Fetal Gene Regulatory Gene Deletions are Associated with Poor Cognition in Schizophrenia and Community-Based Samples
Bibliographic record
Abstract
ABSTRACT Objective Schizophrenia is a neurodevelopmental disorder involving clinical and genetic heterogeneity. Multiple recurrent copy number variants (CNVs) increase risk for schizophrenia spectrum disorders (SSD). However, how known risk CNVs and broader genome-wide CNVs influence clinical variability is unclear. Furthermore, whether biological annotation of CNV scores can improve power for patient stratification is unknown. Methods This study examined associations between severe phenotypes in 617 SSD individuals, namely, child-onset psychosis or borderline intellectual functioning (IQ), and: 1) known risk CNVs; 2) genome-wide deletion burden scores; and 3) novel scores capturing deletion burden in 18 previously validated and mutually exclusive gene-sets, representing distinct aspects of neurodevelopment. Associations with borderline IQ were assessed for replicability in 233 SSD-relatives and 581 controls, and 9,930 youth from the Adolescent Brain Cognitive Development (ABCD) Study. Results Known SSD- (odds ratios (OR)=7.07, 95%CI[1.60,31.32]) and neurodevelopmental disorder (NDD)-risk CNVs (OR=4.56, 95%CI[1.48,14.10]) were associated with borderline IQ in SSD. Furthermore, beyond effects of known NDD-risk CNVs, deletion of genes involved in regulating gene expression during fetal brain development was associated with borderline IQ across SSD cases and non-cases (OR=2.57, 95%CI[1.44,4.60]), and in the ABCD cohort (OR=1.33, 95%CI[1.00,1.76]). Exploratory structural MRI-based analyses showed associations between fetal gene regulatory gene deletions and altered gray matter volume ( b =0.09, 95%CI[0.004,0.17]) and cortical thickness ( b =0.14, 95%CI[0.05,0.24]) across SSD cases and non-cases. Conclusions Results confirm contributions of known risk CNVs to severe phenotypes in SSD, implicate disrupted fetal brain development in poor cognition, and demonstrate the utility of a neurodevelopmental framework for identifying mechanisms underlying severe SSD-relevant phenotypes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".