Multi-Omics Analysis for Identifying Cell-Type-Specific Druggable Targets in Alzheimer's Disease

Abstract

Background: Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) is important for identifying potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) results with tissue-level eQTLs. Recent studies have explored brain cell type-specific eQTLs, but they lack a systematic analysis across various Alzheimer's disease (AD) GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type-specific eQTL datasets. In this study, we integrated brain cell type-specific eQTL datasets with AD GWAS datasets to identify potential causal genes at the cell type level.

Methods: To prioritize disease-causing genes, we used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with cell-type-specific eQTLs. Combining data from five AD GWAS, three single-cell eQTL datasets, and one bulk tissue eQTL meta-analysis, we identified and confirmed both novel and known candidate causal genes. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction and pathway enrichment analyses, and conducted a drug/compound enrichment analysis with the Drug Signatures Database (DSigDB) to support drug repurposing for AD.

Results: We identified 27 candidate causal genes for AD using cell type-specific eQTL datasets, with the highest numbers in microglia, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel astrocyte-specific gene. Our analysis revealed protein-protein interaction (PPI) networks for these causal genes in microglia and astrocytes. We found the "regulation of aspartic-type peptidase activity" pathway being the most enriched among all the causal genes. AD-risk variants associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the genes into three drug tiers and identified druggable interactions, with imatinib mesylate emerging as a key candidate. A drug-target gene network was created to explore potential drug targets for AD.

Conclusions: We systematically prioritized AD candidate causal genes based on cell type-specific molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates the interpretation of AD GWAS results.

Keywords: Alzheimer’s disease; Causal genes; GWAS; Gene expression; SNP; astrocytes; cell type; drug repurposing; eQTL; genetic variant.

Figure 1.

Study workflow.

Figure 2.. SMR beta value signs for candidate causal genes from SMR and colocalization analysis.

Note: all five GWAS datasets results are combined. The candidate causal genes are filtered by SMR FDR < 0.05, HEIDI > 0.05, Coloc PPH4 < 0.75, Coloc PPH4/PPH3 > 3.

Figure 3.. Candidate causal genes network analysis and pathway enrichment.

A. STRING PPI network of Astrocyte candidate causal genes. B. STRING PPI network of Microglia candidate causal genes. C. Pathway enrichment of all 31 detected candidate causal (mRNA) genes

Figure 4.. eQTpLot for colocalization between eQTLs for the gene PABPC1 and a GWAS signal for AD.

The GWAS dataset is from Bellenguez et al., 2022 and the cell type eQTL dataset of astrocyte is from Fujita et al., 2024. A shows the locus of interest, containing the PABPC1 gene, with chromosomal space indicated along the horizontal axis. The position of each point on the vertical axis corresponds to the p-value of association for that variant with AD, while the color scale for each point corresponds to the magnitude of that variant’s p-value for association with PABPC1 expression. Variants with congruous effects are plotted using a blue color scale, while variants with incongruous effects are plotted using a red color scale. The directionality of each triangle corresponds to the GWAS direction of effect, while the size of each triangle corresponds to the effect size for the eQTL data. The default genome-wide p-value significance threshold for the GWAS analysis, 5e-8, is depicted with a horizontal red line. B displays the genomic positions of all genes within AD. C depicts a heatmap of LD information of all PABPC1 eQTL variants, displayed in the same chromosomal space as panels A and B for ease of reference (R2min=0.1, LDmin = 10). D depicts the enrichment of PABPC1 eQTLs among GWAS-significant variants, while E and F depicts the correlation between P_GWAS and P_eQTL for PABPC1 and AD, with the computed Pearson correlation coefficient (r) and p-value (p) displayed on the plot. For E, the analysis is confined only to variants with congruous directions of effect, while for F the analysis includes only variants with incongruous directions of effect. A lead variant is indicated in both E and F, and both are also labeled in panel A.

Figure 5.. Brain cell-type-specific chromatin profiles by UCSC Genome Browser (hg19).

A. H3K27ac and ATAC-seq data for PABPC1, showing active enhancer regions and open chromatin specific to astrocytes, with a yellow vertical line marking the location of the associated disease variant and a dashed square showing the region of active enhancer.

Figure 6.. Potential drugs enrichment analysis and gene-drug interaction network.

A. Top 10 enriched drug/compounds based on DSigDB predictions. B. Interaction network illustrating connections between enriched drugs/compounds and target genes. Blue circles indicate druggable/non-druggable causal genes identified in this study, green circles represent druggable interacting genes linked to non-druggable causal genes, and pink nodes denote the top 10 enriched drugs/compounds.