Diagnostic yield of genome sequencing for prenatal diagnosis of fetal structural anomalies
Bibliographic record
Abstract
OBJECTIVE: Genome sequencing (GS >30x) is beginning to be adopted as a comprehensive genome-wide test for the diagnosis of rare disease in the post-natal setting. Recent studies demonstrated the utility of exome sequencing (ES) in prenatal diagnosis, we investigate the potential benefits for GS to act as a comprehensive prenatal test for diagnosis of fetal abnormalities. METHODS: We performed GS on a prospective cohort of 37 singleton fetuses with ultrasound-identified structural abnormalities undergoing invasive prenatal testing. GS was performed in parallel with standard diagnostic testing, and the prioritized variants were classified according to ACMG guidelines and reviewed by a panel of board-certified laboratory and clinical geneticists. RESULTS: Diagnostic sequence variants were identified in 5 fetuses (14%), with pathogenic variants found in NIPBL, FOXF1, RERE, AMMECR1, and FLT4. A further 7 fetuses (19%) had variants of uncertain significance (VUS) that may explain the phenotypes. Importantly, GS also identified all pathogenic variants reported by clinical microarray (2 CNVs, 5%). CONCLUSION: Prenatal GS offered diagnoses (sequence variants and CNVs) in 19% of fetuses with structural anomalies. GS has the potential of replacing multiple consecutive tests, including microarray, gene panels, and WES, to provide the most comprehensive analysis in a timely manner necessary for prenatal diagnosis.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".