Neonatal DNA methylation and early-onset conduct problems: A genome-wide, prospective study
Bibliographic record
Abstract
Early-onset conduct problems (CP) are a key predictor of adult criminality and poor mental health. While previous studies suggest that both genetic and environmental risks play an important role in the development of early-onset CP, little is known about potential biological processes underlying these associations. In this study, we examined prospective associations between DNA methylation (cord blood at birth) and trajectories of CP (4-13 years), using data drawn from the Avon Longitudinal Study of Parents and Children. Methylomic variation at seven loci across the genome (false discovery rate < 0.05) differentiated children who go on to develop early-onset (n = 174) versus low (n = 86) CP, including sites in the vicinity of the monoglyceride lipase (MGLL) gene (involved in endocannabinoid signaling and pain perception). Subthreshold associations in the vicinity of three candidate genes for CP (monoamine oxidase A [MAOA], brain-derived neurotrophic factor [BDNF], and FK506 binding protein 5 [FKBP5]) were also identified. Within the early-onset CP group, methylation levels of the identified sites did not distinguish children who will go on to persist versus desist in CP behavior over time. Overall, we found that several of the identified sites correlated with prenatal exposures, and none were linked to known genetic methylation quantitative trait loci. Findings contribute to a better understanding of epigenetic patterns associated with early-onset CP.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".