ArticleAlzheimer's research & therapy2025
Proteome-wide association studies using summary pQTL data of brain, CSF, and plasma identify 30 risk genes of Alzheimer's disease dementia.
Article in Alzheimer's research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Integrating dorsolateral prefrontal cortex multi-omics and GWAS summary data reveals genetic etiology of Parkinson's disease.Research square · 2026Article
- Integrating dorsolateral prefrontal cortex multi-omics and GWAS summary data reveals genetic etiology of Parkinson's disease.medRxiv : the preprint server for health sciences · 2026Article
- Integration of Brain Proteomes and Genome-Wide Association Data Identifies GLO1 as a Candidate Causal Gene and Therapeutic Target for Restless Legs Syndrome.International journal of molecular sciences · 2026Article
- Family-Based GWAS of Cognitive Endophenotypes Reveals Genetic Architecture of Memory and Executive Function in Alzheimer's Disease.Current issues in molecular biology · 2026Article
- Cell-type-aware transcriptome-wide association studies identify 91 independent risk genes for Alzheimer's disease dementia.Communications biology · 2026Article
- Multi-tissue transcriptome-wide association study identifies 29 risk genes associated with attention-deficit/hyperactivity disorder.medRxiv : the preprint server for health sciences · 2026Article
- Modulation of Network Plasticity Opens Novel Therapeutic Possibilities in Cancer, Diabetes, and Neurodegeneration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- The evolution of Alzheimer's target identification: Towards a fusion of artificial and cellular intelligence.The journal of prevention of Alzheimer's disease · 2025Article
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Abstract
backgroundA proteome-wide association study (PWAS) that integrates proteomic data with genome-wide association study (GWAS) summary data is a powerful tool for studying Alzheimer's disease (AD) dementia. Existing PWAS analyses of AD often rely on the availability of individual-level proteomic and genetic data of a reference panel. Leveraging summary protein quantitative trait loci (pQTL) reference data of multiple AD-relevant tissues is expected to improve PWAS findings of AD dementia.
methodsWe conducted PWAS by integrating publicly available summary pQTL data of three tissues including brain, cerebrospinal fluid (CSF), and plasma, with the latest GWAS summary data of AD dementia. For each target protein per tissue, we employed our recently published OTTERS tool to obtain omnibus PWAS p-value, testing whether the genetically regulated protein abundance in the corresponding tissue is associated with AD dementia. Protein-protein interactions and enriched pathways of identified significant PWAS risk genes were analyzed by STRING. The potential causal effects of these PWAS risk genes were assessed by probabilistic Mendelian Randomization analyses.
resultsWe identified 30 unique significant PWAS risk genes for AD dementia, including 11 for brain, 10 for CSF, and 16 for plasma tissues. Five of these were shared by at least two tissues, and gene MAPK3 was found in all three tissues. We found that 11 of these PWAS risk genes were associated with AD dementia or AD pathology traits in GWAS Catalog; 18 of these were detected by transcriptome-wide association studies (TWAS) in dorsolateral prefrontal cortex brain tissue; and 25 of these, including 8 out of 9 novel genes, were interconnected within a protein-protein interaction network involving the well-known AD risk gene APOE. These PWAS risk genes were enriched in immune response, glial cell proliferation, and high-density lipoprotein particle clearance pathways. Mediated causal effects were validated for 13 PWAS risk genes (43.3%).
conclusionsOur findings provide novel insights into the genetic mechanisms of AD dementia in brain, CSF, and plasma, and provide targets for developing new therapies. This study also demonstrates the effectiveness of integrating summary pQTL and GWAS data for mapping risk genes of complex human diseases.
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