Cell-type-specific methylome-wide association studies implicate neurodegenerative processes and neuroimmune communication in major depressive disorder
Bibliographic record
Abstract
We studied the methylome in three collections of human postmortem brain (N=206) and blood samples (N=1,132) of subjects with major depressive disorder (MDD) and controls. Using an epigenomic deconvolution approach we performed cell-type-specific methylome-wide association studies (MWAS) within sub-populations of neurons/glia and granulocytes/T-cells/B-cells/monocytes for bulk brain and blood data, respectively. Multiple MWAS findings in neurons/glia replicated across brain collections (ORs=509-538, P-values<1×10 −5 ) and were reproducible in an array-based MWAS of sorted neurons/glia from a fourth brain collection (N=58). Pathway analyses implicated p75 NTR /VEGF signaling, neurodegeneration, and blood-brain barrier perturbation. Cell-type-specific analysis in blood identified associations in CD14+ monocytes -- a cell type strongly linked to neuroimmune processes and stress. Top results in neurons/glia/bulk and monocytes were enriched for genes supported by GWAS for MDD (ORs=2.02-2.87, P-values=0.003 to <1×10 −5 ), neurodegeneration and other psychiatric disorders. In summary, we identified novel MDD-methylation associations by using epigenomic deconvolution that provided important mechanistic insights for the disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".