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Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

View ORCID ProfileMilton Pividori, Sumei Lu, View ORCID ProfileBinglan Li, View ORCID ProfileChun Su, Matthew E. Johnson, Wei-Qi Wei, View ORCID ProfileQiping Feng, Bahram Namjou, View ORCID ProfileKrzysztof Kiryluk, Iftikhar Kullo, Yuan Luo, Blair D. Sullivan, View ORCID ProfileBenjamin F. Voight, View ORCID ProfileCarsten Skarke, View ORCID ProfileMarylyn D. Ritchie, View ORCID ProfileStruan F.A. Grant, View ORCID ProfileCasey S. Greene
doi: https://doi.org/10.1101/2021.07.05.450786
Milton Pividori
1Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Sumei Lu
2Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
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Binglan Li
3Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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Chun Su
2Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
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Matthew E. Johnson
2Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
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Wei-Qi Wei
4Vanderbilt University Medical Center
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Qiping Feng
4Vanderbilt University Medical Center
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Bahram Namjou
5Cincinnati Children’s Hospital Medical Center
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Krzysztof Kiryluk
6Department of Medicine, Division of Nephrology, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York
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Iftikhar Kullo
7Mayo Clinic
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Yuan Luo
8Northwestern University
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Blair D. Sullivan
9School of Computing, University of Utah, Salt Lake City, UT, USA
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Benjamin F. Voight
1Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
10Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
11Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Carsten Skarke
12Institute for Translational Medicine and Therapeutics, Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Marylyn D. Ritchie
1Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Struan F.A. Grant
2Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
13Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
14Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
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Casey S. Greene
15Center for Health AI, University of Colorado School of Medicine, Aurora, CO 80045, USA
16Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA
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  • For correspondence: casey.s.greene@cuanschutz.edu
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Abstract

Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented a significant step forward to test the mediating role of gene expression in GWAS associations. However, it is hard to disentangle causal cell types using eQTL data alone, and other methods generally do not use the large amounts of publicly available RNA-seq data. Here we introduce PhenoPLIER, a polygenic approach that maps both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same tissues. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, and our approach was accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we found that functionally important players lacked TWAS associations but were prioritized in phenotype-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets within associated processes that are missed by single-gene strategies.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵Funded by The Gordon and Betty Moore Foundation (GBMF 4552); The National Human Genome Research Institute (R01 HG010067); The National Cancer Institute (R01 CA237170)

  • miltondp · miltondp

  • sckinta

  • kirylukk

  • bvoight · bvoight28

  • CarstenSkarke

  • MarylynRitchie

  • STRUANGRANT

  • cgreene · GreeneScientist

  • Incorporate feedback from first version of preprint, add missed co-author.

  • https://github.com/greenelab/phenoplier_manuscript/

  • https://github.com/greenelab/phenoplier/

Copyright 
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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Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms
Milton Pividori, Sumei Lu, Binglan Li, Chun Su, Matthew E. Johnson, Wei-Qi Wei, Qiping Feng, Bahram Namjou, Krzysztof Kiryluk, Iftikhar Kullo, Yuan Luo, Blair D. Sullivan, Benjamin F. Voight, Carsten Skarke, Marylyn D. Ritchie, Struan F.A. Grant, Casey S. Greene
bioRxiv 2021.07.05.450786; doi: https://doi.org/10.1101/2021.07.05.450786
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Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms
Milton Pividori, Sumei Lu, Binglan Li, Chun Su, Matthew E. Johnson, Wei-Qi Wei, Qiping Feng, Bahram Namjou, Krzysztof Kiryluk, Iftikhar Kullo, Yuan Luo, Blair D. Sullivan, Benjamin F. Voight, Carsten Skarke, Marylyn D. Ritchie, Struan F.A. Grant, Casey S. Greene
bioRxiv 2021.07.05.450786; doi: https://doi.org/10.1101/2021.07.05.450786

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