Genetics of Obsessive-compulsive Disorder: From Phenotypes to Pharmacogenetics
Bibliographic record
Abstract
Background: Obsessive-compulsive disorder (OCD) is a debilitating neuropsychiatric disorder that is characterized by a diverse clinical presentation. Evidence suggests genetic involvement in the etiology of OCD; however, genetic association studies have yielded mixed results partly due to clinical heterogeneity. Aims: We therefore investigated the genetics of OCD subphenotypes: age at onset (AAO), Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) severity score and symptom dimensions, family history of obsessive-compulsive and related disorders (OCRDs), psychiatric comorbidities, and drug response. We first examined these subphenotypes to ascertain clinically homogeneous dimensions of OCD. We then analyzed these subphenotypes and antidepressant response for genetic association to identify marker(s) for each subphenotype. Methods: The sample consists of 560 OCD individuals. For the subphenotypic analyses, admixture analysis (STATA) was performed to analyze AAO and factor analysis (SPSS) was applied to reduce the Y-BOCS symptom checklist. Family history and psychiatric comorbidity were obtained using the modified Family History Index and SCID-IV interviews respectively. The candidate gene study investigated markers, mostly in the remote regulatory regions, of 17 candidates for association with subphenotypes in 497 OCD participants. Genome-wide association study was conducted in a subset of this sample to examine AAO and Y-BOCS severity. Retrospective antidepressant response data was available in 222 OCD individuals. Statistical analyses were performed using SPSS, PLINK, and R programs, comparing genotype frequencies between different subphenotype groups. Results: We identified early (
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; both teacher heads agree on what is shown here.
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".