Targeted exome sequencing strategy (<scp>NeoEXOME</scp>) for Chinese newborns using a pilot study with 3423 neonates
Bibliographic record
Abstract
BACKGROUND: Newborn screening (NBS) aims to detect congenital anomalies, and next-generation sequencing (NGS) has shown promise in this aspect. However, the NBS strategy for monogenic inherited diseases in China remains insufficient. METHODS: We developed a NeoEXOME panel comprising 601 genes that are relevant to the Chinese population found through extensive research on available databases. An interpretation system to grade the results into positive (high-risk, moderate-risk, and low-risk genotypes), negative, and carrier according to the American College of Medical Genetics (ACMG) guidelines was also developed. We validated the panel to evaluate its efficacy by using data from the "1000 Genomes Project" and conducted a pilot multicenter study involving 3423 neonates. RESULTS: The NGS positive rate in the 1000 Genomes Project was 7.6% (23/301), whereas the rate was 12.0% in the multicenter study, including 3249 recruited neonates. Notably, in 200 neonates, positive per conventional NBS, 58.5% (69/118) showed results consistent with NGS. In the remaining 3049 neonates showing negative results in conventional NBS, 271 (8.9%) were positive per NGS, and nine of them were clinically diagnosed with diseases in the follow-up. CONCLUSION: We successfully designed a NeoEXOME panel for targeted sequencing of monogenic inherited diseases in NBS. The panel demonstrated high performance in the Chinese population, particularly for the early detection of diseases with no biochemical markers.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".