Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer's disease

Abstract

Background: Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) helps identify 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 a systematic analysis across multiple Alzheimer's disease (AD) genome-wide association study (GWAS) datasets or comparisons between tissue-level and cell type-specific effects remain limited. Here, we integrated brain cell type-level and bulk-level eQTL datasets with AD GWAS datasets to identify potential causal genes.

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

Results: We identified 28 candidate causal genes for AD, of which 12 were uniquely detected at the cell-type level, 9 were exclusive to the bulk level and 7 detected in both. Among the 19 cell-type level candidate causal genes, microglia contributed the highest number of candidate genes, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel candidate causal gene in astrocytes. We generated PPI networks for the candidate causal genes and found that pathways such as membrane organization, cell migration, and ERK1/2 and PI3K/AKT signaling were enriched. The AD-risk variant associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the 28 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-level and bulk level molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates interpretation of AD GWAS results.

Keywords: Alzheimer’s disease; Astrocytes; Causal genes; Cell type; Drug repurposing; GWAS; Gene expression; Genetic variant; SNP; eQTL.

Fig. 1

Study workflow. Created by BioRender

Fig. 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

Fig. 3

Candidate causal genes network analysis and pathway enrichment. A STRING PPI network of candidate causal genes. Nodes represent proteins, while edges illustrate the interactions between them. The shape of each node was used to represent the detection context of candidate genes: ellipses indicate genes detected only in cell type-level datasets, diamonds represent genes found only in bulk-level datasets, and rectangles denote genes shared between both cell type-level and bulk-level analyses. The thickness of the edges corresponds to the combined interaction score from STRING. ACE and SCIMP were detected in both excitatory neurons and inhibitory neurons, while ARL17B was detected in both microglia and OPCs. B Pathway enrichment of candidate causal (mRNA) genes based on the Gene Ontology (GO) biological process category. C Pathway enrichment of candidate causal (mRNA) genes based on Reactome pathways

Fig. 4

eQTpLot for colocalization between eQTLs for the gene PABPC1 and a GWAS signal for AD. The GWAS dataset is from Bellenguez et al. [4] and the cell type eQTL dataset of astrocyte is from Mathys et al. [20]. 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 A

Fig. 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

Fig. 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