Year: 2015
Type: article
Abstract: To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.
Source: PLoS Computational Biology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: University of Miami, University of Pennsylvania, Harvard University, University of Chicago, Broad Institute, Université de Genève, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Human Genome Research Institute, NIH Office of the Director, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, Common Fund
Awards: Methods for high-resolution analysis of genetic effects on gene expression, G1206703408, G1217436835, Epigenomic variation atlas across human tissues and individuals in GTEx, G1534570039, G1561323845, G1650773652, Identification and validation of cell specific eQTLs by Bayesian modeling, G1883398070, Using the Transcriptome for SNP and Gene Annotation, G2158443041, MODELING THE EFFECTS OF STRUCTURAL VARIATION IN GTEX DATA AND MENDELIAN DISEASE, G2651523388, G2835668146, G2877753951, G3128394570, G3221727586, G32319252, Epigenetic Marks of Cocaine Addiction, G3387558946, G3443372292, G347407575, G3503792436, G3547560438, G3793778294, G386607036, Genetic Regulation of Gene Expression and its Impact on Phenotypes, G4124418613, G4395118462, Systems approaches to link tissue-specific expression to disease, Harnessing GTEx to Create Transcriptome Knowledge and Inform Disease Biology, G4478373029, G4606556408, G4715193760, G4729234894, Epistatic and Cross Tissue Analysis for Human Gene Expression Traits, G5097752872, G5286804338, G563538711, G5737582129, G5865458551, G5888954722, Statistical analysis of gene expression quantitative trait loci (eQTL), Methods for high-resolution analysis of genetic effects on gene expression, G6317516893, G633025773, G634178657, G6619812508, G664140718, G7075004892, G7215801407, G7274654584, G7353525908, FACILITATING GTEx, DISEASE, AND GxE ANALYSES VIA FAST EXPRESSION (e)QTL MAPPING, Statistical analysis of gene expression quantitative trait loci (eQTL), CNS Mechanisms in Cocaine-Related Sudden Death, G7665525972, G7761642487, G7924626839, G7937240049, G7953026188, G7958282288, Regulatory Morif Discovery in the Human Genome Using Comparative Genomics, G8308451750, G8602100876, G8644024335, G8719970897, G8788848806, G8810271873, G8906494432, Roswell Park Cancer Center Support Grant, Diabetes Research and Training Center
OpenAlex ID: W2951920373