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

• Published in: Nature communications Vol. 10; no. 1; pp. 3834 - 13
• Main 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23-08-2019; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/2401; 631/208/205; 692/308/153; Animals; Biological activity; Convergence; Databases, Genetic; Datasets as Topic; Epigenomics - methods; Gene expression; Gene Expression Profiling - methods; Genetic diversity; Genetic Pleiotropy; Genetic Predisposition to Disease; Humanities and Social Sciences; Humans; Medicin och hälsovetenskap; Mice; Models, Genetic; multidisciplinary; Phenotypes; Pleiotropy; Quantitative Trait Loci; Science; Science (multidisciplinary); Software
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-11874-7

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.
PrediXcan is a widely used gene expression imputation method that links genetic variants to gene expression. Here, the authors develop EpiXcan which leverages epigenetic annotations to inform transcriptomic imputation and further use the obtained gene-trait associations for computational drug repurposing.