Integrating 3D genomic and epigenomic data to enhance target gene discovery and drug repurposing in transcriptome-wide association studies

Chachrit Khunsriraksakul, Daniel McGuire, Renan Sauteraud, Fang Chen, Lina Yang, Lida Wang, Jordan Hughey, Scott Eckert, J. Dylan Weissenkampen, Ganesh Shenoy, Olivia Marx, Laura Carrel, Bibo Jiang () and Dajiang J. Liu ()
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Chachrit Khunsriraksakul: Pennsylvania State University College of Medicine
Daniel McGuire: Pennsylvania State University College of Medicine
Renan Sauteraud: Pennsylvania State University College of Medicine
Fang Chen: Pennsylvania State University College of Medicine
Lina Yang: Pennsylvania State University College of Medicine
Lida Wang: Pennsylvania State University College of Medicine
Jordan Hughey: Pennsylvania State University College of Medicine
Scott Eckert: Pennsylvania State University College of Medicine
J. Dylan Weissenkampen: Pennsylvania State University College of Medicine
Ganesh Shenoy: Pennsylvania State University College of Medicine
Olivia Marx: Pennsylvania State University College of Medicine
Laura Carrel: Pennsylvania State University College of Medicine
Bibo Jiang: Pennsylvania State University College of Medicine
Dajiang J. Liu: Pennsylvania State University College of Medicine

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract Transcriptome-wide association studies (TWAS) are popular approaches to test for association between imputed gene expression levels and traits of interest. Here, we propose an integrative method PUMICE (Prediction Using Models Informed by Chromatin conformations and Epigenomics) to integrate 3D genomic and epigenomic data with expression quantitative trait loci (eQTL) to more accurately predict gene expressions. PUMICE helps define and prioritize regions that harbor cis-regulatory variants, which outperforms competing methods. We further describe an extension to our method PUMICE +, which jointly combines TWAS results from single- and multi-tissue models. Across 79 traits, PUMICE + identifies 22% more independent novel genes and increases median chi-square statistics values at known loci by 35% compared to the second-best method, as well as achieves the narrowest credible interval size. Lastly, we perform computational drug repurposing and confirm that PUMICE + outperforms other TWAS methods.

Date: 2022
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DOI: 10.1038/s41467-022-30956-7

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