ArticleJournal of translational medicine2025
Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer's disease.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging.Cell genomics · 2026Review
- Targeting the astrocyte-microglia EFEMP1-GALNT10 axis: a spatially programmable therapeutic strategy for hippocampal vulnerability in Alzheimer's disease.Journal of translational medicine · 2026Article
- Investigating the causal relationship between diabetes and shoulder periarthritis: A 2-sample Mendelian randomization study.Medicine · 2026Article
- Distinct Regulatory Genomic Architectures Distinguish Early-Onset from Late-Onset Alzheimer's Disease.Genes · 2026Article
- Single-Cell eQTL Revealing Brain Cell-Type-Specific Genetic Control of Insomnia.Journal of molecular neuroscience : MN · 2026Article
- Insomnia Increases Susceptibility to Sciatica: Evidence from Mendelian Randomization and Integrative Genetic Analyses.Journal of pain research · 2026Article
- The evolution of Alzheimer's target identification: Towards a fusion of artificial and cellular intelligence.The journal of prevention of Alzheimer's disease · 2025Article
- Integration of genetic, proteomic, and transcriptomic data identifies therapeutic targets and prognostic biomarkers in bladder cancer.Translational andrology and urology · 2025Article
- Multi-omics profiling uncovers paradoxical Epstein-Barr virus involvement in autoimmune liver disease pathogenesis.AMB Express · 2025Article
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Abstract
backgroundAnalyzing 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.
methodsWe 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.
resultsWe 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.
conclusionsWe 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.
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