Guidelines for genetic ancestry inference created through roundtable discussions
Bibliographic record
Abstract
The use of genetic and genomic technology to infer ancestry is commonplace in a variety of contexts, particularly in biomedical research and for direct-to-consumer genetic testing. In 2013 and 2015, two roundtables engaged a diverse group of stakeholders toward the development of guidelines for inferring genetic ancestry in academia and industry. This report shares the stakeholder groups' work and provides an analysis of, commentary on, and views from the groundbreaking and sustained dialogue. We describe the engagement processes and the stakeholder groups' resulting statements and proposed guidelines. The guidelines focus on five key areas: application of genetic ancestry inference, assumptions and confidence/laboratory and statistical methods, terminology and population identifiers, impact on individuals and groups, and communication or translation of genetic ancestry inferences. We delineate the terms and limitations of the guidelines and discuss their critical role in advancing the development and implementation of best practices for inferring genetic ancestry and reporting the results. These efforts should inform both governmental regulation and self-regulation.
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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 | 0.001 | 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".