Polymorphism of Genes Encoding Selenoproteins in the Indigenous Population of Siberia: Adaptive Variant rs1133238-A of the Gene
Bibliographic record
Abstract
In the present study, we analyzed the distribution of polymorphism variants of 25 genes encoding selenoproteins in indigenous Siberian populations, based on data on the variability of exons and adjacent noncoding DNA sequences. The study showed that there are virtually no polymorphism variants in the indigenous population of Siberia that differ greatly in frequency from those in the East Asian population, which could indicate the effect of selection on genes encoding selenoproteins. Nevertheless, we have proposed a set of loci in exons (rs3732532 of gene, rs1050450 of gene, rs6748996 of gene, rs225014 of gene) and noncoding region of genes (rs2291250 of gene, rs201938903 of gene, rs11258324 of gene), further study of their polymorphism in different ethnic groups of Siberia is quite reasonable. The most promising locus is rs1133238 of the gene. The frequency of the rs1133238-A variant was found to exceed 30% in northeastern Siberia, although its frequency is significantly lower (~12%) in other regions of Siberia. Considering the association between the rs1133238-A variant and blood mercury levels in Canadian Eskimos, it is suggested that the increased frequency of the rs1133238-A variant in populations of the coastal regions of Northeast Siberia (Koryaks, Chukchi, Eskimo) may be due to the protective role of this allele against the effects of mercury compounds on the body.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".