Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects

[1]  Simon C. Potter,et al.  The Architecture of Gene Regulatory Variation across Multiple Human Tissues: The MuTHER Study , 2011, PLoS genetics.

[2]  David Heckerman,et al.  Correction for hidden confounders in the genetic analysis of gene expression , 2010, Proceedings of the National Academy of Sciences.

[3]  Leopold Parts,et al.  A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies , 2010, PLoS Comput. Biol..

[4]  H. Kang,et al.  Variance component model to account for sample structure in genome-wide association studies , 2010, Nature Genetics.

[5]  Chun Jimmie Ye,et al.  Accurate Discovery of Expression Quantitative Trait Loci Under Confounding From Spurious and Genuine Regulatory Hotspots , 2008, Genetics.

[6]  Jingyuan Fu,et al.  Genetical Genomics: Spotlight on QTL Hotspots , 2008, PLoS genetics.

[7]  D. Clayton,et al.  Extreme Clonality in Lymphoblastoid Cell Lines with Implications for Allele Specific Expression Analyses , 2008, PloS one.

[8]  M. McCarthy,et al.  Genome-wide association studies for complex traits: consensus, uncertainty and challenges , 2008, Nature Reviews Genetics.

[9]  L. Kruglyak,et al.  Gene–Environment Interaction in Yeast Gene Expression , 2008, PLoS biology.

[10]  D. Heckerman,et al.  Efficient Control of Population Structure in Model Organism Association Mapping , 2008, Genetics.

[11]  D. Stephan,et al.  A survey of genetic human cortical gene expression , 2007, Nature Genetics.

[12]  D. Koller,et al.  Population genomics of human gene expression , 2007, Nature Genetics.

[13]  John D. Storey,et al.  Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.

[14]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[15]  D. Reich,et al.  Principal components analysis corrects for stratification in genome-wide association studies , 2006, Nature Genetics.

[16]  M. McMullen,et al.  A unified mixed-model method for association mapping that accounts for multiple levels of relatedness , 2006, Nature Genetics.

[17]  Pooja Jain,et al.  The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae , 2005, Nucleic Acids Res..

[18]  J. Castle,et al.  An integrative genomics approach to infer causal associations between gene expression and disease , 2005, Nature Genetics.

[19]  D. Balding,et al.  Handbook of statistical genetics , 2004 .

[20]  Daniel Pinkel,et al.  Large-scale variation among human and great ape genomes determined by array comparative genomic hybridization. , 2003, Genome research.

[21]  G. Churchill.  Fundamentals of experimental design for cDNA microarrays , 2002, Nature Genetics.

[22]  L. Kruglyak,et al.  Genetic Dissection of Transcriptional Regulation in Budding Yeast , 2002, Science.