A comparative GWAS of eye colour in light and dark eye genetic backgrounds defined by <i>HERC2</i> rs12913832 polymorphism
Bibliographic record
Abstract
Abstract rs12913832, a polymorphism located in an enhancer within the HERC2 gene, which is known to regulate OCA2 transcription, is heavily relied upon as a predictor of light versus dark eyes. Individuals with the GG genotype are projected to have blue eyes, while individuals with the AA or AG genotypes are projected to have darker eye colours (primarily brown). However, eye colour is a polygenic trait, and previous studies have revealed that a significant proportion of individuals self-report an eye colour that is not concordant with their genotype at rs12913832. Herein, we address the question: What common markers are influencing eye colour in individual’s whose self-reported phenotype does not correspond to the expected phenotype based on their rs12913832 genotype? Building upon our prior investigation of iris pigmentation genetics in individuals with an expected “blue eye” background (rs12913832:GG genotype) in a sample of the Canadian Partnership for Tomorrow’s Health (CanPath) cohort, this study extends the analysis to include individuals with an expected “brown eye” background (rs12913832:AA+AG). We identified variants in SLC45A2 , TYRP1 , TYR , SLC24A4 and TSPAN10 , which may influence eye colour presentation in individuals with the rs12913832:GG genotype and variants in IRF4 , TYRP1 and OCA2 , which may influence eye colour presentation in individuals with the rs12913832:AA+AG genotype. These markers include well-known pigmentation-associated single nucleotide polymorphisms, such as rs16891982 ( SLC45A2 ), rs1126809 ( TYR ), rs12203592 ( IRF4 ), rs1800407 ( OCA2 ) and rs6420484 ( TSPAN10 ). Several of these loci were replicated using independent quantitative eye colour measures, including heterochromia and CIELAB colour dimensions. This research highlights the importance of gene-gene interactions and the polygenic nature of pigmentation traits, emphasizing modifying effects that can sometimes counteract the dominant influence of rs12913832, contributing to advancements in pigmentation genetics and forensic applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".