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

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.

Alzheimer’s disease, deep learning, PLCγ1, single-nucleotide variation

주소복사
논문정보   
- 형식: Research article
- 게재일: 2021년 01월 (BRIC 등록일 2021-01-11)
- 연구진: 국내(교신)+국외 연구진태극기
- 분야: Neuroscience
댓글 (3)
_S_  |  01.14 16:23     
임박사님 축하드립니다 ^^
Rose-Chick  |  01.20 17:52     
임기환 박사님 축하 드립니다.
먼 곳에서도 응원 드립니다.
수퍼소년  |  03.10 13:56     
멋진 인터뷰네요^^ 축하드립니다~.
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