Uncovering obsessive-compulsive disorder risk genes in a pediatric cohort by high-resolution analysis of copy number variation
Bibliographic record
Abstract
Obsessive-compulsive disorder (OCD) is a heterogeneous neuropsychiatric condition, thought to have a significant genetic component. When onset occurs in childhood, affected individuals generally exhibit different characteristics from adult-onset OCD, including higher prevalence in males and increased heritability. Since neuropsychiatric conditions are associated with copy number variations (CNVs), we considered their potential role in the etiology of OCD. We genotyped 307 unrelated pediatric probands with idiopathic OCD (including 174 that were part of complete parent-child trios) and compared their genotypes with those of 3861 population controls, to identify rare CNVs (<0.5 % frequency) of at least 15 kb in size that might contribute to OCD. We uncovered de novo CNVs in 4/174 probands (2.3 %). Our case cohort was enriched for CNVs in genes that encode targets of the fragile X mental retardation protein (nominal p = 1.85 × 10 −03 ; FDR=0.09), similar to previous findings in autism and schizophrenia. These results also identified deletions or duplications of exons in genes involved in neuronal migration ( ASTN2 ), synapse formation ( NLGN1 and PTPRD ), and postsynaptic scaffolding ( DLGAP1 and DLGAP2 ), which may be relevant to the pathogenesis of OCD. Four cases had CNVs involving known genomic disorder loci (1q21.1-21.2, 15q11.2-q13.1, 16p13.11, and 17p12). Further, we identified BTBD9 as a candidate gene for OCD. We also sequenced exomes of ten “CNV positive” trios and identified in one an additional plausibly relevant mutation: a 13 bp exonic deletion in DRD4 . Our findings suggest that rare CNVs may contribute to the etiology of OCD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".