Brain region and cell type-specific DNA methylation profiles in association with ADHD
Bibliographic record
Abstract
Previous studies identified DNA methylation (DNAm) associations with ADHD in peripheral tissue and the brain. Given that DNAm is highly cell type-specific, it is crucial to understand which cell types are driving the DNAm differences observed in ADHD. Here, we report the first brain cell type-specific epigenome-wide association study (EWAS) for ADHD (25 individuals with ADHD, 33 individuals without ADHD) in postmortem anterior cingulate cortex (ACC) and caudate nucleus (CN) based on epigenomic deconvolution. We identified distinct cell type-specific DNAm patterns in both brain regions. On the single site level, we identified significant associations with ADHD in microglia. We identified that in the ACC most differentially methylated regions (DMRs) differences were driven by glutamatergic neurons, whereas differences in the CN were mainly driven by GABAergic neurons. Enrichment of DMRs implicated genes involved in brain development, both in bulk and on the cell type-specific level. Genome-wide DNAm differences in microglia and GABAergic neurons were enriched in genetic risk variants for ADHD. Lastly, the results of EWAS were dependent on cell type reference panels used in statistical analyses. Altogether, these results could provide new insights into the molecular mechanisms underlying ADHD and considerations for EWAS in brain tissue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".