| First Author | Kim SH | Year | 2021 |
| Journal | Proc Natl Acad Sci U S A | Volume | 118 |
| Issue | 3 | PubMed ID | 33397809 |
| Mgi Jnum | J:300753 | Mgi Id | MGI:6502699 |
| Doi | 10.1073/pnas.2011250118 | Citation | Kim SH, et al. (2021) Prediction of Alzheimer's disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening. Proc Natl Acad Sci U S A 118(3):e2011250118 |
| abstractText | 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 (PLCgamma1) 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 PLCgamma1 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 PLCgamma1 gene, and one of these completely matched with an SNV in exon 27 of PLCgamma1 gene in an AD mouse model. In particular, the SNV in exon 27 of PLCgamma1 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. |
