Validation of low‐pass genome sequencing for prenatal diagnosis
Bibliographic record
Abstract
OBJECTIVE: Chromosomal microarray (CMA), while considered the gold standard for detecting copy number variants (CNVs) in prenatal diagnostics, has its limitations, including the necessity to replace aging microarray equipment, low throughput, a static design, and an inefficient multi-day workflow. This study evaluates the feasibility of low-pass genome sequencing (LP-GS) as a potential replacement for CMA in prenatal diagnostics. METHODS: We comprehensively compared LP-GS at 10x and 5x average depths with CMA in a prenatal laboratory. We examined parameters, including concordance, sensitivity, specificity, workflow efficiency, and cost-effectiveness. RESULTS: We found a high degree of agreement between LP-GS and CMA for detecting CNVs and absence of heterozygosity. Furthermore, compared to CMA, LP-GS increased workflow efficiency and proved to be cost-neutral at 10x and cost-effective at 5x. CONCLUSION: Our study suggests that LP-GS is a promising alternative to CMA in prenatal diagnostics, offering advantages, including a more efficient workflow and scalability for larger testing volumes. Importantly, for clinical laboratories that have adopted next-generation sequencing in a separate capacity, LP-GS facilitates a unified NGS-centric approach, enabling workflow consolidation. By offering a single, streamlined platform for detecting a broad range of genetic variants, LP-GS may represent a critical step toward enhancing the diagnostic capabilities of prenatal laboratories.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".