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Discovery and characterization of variance QTLs in human induced pluripotent stem cells

ABSTRACT: Quantification of gene expression levels at the single cell level has revealed that gene expression can vary substantially even across a population of homogeneous cells. However, it is currently unclear what genomic features control variation in gene expression levels, and whether common genetic variants may impact gene expression variation. Here, we take a genome-wide approach to identify expression variance quantitative trait loci (vQTLs). To this end, we generated single cell RNA-seq (scRNA-seq) data from induced pluripotent stem cells (iPSCs) derived from 53 Yoruba individuals. We collected data for a median of 95 cells per individual and a total of 5,447 single cells, and identified 241 mean expression QTLs (eQTLs) at 10% FDR, of which 82% replicate in bulk RNA-seq data from the same individuals. We further identified 14 vQTLs at 10% FDR, but demonstrate that these can also be explained as effects on mean expression. Our study suggests that dispersion QTLs (dQTLs), which could alter the variance of expression independently of the mean, have systematically smaller effect sizes than eQTLs. We estimate that at least 300 cells per individual and 400 individuals would be required to have modest power to detect the strongest dQTLs in iPSCs. These results will guide the design of future studies on understanding the genetic control of gene expression variance.

ORGANISM(S): Homo sapiens

PROVIDER: GSE118723 | GEO | 2018/09/21

REPOSITORIES: GEO

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Project description:Genetic variants that impact gene regulation are important contributors to human phenotypic variation. For this reason, considerable efforts have been made to identify genetic associations with differences in mRNA levels of nearby genes, namely, cis expression quantitative trait loci (eQTLs). The phenotypic consequences of eQTLs are presumably due, in most cases, to their ultimate effects on protein expression levels. Yet, only few studies have quantified the impact of genetic variation on proteins levels directly. It remains unclear how faithfully eQTLs are reflected at the protein level, and whether there is a significant layer of cis regulatory variation acting primarily on translation or steady state protein levels. To address these questions, we measured ribosome occupancy by high-throughput sequencing, and relative protein levels by high-resolution quantitative mass spectrometry, in a panel of lymphoblastoid cell lines (LCLs) in which we had previously measured transcript expression using RNA sequencing. We then mapped genetic variants that are associated with changes in transcript expression (eQTLs), ribosome occupancy (rQTLs), or protein abundance (pQTLs). Most of the QTLs we detected are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, we found that eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we confirmed the presence of a class of cis QTLs that specifically affect protein abundance with little or no effect on mRNA levels; most of these QTLs have little effect on ribosome occupancy, and hence may arise from differences in post-translational regulation. We measured level of translation transcriptome-wide in lymphoblastoid cell lines derived from 72 HapMap Yoruba individuals using ribosome profiling assay, for which we have transcript level, protein level (62 out of 72) and genotype information collected.

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Discovery and characterization of variance QTLs in human induced pluripotent stem cells

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