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Nature. 2014 Feb 27;506(7489):494-7. doi: 10.1038/nature12904. Epub 2014 Jan 8.

Genetics of single-cell protein abundance variation in large yeast populations.

Author information

1
1] Department of Human Genetics, University of California, Los Angeles, California 90095, USA [2] Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
2
Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
3
Synthetic Genomics, 11149 North Torrey Pines Road, La Jolla, California 92037, USA.
4
1] Department of Human Genetics, University of California, Los Angeles, California 90095, USA [2] Howard Hughes Medical Institute, University of California, Los Angeles, California 90095, USA.
5
1] Department of Human Genetics, University of California, Los Angeles, California 90095, USA [2] Howard Hughes Medical Institute, University of California, Los Angeles, California 90095, USA [3] Department of Biological Chemistry, University of California, Los Angeles, California 90095, USA.

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.

PMID:
24402228
PMCID:
PMC4285441
DOI:
10.1038/nature12904
[Indexed for MEDLINE]
Free PMC Article

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