Identification of Alzheimer's disease susceptibility genes by the integration of genomics and transcriptomics.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease. With the deepening of clinical and genomic research, a series of biomarkers and risk factors related to AD have been identified. However, the exact molecular mechanism of AD is not completely understood.

Methods: By combining expression quantitative trait loci (eQTLs) analysis with the results of genome-wide association studies (GWAS), the candidate genes (CG) related to AD were screened out accurately. We identified that intersection genes of differentially expressed genes (DEGs) and CG are the key genes. Then, GO, KEGG, and GSEA were utilized for functional enrichment analysis. Finally, we predicted AD responses to immunotherapy by the single sample gene set enrichment analysis (ssGSEA).

Results: A total of 253 DEGs were identified. The three key genes (VASP, SURF2, and TARBP1) were identified by taking the intersection of DEGs and CG. Through Mendelian randomization (MR) analysis, it was found that the risk of AD was significantly increased when VASP expression increased (OR = 0.1.046), while the risk of AD was significantly decreased when SURF2 (OR = 0.897) and TARBP1(OR = 0.920) expression increased. Subsequently, the functional analysis indicated that the core genes were mainly enriched in Leukocyte Transendothelial Migration, cGMP-PKG signaling pathway, and Rap1 signaling pathway. Through ssGSEA analysis showed that all three core genes were significantly related to M2 macrophages.

Conclusions: Three core genes were screened by integrating eQTLs data, GWAS data and transfer group data, and the potential mechanism of diagnosis and treatment of AD was revealed.