Sung-Hyun Kim^a,b,1, Sumin Yang^a,b,1, Key-Hwan Lim^a,b,1, Euiseng Ko^c, Hyun-Jun Jang^d, Mingon Kang^c, Pann-Ghill Suh^b, and Jae-Yeol Joo^a,b,2

^aNeurodegenerative Disease Research Group, 41062 Daegu, Republic of Korea; ^bKorea Brain Research Institute, 41062 Daegu, Republic of Korea; ^cDepartment of Computer Science, University of Nevada, Las Vegas, NV 89154; and ^dSchool of Life Sciences, Ulsan National Institute of Science and Technology, 44919 Ulsan, Republic of Korea

¹S.-H.K., S.Y., and K.-H.L. contributed equally to this work.
²To whom correspondence may be addressed. 

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