SNP-Associated Differential Methylation in <i>ARHGEF38:</i> Insights into Genetic-Epigenetic Interactions
Bibliographic record
Abstract
Abstract Objective Associations have been seen between suicidal behavior and differential DNA methylation of certain genes, with one study showing significant hypomethylation of ARHGEF38 in postmortem brain samples from individuals with bipolar disorder who died by suicide. Our objective was to explore ARHGEF38 methylation in individuals with bipolar disorder and a history of suicide attempt. Method With pyrosequencing, we looked at the previously identified region of interest in ARHGEF38. We investigated the methylation levels of 3 CpG sites in 47 individuals with bipolar disorder and a history of suicide attempt, 47 individuals with bipolar disorder without a history of suicide attempt, and 47 non-bipolar disorder controls. Results None of the CpG sites measured had an association between groups, although there were distinct clusters of differential methylation in each group. Applying genotypes of SNPs found in the region of interest, rs2121558 and rs1447093, these clusters showed stepwise methylation at each CpG site, regardless of phenotype. Conclusions In this relatively small sample size study, differential methylation in ARHGEF38 was not associated with history of suicide attempt, failing to replicate findings from a related outcome, suicide death. However, we did provide evidence of SNP and DNA methylation interplay in this region. This highlights the potential relevance of considering genetics when interrogating epigenetic mechanisms. Highlights ARHGEF38 methylation is not associated with bipolar disorder and suicide attempt Methylation of ARHGEF38 is heavily influenced by the presence of SNPs Suicide phenotype, genetics, and sample type impact DNA methylation
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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 | 0.001 |
| 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) | 0.003 | 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".