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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".