696 Whole-genome single nucleotide polymorphism study of Romanov sheep
Bibliographic record
Abstract
The Romanov breed stands out from the global variety of sheep breeds. Romanovs originated from local sheep at the end of 17th century in the Volga Valley. Since the 1970s, the breed has become popular in France, Canada, and the United States. Some unique traits are of permanent interest to this Russian breed. Primarily, Romanovs have outstanding reproduction qualities: early sexual maturity, out-of-season breeding ability, and extraordinary prolificacy. Using Romanov ewes in crossbreeding programs is a profitable, practical method to increase the number of highly viable hybrids. In addition, the Romanov breed has unique wool properties, which are very suitable for manufacturing felt products, rugs, and mats. However, detailed genetic information on the breed is lacking. Therefore, we performed the whole-genome SNP analysis of the original Romanov sheep to study its genetic diversity and genetic relationships with other Russian breeds. We genotyped 42 samples of Romanovs using the OvineSNP50K BeadChip and pooled the data with the set from 24 Russian breeds. Quality filtering was performed in PLINK version 1.07. Calculations were done in PLINK version 1.07, GENETIX version 4.05, and HP-Rare 1.1. We found a lower level of genetic diversity in Romanovs (Ho = 0.350 and Ar = 1.862) in comparison with other coarse wool breeds (Ho = 0.377 and Ar = 1.899). At the same time, Romanovs, compared with the 24 breeds, were characterized by the most insignificant deviation from Hardy–Weinberg equilibrium (Fis = −0.005). In other breeds, Fis values varied from −0.028 to −0.082. Pairwise Fst values ranged from 0.084 to 0.124 between Romanovs and Kuibyshev and Kuchugur, respectively. The MDS analysis revealed the genetic uniqueness of Romanov sheep. Principal component (PC) 1 divided all breeds into 2 groups according the wool type (fine wool + semifine wool and coarse wool) with 4.7% of total genetic variance. By PC1 the Romanov formed an incorporated bunch and logically clustered with coarse-wool breeds. However, the Romanov was clearly separated from the others by PC2, which explained 3.8% of additional genetic variability. Most likely, a high level of consolidation and genetic differentiation of the Romanov breed from other Russian sheep are explained by its “pure gene” origin and was not improved by any breed. Our study is the first attempt to reveal the outstanding genetic nature of original Romanov sheep on whole-genome level. We will extend our study by genotyping more samples with medium-density DNA chip and using NGS technology. The research was performed under financial support of Russian Scientific Foundation (number 14-36-00039).
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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 | 0.001 | 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".