Genetics of single-cell protein abundance variation in large yeast populations
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Abstract
Variation among individuals arises in part from differences in DNA sequences, but the genetic basis for variation in most traits, including common diseases, remains only partly understood. Many DNA variants influence phenotypes by altering the expression level of one or several genes. The effects of such variants can be detected as expression quantitative trait loci (eQTL). Traditional eQTL mapping requires large-scale genotype and gene expression data for each individual in the study sample, which limits sample sizes to hundreds of individuals in both humans and model organisms and reduces statistical power. Consequently, many eQTL are probably missed, especially those with smaller effects. Furthermore, most studies use messenger RNA rather than protein abundance as the measure of gene expression. Studies that have used mass-spectrometry proteomics reported unexpected differences between eQTL and protein QTL (pQTL) for the same genes, but these studies have been even more limited in scope. Here we introduce a powerful method for identifying genetic loci that influence protein expression in the yeast Saccharomyces cerevisiae. We measure single-cell protein abundance through the use of green fluorescent protein tags in very large populations of genetically variable cells, and use pooled sequencing to compare allele frequencies across the genome in thousands of individuals with high versus low protein abundance. We applied this method to 160 genes and detected many more loci per gene than previous studies. We also observed closer correspondence between loci that influence protein abundance and loci that influence mRNA abundance of a given gene. Most loci that we detected were clustered in 'hotspots' that influence multiple proteins, and some hotspots were found to influence more than half of the proteins that we examined. The variants that underlie these hotspots have profound effects on the gene regulatory network and provide insights into genetic variation in cell physiology between yeast strains.
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Studies in model organisms have shown that variation in mRNA and protein expression levels are often uncorrelated.
“Studies in model organisms have shown that variation in mRNA and protein expression levels are often uncorrelated (5-8).”
From Impact of regulatory variation from RNA to protein (2015) · cites this paper as [8]
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- Authors
- Albert, F. W., Treusch, S., Shockley, A. H., Bloom, J. S., & Kruglyak, L.
- Affiliations
- University of California, Los Angeles (US)
- Princeton University (US)
- Synthetic Genomics (United States) (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Albert, Frank W.
- Kruglyak, Leonid
- Venue
- Nature
- Published
- 2014-01-08
- Volume
- 506
- Issue
- 7489
- Pages
- 494-497
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4285441
- OpenAlex
- W1973022936
- Cited by
- 141
- References
- 51
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 3 · 2023: 7 · 2022: 10 · 2021: 13 · 2020: 9 · 2019: 13 · 2018: 8 · 2017: 11 · 2016: 17 · 2015: 17 · 2014: 20
- FWCI
- 8.88
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- Howard Hughes Medical Institute
- National Institutes of Health (5f32gm101857-02)
- National Institute of General Medical Sciences (F32 GM101857-02, F32 GM101857, R01GM102308)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- expression quantitative trait loci, protein quantitative trait loci, Saccharomyces cerevisiae, GFP tagging, pooled sequencing, mRNA-protein correlation, gene regulatory networks
- MeSH terms
- Gene Frequency, Genes, Fungal, Multigene Family, Genotype, RNA, Fungal, RNA, Messenger, Saccharomyces cerevisiae, Genetic Variation, Gene Expression Regulation, Fungal, Genome, Fungal, Sequence Analysis, DNA, Gene Expression Profiling, Saccharomyces cerevisiae Proteins, Quantitative Trait Loci, Proteomics, Green Fluorescent Proteins, Gene Regulatory Networks, Single-Cell Analysis
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