%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Cheung:2005p446,
author = {Vivian G Cheung and Richard S Spielman and Kathryn G Ewens and Teresa M Weber and Michael Morley and Joshua T Burdick}, 
journal = {Nature},
title = {Mapping determinants of human gene expression by regional and genome-wide association},
number = {7063},
pages = {1365--9},
volume = {437},
year = {2005},
month = {Oct},
language = {eng},
date-added = {2009-07-06 09:26:47 -0400},
date-modified = {2010-12-02 22:35:13 -0500},
doi = {10.1038/nature04244},
pii = {nature04244},
pmid = {16251966},
}


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%% http://mekentosj.com/papers/

@article{Gaffney:2012p5397,
author = {Daniel J Gaffney and Jean-Baptiste Veyrieras and Jacob F Degner and Roger Pique-Regi and Athma A Pai and Gregory E Crawford and Matthew Stephens and Yoav Gilad and Jonathan K Pritchard}, 
journal = {Genome Biol},
title = {Dissecting the regulatory architecture of gene expression QTLs},
abstract = {BACKGROUND: Expression quantitative trait loci (eQTLs) are likely to play an important role in the genetics of complex traits; however, their functional basis remains poorly understood. Using the HapMap lymphoblastoid cell lines, we combine 1000 Genomes genotypes and an extensive catalogue of human functional elements to investigate the biological mechanisms that eQTLs perturb.

RESULTS: We use a Bayesian hierarchical model to estimate the enrichment of eQTLs in a wide variety of regulatory annotations. We find that approximately 40% of eQTLs occur in open chromatin, and that they are particularly enriched in transcription factor binding sites, suggesting that many directly impact protein-DNA interactions. Analysis of core promoter regions shows that eQTLs also frequently disrupt some known core promoter motifs but, surprisingly, are not enriched in other well-known motifs such as the TATA box. We also show that information from regulatory annotations alone, when weighted by the hierarchical model, can provide a meaningful ranking of the SNPs that are most likely to drive gene expression variation.

CONCLUSIONS: Our study demonstrates how regulatory annotation and the association signal derived from eQTL-mapping can be combined into a single framework. We used this approach to further our understanding of the biology that drives human gene expression variation, and of the putatively causal SNPs that underlie it.},
affiliation = {Department of Human Genetics, University of Chicago, 920 E58th Street, Chicago, IL 60637, USA. dg13@sanger.ac.uk},
number = {1},
pages = {R7},
volume = {13},
year = {2012},
month = {Jan},
language = {eng},
keywords = {Genome: Human, Deoxyribonuclease I, Cell Line, Genotype, DNA-Binding Proteins, Transcription Factors, Bayes Theorem, Gene Expression, Chromatin, Polymorphism: Single Nucleotide, Quantitative Trait Loci, Regulatory Sequences: Nucleic Acid, Humans, Promoter Regions: Genetic, HapMap Project}, 
date-added = {2013-09-24 11:35:20 -0400},
date-modified = {2013-09-24 11:35:27 -0400},
doi = {10.1186/gb-2012-13-1-r7},
pii = {gb-2012-13-1-r7},
pmid = {22293038},
URL = {http://genomebiology.com/content/13/1/R7},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2012/Gaffney/Genome%20Biol%202012%20Gaffney-2.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p5397},
rating = {0}
}


