The landscape of genetic complexity across 5,700 gene expression traits in yeast.

By Rachel B. Brem, Leonid Kruglyak. Proceedings of the National Academy of Sciences of the United States of America, 2005

651 citations recorded by the source corpus.

Subfields: Food Science, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9af732a0026ca1db41d8d79485874e7e8c97be61

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Food Science

In Genetics

In Molecular Biology

Citations in the corpus

A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies (2010, methodology): “The yeast expression dataset [2] (GEO [23] accession GSE1990 with genotypes provided by authors) contained 7084 expression measurements and 2925 genotyped loci in 112 crosses of segregating yeast strains.”
Exploring genetic interactions and networks with yeast (2007, methodology): “The Kruglyak group used yeast to address the issue of polymorphism in genetic interaction”
Robustness and Evolvability (2010, background): “This often manifests as “transgressive segregation” in which hybrid populations show phenotypes more extreme than either parent [40,41].”
Harnessing naturally randomized transcription to infer regulatory relationships among genes (2007, methodology): “Materials and methods Expression measurements and genotyping The expression and genotype data were recently reported elsewhere [12,33].”
Adaptive Multi-Task Lasso: with Application to eQTL Detection (2010, methodology): “We analyze the yeast eQTL dataset [16] that contains expression levels of 5,637 genes and 2,956 SNPs.”
Advantages and limitations of current network inference methods (2010, background): “to altered expression behaviour will unveil fundamental insights into microbial evolution and will identify the multifactorial changes that underlie industrially relevant properties of naturally occurring bacterial or yeast strain”
Genetics of global gene expression (2006, background): “Such arguments have been used to estimate that in a yeast cross only 3% of expression traits are consistent with single-locus inheritance, that most traits require more than two additive QTLs, and that segregation of many traits can only be explained by very complex genetic”
Systems genetics approaches to understand complex traits (2013, background): “…a laboratory strain and a wine strain of S. cerevisiae has been used to investigate interactions among multiple cellular and molecular traits 15 , 57 , 62 , 124 • The haploid segregants from the cross constitute a permanent resource that can be grown in large quantities and studied under a…”
Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks (2008, background): “We demonstrate that the integration of these diverse data types in the context of a genetic cros”
Robustness: mechanisms and consequences (2009, methodology): “With either genomewide approach, the trait(s) studied can be morphological [10,52,54], or physiological [31], or can take the form of RNA and protein concentrations [49,51,53].”
Protein networks in disease. (2008, background): “In yeast, genetic perturbations have been profiled by expression profiling of gene knockout strains (Hughes et al. 2000; Workman et al. 2006; Hu et al. 2007) or by analysis of expression quantitative trait loci (eQTLs) ( Brem and Kruglyak 2005 ), which identifies associations between polymorphic gen”
PC algorithm for nonparanormal graphical models (2013, methodology): “We illustrate this for data on gene expression in yeast from Brem and Kruglyak (2005), where we focus on p = 54 genes from the MAPK signaling pathway as was done in Sun and Li (2012).”
Population genomics of human gene expression (2007, neutral): “In general, the detection of trans effects has been less successful in humans than in yeas”
Mapping complex disease traits with global gene expression (2009, result): “This explanation would be consistent with data in yeast, in which only 3% of highly heritable transcript abundances are explained by single-locus (monogenic) inheritance and 50% are consistent with more than five controlling loci of equal effec”
The QTN Program and the Alleles That Matter for Evolution: All That’s Gold Does Not Glitter (2011, positive): “Brem and Kruglyak (2005) characterized the genetic architectures of 5727 arbitrary traits, the abundances of each gene’s transcript in a cross of two Saccharomyces cerevisiae strains.”