WPSII-4 Population structure and Holstein ancestry analysis of modern Russian Black and White cattle for accurate genetic evaluation in Leningrad region.
Bibliographic record
Abstract
Worldwide popularity of Holstein (HOL) breed is based on high milk abilities and perfect genetic response. During early 1900th Soviet government registered new Russian Black and White (RBW) dairy breed made by crossing of local cows with imported Dutch, German and Latvian Frisian bulls. After 1950th semen and animals importation strategy was changed to pure breeding. Starting from 1990 in an attempt to gain milk production farmers start actively use US and Canadian Holstein bulls for mating purpose. Leningrad region (LR), as highest average milk producing region in Russia, intensively using North American semen and Europe young bulls for AI purpose. Process of moving forward from old official Contemporary Comparison to modern Single-Step SNP-BLUP evaluation model cause question: should be effect of heterosis accounted in the mixed model equation? Due to pedigree data pitfalls, accurate estimation of ancestry proportion between RBW and HOL breed sometimes was difficult. Aim of our research was to check population structure of modern RBW breed in LR using modern genomic tools. The study included 1100 cows and 400 bulls genotyped using illumina 50Kv2 SNP BeadChip and IBDv3 BeadChip by Weatherbys Scientific. Cows were randomly selected from 13 large (>700 milking cows) breeding herds placed in different parts of region. Population structure was estimated using Multi-dimensional scaling (MDS) analysis and Fst statistics in Plink 1.9, maximum likelihood estimation of individual ancestries from multilocus SNP genotype in Admixture software. According to MDS plot (Fig.1) on vector C1, 6 and 42 animals were respectively extremely (-0.4) and slightly (-0.3 to -0.1) outlying from main cluster. Admixture plot shown no critical difference between animals for all tested K (Fig.2). Calculated Fst was 0.008. Using thous evidences, we conclude there is no need to use multi-breed statement in genetic and genomic evaluation model. Supported by FASO state assignment AAAA-A18-118021590138-1.
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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) | 0.002 | 0.001 |
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".