Document Type
Article
Publication Date
1-19-2021
Publication Title
Proceedings of the National Academy of Sciences of the United States of America
Volume
118
Issue
3
First page number:
1
Last page number:
9
Abstract
© 2021 National Academy of Sciences. All rights reserved. Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover Alzheimer’s disease (AD)-specific single-nucleotide variants (SNVs) and abnormal exon splicing of phospholipase c gamma-1 (PLCγ1) gene, using genome-wide association study (GWAS) and a deep learning-based exon splicing prediction tool. GWAS revealed that the identified single-nucleotide variations were mainly distributed in the H3K27ac-enriched region of PLCγ1 gene body during brain development in an AD mouse model. A deep learning analysis, trained with human genome sequences, predicted 14 splicing sites in human PLCγ1 gene, and one of these completely matched with an SNV in exon 27 of PLCγ1 gene in an AD mouse model. In particular, the SNV in exon 27 of PLCγ1 gene is associated with abnormal splicing during messenger RNA maturation. Taken together, our findings suggest that this approach, which combines in silico and deep learning-based analyses, has potential for identifying the clinical utility of critical SNVs in AD prediction.
Keywords
Alzheimer’s disease; deep learning; PLCγ1; single-nucleotide variation
Disciplines
Cognitive Neuroscience
File Format
File Size
2002 KB
Language
English
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Kim, S.,
Yang, S.,
Lim, K.,
Ko, E.,
Jang, H.,
Kang, M.,
Suh, P.,
Joo, J.
(2021).
Prediction of Alzheimer’s Disease-Specific Phospholipase C Gamma-1 SNV by Deep Learning-Based Approach for High-Throughput Screening.
Proceedings of the National Academy of Sciences of the United States of America, 118(3),
1-9.
http://dx.doi.org/10.1073/pnas.2011250118