Mapping Quantitative Trait Loci Affecting Female Reproductive Traits on Porcine Chromosome 81
Bibliographic record
Abstract
An understanding of the genetic control of porcine female reproductive performance would offer the opportunity to utilize natural variation and improve selective breeding programs through marker-assisted selection. The Chinese Meishan is one of the most prolific pig breeds known, farrowing three to five more viable piglets per litter than the European Large White breed. This difference in prolificacy is attributed to the Meishan's superior prenatal survival levels. The present study utilized a three-generation cross in which the founder grandparental animals were purebred Meishan and Large White pigs in a scan for quantitative trait loci (QTL) on porcine chromosome 8 (SSC8) associated with reproductive performance. Reproductive traits, including number of corpora lutea (ovulation rate), teat number, litter size, and prenatal survival, were recorded for as many as 220 F2 females. Putative QTL for the related traits of litter size and prenatal survival were identified at the distal end of the long arm of SSC8. A physiological candidate gene, SPP1, was found to lie within the 95% confidence interval of these QTL. A suggestive QTL for teat number was revealed on the short arm of SSC8. The present study demonstrates, to our knowledge, the first independent confirmation of QTL for fecundity on SSC8, and these QTL regions provide a crucial starting point in the search for the causal genetic variants.
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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 | 0.000 | 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) | 0.001 | 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".