Alzheimer's Disease (AD) is the leading cause of dementia, imposing significant economic and social burdens. Although genome-wide association studies (GWAS) have identified approximately 70 risk loci, the functional mechanisms underlying AD remain unclear. In this study, we integrated the GWAS summary statistics from Jiang et al. with gene expression data from the GTEx project using S-PrediXcan method, encompassing 61 brain-related traits across 49 tissues. Comprehensive analysis identified five significant traits, including family history of AD, and highlighted key genes such as APOE, APOC1, and TOMM40, which play crucial roles in cholesterol metabolism, immune response, and neuroinflammation. Validation using the ROSMAP dataset confirmed the association of these genes with AD phenotypes. Furthermore, we developed AD-MIF, a novel deep multi-layer information fusion model that integrates multi-omics data, achieving a 10-20% improvement in AUC performance for predicting AD-related traits compared to traditional models. Gene enrichment analysis emphasized the importance of pathways such as cholesterol metabolism and immune response in the pathogenesis of AD. Additionally, drug repositioning analysis identified candidate drugs, including Dasatinib and Sirolimus, which may alleviate AD progression by reducing neuroinflammation and clearing senescent cells. Our findings advance the understanding of the genetic architecture of AD, improve predictive models, and propose potential therapeutic drugs.

The authors have declared no competing interest.

This study did not receive any funding

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