Journal ArticleDOI
Trans -acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors
Gaël Yvert,Rachel B. Brem,Jacqueline Whittle,Joshua M. Akey,Eric J. Foss,Erin N. Smith,Erin N. Smith,Rachel Mackelprang,Rachel Mackelprang,Leonid Kruglyak +9 more
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TLDR
Analysis of regulatory variation in a cross between laboratory and wild strains of Saccharomyces cerevisiae showed that polymorphisms in GPA1 and AMN1 affect expression of genes involved in pheromone response and daughter cell separation.Abstract:
Natural genetic variation can cause significant differences in gene expression, but little is known about the polymorphisms that affect gene regulation. We analyzed regulatory variation in a cross between laboratory and wild strains of Saccharomyces cerevisiae. Clustering and linkage analysis defined groups of coregulated genes and the loci involved in their regulation. Most expression differences mapped to trans-acting loci. Positional cloning and functional assays showed that polymorphisms in GPA1 and AMN1 affect expression of genes involved in pheromone response and daughter cell separation, respectively. We also asked whether particular classes of genes were more likely to contain trans-regulatory polymorphisms. Notably, transcription factors showed no enrichment, and trans-regulatory variation seems to be broadly dispersed across classes of genes with different molecular functions.read more
Citations
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Capturing heterogeneity in gene expression studies by surrogate variable analysis.
Jeffrey T. Leek,John D. Storey +1 more
TL;DR: This work introduces “surrogate variable analysis” (SVA) to overcome the problems caused by heterogeneity in expression studies and shows that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.
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Evidence for dynamically organized modularity in the yeast protein–protein interaction network
Jing-Dong J. Han,Nicolas Bertin,Tong Hao,Debra S. Goldberg,Gabriel F. Berriz,Lan V. Zhang,Denis Dupuy,Albertha J.M. Walhout,Albertha J.M. Walhout,Michael E. Cusick,Frederick P. Roth,Marc Vidal +11 more
TL;DR: This work investigated how hubs might contribute to robustness and other cellular properties for protein–protein interactions dynamically regulated both in time and in space, and uncovered two types of hub: ‘party’ hubs, which interact with most of their partners simultaneously, and ‘date’ Hubs, which bind their different partners at different times or locations.
Journal ArticleDOI
Genetic analysis of genome-wide variation in human gene expression
Michael Morley,Cliona Molony,Teresa M. Weber,Teresa M. Weber,James L. Devlin,Kathryn G. Ewens,Richard S. Spielman,Vivian G. Cheung,Vivian G. Cheung +8 more
TL;DR: This work used microarrays to measure gene expression levels and performed genome-wide linkage analysis for expression levels of 3,554 genes in 14 large families to localize the genetic determinants of these quantitative traits in humans.
Journal ArticleDOI
Population genomics of human gene expression
Barbara E. Stranger,Alexandra C. Nica,Matthew S. Forrest,Antigone S. Dimas,Christine P. Bird,Claude Beazley,Catherine E. Ingle,Mark J Dunning,Paul Flicek,Daphne Koller,Stephen B. Montgomery,Simon Tavaré,Panagiotis Deloukas,Emmanouil T. Dermitzakis +13 more
TL;DR: It is found that gene expression is heritable and that differentiation between populations is in agreement with earlier small-scale studies, and the results strongly support an abundance of cis-regulatory variation in the human genome.
Journal ArticleDOI
An integrative genomics approach to infer causal associations between gene expression and disease
Eric E. Schadt,John Lamb,Xia Yang,Jun Zhu,Steve Edwards,Debraj GuhaThakurta,Solveig K. Sieberts,Stephanie A. Monks,Marc L. Reitman,Chunsheng Zhang,Pek Yee Lum,Amy Leonardson,Rolf Thieringer,Joseph M. Metzger,Liming Yang,John C. Castle,Haoyuan Zhu,Shera F Kash,Thomas A. Drake,Alan B. Sachs,Aldons J. Lusis +20 more
TL;DR: It is shown that this approach can predict transcriptional responses to single gene–perturbation experiments using gene-expression data in the context of a segregating mouse population and the utility of this approach is demonstrated by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.
References
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TL;DR: It is shown that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment, and less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal Growth in four of the tested conditions.