Estimate of the contemporary live-birth prevalence of recurrent 22q11.2 deletions: a cross-sectional analysis from population-based newborn screening
Bibliographic record
Abstract
Background: Although pathogenic 22q11.2 deletions are an important cause of developmental delays and lifelong disease burden, their variable and complex clinical expression contributes to under-recognition, delayed molecular diagnosis and uncertainty about prevalence. We sought to estimate the contemporary live-birth prevalence of typical 22q11.2 deletions using a population-based newborn screening sample and to examine data available for associated clinical features. Methods: Using DNA available from an unbiased sample of about 12% of all dried blood spots collected for newborn screening in Ontario between January 2017 and September 2018, we prospectively screened for 22q11.2 deletions using multiplex quantitative polymerase chain reaction assays and conducted independent confirmatory studies. We used cross-sectional analyses to compare available clinical and T-cell receptor excision circle (TREC, used in newborn screening for severe combined immunodeficiency) data between samples with and without 22q11.2 deletions. Results: The estimated minimum prevalence of 22q11.2 deletions was 1 in 2148 (4.7 per 10 000) live births (95% confidence interval [CI] 2.5 to 7.8 per 10 000), based on a total of 30 074 samples screened, with 14 having confirmed 22q11.2 deletions. Of term singletons, samples with 22q11.2 deletions had significantly younger median maternal age (25.5 v. 32.0 yr, difference −6.5 yr, 95% CI −7 to −2 yr), a greater proportion with small birth weight for gestational age (odds ratio 7.00, 95% CI 2.36 to 23.18) and lower median TREC levels (108.9 v. 602.5 copies/3 μL, p < 0.001). Interpretation: These results indicate that the 22q11.2 deletion syndrome is one of the most common of rare genetic conditions and may be associated with relatively younger maternal ages and with prenatal growth abnormalities. The findings support the public health importance of early — prenatal and neonatal — diagnosis that would enable prompt screening for and management of well-known actionable features associated with 22q11.2 deletions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".