Genomic Estimated Breeding Valueof Milk Performance and Fertility Traits in the Russian Black-and-White Cattle Population
Bibliographic record
Abstract
A breakthrough in cattle breeding was achieved with the incorporation of animal genomic data into breeding programs. The introduction of genomic selection has a major impact on traditional genetic assessment systems and animal genetic improvement programs. Since 2010, genomic selection has been officially introduced in the evaluation of the breeding and genetic potential of cattle in Europe, the U.S., Canada, and many other developed countries. The purpose of this study is to develop a system for a genomic evaluation of the breeding value of the domestic livestock of Black-and-White and Russian Holstein cattle based on 3 milk performance traits: daily milk yield (kg), daily milk fat (%), and daily milk protein content (%) and 6 fertility traits: age at first calving (AFC), calving interval (CI), calving to first insemination interval (CFI), interval between first and last insemination (IFL), days open (DO), and number of services (NS). We built a unified database of breeding animals from 523 breeding farms in the Russian Federation. The database included pedigree information on 2,551,529 cows and 69,131 bulls of the Russian Holstein and Black-and-White cattle breeds, as well as information on the milk performance of 1,597,426 cows with 4,771,366 completed lactations. The date of birth of the animals included in the database was between 1975 and 2017. Genotyping was performed in 672 animals using a BovineSNP50 v3 DNA Analysis BeadChip microarray (Illumina, USA). The genomic estimated breeding value (GEBV) was evaluated only for 644 animals (427 bulls and 217 cows) using the single-step genomic best linear unbiased prediction - animal model (ssGBLUP-AM). The mean genetic potential was +0.88 and +1.03 kg for the daily milk yield, -0.002% for the milk fat content, and -0.003 and 0.001% for the milk protein content in the cows and bulls, respectively. There was negative genetic progress in the fertility traits in the studied population between 1975 and 2017. The reliability of the estimated breeding value (EBV) for genotyped bulls ranged from 89 to 93% for the milk performance traits and 85 to 90% for the fertility traits, whereas the reliability of the genomic estimated breeding value (GEBV) varied 54 to 64% for the milk traits and 23 to 60% for the fertility traits. This result shows that it is possible to use the genomic estimated breeding value with rather high reliability to evaluate the domestic livestock of Russian Holstein and Black-and-White cattle breeds for fertility and milk performance traits. This system of genomic evaluation may help bring domestic breeding in line with modern competitive practices and estimate the breeding value of cattle at birth based on information on the animal's genome.
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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".