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@article{Shabalin:2012p4753,
author = {Andrey A Shabalin}, 
journal = {Bioinformatics (Oxford, England)},
title = {Matrix eQTL: ultra fast eQTL analysis via large matrix operations},
abstract = {MOTIVATION: Expression quantitative trait loci (eQTL) analysis links variations in gene expression levels to genotypes. For modern datasets, eQTL analysis is a computationally intensive task as it involves testing for association of billions of transcript-SNP (single-nucleotide polymorphism) pair. The heavy computational burden makes eQTL analysis less popular and sometimes forces analysts to restrict their attention to just a small subset of transcript-SNP pairs. As more transcripts and SNPs get interrogated over a growing number of samples, the demand for faster tools for eQTL analysis grows stronger. RESULTS: We have developed a new software for computationally efficient eQTL analysis called Matrix eQTL. In tests on large datasets, it was 2-3 orders of magnitude faster than existing popular tools for QTL/eQTL analysis, while finding the same eQTLs. The fast performance is achieved by special preprocessing and expressing the most computationally intensive part of the algorithm in terms of large matrix operations. Matrix eQTL supports additive linear and ANOVA models with covariates, including models with correlated and heteroskedastic errors. The issue of multiple testing is addressed by calculating false discovery rate; this can be done separately for cis- and trans-eQTLs.},
affiliation = {Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. shabalin@email.unc.edu},
number = {10},
pages = {1353--8},
volume = {28},
year = {2012},
month = {May},
language = {eng},
date-added = {2012-11-12 17:40:32 -0500},
date-modified = {2012-11-16 16:55:36 -0500},
doi = {10.1093/bioinformatics/bts163},
pii = {bts163},
pmid = {22492648},
URL = {http://bioinformatics.oxfordjournals.org/content/28/10/1353.long},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2012/Shabalin/Bioinformatics%20(Oxford%20England)%202012%20Shabalin.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p4753},
read = {Yes},
rating = {0}
}


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@article{Majewski:2011p3139,
author = {Jacek Majewski and Tomi Pastinen}, 
journal = {Trends Genet},
title = {The study of eQTL variations by RNA-seq: from SNPs to phenotypes},
abstract = {Common DNA variants alter the expression levels and patterns of many human genes. Loci responsible for this genetic control are known as expression quantitative trait loci (eQTLs). The resulting variation of gene expression across individuals has been postulated to be a determinant of phenotypic variation and susceptibility to complex disease. In the past, the application of expression microarray and genetic variation data to study populations enabled the rapid identification of eQTLs in model organisms and humans. Now, a new technology promises to revolutionize the field. Massively parallel RNA sequencing (RNA-seq) provides unprecedented resolution, allowing us to accurately monitor not only the expression output of each genomic locus but also reconstruct and quantify alternatively spliced transcripts. RNA-seq also provides new insights into the regulatory mechanisms underlying eQTLs. Here, we discuss the major advances introduced by RNA-seq and summarize current progress towards understanding the role of eQTLs in determining human phenotypic diversity.},
affiliation = {Department of Human Genetics, McGill University and Genome Quebec Innovation Centre, 740 Dr. Penfield Avenue, Rm 7210, Montreal, Quebec, H3A 1A4, Canada. jacek.majewski@mcgill.ca},
number = {2},
pages = {72--9},
volume = {27},
year = {2011},
month = {Feb},
language = {eng},
keywords = {Humans, Polymorphism: Single Nucleotide, Gene Expression Regulation, Phenotype, Quantitative Trait Loci, Transcription: Genetic, Sequence Analysis: RNA, Animals}, 
date-added = {2011-05-16 13:05:06 -0400},
date-modified = {2011-05-16 13:05:06 -0400},
doi = {10.1016/j.tig.2010.10.006},
pii = {S0168-9525(10)00212-X},
pmid = {21122937},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2011/Majewski/Trends%20Genet%202011%20Majewski.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p3139},
read = {Yes},
rating = {0}
}


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%% http://mekentosj.com/papers/

