The distribution of three candidate cold-resistant SNPs in six minorities in North China
Bibliographic record
Abstract
BACKGROUND: Heilongjiang Province located in northeast China is a multi-ethnic region with people who have lived in cold conditions for several generations. Fatty acids are important to people with cold resistance. CPT1A encodes a protein that imports long-chain fatty acids into the mitochondria for fatty-acid oxidation. FADS is an essential enzyme for the synthesis of long-chain polyunsaturated fatty acids. RESULTS: In the present study, we investigated the distributions of three cold resistance-related SNPs (rs80356779 G > A in CPT1A, rs7115739 T > G in FADS3 and rs174570 C > T in FADS2) from six populations that included 1093 individuals who have lived in Heilongjiang Province for at least three generations. The frequencies of rs174570 and rs7115739 were different in our six north minorities compared to the Chinese Dai in Xishuangbanna (CDX) in southern China. All the SNPs in Hezhen were significantly different from other five studied populations. In addition, the genetic distribution of rs174570 in Daur was significantly different from Manchu and Korea, and the frequency of rs7115739 in Ewenki was significantly different from the other populations. The results also showed that the frequencies of the three SNPs in the six minorities were different from those of Greenlandic Inuit and Siberian population. CONCLUSIONS: Our results showed the distributions of the three cold resistance-related SNPs from six populations that included 1093 individuals in northern China. Distributions of the allele frequencies for the cold resistance-related SNPs in northern China were statistically different from those in southern China. These data help to establish the DNA genome database for the six populations and fully preserve existing minority genetic information.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".