Pedigree analysis and inbreeding effects over morphological traits in Campolina horse population
Bibliographic record
Abstract
Genetic improvement, without control of inbreeding, can go to loss of genetic variability, reducing the potential for genetic gains in the domestic populations. The aim of this study was to analyze the population structure and the inbreeding depression in Campolina horses. Phenotype information from 43 465 individuals was analyzed, data provided by the Campolina Breeders Association. A pedigree file containing 107 951 horses was used to connected the phenotyped individuals. The inbreeding coefficient was performed by use of the diagonal of the relationship matrix and the genealogical parameters were computed using proper softwares. The effective population size was estimated based on the rate of inbreeding and census information, and the stratification of the population was verified by the average relationship coefficient between animals born in different regions of Brazil. The effects of inbreeding on morphological traits were made by the use of inbreeding coefficient as a covariate in the model of random regression. The inbreeding coefficient increased from 1990 on, impacting effective population size and, consequently, shrinking genetic variability. The paternal inbreeding was greater than maternal, which may be attributed to the preference for inbred animals in reproduction. The average genetic relationship coefficient of animals born in different states was lower than individuals born within the same state. The increase in the inbreeding coefficient was negatively associated with all studied traits, showing the importance to avoid genetic losses in the long term. Although results do not indicate a severe narrowing of the population until the present date, the average relationship coefficient shows signs of increase, which could cause a drastic reduction in genetic variability if inbred mating is not successfully controlled in the Campolina horse population.
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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 | 0.000 | 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 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".