DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.

ABSTRACT:

Motivation

Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited statistical power and linkage disequilibrium, and thus are less optimal to pinpoint the causal variants. Moreover, most existing machine-learning approaches, which exploit the functional annotations to interpret and prioritize putative causal variants, cannot accommodate the heterogeneity of personal genetic variations and traits in a population study, targeting a specific disease.

Results

By leveraging paired whole-genome sequencing data and epigenetic functional assays in a population study, we propose a multi-modal deep learni

SUBMITTER: Wang Y 

PROVIDER: S-EPMC9750124 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature