Precision of Fetal DNA Fraction Estimation by Quantitative Polymerase Chain Reaction Quantification of a Differently Methylated Target in Noninvasive Prenatal Testing
Bibliographic record
Abstract
BACKGROUND: The performance of noninvasive prenatal testing (NIPT) assays is critically determined by the proportion of fetal DNA or fetal fraction (FF). Fetomaternal differential methylation of certain genomic regions has been proposed as a universal marker of fetal origin, and previous reports have suggested the use of methylation-sensitive restriction enzyme (MSRE) assays to estimate FF. METHODS: We analyzed the performance of FF estimation using an MSRE assay with duplex quantitative polymerase chain reaction (qPCR). Mixtures of genomic DNA from placental cells and from adult women were digested with 2 MSRE and FF estimates obtained, for a total of 221 pairwise treatment/control comparisons. RESULTS: The coefficient of variance (CV) of the MSRE assays was high, ranging from 24% to 60%. An alternative in silico FF estimation algorithm, SeqFF, displayed slightly lower variability, with a CV of 22%. CONCLUSION: These results cast doubts on the usefulness of the MSRE-based assay of differentially methylated markers for FF estimation. The lack of a universal method capable of precisely estimating FF remains an incompletely solved issue.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".