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Vulnerabilities of transcriptome-wide association studies

Michael Wainberg, Nasa Sinnott-Armstrong, David Knowles, David Golan, Raili Ermel, Arno Ruusalepp, Thomas Quertermous, Ke Hao, Johan LM Björkegren, Manuel A Rivas, Anshul Kundaje
doi: https://doi.org/10.1101/206961
Michael Wainberg
1Department of Computer Science, Stanford University, Stanford, California, USA
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Nasa Sinnott-Armstrong
2Department of Genetics, Stanford University, Stanford, California, USA
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David Knowles
2Department of Genetics, Stanford University, Stanford, California, USA
3Department of Radiology, Stanford University, Stanford, California, USA
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David Golan
2Department of Genetics, Stanford University, Stanford, California, USA
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Raili Ermel
4Department of Cardiac Surgery, Tartu University Hospital, Tartu, Estonia
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Arno Ruusalepp
4Department of Cardiac Surgery, Tartu University Hospital, Tartu, Estonia
5Clinical Gene Networks AB, Stockholm, Sweden
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Thomas Quertermous
6Division of Cardiovascular Medicine, Stanford University, Stanford, California, USA
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Ke Hao
7Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Johan LM Björkegren
5Clinical Gene Networks AB, Stockholm, Sweden
7Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
8Department of Pathophysiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
9Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
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  • For correspondence: johan.bjorkegren@mssm.edu mrivas@stanford.edu akundaje@stanford.edu
Manuel A Rivas
10Department of Biomedical Data Science, Stanford University, Stanford, California, USA
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  • For correspondence: johan.bjorkegren@mssm.edu mrivas@stanford.edu akundaje@stanford.edu
Anshul Kundaje
1Department of Computer Science, Stanford University, Stanford, California, USA
2Department of Genetics, Stanford University, Stanford, California, USA
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  • For correspondence: johan.bjorkegren@mssm.edu mrivas@stanford.edu akundaje@stanford.edu
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Abstract

Transcriptome-wide association studies (TWAS) integrate GWAS and expression quantitative trait locus (eQTL) datasets to discover candidate causal gene-trait associations. We integrate multi-tissue expression panels and summary GWAS for LDL cholesterol and Crohn’s disease to show that TWAS are highly vulnerable to discovering non-causal genes, because variants at a single GWAS hit locus are often eQTLs for multiple genes. TWAS exhibit acute instability when the tissue of the expression panel is changed: candidate causal genes that are TWAS hits in one tissue are usually no longer hits in another, due to lack of expression or strong eQTLs, even though non-causal genes at the same loci remain. Because of these vulnerabilities, it is invalid to use TWAS as a method for finding causal genes, though it can be used as a weighted burden test to identify trait-associated loci. More broadly, our results showcase limitations of using expression variation across individuals to determine causal genes at GWAS loci.

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Posted October 20, 2017.
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Vulnerabilities of transcriptome-wide association studies
Michael Wainberg, Nasa Sinnott-Armstrong, David Knowles, David Golan, Raili Ermel, Arno Ruusalepp, Thomas Quertermous, Ke Hao, Johan LM Björkegren, Manuel A Rivas, Anshul Kundaje
bioRxiv 206961; doi: https://doi.org/10.1101/206961
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Vulnerabilities of transcriptome-wide association studies
Michael Wainberg, Nasa Sinnott-Armstrong, David Knowles, David Golan, Raili Ermel, Arno Ruusalepp, Thomas Quertermous, Ke Hao, Johan LM Björkegren, Manuel A Rivas, Anshul Kundaje
bioRxiv 206961; doi: https://doi.org/10.1101/206961

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