QTL detection for growth and carcass quality traits thanks to a high density SNP chip in pig
Bibliographic record
Abstract
Genetic improvement of carcass quality in pig implies traits measurements on related animals. In such situation, marker-assisted selection could lead to greater genetic gain than phenotypic selection. A family structured population of 454 F2 pigs was produced as an inter-cross between 2 commercial sire lines in order to detect quantitative trait loci (QTL) for growth and carcass quality traits. Animals from the 3 generations of the experimental design were genotyped using the Porcine 60k SNP Illumina Beadchip. Linkage Disequilibrium and Linkage Analyses were performed according to a maximum-likelihood interval mapping methodology using the QTLMap software. A total of 77 QTL were detected at the 5% significance chromosome-wide level. Four of these QTL exceeded the genome-wide 5% significance threshold. Thirty nine QTL influence growth or body composition traits and 34 QTL influence meat quality traits. QTL affecting the average daily gain were detected on SSC4, 6, 14 and 15. Q TL affecting carcass composition traits were detected on all chromosomes, except SSC10, 17 and 18. Finally, QTL were detected for early and ultimate pH, colour measurements (L*a*b Minolta coordinates), shear force measured on raw and cooked meat, intramuscular fat content and glycolytic potential, on all chromosomes, except SSC12 and 13. Several QTL were co-localized suggesting pleiotropic effects for some chromosomal regions. Thus, significant QTL were detected in the present study for a large scale of production traits. Additionally, a transcriptome analysis of LM and SM samples, obtained shortly after slaughter, was realized to detect expression QTL (data not shown). These data may be useful to identify causal polymorphisms of QTL and to exploit them in efficient marker-assisted selection programs.
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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 | 0.000 |
| 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".