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

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 the...

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Published in:Nature communications Vol. 14; no. 1; pp. 5562 - 18
Main Authors: Pividori, Milton, Lu, Sumei, Li, Binglan, Su, Chun, Johnson, Matthew E., Wei, Wei-Qi, Feng, Qiping, Namjou, Bahram, Kiryluk, Krzysztof, Kullo, Iftikhar J., Luo, Yuan, Sullivan, Blair D., Voight, Benjamin F., Skarke, Carsten, Ritchie, Marylyn D., Grant, Struan F. A., Greene, Casey S.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 09.09.2023
Nature Publishing Group
Nature Portfolio
Subjects:
631/114
631/208/205
Causality
Clustered Regularly Interspaced Short Palindromic Repeats
CRISPR
Disease
Epistasis, Genetic
Gene expression
Gene mapping
Gene Regulatory Networks
Genes
Humanities and Social Sciences
Lipids
Modules
multidisciplinary
Perturbation
Representations
Science
Science (multidisciplinary)
Transcriptome
Transcriptomes
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Summary: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. PhenoPLIER integrates genetic studies with gene modules expressed in specific contexts. This aids in extracting mechanistic insight from statistical associations to enhance our understanding of complex diseases and their therapeutic modalities.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-41057-4