Population Genomics Analysis Revealed Origin and High-altitude Adaptation of Tibetan Pigs
Bibliographic record
Abstract
Tibetan pig is native to the Qinghai-Tibet Plateau and has adapted to the high-altitude environmental condition such as hypoxia. However, its origin and genetic mechanisms underlying high-altitude adaptation still remain controversial and enigmatic. Herein, we analyze 229 genomes of wild and domestic pigs from Eurasia, including 63 Tibetan pigs, and detect 49.6 million high-quality variants. Phylogenomic and structure analyses show that Tibetan pigs have a close relationship with low-land domestic pigs in China, implying a common domestication origin. Positively selected genes in Tibetan pigs involved in high-altitude physiology, such as hypoxia, cardiovascular systems, UV damage, DNA repair. Three of loci with strong signals of selection are associated with EPAS1, CYP4F2, and THSD7A genes, related to hypoxia and circulation. We validated four non-coding mutations nearby EPAS1 and CYP4F2 showing reduced transcriptional activity in Tibetan pigs. A high-frequency missense mutation is found in THSD7A (Lys561Arg) in Tibetan pigs. The selective sweeps in Tibetan pigs was found in association with selection against non-coding variants, indicating an important role of regulatory mutations in Tibetan pig evolution. This study is important in understanding the evolution of Tibetan pigs and advancing our knowledge on animal adaptation to high-altitude environments.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".