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 J. Kullo, Yuan Luo, Blair D. Sullivan, Benjamin F. Voight, Carsten Skarke, Marylyn D. Ritchie, Struan F. A. Grant and Casey S. Greene ()
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Milton Pividori: University of Pennsylvania
Sumei Lu: Children’s Hospital of Philadelphia
Binglan Li: Stanford University
Chun Su: Children’s Hospital of Philadelphia
Matthew E. Johnson: Children’s Hospital of Philadelphia
Wei-Qi Wei: Vanderbilt University Medical Center
Qiping Feng: Vanderbilt University Medical Center
Bahram Namjou: Cincinnati Children’s Hospital Medical Center
Krzysztof Kiryluk: Columbia University
Iftikhar J. Kullo: Mayo Clinic
Yuan Luo: Northwestern University
Blair D. Sullivan: Kahlert School of Computing, University of Utah
Benjamin F. Voight: University of Pennsylvania
Carsten Skarke: University of Pennsylvania
Marylyn D. Ritchie: University of Pennsylvania
Struan F. A. Grant: University of Pennsylvania
Casey S. Greene: University of Colorado School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: Abstract Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps 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 conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.

Date: 2023
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DOI: 10.1038/s41467-023-41057-4

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