Neurodevelopmental Trajectories and Psychiatric Morbidity: Lessons Learned From the 22q11.2 Deletion Syndrome
Bibliographic record
Abstract
PURPOSE OF REVIEW: The 22q11.2 deletion syndrome (22q11DS) is associated with a broad spectrum of neurodevelopmental phenotypes and is the strongest known single genetic risk factor for schizophrenia. Compared to other rare structural pathogenic genetic variants, 22q11DS is relatively common and one of the most extensively studied. This review provides a state-of-the-art overview of current insights regarding associated neurodevelopmental phenotypes and potential implications for 22q11DS and beyond. RECENT FINDINGS: We will first discuss recent findings with respect to neurodevelopmental phenotypic expression associated with 22q11DS, including psychotic disorders, intellectual functioning, autism spectrum disorders, as well as their interactions. Second, we will address considerations that are important in interpreting these data and propose potential implications for both the clinical care for and the empirical study of individuals with 22q11DS. Third, we will highlight variable penetrance and pleiotropy with respect to neurodevelopmental phenotypes in 22q11DS. We will discuss how these phenomena are consistently observed in the context of virtually all rare pathogenic variants and that they pose substantial challenges from both a clinical and a research perspective. We outline how 22q11DS could be viewed as a genetic model for studying neurodevelopmental phenotypes. In addition, we propose that 22q11DS research can help elucidate mechanisms underlying variable expression and pleiotropy of neurodevelopmental phenotypes, insights that are likely relevant for 22q11DS and beyond, including for individuals with other rare pathogenic genetic variants and for individuals with idiopathic neurodevelopmental conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".