Etiology of obsessions and compulsions: A behavioral-genetic analysis.
Bibliographic record
Abstract
It is unknown whether various types of obsessive-compulsive (OC) symptoms have a common genetic or environmental etiology. For example, it is unknown whether hoarding is etiologically associated with prototypic OC symptoms, such as washing, checking, and obsessing. Also unknown is whether particular OC-related symptoms are etiologically linked to the general tendency to experience emotional distress (negative emotionality). To investigate these and other issues, a community sample of 307 pairs of monozygotic and dizygotic adult twins provided scores on 6 OC-related symptoms (obsessing, neutralizing, checking, washing, ordering, and hoarding) and 2 markers of negative emotionality (trait anxiety and affective lability). Genetic factors accounted for 40%-56% of variance in the 8 phenotypic scores (M = 49% of variance for OC-related symptoms). Remaining variance was due to nonshared (person-specific) environment. More detailed analyses revealed a complex etiologic architecture, where OC-related symptoms arise from a mix of common and symptom-specific genetic and environmental factors. A general genetic factor was identified, which influenced all symptoms and negative emotionality. An environmental factor was identified that influenced all symptoms but did not influence negative emotionality. Each of the 6 types of symptoms was also shaped by its own set of symptom-specific genetic and environmental factors. The importance of genetic factors did not vary as a function of age or sex, and the architecture of general and specific etiologic factors was replicated for participants having relatively more severe OC symptoms. Gene-environment interactions were identified. Implications for an etiology-based classification system 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".