Detangling red hair from pain: phenotype-specific contributions from different genetic variants in melanocortin-1 receptor
Bibliographic record
Abstract
Genetic variation in melanocortin-1 receptor (MC1R) has a known role in red hair. Studies on responses to noxious stimuli in red-haired individuals have also been conducted, with mixed findings. To investigate a possible divergence between variants responsible for red hair and pain sensitivity, we performed a genewide association analysis in the Orofacial Pain: Prospective Evaluation and Risk Assessment cohort. All genotyped (17) MC1R variants were tested for association with heat pain temporal summation and sensitivity. Our analyses showed an association for pain sensitivity with the 5'-UTR, tagged by rs3212361, and 1 missense variant, rs885479 (R163Q), previously shown to be weakly associated with red hair. For both variants, the minor allele was protective. These results were validated in the 500,000-person UK Biobank cohort, where the minor alleles of rs3212361 and rs885479 were associated with a reduced count of persistent pain conditions as well as individual pain conditions. Haplotype association analysis revealed a possible joint effect from the 2 individual variants. The 5'-UTR variant rs3212361 was further identified as an expression quantitative trait locus, associated with reduced transcript levels of MC1R in the brain and in the peripheral tibial nerve. Hair colour association analysis of the loss-of-function 5'-UTR rs3212361 allele identified association with red hair, and red hair colour itself was associated with a reduced count of persistent pain conditions. Together, our results suggest that primarily different mechanisms-affecting expression levels vs protein activity-mediated by different genetic variants in the MC1R locus contribute to red hair and pain.
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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) | 0.001 | 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".