@article{Stranger:2012p5427,
author = {Barbara E Stranger and Stephen B Montgomery and Antigone S Dimas and Leopold Parts and Oliver Stegle and Catherine E Ingle and Magda Sekowska and George Davey Smith and David Evans and Maria Gutierrez-Arcelus and Alkes Price and Towfique Raj and James Nisbett and Alexandra C Nica and Claude Beazley and Richard Durbin and Panos Deloukas and Emmanouil T Dermitzakis}, 
journal = {PLoS Genet},
title = {Patterns of cis regulatory variation in diverse human populations},
abstract = {The genetic basis of gene expression variation has long been studied with the aim to understand the landscape of regulatory variants, but also more recently to assist in the interpretation and elucidation of disease signals. To date, many studies have looked in specific tissues and population-based samples, but there has been limited assessment of the degree of inter-population variability in regulatory variation. We analyzed genome-wide gene expression in lymphoblastoid cell lines from a total of 726 individuals from 8 global populations from the HapMap3 project and correlated gene expression levels with HapMap3 SNPs located in cis to the genes. We describe the influence of ancestry on gene expression levels within and between these diverse human populations and uncover a non-negligible impact on global patterns of gene expression. We further dissect the specific functional pathways differentiated between populations. We also identify 5,691 expression quantitative trait loci (eQTLs) after controlling for both non-genetic factors and population admixture and observe that half of the cis-eQTLs are replicated in one or more of the populations. We highlight patterns of eQTL-sharing between populations, which are partially determined by population genetic relatedness, and discover significant sharing of eQTL effects between Asians, European-admixed, and African subpopulations. Specifically, we observe that both the effect size and the direction of effect for eQTLs are highly conserved across populations. We observe an increasing proximity of eQTLs toward the transcription start site as sharing of eQTLs among populations increases, highlighting that variants close to TSS have stronger effects and therefore are more likely to be detected across a wider panel of populations. Together these results offer a unique picture and resource of the degree of differentiation among human populations in functional regulatory variation and provide an estimate for the transferability of complex trait variants across populations.},
affiliation = {Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.},
number = {4},
pages = {e1002639},
volume = {8},
year = {2012},
month = {Jan},
language = {eng},
keywords = {Genetics: Population, Polymorphism: Single Nucleotide, Cell Line, European Continental Ancestry Group, Regulatory Sequences: Nucleic Acid, Genome: Human, Asian Continental Ancestry Group, Gene Expression Regulation, Humans, Transcription Initiation Site, African Continental Ancestry Group, HapMap Project, Quantitative Trait Loci}, 
date-added = {2013-11-20 13:13:21 -0500},
date-modified = {2013-11-20 13:15:51 -0500},
doi = {10.1371/journal.pgen.1002639},
pii = {PGENETICS-D-11-00883},
pmid = {22532805},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2012/Stranger/PLoS%20Genet%202012%20Stranger.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p5427},
rating = {5}
}


%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/

@article{Leek:2007p1723,
author = {Jeffrey T Leek and John D Storey}, 
journal = {PLoS Genet},
title = {Capturing heterogeneity in gene expression studies by surrogate variable analysis},
abstract = {It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce "surrogate variable analysis" (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.},
affiliation = {Department of Biostatistics, University of Washington, Seattle, Washington, USA.},
number = {9},
pages = {1724--35},
volume = {3},
year = {2007},
month = {Sep},
language = {eng},
keywords = {Genome: Human, Algorithms, Computer Simulation, Data Interpretation: Statistical, Gene Expression, Breast Neoplasms, Humans, Female, Time Factors, Genes: BRCA1, Epigenesis: Genetic, Mutation, Oligonucleotide Array Sequence Analysis, Genome: Fungal, Linkage (Genetics), Kidney, Genes: BRCA2, Reproducibility of Results, Linear Models, Saccharomyces cerevisiae, Genetic Heterogeneity, Quantitative Trait: Heritable}, 
date-added = {2010-05-27 12:10:21 -0400},
date-modified = {2010-05-27 12:10:39 -0400},
doi = {10.1371/journal.pgen.0030161},
pii = {07-PLGE-RA-0237},
pmid = {17907809},
URL = {http://www.plosgenetics.org/article/info%253Adoi%252F10.1371%252Fjournal.pgen.0030161},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2007/Leek/PLoS%20Genet%202007%20Leek.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1723},
read = {Yes},
rating = {0}
}