Iris pigmentation as a quantitative trait: variation in populations of European, East Asian and South Asian ancestry and association with candidate gene polymorphisms
Bibliographic record
Abstract
In this study, we present a new quantitative method to measure iris colour based on high-resolution photographs. We applied this method to analyse iris colour variation in a sample of individuals of East Asian, European and South Asian ancestry. We show that measuring iris colour using the coordinates of the CIELAB colour space uncovers a significant amount of variation that is not captured using conventional categorical classifications, such as 'brown', 'blue' or 'green'. We tested the association of a selected panel of polymorphisms with iris colour in each population group. Six markers showed significant associations with iris colour in the European sample, three in the South Asian sample and two in the East Asian sample. We also observed that the marker HERC2 rs12913832, which is the main determinant of 'blue' versus 'brown' iris colour in European populations, is also significantly associated with central heterochromia in the European sample.
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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 | 0.001 | 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".