ArticleNature2014
Genetics of single-cell protein abundance variation in large yeast populations.
Article in Nature, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed, 164 citations in OpenAlex.
- High-resolution genotype-free mapping of genetic variation with CRI-SPA-Map.Genome research · 2026Article
- Molecular systems, human noncoding sequence variants, and blood pressure.Physiological reviews · 2026Review
- Adaptive laboratory evolution with ethionine identifies novel genetic determinants for enhanced protein and methionine accumulation in Saccharomyces cerevisiae.Journal of biological engineering · 2026Article
- Article
- A genome-to-proteome map reveals how natural variants drive proteome diversity and shape fitness.Science (New York, N.Y.) · 2025Article
- High-resolution, genotype-free mapping of genetic variation with CRI-SPA-Map.bioRxiv : the preprint server for biology · 2025Article
- Highly replicated experiments studying complex genotypes using nested DNA barcodes.G3 (Bethesda, Md.) · 2025Article
- Unveiling therapeutic targets for spinal stenosis from genetic insights: a Mendelian randomization analysis.Scientific reports · 2024Article
- A genome-to-proteome atlas charts natural variants controlling proteome diversity and forecasts their fitness effects.bioRxiv : the preprint server for biology · 2024Article
- Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Mendelian Randomization Using the Druggable Genome Reveals Genetically Supported Drug Targets for Psychiatric Disorders.Schizophrenia bulletin · 2023Article
- Article
- Substrate-specific effects of natural genetic variation on proteasome activity.PLoS genetics · 2023Article
- Multi-omics analysis identifies drivers of protein phosphorylation.Genome biology · 2023Article
- Article
- Variation in ubiquitin system genes creates substrate-specific effects on proteasomal protein degradation.eLife · 2022Article
- High-throughput approaches to functional characterization of genetic variation in yeast.Current opinion in genetics & development · 2022Review
- Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits.PLoS computational biology · 2022Article
- The impact of genomic variation on protein phosphorylation states and regulatory networks.Molecular systems biology · 2022Article
- Article
21 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
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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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.