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

Author:

Khunsriraksakul ChachritORCID,McGuire DanielORCID,Sauteraud Renan,Chen Fang,Yang Lina,Wang LidaORCID,Hughey Jordan,Eckert Scott,Dylan Weissenkampen J.,Shenoy Ganesh,Marx Olivia,Carrel Laura,Jiang BiboORCID,Liu Dajiang J.ORCID

Abstract

AbstractTranscriptome-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.

Funder

U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences

U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine

Robert and Sevia Finkelstein

Lupus Research Alliance

Pennsylvania Department of Health

U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute

U.S. Department of Health & Human Services | NIH | NIH Office of the Director

Publisher

Springer Science and Business Media LLC

Subject

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

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