154 A genetic marker for PRRS resistance has no adverse effect on economically important traits in pigs
Bibliographic record
Abstract
Abstract Porcine Respiratory and Reproductive Syndrome (PRRS) is a very costly viral disease of pigs. A genetic marker for host response to PRRS on chromosome 4 (rs80800372, (WUR)) could be used to select for resilience to PPRS. However, before including WUR in selection, it is necessary to know effects of WUR on economically important traits without PRRS. A dataset with both genotypes and phenotypes on purebred Duroc (D), Landrace (L), Yorkshire (Y) and crossbred (LY or DLY) pigs was used. A total of 20 traits were tested. Sow reproduction and litter traits were available on > 13,000 D, L and Y and 1,549 LY sows. Growth and ultrasound traits were recorded on >35,000 D, L and Y and 2,622 DLY pigs. Daily feed intake (70-120kg) was from 4,133 Durocs. Carcass and meat quality traits were from 2,184 Durocs, 1,160 Yorkshires and 2,184 DLY pigs. All animals were genotyped using a custom SNP chip ( > 55K) including WUR. Analyses were done within each breed using an animal model. WUR was fitted as a fixed effect by classifying pigs with one or two favourable alleles (B) into one group and others into a second group. Contemporary group, parity, litter, lactation length and net fostering effects were considered depending on the traits. Relationship matrices were constructed using pedigree in purebreds and genotypes in crossbreds. WUR had no significant ( P >0.05 ) effect on any trait, except for number of pigs alive at 24hrs in Y and ultrasound loin depth in D and Y ( P< 0.05 ) but the favorable WUR allele also had favorable effects on these two traits. These results were similar to the report by Dunkelberger et al. (J. Anim. Sci. 2017, 95: 2838). In conclusion, WUR had no adverse effects on any trait and can be used to select pigs with increased resilience to PRRS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".