Genetic relationships between Japanese native and commercial breeds using 70 chicken autosomal SNP genotypes by the DigiTag2 assay
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, single nucleotide polymorphisms (SNPs) have been used to identify genes or genomic regions responsible for economic traits, including genetic diseases in domestic animals, and to examine genetic diversity of populations. In this study, we genotyped 70 chicken autosomal SNPs using DigiTag2 assay to understand the genetic structure of the Japanese native chicken breeds Satsumadori and Ingie, and the relationship of these breeds with other established breeds, Rhode Island Red (RIR), commercial broiler and layer. Five breeds, each consisting of approximately 20 chickens, were subjected to the assay, revealing the following: Average expected heterozygosities of broiler, Satsumadori, RIR, layer and Ingie were 0.265, 0.254, 0.244, 0.179 and 0.176, respectively. Phylogenetic analysis using the concatenated 70 autosomal SNP genotypes distinguished all chickens and formed clusters of chickens belonging to the respective breeds. In addition, the 2-D scatter plot of the first two principal components was consistent with the phylogenic tree. Taken together with the pairwise F(st) distances, broiler and RIR were closely positioned near each other, while Ingie was positioned far from the other breeds. Structure analysis revealed that the probable number of genetic clusters (K) was six and four with maximum likelihood and ΔK values, respectively. The clustering with maximum likelihood revealed that, in addition to the clustering of the other five breeds, the Satsumadori was subdivided into two genetic clusters. The clustering with ΔK value indicated that the broiler and Rhode Island Red were assigned to the same genetic cluster.
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.
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 | 0.001 | 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 it