Influence of Parent-of-Origin on Intellectual Outcomes in the Chromosome 22q11.2 Deletion Syndrome
Bibliographic record
Abstract
Learning and intellectual disabilities are hallmark features of 22q11.2 deletion syndrome. Data are limited, however, regarding influences on full-scale IQ (FSIQ). Here, we investigated possible 22q11.2 deletion parent-of-origin effects. In 535 individuals, we compared FSIQ (≥50), 481 with de novo and 54 with inherited 22q11.2 deletions. In the subsets with data available, we examined parent-of-origin effects on FSIQ. We used linear regression models to account for covariates. Median FSIQ was significantly higher in de novo vs. inherited deletions (77; range 50−116 vs. 67; range 50−96, p < 0.0001). Results remained significant using a regression model accounting for age at IQ testing, sex and cohort site. No significant parent-of-origin differences in FSIQ were observed for de novo deletions (n = 81, 63.0% maternal; p = 0.6882). However, median FSIQ was significantly lower in maternally than in paternally inherited familial deletions (65, range 50−86 vs. 71.5, range 58−96, respectively, p = 0.0350), with the regression model indicating an ~8 point decrement in FSIQ for this variable (p = 0.0061). FSIQ is higher on average in de novo than in inherited 22q11.2 deletions, regardless of parental origin. However, parent-of-origin appears relevant in inherited deletions. The results have potential clinical implications with further research needed to delineate possible actionable mechanisms.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| 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".