Admixture mapping: from paradigms of race and ethnicity to population history
Bibliographic record
Abstract
Admixture mapping is a whole genome association strategy that takes advantage of population history-or genetic ancestry-to map genes for complex diseases. However, because it uses racial/ethnic groupings to examine differential disease risk, admixture mapping raises ethical and social concerns. While there has been much theoretical commentary regarding the ethical and social implications of population-based genetic research, empirical data from stakeholders most closely involved with these studies is limited. One of the first admixture mapping studies carried out was a scan for Multiple Sclerosis (MS) risk factors in an African-American population. Applying qualitative research methods, we used this example to explore developing views, experiences and perceptions of the ethical and social implications of admixture mapping and other population-based research-their value, risks and benefits, and the future prospects of the field. Additionally, we sought to understand how social and ethical risks might be mitigated, and the benefits of this research optimized. We draw on in-depth, one-on-one interviews with leading population geneticists, genome scientists, bioethicists, and African-Americans with MS. Here we present our findings from this unique group of key informants and stakeholders.
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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 | 0.028 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.063 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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; 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".