Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds
Bibliographic record
Abstract
Breed utilization, genetic improvement, and industry consolidation are predicted to have major impacts on the genetic composition of commercial chickens. Consequently, the question arises as to whether sufficient genetic diversity remains within industry stocks to address future needs. With the chicken genome sequence and more than 2.8 million single-nucleotide polymorphisms (SNPs), it is now possible to address biodiversity using a previously unattainable metric: missing alleles. To achieve this assessment, 2551 informative SNPs were genotyped on 2580 individuals, including 1440 commercial birds. The proportion of alleles lacking in commercial populations was assessed by (1) estimating the global SNP allele frequency distribution from a hypothetical ancestral population as a reference, then determining the portion of the distribution lost, and then (2) determining the relationship between allele loss and the inbreeding coefficient. The results indicate that 50% or more of the genetic diversity in ancestral breeds is absent in commercial pure lines. The missing genetic diversity resulted from the limited number of incorporated breeds. As such, hypothetically combining stocks within a company could recover only preexisting within-breed variability, but not more rare ancestral alleles. We establish that SNP weights act as sentinels of biodiversity and provide an objective assessment of the strains that are most valuable for preserving genetic diversity. This is the first experimental analysis investigating the extant genetic diversity of virtually an entire agricultural commodity. The methods presented are the first to characterize biodiversity in terms of allelic diversity and to objectively link rate of allele loss with the inbreeding coefficient.
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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) | 0.000 | 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".