Prediction of Alzheimer's disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening

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 (PLC.1) 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 PLC.1 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 PLC.1 gene, and one of these completely matched with an SNV in exon 27 of PLC.1 gene in an AD mouse model. In particular, the SNV in exon 27 of PLC.1 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.

Automated summary

Using GWAS and deep learning techniques, specific SNVs related to Alzheimer's disease and abnormal exon splicing of the PLC.1 gene were identified, highlighting their association with AD. This study suggests the potential clinical utility of critical SNVs in AD prediction through a combination of computational and deep learning-based analyses.