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

Nature Communications · 17 authors, 16 centres

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TL;DR

PhenoPLIER is a computational framework that maps gene-trait associations through modules of co-expressed genes in a shared latent representation, enabling joint analysis of diseases, genes, and drugs. The latent variable-based approach outperformed single-gene strategies in predicting known drug-disease pairs and prioritized functionally important genes missed by individual gene association tests.

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PhenoPLIER integrates transcriptome-wide association study (TWAS) results with groups of co-expressed genes (latent variables) inferred by MultiPLIER from a large expression compendium. Validation on 322 drugs across 53 diseases showed the LV-based drug-repurposing approach outperformed the gene-based one. CRISPR screen analysis of lipid regulation found that functionally important genes lacking single-gene associations were prioritized in trait-associated modules. Limitations include that the underlying TWAS captures only expression-mediated effects and the MultiPLIER model was designed for rare disorders and uses linear factorization. The approach rests on the assumption that coordinated expression patterns manifest coordinated pathological effects.

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research methods

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ORIGINAL PAPER

PMC10492839

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

Nature Communications

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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.

CITATION EXPORT

Milton Pividori, Sumei Lu, Binglan Li et al.. Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms. Nature Communications. (2023). https://doi.org/10.1038/s41467-023-41057-4

AUTHORS

17 · 16 CENTRES

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, Casey S. Greene

1grid.25879.310000 0004 1936 8972Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
2grid.430503.10000 0001 0703 675XDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045 USA
3https://ror.org/01z7r7q48grid.239552.a0000 0001 0680 8770Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
4https://ror.org/00f54p054grid.168010.e0000 0004 1936 8956Department of Biomedical Data Science, Stanford University, Stanford, CA 94305 USA
5https://ror.org/05dq2gs74grid.412807.80000 0004 1936 9916Vanderbilt University Medical Center, Nashville, TN 37232 USA
6https://ror.org/01hcyya48grid.239573.90000 0000 9025 8099Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229 USA
7https://ror.org/00hj8s172grid.21729.3f0000 0004 1936 8729Department of Medicine, Division of Nephrology, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY 10032 USA
8https://ror.org/02qp3tb03grid.66875.3a0000 0004 0459 167XMayo Clinic, Rochester, MN 55905 USA
9https://ror.org/000e0be47grid.16753.360000 0001 2299 3507Northwestern University, Chicago, IL 60611 USA
10https://ror.org/03r0ha626grid.223827.e0000 0001 2193 0096Kahlert School of Computing, University of Utah, Salt Lake City, UT 84112 USA
11grid.25879.310000 0004 1936 8972Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
12grid.25879.310000 0004 1936 8972Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
13https://ror.org/01z7r7q48grid.239552.a0000 0001 0680 8770Division of Endocrinology and Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
14https://ror.org/01z7r7q48grid.239552.a0000 0001 0680 8770Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
15grid.25879.310000 0004 1936 8972Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
16grid.430503.10000 0001 0703 675XCenter for Health AI, University of Colorado School of Medicine, Aurora, CO 80045 USA

Milton Pividori, Sumei Lu, Binglan Li et al.. Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms. Nature Communications. (2023). https://doi.org/10.1038/s41467-023-41057-4