TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.

ABSTRACT:

Motivation

Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk.

Results

With thousands of tissue- and cell-type-specific epigenomic features, we propose TVAR. This multi-label learning-based deep neural network predicts the functionality of non-coding variants in the genome based on eQTLs across 49 human tissues in the GTEx project. TVAR learns the relationships between high-dimensional epigenomics and eQTLs across tissues, taking the correlation among tissues into

SUBMITTER: Yang H 

PROVIDER: S-EPMC9563698 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature