Genetic and environmental influences on psychological distress in the population: General Health Questionnaire analyses in UK twins
Bibliographic record
Abstract
BACKGROUND: The General Health Questionnaire (GHQ) is the most popular screening instrument for detecting psychiatric disorders in community samples. Using longitudinal data of a large sample of UK twin pairs, we explored (i) heritabilities of the four scales and the total score; (ii) the genetic stability over time; and (iii) the existence of differential heritable influences at the high (ill) and low (healthy) tail of the distribution. METHOD: At baseline we assessed the GHQ in 627 MZ and 1323 DZ female pairs and at a second occasion (3.5 years later) for a small subsample (90 MZ and 270 DZ pairs). Liability threshold models and raw ordinal maximum likelihood were used to estimate twin correlations and to fit longitudinal genetic models. We estimated extreme group heritabilities of the GHQ distribution by using a model-fitting implementation of the DeFries-Fulker regression method for selected twin data. RESULTS: Heritabilities for Somatic Symptoms, Anxiety, Social Dysfunction, Depression and total score were 0.37, 0.40, 0.20, 0.42 and 0.44, respectively. The contribution of shared genetic factors to the correlations between time points is substantial for the total score (73%). Group heritabilities of 0.48 and 0.43 were estimated for the top and bottom 10% of the total GHQ score distribution, respectively. CONCLUSION: The overall heritability of the GHQ as a measure of psychosocial distress was substantial (44%), with all scales having significant additive genetic influences that persisted across time periods. Extreme group analyses suggest that the genetic control of resilience is as important as the genetic control of vulnerability.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".