Opportunities for genome-wide selection for pig breeding in developing countries1
Bibliographic record
Abstract
Genetic improvement of exotic and indigenous pigs in tropical developing countries is desired. Implementations of traditional selection methods on tropical pig populations are limited by lack of data recording and analysis infrastructure. Genome-wide selection (GS) provides an approach for achieving faster genetic progress without developing a pedigree recording system. The implications of GS on long-term gain and inbreeding should be studied before actual implementation, especially where low linkage disequilibrium (LD) is anticipated in the target population. A simulation case study of this option was performed on the basis of the available 60,000 SNP panel for porcine genome. Computer simulation was used to explore the effects of various selection methods, trait heritability, and different breeding programs when applying GS. Genomic predictions were based on the ridge regression method. Genome-wide selection performed better than BLUP and phenotypic selection methods by increasing genetic gain and maintaining genetic variation while lowering inbreeding, especially for traits with low heritability. Indigenous pig populations with low LD can be improved by using GS if high-density marker panels are available. The combination of GS with repeated backcrossing of crossbreds to exotic pigs in developing countries promises to rapidly improve the genetic merit of the commercial population. Application of this novel method on a real population will need to be performed to validate these results.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".