Study of a Polymorphic Variant of the TRPM8 Cold Receptor Gene (rs7593557) in 15 Populations of the Altai-Sayan Region and the Far East
Bibliographic record
Abstract
Abstract— The TRPM8 thermoreceptor gene (rs7593557) was analyzed in 15 population samples living in different regions of Northern Asia, the Altai-Sayan Highlands, and Canada. High frequencies of a rare genotype and allele in the populations of the Altai-Sayan region and Western Siberia were found among the Telengits (22.2 and 41.7% respectively) and were low among the Siberian Tatars (3.2 and 18.5% respectively), while among the peoples of northern Siberia and the Far East high frequency was found among the Nanais (22.2 and 42.6% respectively) and a low frequency among the Yakuts (1.1 and 10.9% respectively). No deviations from the Hardy–Weinberg equilibrium were found. Trans-association of TRPV1, TRPA1, and TRPM8 gene polymorphisms was carried out using the regression analysis method in 14 populations. Of all three pairs, only one, TRPV1/TRPM8, showed a positive correlation (0.55, df = 13, P-value, 0.032). In TRPV1/TRPA and TRPA1/TRPM8 pairs a negative correlation was revealed (–0.545, df = 13, P-value, 0.048) and (–0.46, df = 13, P-value, 0.097) respectively. Our data indicate that the studied polymorphisms of the TRPV1 and TRPM8 genes are correlated with each other and negatively correlated with TRPA. The results obtained may indicate the coevolution of these genes. It was previously shown that TRPV1, as well as TRPM8, was detected with high frequency in the Nanai population. It is likely that these two mutations came simultaneously from East Asia and spread throughout Russia, which explains the fact that they are positively correlated with each other.
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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 | 0.001 |
| 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".