A Statistical Framework for Joint eQTL Analysis in Multiple Tissues
Publication Date
May 09, 2013
Journal
PLOS Genetics
Authors
Timothée Flutre, Xiaoquan Wen, Jonathan Pritchard & Matthew Stephens
Volume
9
Issue
5
Pages
e1003486
DOI
http://doi.org/10.1371/journal.pgen.1003486
Publisher URL
http://journals.plos.org/plosgenetics/article?id=10.1371%2Fjournal.pgen.1003486
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/23671422
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649995
Europe PMC
http://europepmc.org/abstract/MED/23671422
Web of Science
000320030000010
Scopus
84878476783
Mendeley
http://www.mendeley.com/research/statistical-framework-joint-eqtl-analysis-multiple-tissues
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1057779"], "description"=>"<p>A. Five tissues are simulated, each with the error variance equal to 1. B. Five tissues are simulated, with error variances being 1, 1.5 or 2. C. Twenty tissues are simulated, each with the error variance equal to 1.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Trait locus analysis", "transcriptomes", "Functional genomics", "Genome expression analysis", "Molecular genetics", "Gene regulation", "genetics", "Human genetics", "Genetic association studies", "gene expression", "Genetics of disease", "Genome-wide association studies", "Molecular cell biology", "Cellular types"], "article_id"=>700841, "categories"=>["Biological Sciences"], "users"=>["Timothée Flutre", "Xiaoquan Wen", "Jonathan Pritchard", "Matthew Stephens"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003486.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_joint_analysis_has_more_power_across_a_range_of_alternatives_/700841", "title"=>"The joint analysis has more power across a range of alternatives.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-09 00:14:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1057780"], "description"=>"<p>Five tissues are simulated. Some eQTLs were shared by all tissues, some were specific to each tissue, and, as depicted by the cladogram, some were shared by Tissues 1 and 2 only, while others were shared by Tissues 3, 4 and 5. Each tissue has 100 samples, except tissue 1 which has only 60.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Trait locus analysis", "transcriptomes", "Functional genomics", "Genome expression analysis", "Molecular genetics", "Gene regulation", "genetics", "Human genetics", "Genetic association studies", "gene expression", "Genetics of disease", "Genome-wide association studies", "Molecular cell biology", "Cellular types", "efficiently", "borrows"], "article_id"=>700842, "categories"=>["Biological Sciences"], "users"=>["Timothée Flutre", "Xiaoquan Wen", "Jonathan Pritchard", "Matthew Stephens"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003486.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_joint_analysis_efficiently_borrows_information_across_genes_/700842", "title"=>"The joint analysis efficiently borrows information across genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-09 00:14:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/1057782"], "description"=>"<p>A and B. Histograms of gene obtained by the tissue-by-tissue analysis and the joint analysis. C. Scatter plot of the from the joint analysis versus the of the tissue-by-tissue analysis. D. Numbers of eQTLs called by both methods or either one of them.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Trait locus analysis", "transcriptomes", "Functional genomics", "Genome expression analysis", "Molecular genetics", "Gene regulation", "genetics", "Human genetics", "Genetic association studies", "gene expression", "Genetics of disease", "Genome-wide association studies", "Molecular cell biology", "Cellular types", "dimas"], "article_id"=>700843, "categories"=>["Biological Sciences"], "users"=>["Timothée Flutre", "Xiaoquan Wen", "Jonathan Pritchard", "Matthew Stephens"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003486.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_joint_analysis_is_more_powerful_on_the_data_set_from_Dimas_et_al_/700843", "title"=>"The joint analysis is more powerful on the data set from Dimas <i>et al.</i>", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-09 00:14:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1057783"], "description"=>"<p>A. Boxplots of the PC-corrected expression levels from gene <i>ASCC1</i> (Ensembl id ENSG00000138303) in all three cell types, color-coded by genotype class at SNP rs1678614. B. Forest plot of estimated standardized effect sizes of this eQTL. Note that none of the from the tissue-by-tissue analysis are significant at FDR = 0.05.