Approaches to carrier testing and results disclosure in translational genomics research: The clinical sequencing exploratory research consortium experience
Bibliographic record
Abstract
BACKGROUND: Clinical genome and exome sequencing (CGES) is primarily used to address specific clinical concerns by detecting risk of future disease, clarifying diagnosis, or directing treatment. Additionally, CGES makes possible the disclosure of autosomal recessive and X-linked carrier results as additional secondary findings, and research about the impact of carrier results disclosure in this context is needed. METHODS: Representatives from 11 projects in the clinical sequencing exploratory research (CSER) consortium collected data from their projects using a structured survey. The survey focused on project characteristics, which variants were offered and/or disclosed to participants as carrier results, methods for carrier results disclosure, and project-specific outcomes. We recorded quantitative responses and report descriptive statistics with the aim of describing the variability in approaches to disclosing carrier results in translational genomics research projects. RESULTS: The proportion of participants with carrier results was related to the number of genes included, ranging from 3% (three genes) to 92% (4,600 genes). Between one and seven results were disclosed to those participants who received any positive result. Most projects offered participants choices about whether to receive some or all of the carrier results. There were a range of approaches to communicate results, and many projects used separate approaches for disclosing positive and negative results. CONCLUSION: Future translational genomics research projects will need to make decisions regarding whether and how to disclose carrier results. The CSER consortium experience identifies approaches that balance potential participant interest while limiting impact on project resources.
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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 | 0.245 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".