Global Population Genomics of Chickens and Their Adaptation to Diverse Environments
Bibliographic record
Abstract
Chickens ( Gallus gallus domesticus ) represent one of the most widely domesticated and distributed livestock species globally, offering a unique model for exploring population genomics and adaptive evolution. In this study, we investigated the global genomic diversity and environmental adaptation of chickens by analyzing whole-genome resequencing data and single nucleotide polymorphism (SNP) arrays across diverse geographic populations. We characterized regional genomic structures, examined the impact of artificial selection, and highlighted the role of indigenous breeds in shaping genetic landscapes. Specific adaptation signatures were identified in populations exposed to high altitudes, extreme temperatures, and pathogen pressures, revealing key loci associated with physiological resilience and immune function. Methodologically, we employed population structure analyses, phylogenetic reconstruction, selection scans, and functional annotation to uncover evolutionary trajectories. A focused case study on East African chickens demonstrated context-specific adaptations driven by unique environmental and cultural factors. Our findings underscore the underrepresentation of certain populations in genomic surveys and emphasize the importance of integrating genomics with ecological and phenotypic data. This study not only advances our understanding of chicken evolution and adaptation but also provides genomic insights that can inform sustainable breeding, conservation efforts, and global food security strategies.
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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) | 0.000 | 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".