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, Dajiang J. Liu

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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.

Original languageEnglish (US)
Article number3258
JournalNature communications
Volume13
Issue number1
DOIs
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

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

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