DRD2, DAT1, COMT, and OXTR Genes as Potential Moderators of the Relationship Between Maternal History of Maltreatment and Infant Emotion Regulation
Bibliographic record
Abstract
Background: Gene-environment (GXE) interaction models have demonstrated that DRD2, DAT1, COMT, and OXTR SNPs moderate parental factors (i.e., maternal depression, parenting) to predict outcomes related to emotion regulation (e.g., affective problems, attentional control). No studies have investigated the connections between maternal maltreatment history and infant dopamine and oxytocin gene variants as they relate to infant emotion regulation. The current study addresses these gaps, evaluating the interaction of selected genes as they interact with maternal history of maltreatment to predict infant emotion regulation. Method: I investigated five infant genotypes (DRD2, DAT1, COMT, OXTR rs53576, and OXTR rs2254298) as they interacted with maternal history of self-reported maltreatment to predict observed infant emotion regulation behaviours. Self-reported maternal depressive symptoms were covaried. Infant emotion regulation was observed in the context of a potent stressor. I assessed three potential models of interaction, diathesis-stress, differential sensitivity, or vantage sensitivity. Results: Analyses demonstrated that, over and above maternal depressive symptoms, DRD2 and COMT significantly interacted with self-reported maternal maltreatment scores in a ‘vantage sensitivity’ model and DAT1 significantly interacted with maternal maltreatment history in a ‘diathesis-stress’ model. A cumulative vantage sensitivity (CVS) index significantly interacted with maternal maltreatment history to predict emotion regulation scores, consistent with a vantage sensitivity model. Conclusions: Findings indicated that infants with the “vantage” DRD2 (A1+) and COMT (Met) alleles, when exposed to mothers with lesser histories of maltreatment, fair better in terms of regulation than their non-susceptible counterparts. Infants with the “risk” DAT1 (presence of the 9-repeat) allele, when exposed to a parent with a greater history of maltreatment, tended to fare worse in terms of regulation behaviours. These differences in genetic interaction models suggest that an adaptive variation in genetic vulnerability and vantage-sensitivity, across an infant’s genome, can increase the possibility for optimal self-regulation outcomes, whether the environment is favourable or less favourable (i.e., lower versus higher history of maternal maltreatment, respectively).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".