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; Casey S. Greene

doi:10.1038/s41467-023-41057-4

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, Casey S. Greene^*

^*Corresponding author for this work

Preventive Medicine

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

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.

Original language	English (US)
Article number	5562
Journal	Nature communications
Volume	14
Issue number	1
DOIs	https://doi.org/10.1038/s41467-023-41057-4
State	Published - Dec 2023

Funding

This study was funded by: the Gordon and Betty Moore Foundation (GBMF 4552 to C.S.G.; GBMF 4560 to B.D.S.), the National Human Genome Research Institute (R01 HG010067 to C.S.G., S.F.A. Grant, and B.D.S.; K99 HG011898 and R00 HG011898 to M. Pividori; U01 HG011181 to W.W.), the National Cancer Institute (R01 CA237170 to C.S.G.), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD109765 to C.S.G.), the National Institute of Aging (R01AG069900 to W.W.), the National Institute of General Medical Sciences (R01 GM139891 to W.W.); the National Heart, Lung, and Blood Institute (R01 HL163854 to Q.F.); the National Institute of Diabetes and Digestive and Kidney Diseases (DK126194 to B.F.V.); the Daniel B. Burke Endowed Chair for Diabetes Research to S.F.A. Grant; the Robert L. McNeil Jr. Endowed Fellowship in Translational Medicine and Therapeutics to C. Skarke. Phase III of the eMERGE Network was initiated and funded by the NHGRI through the following grants: U01 HG8657 (Group Health Cooperative/University of Washington); U01 HG8685 (Brigham and Womens Hospital); U01 HG8672 (Vanderbilt University Medical Center); U01 HG8666 (Cincinnati Childrens Hospital Medical Center); U01 HG6379 (Mayo Clinic); U01 HG8679 (Geisinger Clinic); U01 HG8680 (Columbia University Health Sciences); U01 HG8684 (Childrens Hospital of Philadelphia); U01 HG8673 (Northwestern University); U01 HG8701 (Vanderbilt University Medical Center serving as the Coordinating Center); U01 HG8676 (Partners Healthcare/Broad Institute); and U01 HG8664 (Baylor College of Medicine). The Penn Medicine BioBank (PMBB) is funded by the Perelman School of Medicine at the University of Pennsylvania, a gift from the Smilow family, and the National Center for Advancing Translational Sciences of the National Institutes of Health under CTSA Award Number UL1TR001878. We thank D. Birtwell, H. Williams, P. Baumann, and M. Risman for their informatics support regarding the PMBB. We thank the staff of the Regeneron Genetics Center for whole-exome sequencing of DNA from PMBB participants. Figure a was created with BioRender.com.

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

UN SDGs

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10.1038/s41467-023-41057-4

Cite this

Pividori, M., Lu, S., Li, B., Su, C., Johnson, M. E., Wei, W. Q., Feng, Q., Namjou, B., Kiryluk, K., Kullo, I. J., Luo, Y., Sullivan, B. D., Voight, B. F., Skarke, C., Ritchie, M. D., Grant, S. F. A., & Greene, C. S. (2023). Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms. Nature communications, 14(1), Article 5562. https://doi.org/10.1038/s41467-023-41057-4

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title = "Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms",

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.",

author = "Milton Pividori and Sumei Lu and Binglan Li and Chun Su and Johnson, {Matthew E.} and Wei, {Wei Qi} and Qiping Feng and Bahram Namjou and Krzysztof Kiryluk and Kullo, {Iftikhar J.} and Yuan Luo and Sullivan, {Blair D.} and Voight, {Benjamin F.} and Carsten Skarke and Ritchie, {Marylyn D.} and Grant, {Struan F.A.} and Greene, {Casey S.}",

note = "Publisher Copyright: {\textcopyright} 2023, Springer Nature Limited.",

year = "2023",

month = dec,

doi = "10.1038/s41467-023-41057-4",

language = "English (US)",

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Pividori, M, Lu, S, Li, B, Su, C, Johnson, ME, Wei, WQ, Feng, Q, Namjou, B, Kiryluk, K, Kullo, IJ, Luo, Y, Sullivan, BD, Voight, BF, Skarke, C, Ritchie, MD, Grant, SFA & Greene, CS 2023, 'Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms', Nature communications, vol. 14, no. 1, 5562. https://doi.org/10.1038/s41467-023-41057-4

TY - JOUR

T1 - Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

AU - Pividori, Milton

AU - Lu, Sumei

AU - Li, Binglan

AU - Su, Chun

AU - Johnson, Matthew E.

AU - Wei, Wei Qi

AU - Feng, Qiping

AU - Namjou, Bahram

AU - Kiryluk, Krzysztof

AU - Kullo, Iftikhar J.

AU - Luo, Yuan

AU - Sullivan, Blair D.

AU - Voight, Benjamin F.

AU - Skarke, Carsten

AU - Ritchie, Marylyn D.

AU - Grant, Struan F.A.

AU - Greene, Casey S.

PY - 2023/12

Y1 - 2023/12

N2 - 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.

AB - 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.

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ER -

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

Abstract

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ASJC Scopus subject areas

UN SDGs

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