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

• DOI: 10.1073/pnas.2011250118
• Publication Date: 2021-01-04T21:25:14Z
• Authors: Sung-Hyun Kim; Sumin Yang; Key-Hwan Lim; Euiseng Ko; Hyun-Jun Jang; Mingon Kang; Pann-Ghill Suh; Jae-Yeol Joo
• Keywords: Genome, Human; Phospholipase C gamma; PLCγ1; Cognitive Neuroscience; RNA Splicing; deep learning; single-nucleotide variation; Exons; Biological Sciences; Polymorphism, Single Nucleotide; Alzheimer’s disease; High-Throughput Screening Assays; Disease Models, Animal; Mice; Deep Learning; Alzheimer Disease; Animals; Humans; Computer Simulation; Genetic Predisposition to Disease; RNA, Messenger; Genome-Wide Association Study

Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discovered Alzheimer’s disease (AD)-specific single-nucleotide variants (SNVs) and abnormal exon splicing of the 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 the 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 the human PLCγ1 gene, one of which completely matched an SNV in exon 27 of the PLCγ1 gene in an AD mouse model. In particular, the SNV in exon 27 of the 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.