Discovery and characterization of variance QTLs in human induced pluripotent stem cells
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Abstract
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 summary Common 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 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.
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We did not address power for detection of variance QTLs from scRNAseq data due to lack of data-driven priors for effect sizes.
“Lastly, we did not address the power for the detection of variance QTLs (quantitative trait loci associated with gene expression variance across cells) from scRNAseq data48 due to the lack of data driven priors for the effect sizes.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [48]
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- Authors
- Sarkar, A. K., Tung, P.-Y., Blischak, J. D., Burnett, J. E., Li, Y. I., Stephens, M., & Gilad, Y.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Sarkar, Abhishek K.
- Tung, Po-Yuan
- Blischak, John D.
- Burnett, Jonathan E.
- Li, Yang I.
- Stephens, Matthew
- Gilad, Yoav
- Venue
- PLOS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2019-04-19
- Volume
- 15
- Issue
- 4
- Pages
- e1008045
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC6474585
- OpenAlex
- W2890998227
- Cited by
- 72
- References
- 54
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 7 · 2023: 9 · 2022: 10 · 2021: 23 · 2020: 12 · 2019: 3
- FWCI
- 7.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Knowledge@UChicago (University of Chicago) (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- University of Chicago (cc-by)
- Funders
- National Institutes of Health (RO1GM122930)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- gene expression variation, induced pluripotent stem cells, single-cell RNA sequencing, expression QTL, Yoruba, power analysis
- MeSH terms
- Cell Line, Computer Simulation, Humans, Models, Genetic, Nigeria, Phenotype, Genetic Variation, Sequence Analysis, RNA, Gene Expression Profiling, Quantitative Trait Loci, Black People, Genome-Wide Association Study, Induced Pluripotent Stem Cells, Single-Cell Analysis
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