Dataset Information

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 learning framework to predict genome-wide quantitative epigenetic signals by considering both personal genetic variations and traits. The proposed approach can further evaluate the functional consequence of non-coding variants on an individual level by quantifying the allelic difference of predicted epigenetic signals. By applying the approach to the ROSMAP cohort studying Alzheimer's disease (AD), we demonstrate that the proposed approach can accurately predict quantitative genome-wide epigenetic signals and in key genomic regions of AD causal genes, learn canonical motifs reported to regulate gene expression of AD causal genes, improve the partitioning heritability analysis and prioritize putative causal variants in a GWAS risk locus. Finally, we release the proposed deep learning model as a stand-alone Python toolkit and a web server.

Availability and implementation

https://github.com/lichen-lab/DeepPerVar.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Wang Y

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Wang Ye Y Chen Li L

Bioinformatics (Oxford, England) 20221201 24

<h4>Motivation</h4>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 ...[more]

PMID: 36271868

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Dataset Information

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

Motivation

Results

Availability and implementation

Supplementary information

Publications

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

Similar Datasets

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