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Trait locus analysis", "transcriptomes", "Functional genomics", "Genome expression analysis", "Molecular genetics", "Gene regulation", "genetics", "Human genetics", "Genetic association studies", "gene expression", "Genetics of disease", "Genome-wide association studies", "Molecular cell biology", "Cellular types", "eqtl"], "article_id"=>700844, "categories"=>["Biological Sciences"], "users"=>["Timothée Flutre", "Xiaoquan Wen", "Jonathan Pritchard", "Matthew Stephens"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003486.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_an_eQTL_with_weak_yet_consistent_effects_/700844", "title"=>"Example of an eQTL with weak, yet consistent effects.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-09 00:14:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1057784"], "description"=>"<p>A. Boxplots of the PC-corrected expression levels from gene <i>CHPT1</i> (Ensembl id ENSG00000111666) in all three cell types, color-coded by genotype class at SNP rs10860794. B. Forest plot of estimated standardized effect sizes of this eQTL. Note that, from the of the tissue-by-tissue analysis, the eQTL is significant at FDR = 0.05 only in fibroblasts.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Trait locus analysis", "transcriptomes", "Functional genomics", "Genome expression analysis", "Molecular genetics", "Gene regulation", "genetics", "Human genetics", "Genetic association studies", "gene expression", "Genetics of disease", "Genome-wide association studies", "Molecular cell biology", "Cellular types", "eqtl", "wrongly", "called", "tissue-specific", "tissue-by-tissue"], "article_id"=>700845, "categories"=>["Biological Sciences"], "users"=>["Timothée Flutre", "Xiaoquan Wen", "Jonathan Pritchard", "Matthew Stephens"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003486.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_an_eQTL_wrongly_called_as_tissue_specific_by_the_tissue_by_tissue_analysis_/700845", "title"=>"Example of an eQTL wrongly called as tissue-specific by the tissue-by-tissue analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-09 00:14:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1057785"], "description"=>"<p>The configurations are denoted here using the first letter of each tissue, e.g. “F-L-T” corresponds to the consistent configuration . The results for the hierarchical model were obtained with the multivariate Bayes Factors allowing correlated residuals and the EM algorithm. The results for the tissue-by-tissue analysis were obtained by calling eQTLs at an FDR of 0.05 after performing permutations in each tissue separately, and calculating the overlaps among tissues.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Trait locus analysis", "transcriptomes", "Functional genomics", "Genome expression analysis", "Molecular genetics", "Gene regulation", "genetics", "Human genetics", "Genetic association studies", "gene expression", "Genetics of disease", "Genome-wide association studies", "Molecular cell biology", "Cellular types"], "article_id"=>700846, "categories"=>["Biological Sciences"], "users"=>["Timothée Flutre", "Xiaoquan Wen", "Jonathan Pritchard", "Matthew Stephens"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003486.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Inference_of_the_proportion_of_tissue_specificity_/700846", "title"=>"Inference of the proportion of tissue specificity.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-09 00:14:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1057786", "https://ndownloader.figshare.com/files/1057787", "https://ndownloader.figshare.com/files/1057788", "https://ndownloader.figshare.com/files/1057789"], "description"=>"<div><p>Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with “tissue-by-tissue” analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells, and fibroblasts, we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR = 0.05). Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Trait locus analysis", "transcriptomes", "Functional genomics", "Genome expression analysis", "Molecular genetics", "Gene regulation", "genetics", "Human genetics", "Genetic association studies", "gene expression", "Genetics of disease", "Genome-wide association studies", "Molecular cell biology", "Cellular types", "eqtl"], "article_id"=>700847, "categories"=>["Biological Sciences"], "users"=>["Timothée Flutre", "Xiaoquan Wen", "Jonathan Pritchard", "Matthew Stephens"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003486.s001", "https://dx.doi.org/10.1371/journal.pgen.1003486.s002", "https://dx.doi.org/10.1371/journal.pgen.1003486.s003", "https://dx.doi.org/10.1371/journal.pgen.1003486.s004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Statistical_Framework_for_Joint_eQTL_Analysis_in_Multiple_Tissues_/700847", "title"=>"A Statistical Framework for Joint eQTL Analysis in Multiple Tissues", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-05-09 00:14:07"}

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Relative Metric

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