Distinct glial functions are associated with Alzheimer's disease based on cell-type- and pathway-specific polygenic risk score analysis
Bibliographic record
Abstract
Background Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, amyloid plaques, neurofibrillary tangles, and neuroinflammation. Glial cells—astrocytes, microglia, and oligodendrocytes—play essential roles in AD progression, but their pathway-specific genetic contributions remain unclear. Objective To identify glial cell type-specific biological pathways associated with AD using pathway-based polygenic risk score (PRS) analysis. Methods We applied PRSet to evaluate associations between glial-specific pathways and AD in a discovery dataset (ADc1234ADA), adjusting for the top two principal components (Model 1), and additionally for sex, age, and APOE ε4 status (Model 2). Pathways with nominal significance ( p < 0.05) were further tested in an independent replication dataset (ADNI). Results from both datasets were meta-analyzed and assessed for statistical significance using Bonferroni correction. Competitive p -values were used to determine the relative contribution of each pathway within glial types. Genes from significant pathways were used in a follow-up gene-based PRS analysis, following the same modeling and validation steps. Results In Model 1, we identified four significant astrocytic, six microglial, and one oligodendrocyte pathways. In Model 2, five astrocytic and three oligodendrocytic pathways remained significant; no microglial pathways met significance. The top pathways were the immune system in astrocytes, antigen processing in microglia, and transport and trafficking in oligodendrocytes. At the gene level, BCL3 and BIN1 were significant in Model 1, while only BIN1 remained in Model 2. Conclusions These findings highlight distinct glia-specific genetic contributions to AD, particularly involving immune-related pathways, and demonstrate the value of cell-type-specific PRS approaches in AD research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".