’ome on the Range: Altitude Adaptation, Positive Selection, and Himalayan Genomics
Bibliographic record
Abstract
In 2010, a number of papers were published describing data from genome-wide studies designed to identify genes and genetic variants that contribute (or contributed) to human adaptation to altitude in the Himalaya. The results were exciting, intriguing, and controversial. Several genes, most notably EGLN1 and EPAS1, were identified as strong candidates for a role in evolutionary adaptation to high altitude, and the time course over which this adaptation occurred was calculated by one team to be remarkably brief. Overall, the data suggest that, at least in the ancestors of the modern Tibetans, there was a powerful selective pressure favoring variants in genes central to the molecular response to hypoxia. The most obvious manifestation of this selection seems to be the Tibetan's well known blunted erythropoietic response to hypoxemia. This article briefly reviews recent developments in 'omic' analysis of Tibetan highland natives, with a focus both on the answers found and the questions raised.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".