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@ARTICLE{Brem2005,
  author = {Rachel B Brem and John D Storey and Jacqueline Whittle and Leonid
	Kruglyak},
  title = {Genetic interactions between polymorphisms that affect gene expression
	in yeast.},
  journal = {Nature},
  year = {2005},
  volume = {436},
  pages = {701--703},
  number = {7051},
  month = {Aug},
  abstract = {Interactions between polymorphisms at different quantitative trait
	loci (QTLs) are thought to contribute to the genetics of many traits,
	and can markedly affect the power of genetic studies to detect QTLs.
	Interacting loci have been identified in many organisms. However,
	the prevalence of interactions, and the nucleotide changes underlying
	them, are largely unknown. Here we search for naturally occurring
	genetic interactions in a large set of quantitative phenotypes--the
	levels of all transcripts in a cross between two strains of Saccharomyces
	cerevisiae. For each transcript, we searched for secondary loci interacting
	with primary QTLs detected by their individual effects. Such locus
	pairs were estimated to be involved in the inheritance of 57\% of
	transcripts; statistically significant pairs were identified for
	225 transcripts. Among these, 67\% of secondary loci had individual
	effects too small to be significant in a genome-wide scan. Engineered
	polymorphisms in isogenic strains confirmed an interaction between
	the mating-type locus MAT and the pheromone response gene GPA1. Our
	results indicate that genetic interactions are widespread in the
	genetics of transcript levels, and that many QTLs will be missed
	by single-locus tests but can be detected by two-stage tests that
	allow for interactions.},
  doi = {10.1038/nature03865},
  institution = {Program in Computational Biology, Fred Hutchinson Cancer Research
	Center, 1100 Fairview Avenue N, M2-B876, Seattle, Washington 98109,
	USA. rbrem@fhcrc.org},
  keywords = {Crosses, Genetic; GTP-Binding Protein alpha Subunits, Gq-G11; GTP-Binding
	Protein alpha Subunits, genetics/metabolism; Gene Expression Regulation,
	Fungal, genetics; Genes, Fungal, genetics; Genes, Mating Type, Fungal;
	Genome, Fungal; Haploidy; Mutation, genetics; Pheromones, pharmacology;
	Polymorphism, Genetic, genetics; Quantitative Trait Loci, genetics;
	RNA, Fungal, genetics/metabolism; RNA, Messenger, genetics/metabolism;
	Saccharomyces cerevisiae Proteins, genetics/metabolism; Saccharomyces
	cerevisiae, drug effects/genetics/physiology},
  owner = {dipen},
  pii = {nature03865},
  pmid = {16079846},
  timestamp = {2011.10.06},
  url = {http://dx.doi.org/10.1038/nature03865}
}

@ARTICLE{Chen2007,
  author = {Lin S Chen and Frank Emmert-Streib and John D Storey},
  title = {Harnessing naturally randomized transcription to infer regulatory
	relationships among genes.},
  journal = {Genome Biol},
  year = {2007},
  volume = {8},
  pages = {R219},
  number = {10},
  abstract = {We develop an approach utilizing randomized genotypes to rigorously
	infer causal regulatory relationships among genes at the transcriptional
	level, based on experiments in which genotyping and expression profiling
	are performed. This approach can be used to build transcriptional
	regulatory networks and to identify putative regulators of genes.
	We apply the method to an experiment in yeast, in which genes known
	to be in the same processes and functions are recovered in the resulting
	transcriptional regulatory network.},
  doi = {10.1186/gb-2007-8-10-r219},
  institution = {Department of Biostatistics, University of Washington, 1705 NE Pacific
	St, Seattle, WA 98195, USA. chenlin@u.washington.edu},
  keywords = {Algorithms; Gene Expression Profiling, methods; Gene Expression Regulation,
	genetics; Gene Regulatory Networks, genetics; Genotype; Models, Genetic;
	Yeasts, genetics/metabolism},
  owner = {dipen},
  pii = {gb-2007-8-10-r219},
  pmid = {17931418},
  timestamp = {2011.10.06},
  url = {http://dx.doi.org/10.1186/gb-2007-8-10-r219}
}

@ARTICLE{Chen2008,
  author = {Lin S Chen and John D Storey},
  title = {Eigen-{R}2 for dissecting variation in high-dimensional studies.},
  journal = {Bioinformatics},
  year = {2008},
  volume = {24},
  pages = {2260--2262},
  number = {19},
  month = {Oct},
  abstract = {We provide a new statistical algorithm and software package called
	'eigen-R(2)' for dissecting the variation of a high-dimensional biological
	dataset with respect to other measured variables of interest. We
	apply eigen-R(2) to two real-life examples and compare it with simply
	averaging R(2) over many features. AVAILABILITY: An R-package eigenR2
	is available at http://www.genomine.org/eigenr2/ and will be made
	publicly available via Bioconductor.},
  doi = {10.1093/bioinformatics/btn411},
  institution = {Lewis-Sigler Institute and Department of Molecular Biology, Princeton
	University, Princeton, NJ 08544, USA.},
  keywords = {Algorithms; Gene Expression Profiling; Genetic Variation; Models,
	Statistical; Software},
  owner = {dipen},
  pii = {btn411},
  pmid = {18718946},
  timestamp = {2011.10.06},
  url = {http://dx.doi.org/10.1093/bioinformatics/btn411}
}

