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

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 multiple genes. The effects o...

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Detalles Bibliográficos
Autores principales: Albert, Frank W., Treusch, Sebastian, Shockley, Arthur H., Bloom, Joshua S., Kruglyak, Leonid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4285441/
https://www.ncbi.nlm.nih.gov/pubmed/24402228
http://dx.doi.org/10.1038/nature12904
Descripción
Sumario: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 multiple genes. The effects of such variants can be detected as expression quantitative trait loci (eQTL) (1). 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 (2–6). Consequently, many eQTL are likely missed, especially those with smaller effects (7). Further, most studies use mRNA rather than protein abundance as the measure of gene expression. Studies that have used mass-spectrometry proteomics (8–13) reported surprising differences between eQTL and protein QTL (pQTL) for the same genes (9,10), 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 Saccharomyes 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 vs. 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 cluster at hotspot locations that influence multiple proteins—in some cases, more than half of those 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.