Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits.

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ID: 43614
2019
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Abstract
Transcriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. By incorporating epigenome data to estimate the functional importance of genetic variation on gene expression, we generate a small but significant improvement in the accuracy of transcriptome prediction and increase the power to detect significant expression-trait associations. Joint analysis of 14 large-scale transcriptome datasets and 58 traits identify 13,724 significant expression-trait associations that converge on biological processes and relevant phenotypes in human and mouse phenotype databases. We perform drug repurposing analysis and identify compounds that mimic, or reverse, trait-specific changes. We identify genes that exhibit agonistic pleiotropy for genetically correlated traits that converge on shared biological pathways and elucidate distinct processes in disease etiopathogenesis. Overall, this comprehensive analysis provides insight into the specificity and convergence of gene expression on susceptibility to complex traits.
Reference Key
zhang2019integrativenature Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhang, Wen;Voloudakis, Georgios;Rajagopal, Veera M;Readhead, Ben;Dudley, Joel T;Schadt, Eric E;Björkegren, Johan L M;Kim, Yungil;Fullard, John F;Hoffman, Gabriel E;Roussos, Panos;
Journal Nature communications
Year 2019
DOI
10.1038/s41467-019-11874-7
URL
Keywords
electrophysiology liraglutide melanocortin npy/agrp pomc patch-clamp

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