Genome-Wide Selection for Improvement of Indigenous Pigs in Tropical Developing Countries
Bibliographic record
Abstract
Genetic improvement of indigenous pig populations in tropical developing countries can make a significant contribution to the conservation and utilization of local genetic resources. Designing a swine breeding program requires knowledge of genetic parameters for economically important traits. A meta-analysis of genetic parameters determined under tropical conditions and published from 1974 to 2009 was carried out to provide consensus estimates of genetic parameters. Given that the data recording and analysis infrastructure for implementing the conventional best linear unbiased prediction (BLUP) methods is generally lacking in developing countries, Genome-wide selection (GS) provides an approach for achieving faster genetic progress without developing a pedigree recording system. A simulation study was carried out to evaluate the option of using available 60 K single nucleotide polymorphism marker panel. The observed levels of linkage disequilibrium (LD) in the tropical pig populations were simulated and utilized. Genomic predictions were from ridge regression analysis. The results showed that expected accuracies of genomic breeding values (GBV) were in the range of 0.31 - 0.86 for the validation set. Genome-wide selection improved accuracy of GBVs over conventional BLUP method for traits with low heritability and in young animals with no performance data. Crossbred training populations had higher accuracy than purebred training populations. An assessment of the opportunities for GS in tropical pig breeding was conducted. Genome-wide selection performed better than conventional methods by increasing genetic gain and maintaining genetic variation while lowering inbreeding especially for traits with low heritability, by exploiting LD and the Mendelian sampling effects. Combining GS with repeated backcrossing of crossbreds to the selected exotic population in moderate LD promises faster improvements of the commercial population. A two-step selection strategy that involves the use of GS to pre-select candidates that entered the performance test station and for selecting replacement candidates in a nucleus swine breeding program was evaluated and compared to other conventional approaches. Genome-wide selection generated an increase of about 38% to 172% in annual returns compared to other conventional approaches for previously selected population in moderate LD and about 2% to 50% increases in return for unselected population in low LD.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".