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PrediXcan: Trait Mapping Using Human Transcriptome Regulation

Eric R. Gamazon, Heather E. Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler, Joshua C. Denny, GTEx Consortium, Dan L. Nicolae, Nancy J. Cox, View ORCID ProfileHae Kyung Im
doi: https://doi.org/10.1101/020164
Eric R. Gamazon
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, ILDivision of Genetic Medicine, Vanderbilt University, Nashville, TN
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Heather E. Wheeler
Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL
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Kaanan P. Shah
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL
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Sahar V. Mozaffari
Department of Human Genetics, University of Chicago, Chicago, IL
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Keston Aquino-Michaels
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL
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Robert J. Carroll
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN
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Anne E. Eyler
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN
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Joshua C. Denny
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN
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Dan L. Nicolae
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, ILDepartment of Human Genetics, University of Chicago, Chicago, ILDepartment of Statistics, University of Chicago, Chicago, IL
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Nancy J. Cox
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, ILDivision of Genetic Medicine, Vanderbilt University, Nashville, TNDepartment of Human Genetics, University of Chicago, Chicago, IL
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Hae Kyung Im
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL
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  • ORCID record for Hae Kyung Im
  • For correspondence: haky@uchicago.edu
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ABSTRACT

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual’s genetic profile and correlates the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 17, 2015.
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PrediXcan: Trait Mapping Using Human Transcriptome Regulation
Eric R. Gamazon, Heather E. Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler, Joshua C. Denny, GTEx Consortium, Dan L. Nicolae, Nancy J. Cox, Hae Kyung Im
bioRxiv 020164; doi: https://doi.org/10.1101/020164
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PrediXcan: Trait Mapping Using Human Transcriptome Regulation
Eric R. Gamazon, Heather E. Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler, Joshua C. Denny, GTEx Consortium, Dan L. Nicolae, Nancy J. Cox, Hae Kyung Im
bioRxiv 020164; doi: https://doi.org/10.1101/020164

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