RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants.

ABSTRACT: Non-coding genomic variants constitute the majority of trait-associated genome variations; however, the identification of functional non-coding variants is still a challenge in human genetics, and a method for systematically assessing the impact of regulatory variants on gene expression and linking these regulatory variants to potential target genes is still lacking. Here, we introduce a deep neural network (DNN)-based computational framework, RegVar, which can accurately predict the tissue-specific impact of non-coding regulatory variants on target genes. We show that by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues, RegVar vastly surpasses all current non-coding variant prioritization methods in predicting regu

SUBMITTER: Lu H 

PROVIDER: S-EPMC10626172 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature