜在用途。我们还提供了一组用于未来单细胞 eQTL 研究的研究设计注意事项。