Abstract
Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.
Highlights
A primary aim of human genetics and genomics is to understand the genetic architecture of complex traits
This work demonstrates the importance of searching for variant-gene associations in cell types that change over time or exist only during fleeting stages of cellular differentiation, and provides a framework for identifying these associations in the presence of bifurcating trajectories that are characteristic of human development
Studies mapping expression quantitative trait loci, identifying genetic variants associated with gene expression levels, reveal the impact of genetic variation on gene regulation and can inform molecular mechanisms underlying trait-associated loci. eQTLs have been identified for a wide variety of tissues, and their study has contributed to the understanding of gene regulation and disease [4,5,6,7,8,9,10]
Summary
A primary aim of human genetics and genomics is to understand the genetic architecture of complex traits. Large studies including the Genotype-Tissue Expression Project (GTEx) have been been successful in identifying thousands of eQTLs in diverse human tissues [4,11] Despite these efforts, we are still unable to identify a regulatory mechanism for the genetic contribution of a majority of disease-associated loci [12,13,14,15,16]. One reason for this knowledge gap may be that most large-scale eQTL studies are based on expression data from adult, bulk tissue samples that do not represent the specific cell types and contexts in which disease-relevant dysregulation occurs [17]
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