A Parkinson’s disease genetic risk score associates with blood DNAm on chromosome 17
Bibliographic record
Abstract
Abstract Although Parkinson’s disease (PD) coincides with altered immune functioning, there are few reproducible associations between blood DNA methylation (DNAm) and PD case-control status. Integrative analyses of genotype and blood DNAm can address this gap and can help us characterize the biological function of PD genetic risk loci. First, we tested for associations between a PD genetic risk score (GRS) and DNAm. Our GRS included 36 independent genome-wide significant variants from the largest GWAS of PD to date. Our discovery sample was TERRE, consisting of French agricultural workers (71 PD cases and 147 controls). The GRS associated with DNAm at 85 CpG sites, with 19 associations replicated in an independent sample (DIG-PD). The majority of CpG sites (73) are within a 1.5 Mb window on chromosome 17, and 36 CpG sites annotate to MAPT and KANSL1 , neighboring genes that affect neurodegeneration. All associations were invariant to non-genetic factors, including exposure to commercial-grade pesticides, and omitting chromosome 17 variants from the GRS had little effect on association. Second, we compared our findings to the relationship between individual PD risk loci and blood DNAm using blood mQTL from a large independent meta-analysis (GoDMC). We found 79 CpG sites that colocalized with PD loci, and via summary Mendelian randomization analysis, we show 25/79 CpG sites where DNAm causally affects PD risk. The nine largest causal effects are within chromosome 17, including an effect within MAPT . Thus, all integrative analyses prioritized DNAm on chromosome 17, drawing from multiple independent data sets, meriting further study of this region.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".