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
Cited by
20 articles.
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