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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.
Highlights
Transcriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations
Since transcriptome-wide association studies (TWASs) is limited to genes that can be accurately predicted from genotype data, increasing prediction accuracy can increase the scope and power of analyses
The actual R2CV achieved by both methods is generally low, in all simulated scenarios, EpiXcan improves the average R2CV compared to PrediXcan models
Summary
Transcriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. 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. The generated trait-associated imputed transcriptomes can be leveraged for diverse downstream applications such as the identification of candidate compounds, for which we have reference transcriptomic data, that are predicted to reverse trait-specific, genetically driven, gene expression changes[14]. We utilize 14 large-scale transcriptome datasets of genotyped individuals to train prediction models and integrate with 58 complex traits and diseases to define significant GTAs. GTAs exhibit significant enrichment for relevant biological pathways and known genes linked to trait-related phenotypes in humans and mice. Our analysis provides insight into the specificity and convergence of gene expression mediating the genetic risk architecture underlying susceptibility to complex traits and diseases
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