Genetics of environmental sensitivity and its association with variations in emotional problems, autistic traits, and wellbeing
Bibliographic record
Abstract
Greater environmental sensitivity has been associated with increased risk of mental health problems, especially in response to stressors, and lower levels of subjective wellbeing. Conversely, sensitivity also correlates with lower risk of emotional problems in the absence of adversity, and in response to positive environmental influences. Additionally, sensitivity has been found to correlate positively with autistic traits. Individual differences in environmental sensitivity are partly heritable, but it is unknown to what extent the aetiological factors underlying sensitivity overlap with those on emotional problems (anxiety and depressive symptoms), autistic traits and wellbeing. The current study used multivariate twin models and data on sensitivity, emotional problems, autistic traits, and several indices of psychological and subjective wellbeing, from over 2800 adolescent twins in England and Wales. We found that greater overall sensitivity correlated with greater emotional problems, autistic traits, and lower subjective wellbeing. A similar pattern of correlations was found for the Excitation and Sensory factors of sensitivity, but, in contrast, the Aesthetic factor was positively correlated with psychological wellbeing, though not with emotional problems nor autistic traits. The observed correlations were largely due to overlapping genetic influences. Importantly, genetic influences underlying sensitivity explained between 2 and 12% of the variations in emotional problems, autistic traits, and subjective wellbeing, independent of trait-specific or overlapping genetic influences. These findings encourage incorporating the genetics of environmental sensitivity in future genomic studies aiming to delineate the heterogeneity in emotional problems, autistic traits, and wellbeing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".