The effect of new and ancestral inbreeding on milk production traits in Iranian Holstein cattle
Bibliographic record
Abstract
Inbreeding depression, the reduction of fitness and performance, is due to an increase in the mating of related individuals. Based on the purge hypothesis, inbreeding and breeding over generations reduce the effect of deleterious alleles responsible for inbreeding depression. Thus, recent inbreeding is assumed to be more harmful than ancestral inbreeding. This study aimed at evaluating the effects of new and ancestral inbreeding on milk, fat and protein production in Iranian Holstein cattle. The secondary objective was to examine the changes in predicted breeding values when the inbreeding effect was included in the model's analysis. To this end, inbreeding coefficients were calculated using the pedigree of 2,394,517 Holstein cattle to achieve these goals. In addition, 419,132 records of milk, fat and protein yields of first parity cows were collected to assess inbreeding depression and breeding values. The average inbreeding coefficients were 0.83% and 1.68% for the whole population and the inbred animals, respectively. A 1% increase in classical pedigree-based inbreeding coefficient was associated with a decrease of 11.99 kg in milk, 0.39 kg in fat and 0.29 kg in protein. The effect of ancestral inbreeding was more detrimental to performance traits than the effect of new inbreeding. This result contradicted the hypothesis of purging. By including the inbreeding coefficient in the model, the rank of animals remained unchanged, but the average predicted breeding values increased. In general, inbreeding depression was observed in Iranian Holstein cows; however, no evidence of purging was observed. The average of inbreeding coefficients was not high in this population, although accounting for inbreeding coefficients in the analytical model did significantly increase the predicted breeding values. It is recommended that the analytical model incorporate the inbreeding coefficient to improve the accuracy of genetic evaluation. In future studies, inbreeding depression should be assessed using genomic data for performance and reproduction traits.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".