DeepPerVar: a multimodal deep learning framework for functional interpretation of genetic variants in personal genome

Understanding the functional consequence of genetic variants, especially the noncoding ones, is important but particularly challenging. Genome-wide association studies or quantitative trait locus analyses may be subject to limited statistical power and linkage disequilibrium, and thus are less optim...

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Veröffentlicht in:bioRxiv
Hauptverfasser: Chen, Li, Wang, Ye
Format: Paper
Sprache:Englisch
Veröffentlicht: Cold Spring Harbor Cold Spring Harbor Laboratory Press 11.04.2022
Cold Spring Harbor Laboratory
Ausgabe:1.1
Schlagworte:
Alzheimer's disease
Bioinformatics
Deep learning
Epigenetics
Gene expression
Genetic diversity
Genome-wide association studies
Genomes
Heritability
Linkage disequilibrium
Neurodegenerative diseases
Population studies
Quantitative trait loci
Whole genome sequencing
ISSN:2692-8205, 2692-8205
Online-Zugang:Volltext
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Zusammenfassung:Understanding the functional consequence of genetic variants, especially the noncoding ones, is important but particularly challenging. Genome-wide association studies 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. 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 noncoding 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. Competing Interest Statement The authors have declared no competing interest.
Bibliographie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
Competing Interest Statement: The authors have declared no competing interest.
ISSN:2692-8205
2692-8205
DOI:10.1101/2022.04.10.487809