Non-invasive Prenatal Testing for Fetal Whole Genome Sequencing: An Interpretive Critical Review of the Ethical, Legal, Social, and Policy Implications
Bibliographic record
Abstract
Introduction: Non-invasive prenatal testing (NIPT) allows for genetic testing of a fetus through the analysis of cell-free DNA from the mother’s plasma. NIPT is easy and safe for the fetus, since it only requires a blood draw from the mother and therefore holds no risk of miscarriage. It is considered superior to other prenatal screening tests and can also be performed earlier in the pregnancy. NIPT has the future potential for fetal whole genome sequencing (FWGS) for an expanded range of conditions, such as late onset genetic conditions and carrier status. Objective: To review ethical, legal, social, and policy implications of the potential use of non-invasive prenatal testing for FWGS. Methods: This study is a critical interpretive literature review exploring and reporting ethical, legal, social, and policy implications of potential future implementation of NIPT for FWGS, which will be referred to as non-invasive prenatal whole genome sequencing (NIPW). Database and reference list searching was conducted between 2010 and 2019 for terms related to “non-invasive prenatal testing” AND “fetal whole genome sequencing” and derivatives. Results: Following screening, 32 articles were included. Data were grouped into four thematic categories: 1) ethical implications for the future child concerning autonomy and harms, as well as for prospective parents involving autonomy, informed consent concerns, and harms; 2) legal implications including privacy concerns; 3) social implications including changes in family dynamics, altered societal perceptions and disability concerns, justice and equity in accessing the test, and social pressure to use the test; and 4) policy implications including cost and funding concerns, limiting the scope of testing, as well as counseling, education, and support. Discussion: The discussion of results highlights several ethical, legal, social, and policy implications of NIPT use for FWGS. These findings have implications on NIPT implementation for FWGS including how the autonomy of the future child should be balanced with the autonomy of prospective parents, the scope of conditions that should or should not be tested for – and covered or not covered by the healthcare system – and the regulation of FWGS introduction, among others. Further research needs to be performed to address these concerns and hence guide the discussion about the clinical implementation of FWGS through NIPT.
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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 | 0.180 | 0.392 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.034 | 0.015 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| 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".