Andean and Tibetan patterns of adaptation to high altitude
Bibliographic record
Abstract
OBJECTIVES: High-altitude hypoxia, or decreased oxygen levels caused by low barometric pressure, challenges the ability of humans to live and reproduce. Despite these challenges, human populations have lived on the Andean Altiplano and the Tibetan Plateau for millennia and exhibit unique circulatory, respiratory, and hematological adaptations to life at high altitude. We and others have identified natural selection candidate genes and gene regions for these adaptations using dense genome scan data. One gene previously known to be important in cellular oxygen sensing, egl nine homolog 1 (EGLN1), shows evidence of positive selection in both Tibetans and Andeans. Interestingly, the pattern of variation for this gene differs between the two populations. Continued research among Tibetan populations has identified statistical associations between hemoglobin concentration and single nucleotide polymorphism (SNP) genotype at EGLN1 and a second gene, endothelial PAS domain protein 1 (EPAS1). METHODS: To measure for the effects of EGLN1 and EPAS1 altitude genotypes on hemoglobin concentration among Andean highlanders, we performed a multiple linear regression analysis of 10 candidate SNPs in or near these two genes. RESULTS: Our analysis did not identify significant associations between EPAS1 or EGLN1 SNP genotypes and hemoglobin concentration in Andeans. CONCLUSIONS: These results contribute to our understanding of the unique set of adaptations developed in different highland groups to the hypoxia of high altitude. Overall, the results provide key insights into the patterns of genetic adaptation to high altitude in Andean and Tibetan populations.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".