In Cell reports ; h5-index 119.0
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.
Xu Jielin, Mao Chengsheng, Hou Yuan, Luo Yuan, Binder Jessica L, Zhou Yadi, Bekris Lynn M, Shin Jiyoung, Hu Ming, Wang Fei, Eng Charis, Oprea Tudor I, Flanagan Margaret E, Pieper Andrew A, Cummings Jeffrey, Leverenz James B, Cheng Feixiong
2022-Nov-29
AD, Alzheimer’s disease, CP: Neuroscience, EHR, GWAS, deep learning, drug repurposing, drug target, electronic health record, gemfibrozil, genome-wide association studies, multi-omics, pathobiology, protein-protein Interactome
