First, we leveraged pQTL data derived from brain tissue and findings from two large-scale AD GWASs to conduct a proteome-wide association study (PWAS) analysis aimed at identifying the candidate protein biomarkers. Second, we integrated these data using a Mendelian randomization (MR) framework which harnesses genetic colocalization to highlight genes and AD are influenced by a shared causal variant. Third, by leveraging gene-expression data, we identified the significant genes driving GWAS signals at the transcriptional level. Fourth, a specificity analysis was conducted to detect the cell type that targeted genes express on the highest levels. Last, we verified the findings by applying them to the proteomic data derived from blood serum to assess the consistency between the two tissues.

Collectively, we identified the brain protein abundance of 7 genes (ACE, ICA1L, TOM1L2, SNX32, EPHX2, CTSH, and RTFDC1) are causal in AD (P<0.05/proteins identified for PWAS and MR; PPH4>80% for Bayesian colocalization). The proteins encoded by these genes were mainly expressed on the surface of glutamatergic neurons and astrocytes. Of them, ACE with its protein abundance was also identified in significant association with AD on the blood-based studies and showed significance at the transcriptomic level. SNX32 was also found to be associated with AD at the blood transcriptomic level.