Inbreeding and Genetic Diversity in Three Imported Swine Breeds in China Using Pedigree Data
Bibliographic record
Abstract
The accumulation of inbreeding and the loss of genetic diversity is a potential problem in the modern swine breeds in China. Therefore, the purpose of this study was to analyze the pedigrees of Chinese Duroc (CD), Landrace (CL) and Yorkshire (CY) swine to estimate the past and current rates of inbreeding, and to identify the main causes of genetic diversity loss. Pedigree files from CD, CL and CY containing, 4529, 16,776 and 22,600 records, respectively, were analyzed. Pedigree completeness indexes of the three breeds, accounting for one generation back, were 83.72, 93.93 and 93.59%, respectively. The estimated average annual inbreeding rates for CD, CL and CY in recent three years were 0.21, 0.19 and 0.13%, respectively. The estimated average percentage of genetic diversity loss within each breed in recent three years was about 8.92, 2.19, and 3.36%, respectively. The average relative proportion of genetic diversity loss due to unequal contributions of founders in CD, CL and CY was 69.09, 57.95 and 60.57%, and due to random genetic drift was 30.91, 42.05 and 39.43%, respectively. The estimated current effective population size for CD, CL and CY was 76, 117 and 202, respectively. Therefore, CD has been found to have lost considerable genetic diversity, demanding priority for optimizing the selection and mating to control future coancestry and inbreeding. Unequal contribution of founders was a major cause of genetic diversity loss in Chinese swine breeds and random genetic drift also showed substantial impact on the loss of diversity.
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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 | 0.002 | 0.002 |
| 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".