Linking DNA methylation in brain regions to Alzheimer’s disease risk: a Mendelian randomization study
Bibliographic record
Abstract
AIM: DNA methylation in brain regions represents a potential mechanism linking genetic variation to Alzheimer's disease (ad) risk, yet most studies have focused on blood-derived methylation markers. In this study, we conducted a systematic Mendelian randomization (MR) study to evaluate associations between predicted brain region-specific DNA methylation levels and ad risk, using methylation quantitative trait loci (mQTL) as genetic instruments. METHODS: We analyzed mQTLs from five human brain regions: cerebellum (CRBLM), frontal cortex (FCTX), causal pons (PONS), and temporal cortex (TCTX) from 600 individuals in Gibbs et al's study, as well as mQTLs from dorsolateral prefrontal cortex (DLPFC) of 543 participants in the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP). In our MR analyses, we integrated these mQTLs with single nucleotide polymorphisms (SNP)-ad risk summary statistics derived from 85 934 ad-related cases and 401 577 normal controls. RESULTS: Among 62 554 cytosine-guanine dinucleotide (CpG) sites, we identified 597 CpG sites (CpGs) significantly associated with ad risk (false discovery rate (FDR) < 0.05). Of these, 289 were confirmed through colocalization and summary-based MR (SMR) analyses, including one CpG site in CRBLM, 285 in DLPFC, one in FCTX, two in PONS, and one in TCTX. By integrating gene expression data, we identified 19 CpG sites with consistent associations across methylation levels, expression of eight target genes, and ad risk, including novel regulatory mechanisms involving RITA1's modulation of cg11558705 and PCGF3's regulation of cg10009224. CONCLUSION: Our findings highlight brain region-specific DNA methylation as a mediator of genetic risk for ad, offering insights into ad pathogenesis and identifying potential therapeutic targets.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".