The omics of our lives: practices and policies of direct-to-consumer epigenetic and microbiomic testing companies
Bibliographic record
Abstract
While much attention has gone towards ethical, legal, and social implications of direct-to-consumer genetic testing over the past decades, the rise of new forms of consumer omics has largely escaped scrutiny. In this paper, we analyze the product descriptions, promotional messages, terms of service, and privacy policies of five epigenetic and seven microbiomic testing companies. The advent of such tests online represents a significant shift in consumer omics, from a focus on inherited molecules with genetic tests, to broader interest for information about the lives of individuals, such as chronological and biological age, exposures, and lifestyle. Building on previous literature about direct-to-consumer genetic testing, and taking this shift into account, we identify limitations, gaps and inconsistencies in current practices and policies of the new companies. Best practice standards and regulations applicable across different omic sample and data types is a necessary first step in the promotion of responsible consumer omics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".