ISDN2014_0418: DNA methylation changes in fetal alcohol spectrum disorder
Bibliographic record
Abstract
Prenatal alcohol exposure is a major, preventable cause of behavioural and cognitive deficits in children. Despite extensive research, a unique neurobehavioural profile for children affected by prenatal alcohol exposure remains elusive. The NeuroDevNet Fetal Alcohol Spectrum Disorder (FASD) project is investigating how genetic and environmental factors interact with gestational alcohol exposure to produce neurobehavioural and neurobiological deficits in children. To investigate differences in DNA methylation between FASD cases and controls and gain understanding in the molecular mechanisms underlying brain function. The epigenetics cohort included 214 children from 5 to 18 years of age (112 FASD:102 Control). DNA methylation was assessed using the Illumina HumanMethylation450 array on DNA extracted from buccal swabs. Each child also completed an extensive battery of psychometric tests, neuroimaging, and novel eye tracking assessments. We identified 1661 differentially methylated probes. After correcting for confounding effects of genetic background we were left with 658 significantly differentially methylated probes that were further investigated for biological significance. Five of these probes were selected for verification with pyrosequencing and confirmed our results. Enrichment analyses using the genes up-methylated in FASD show a significant enrichment in genes associated with neurodevelopmental processes and diseases. This finding could reflect the fact that these genes are generally involved in brain development and affected in FASD cases.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".