Whole-genome sequencing identifies genetic diversity and adaptive signatures of hypoxia and ultraviolet radiation in Chinese chickens
Bibliographic record
Abstract
INTRODUCTION: Domestic chickens primarily descended from the wild red junglefowl, play a crucial role in global egg and meat production. China hosts diverse indigenous chicken populations that have adapted to various environmental conditions, including high-altitude with hypoxic and ultraviolet radiation stress. METHOD: We analyzed whole-genome sequences of 118 birds from five Indigenous Chinese chicken populations and 295 chicken genomes from publicly available databases to identify genomic diversity, admixture, and selection signatures of chickens adapted to high-altitude environments. Selection signatures were identified using nucleotide diversity (π), Tajima's D, XPEHH, and XP-CLR, selection scan methods. RESULTS: We observed a reduction in genetic diversity and historical declines in effective population size in high-altitude chicken, suggesting ongoing selection pressures shaping these populations. Selection scans identified nine genomic regions under strong positive selection, enriched for genes associated with hypoxia and ultraviolet radiation. Notably, five genes (TPK1, BAZ2B, MARCHF7, LLGL2, and RCAN3) were repeatedly detected across multiple selection signature analyses. RNA-seq analysis further confirmed the differential expression of these genes in the lung and heart tissues of chickens adapted to high and low altitudes, reinforcing their role in physiological adaptation to hypoxic environments. Altitude adaptation is driven by the selection of genes involved in oxygen metabolism, cellular stress response, and energy regulation. CONCLUSION: Our study provides compelling genetic evidence for differentiation between high and low and high-altitude Chinese chicken populations. These findings also ensure our understanding of local adaptation in poultry and establish a genomic framework for breeding strategies to improve environmental resilience to altitude-related stressors.
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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".