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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".