@ARTICLE{Storey2005,
  author = {John D Storey and Joshua M Akey and Leonid Kruglyak},
  title = {Multiple locus linkage analysis of genomewide expression in yeast.},
  journal = {PLoS Biol},
  year = {2005},
  volume = {3},
  pages = {e267},
  number = {8},
  month = {Aug},
  abstract = {With the ability to measure thousands of related phenotypes from a
	single biological sample, it is now feasible to genetically dissect
	systems-level biological phenomena. The genetics of transcriptional
	regulation and protein abundance are likely to be complex, meaning
	that genetic variation at multiple loci will influence these phenotypes.
	Several recent studies have investigated the role of genetic variation
	in transcription by applying traditional linkage analysis methods
	to genomewide expression data, where each gene expression level was
	treated as a quantitative trait and analyzed separately from one
	another. Here, we develop a new, computationally efficient method
	for simultaneously mapping multiple gene expression quantitative
	trait loci that directly uses all of the available data. Information
	shared across gene expression traits is captured in a way that makes
	minimal assumptions about the statistical properties of the data.
	The method produces easy-to-interpret measures of statistical significance
	for both individual loci and the overall joint significance of multiple
	loci selected for a given expression trait. We apply the new method
	to a cross between two strains of the budding yeast Saccharomyces
	cerevisiae, and estimate that at least 37\% of all gene expression
	traits show two simultaneous linkages, where we have allowed for
	epistatic interactions. Pairs of jointly linking quantitative trait
	loci are identified with high confidence for 170 gene expression
	traits, where it is expected that both loci are true positives for
	at least 153 traits. In addition, we are able to show that epistatic
	interactions contribute to gene expression variation for at least
	14\% of all traits. We compare the proposed approach to an exhaustive
	two-dimensional scan over all pairs of loci. Surprisingly, we demonstrate
	that an exhaustive two-dimensional scan is less powerful than the
	sequential search used here. In addition, we show that a two-dimensional
	scan does not truly allow one to test for simultaneous linkage, and
	the statistical significance measured from this existing method cannot
	be interpreted among many traits.},
  doi = {10.1371/journal.pbio.0030267},
  institution = {Department of Biostatistics, University of Washington, Seattle, Washington,
	USA. jstorey@u.washington.edu},
  keywords = {Chromosome Mapping, methods; Epistasis, Genetic; Gene Expression Profiling;
	Genetic Linkage; Genome, Fungal; Phenotype; Probability; Quantitative
	Trait Loci; Saccharomyces cerevisiae, genetics},
  owner = {dipen},
  pii = {05-PLBI-RA-0206R1},
  pmid = {16035920},
  timestamp = {2011.10.06},
  url = {http://dx.doi.org/10.1371/journal.pbio.0030267}
}

@ARTICLE{Storey2003,
  author = {John D Storey and Robert Tibshirani},
  title = {Statistical significance for genomewide studies.},
  journal = {Proc Natl Acad Sci U S A},
  year = {2003},
  volume = {100},
  pages = {9440--9445},
  number = {16},
  month = {Aug},
  abstract = {With the increase in genomewide experiments and the sequencing of
	multiple genomes, the analysis of large data sets has become commonplace
	in biology. It is often the case that thousands of features in a
	genomewide data set are tested against some null hypothesis, where
	a number of features are expected to be significant. Here we propose
	an approach to measuring statistical significance in these genomewide
	studies based on the concept of the false discovery rate. This approach
	offers a sensible balance between the number of true and false positives
	that is automatically calibrated and easily interpreted. In doing
	so, a measure of statistical significance called the q value is associated
	with each tested feature. The q value is similar to the well known
	p value, except it is a measure of significance in terms of the false
	discovery rate rather than the false positive rate. Our approach
	avoids a flood of false positive results, while offering a more liberal
	criterion than what has been used in genome scans for linkage.},
  doi = {10.1073/pnas.1530509100},
  institution = {Department of Biostatistics, University of Washington, Seattle, WA
	98195, USA. jstorey@u.washington.edu},
  keywords = {Algorithms; Alternative Splicing; Animals; Binding Sites; Exons; Gene
	Expression Regulation; Genetic Linkage; Genetic Techniques; Genome;
	Humans; Oligonucleotide Array Sequence Analysis, methods; Statistics
	as Topic; Transcription, Genetic},
  owner = {dipen},
  pii = {1530509100},
  pmid = {12883005},
  timestamp = {2011.10.06},
  url = {http://dx.doi.org/10.1073/pnas.1530509100}
}

@ARTICLE{Yvert2003,
  author = {Ga\"el Yvert and Rachel B Brem and Jacqueline Whittle and Joshua
	M Akey and Eric Foss and Erin N Smith and Rachel Mackelprang and
	Leonid Kruglyak},
  title = {Trans-acting regulatory variation in Saccharomyces cerevisiae and
	the role of transcription factors.},
  journal = {Nat Genet},
  year = {2003},
  volume = {35},
  pages = {57--64},
  number = {1},
  month = {Sep},
  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.},
  doi = {10.1038/ng1222},
  institution = {Howard Hughes Medical Institute and Division of Human Biology, Fred
	Hutchinson Cancer Research Center, Mailstop D4-100, 1100 Fairview
	Avenue North, Seattle, Washington 98103 USA.},
  keywords = {Amino Acid Sequence; Cell Cycle Proteins, physiology; Cloning, Molecular;
	Gene Expression Regulation, Fungal; Genetic Linkage; Genetic Variation;
	Molecular Sequence Data; Multigene Family; Regulatory Sequences,
	Nucleic Acid; Saccharomyces cerevisiae Proteins, physiology; Saccharomyces
	cerevisiae, genetics; Transcription Factors, metabolism},
  owner = {dipen},
  pii = {ng1222},
  pmid = {12897782},
  timestamp = {2011.10.06},
  url = {http://dx.doi.org/10.1038/ng1222}
}

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