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Nat Commun. 2019 Aug 23;10(1):3834. doi: 10.1038/s41467-019-11874-7.

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

Author information

1
Department of Psychiatry, Pamela Sklar Division of Psychiatric Genomics and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
2
Department of Genetics & Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA.
3
Department of Biomedicine, Aarhus University, 8000, Aarhus, Denmark.
4
The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus University, 8000, Aarhus, Denmark.
5
Centre for Integrative Sequencing (iSEQ), Aarhus University, 8000, Aarhus, Denmark.
6
The Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA.
7
Clinical Gene Networks AB, 114 44, Stockholm, Sweden.
8
Vascular Biology Unit, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77, Stockholm, Sweden.
9
Department of Physiology, Institute of Biomedicine and Translation Medicine, University of Tartu, 50411, Tartu, Estonia.
10
Department of Psychiatry, Pamela Sklar Division of Psychiatric Genomics and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. panagiotis.roussos@mssm.edu.
11
Department of Genetics & Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA. panagiotis.roussos@mssm.edu.
12
Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, 10468, NY, USA. panagiotis.roussos@mssm.edu.

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.

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