Genomic insight into the influence of selection, crossbreeding, and geography on population structure in poultry
Bibliographic record
Abstract
BACKGROUND: In poultry, the population structure of local breeds is usually complex mainly due to unrecorded breeding. Local chicken breeds offer an interesting proxy to understand the complexity of population structure in the context of human-mediated development of diverse morphologies and varieties. We studied 37 traditional Dutch chicken breeds to investigate population structure and the corresponding genomic impact using whole-genome sequence data. RESULTS: Looking at the genetic differences between breeds, the Dutch chicken breeds demonstrated a complex and admixed subdivided structure. The dissection of this complexity highlighted the influence of selection adhering to management purposes, as well as the role of geographic distance within subdivided breed clusters. Identification of signatures of genetic differentiation revealed genomic regions that are associated with diversifying phenotypic selection between breeds, including dwarf size (bantam) and feather color. In addition, with a case study of a recently developed bantam breed developed by crossbreeding, we provide a genomic perspective on the effect of crossbreeding. CONCLUSIONS: This study demonstrates the complex population structure of local traditional Dutch chicken, and provides insight into the genomic basis and the factors involved in the formation of this complexity.
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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".