Etiology of obsessive-compulsive symptoms and obsessive-compulsive personality traits: common genes, mostly different environments
Bibliographic record
Abstract
BACKGROUND: Little is known about the etiologic relationship between obsessive-compulsive (OC) symptoms and traits of OC personality disorder. The traits include perfectionism and rigidity. Some theorists have proposed that OC personality disorder is one of several disorders falling within an OC spectrum. This implies that OC personality traits and symptoms should have etiologic factors in common, and this should not be simply because symptoms and traits are both shaped by nonspecific etiological influences, such as those shaping negative emotionality (neuroticism). METHODS: To investigate these issues, a community sample of 307 pairs of monozygotic and dizygotic adult twins provided scores on six types of OC-related symptoms, two markers of negative emotionality, and a measure of OC personality traits. RESULTS: Analyses indicated that symptoms and traits arose from a combination of genetic and nonshared environmental factors. A matrix of genetic correlations was computed among the variables, which represented the correlations between the genetic components of pairs of variables. A matrix of environmental correlations was similarly computed. Each matrix was factor analyzed. One genetic factor was obtained, indicating that all variables were influenced by a common genetic factor. Three environmental factors were obtained, with salient loadings on either (a) all six OC symptoms, (b) negative emotionality and obsessing, or (c) OC personality traits and ordering. CONCLUSIONS: OC symptoms and traits were etiologically related primarily because they are shaped by the same nonspecific genetic factor that influenced negative emotionality. Implications for the concept of the OC spectrum are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".