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Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases

Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on...

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Autores principales: Lan, Alexander Y., Corces, M. Ryan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9716280/
https://www.ncbi.nlm.nih.gov/pubmed/36466610
http://dx.doi.org/10.3389/fnagi.2022.1027224
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author Lan, Alexander Y.
Corces, M. Ryan
author_facet Lan, Alexander Y.
Corces, M. Ryan
author_sort Lan, Alexander Y.
collection PubMed
description Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer’s-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.
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spelling pubmed-97162802022-12-03 Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases Lan, Alexander Y. Corces, M. Ryan Front Aging Neurosci Aging Neuroscience Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer’s-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning. Frontiers Media S.A. 2022-11-18 /pmc/articles/PMC9716280/ /pubmed/36466610 http://dx.doi.org/10.3389/fnagi.2022.1027224 Text en Copyright © 2022 Lan and Corces. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Aging Neuroscience
Lan, Alexander Y.
Corces, M. Ryan
Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases
title Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases
title_full Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases
title_fullStr Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases
title_full_unstemmed Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases
title_short Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases
title_sort deep learning approaches for noncoding variant prioritization in neurodegenerative diseases
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9716280/
https://www.ncbi.nlm.nih.gov/pubmed/36466610
http://dx.doi.org/10.3389/fnagi.2022.1027224
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