ArticlePLoS genetics2019
Discovery and characterization of variance QTLs in human induced pluripotent stem cells.
Article in PLoS genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it, 80 citations in OpenAlex.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Decoding Immune Regulation: From Genetic Variation to Mechanism Through Single-Cell Genomics.Immune network · 2026Review
- Article
- Distributional genetic effects reveal context-dependent molecular regulation in human brain aging and Alzheimer's disease.Research square · 2025Article
- distQTL: distribution quantitative trait loci identification by population-scale single-cell data.NAR genomics and bioinformatics · 2025Article
- distQTL: Distribution Quantitative Trait Loci Identification by Population-Scale Single-Cell Data.bioRxiv : the preprint server for biology · 2025Article
- Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction.Nature communications · 2025Article
- GhLPF1 Associated Network Is Involved with Cotton Lint Percentage Regulation Revealed by the Integrative Analysis of Spatial Transcriptome.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction.bioRxiv : the preprint server for biology · 2024Article
- Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison.G3 (Bethesda, Md.) · 2024Review
- The dynamic genetic determinants of increased transcriptional divergence in spermatids.Nature communications · 2024Article
- scQTLbase: an integrated human single-cell eQTL database.Nucleic acids research · 2024Article
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- Methods and Insights from Single-Cell Expression Quantitative Trait Loci.Annual review of genomics and human genetics · 2023Review
- Single-cell genomics meets human genetics.Nature reviews. Genetics · 2023Review
- Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution.Genome research · 2023Article
- eQTL Studies: from Bulk Tissues to Single Cells.ArXiv · 2023Article
- Expression quantitative trait locus studies in the era of single-cell omics.Frontiers in genetics · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
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 235 mean expression QTLs (eQTLs) at 10% FDR, of which 79% replicate in bulk RNA-seq data from the same individuals. We further identified 5 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 4,015 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.