33 Genetic parameters for reproductive traits in purebred and crossbred swine
Bibliographic record
Abstract
Abstract For swine breeding programs, testing and selection programs are located in nucleus units that are generally managed differently and with higher health levels than commercial herds where descendants of nucleus animals are expected to perform. This approach assumes that superior animals selected in nucleus herds will have progeny with superior performance at the commercial level. There is clear evidence that this may not be true for all traits of economic importance and thus methods including data collected at the commercial level may increase accuracy of selection at the nucleus level. This study’s goal was to estimate genetic parameters for five reproductive traits between two purebred maternal nucleus populations and their commercial F1 offspring: Total Number Born, Number Born Alive, Number Born Alive > 1kg, Number Weaned, and Litter Weight. Estimates were based on single-step GBLUP in the BLUPF90 programs by utilizing any two combinations of a purebred and the F1 population, and by using all three populations jointly. The genomic relationship matrix between the three populations was generated by using within-population allele frequencies for relationships within a population, and across-population allele frequencies for relationships of the F1 with the purebred animals. The two purebred populations were assumed to be genetically unrelated. The use of two versus three populations did not impact estimates of heritability, additive variance, or genetic correlations. Heritabilities ranged from 0.02 to 0.09 for the F1, from 0.07 to 0.18 for Landrace, and from 0.07 to 0.21 for Yorkshire. Genetic correlations between the same traits in F1 and Landrace ranged from 0.22 to 0.93, and from 0.31 to 0.84 for F1 and Yorkshire. This range of genetic correlations indicates that the use of crossbred information can aid in the selection of purebreds for commercial crossbred performance for some traits. Funded by Genome Canada Genomic Applications Partnerships Program.
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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 | 0.003 |
| 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".