Persistence and innovation effects in genetic and environmental factors in negative emotionality during infancy: A twin study
Bibliographic record
Abstract
BACKGROUND: Difficult temperament in infancy is a risk factor for forms of later internalizing and externalizing psychopathology, including depression and anxiety. A better understanding of the roots of difficult temperament requires assessment of its early development with a genetically informative design. The goal of this study was to estimate genetic and environmental contributions to individual differences in infant negative emotionality, their persistence over time and their influences on stability between 5 and 18 months of age. METHOD: Participants were 244 monozygotic and 394 dizygotic twin pairs (49.7% male) recruited from birth. Mothers rated their twins for negative emotionality at 5 and 18 months. Longitudinal analysis of stability and innovation between the two time points was performed in Mplus. RESULTS: There were substantial and similar heritability (approximately 31%) and shared environmental (57.3%) contributions to negative emotionality at both 5 and 18 months. The trait's interindividual stability across time was both genetically- and environmentally- mediated. Evidence of innovative effects (i.e., variance at 18 months independent from variance at 5 months) indicated that negative emotionality is developmentally dynamic and affected by persistent and new genetic and environmental factors at 18 months. CONCLUSIONS: In the first two years of life, ongoing genetic and environmental influences support temperamental negative emotionality but new genetic and environmental factors also indicate dynamic change of those factors across time. A better understanding of the source and timing of factors on temperament in early development, and role of sex, could improve efforts to prevent related psychopathology.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".