Multi-omic derived cell-type specific Alzheimer disease polygenic risk scores
Bibliographic record
Abstract
Alzheimer disease (AD) polygenic risk scores (ADPRS) built from cell-type (ct) specific genetic variants can be used to infer cell-type contributions to AD. We derived two ct-ADPRSs using variants near single-nuclei RNA-seq (snRNA) derived cell-type specific genes or on single-nuclei ATAC-seq (snATAC) derived cell-type specific accessible chromatin regions. We generated a multi-omic ct-ADPRS for eight neuron subtypes using both single-nuclei datasets. SnATAC-derived ct-ADPRSs demonstrated considerably lower correlations among cell types (average r=0.071) than snRNA-derived ct-ADPRSs (average r=0.19), indicating their heightened cell-type specificity. The association of these ct-ADPRSs with AD endophenotypes was evaluated using logistic and linear regression models. Tau tangle burden was associated with astrocyte (AST) ct-ADPRS derived from snATAC (β=0.82, FDR=0.0013) and snRNA (β=0.60, FDR=0.045) as well as microglia (MIC) ct-ADPRS from both (snATAC: β=0.75, FDR=0.0047) (snRNA: β=0.63, FDR=0.028). AST ct-ADPRS was significantly associated with Mini-Mental State Examination score only when derived from snATAC data (β=-0.82, FDR=0.011). SST expressing GABAergic neuron ADPRS was strongly associated ct-ADPRS with neuritic plaque burden (β=0.087, FDR=0.0014) and the only neuron subtype ct-ADPRS significantly associated with AD endophenotypes. We investigated 1,954 SNPs contributing to this ct-ADPRS and found the strongest association with variants upstream of the neuropeptide Y gene, NPY , particularly rs3940268 (β=-0.13, P=8.2x10 -5 ). This association is significant even after adjusting for diffuse plaque (β=-0.12, P=1.5x10 -4 ) or neurofibrillary tangle burden (β=-0.08, P=3.9x10 -3 ). NPY was expressed in a small subset of neurons, and these findings suggest its strong impact on the association of SST+ GABAergic neurons with early AD pathology.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".