Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer’s disease
Abstrak
SUMMARY Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer’s disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51–0.63, p < 0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.
Topik & Kata Kunci
Penulis (17)
Jielin Xu
Chengsheng Mao
Yuan Hou
Yuan Luo
Jessica L. Binder
Yadi Zhou
L. Bekris
Jiyoung Shin
Ming Hu
Fei Wang
Ch. Eng
Tudor I. Oprea
M. Flanagan
A. Pieper
J. Cummings
J. Leverenz
F. Cheng
Akses Cepat
- Tahun Terbit
- 2022
- Bahasa
- en
- Total Sitasi
- 86×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1016/j.celrep.2022.111717
- Akses
- Open Access ✓