Analysis of genetic diversity in Brown Swiss, Jersey and Holstein populations using genome-wide single nucleotide polymorphism markers
Bibliographic record
Abstract
BACKGROUND: Studies of genetic diversity are essential in understanding the extent of differentiation between breeds, and in designing successful diversity conservation strategies. The objective of this study was to evaluate the level of genetic diversity within and between North American Brown Swiss (BS, n = 900), Jersey (JE, n = 2,922) and Holstein (HO, n = 3,535) cattle, using genotyped bulls. GENEPOP and FSTAT software were used to evaluate the level of genetic diversity within each breed and between each pair of the three breeds based on genome-wide SNP markers (n = 50,972). RESULTS: Hardy-Weinberg equilibrium (HWE) exact test within breeds showed a significant deviation from equilibrium within each population (P < 0.01), which could be a result of selection, genetic drift and inbreeding within each breed. Hardy-Weinberg test also confirmed significant heterozygote deficit in each breed over several loci. Moreover, results from population differentiation tests showed that the majority of loci have alleles or genotypes drawn from different distributions in each breed. Average gene diversity, expressed in terms of observed heterozygosity, over all loci in BS, JE and HO was 0.27, 0.26 and 0.31, respectively. The proportion of genetic diversity due to allele frequency differences among breeds (Fst) indicated that the combination of BS and HO in an ideally amalgamated population had higher genetic diversity than the other pairs of breeds. CONCLUSION: Results suggest that the three bull populations have substantially different gene pools. BS and HO show the largest gene differentiation and jointly the highest total expected gene diversity compared to when JE is considered. If the loss of genetic diversity within breeds worsens in the future, the use of crossbreeding might be an option to recover genetic diversity, especially for the breeds with small population size.
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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".