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 can have larger fold changes, but explain less phenotypic variance than eQTLs. We estimate 424 individuals as a lower bound to achieve 80% 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. Author summaryCommon genetic variation can alter the level of average gene expression in human tissues, and through changes in gene expression have downstream consequences on cell function, human development, and human disease. However, human tissues are composed of many cells, each with its own level of gene expression. With advances in single cell sequencing technologies, we can now go beyond simply measuring the average level of gene expression in a tissue sample and directly measure cell-to-cell variance in gene expression. We hypothesized that genetic variation could also alter gene expression variance, potentially revealing new insights into human development and disease. To test this hypothesis, we used single cell RNA sequencing to directly measure gene expression 1 variance in multiple individuals, and then associated the gene expression variance with genetic variation in those same individuals. Our results suggest that effects on gene expression variance are smaller than effects on mean expression, relative to how much the phenotypes vary between individuals, and will require much larger studies than previously thought to detect.

Cell-to-cell expression dispersion of B-cell surface proteins displays genetic variation among humans

“…Our results complement the study of Lu et al 14 Recently, Sarkar et al 13 searched for cis-acting SNPs of expression variability in another cell type: induced pluripotent stem cells, which were derived from the Yoruba LCL lines analyzed here. The authors used single-cell RNAseq, generating data on many more genes but much fewer cells as compared to flow-cytometry.…”

“…It is therefore important to clarify the meaning of several terms that are used hereafter. Expression variability will refer to differences in expression level of a protein between cells of the same genotype, same cell type and subtype, and which are extracted simultaneously from the same environment; given that expression variability often co-varies with mean expression, we also use the term expression dispersion of Sarkar et al 13 to refer to the amount of expression variability that is not explained by the mean; an individual will refer to a human person; variation will refer to differences of a given scalar value, for example a summary statistics of single-cell values, between individuals having different genotypes; cell line will refer to a population of immortalized cells of the same cell-type that can be propagated in vitro and which derives from a single donor individual; clone will refer to a cell line deriving from a single primary cell extracted from an individual; sample will refer to a population of cells that were cultured together in a single well and collected in a single tube for investigation.…”

“…In humans, however, co-variation of mean and variability cannot be decoupled experimentally. Even if single-cell data can provide finescale descriptions of the genetic control of gene expression 12 , loci affecting variability are sometimes identified simply because of their effect on mean expression 13 .…”

Design and power analysis for multi-sample single cell genomics experiments

“…Some imputation methods such as [73,74] do model the negative binomial means per gene, which we use in our model, so these results could in principle be integrated in our model. Fourth, we did not address the power for the detection of variance QTLs from scRNAseq data [24] due to the lack of data driven priors for the effect sizes. Several practical considerations should be addressed when using our approach.…”

“…A genetic variant associated with the transcription of a gene is called eQTL and allows for gaining insights into the molecular underpinnings of trait associated genetic variants. Using scRNA-seq, it is now possible to identify eQTLs in a cell type specific manner [22][23][24][25] and large scale efforts are currently underway [26]. In contrast to differential expression methods, linear regression models are typically used for the detection of eQTLs even in RNA-seq data sets [20,27], after transforming the count data to a normal distribution.…”