Return of incidental findings and individual results to participants in the context of research conducted by direct-to-consumer genetic testing companies
Bibliographic record
Abstract
Direct-to-consumer genetic testing (DTC-GT) companies have created some of the largest genomic data repositories in the world by storing the genetic and phenotypic information they collect from customers.As many of these companies have ventured into research endeavors, this model has elicited numerous ethical concerns related to informed consent, privacy, and commercialization.In this study, I focused on a largely unexamined issue: the return of incidental findings and individual research results.As it is increasingly common to discover medically or personally significant information about participants during genomic research, investigators are encouraged to address this possibility when recruiting participants.To examine how this is being managed in the DTC-GT context, I analyzed the research consent forms and policies of 26 web-based companies offering genetic testing services to Canadian consumers and involved in research.This thematic analysis was informed by Canadian and international guidance for the return of findings in genomic research.Most firms indicated that they retain personal identifiers and contact information, suggesting that the disclosure of findings is feasible.However, less than a third of companies discussed the issue of potential findings in their consent documents and even fewer articulated their position on whether these results would be returned to participants.They also omitted important details regarding the return strategy, including the timing and method of communication and any criteria such as validity and actionability.To address the shortcomings identified in the analysis of DTC-GT research policies, I propose several points to consider informed by existing normative guidance and the scholarly literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.339 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.026 | 0.069 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".