PSIII-5 Accuracy of genomic prediction of antibody response to common infectious diseases in commercial sows
Bibliographic record
Abstract
Abstract Previous results indicated that antibody response to PRRSV has moderate genomic prediction accuracy; however, little is known about this for other common infectious diseases. Therefore, the objective of this study was to estimate the accuracy of genomic prediction for antibody response to infectious diseases in commercial sows. A total of 2,848 Large White x Landrace replacement gilts were sourced from 17 high-health multipliers (7 breeding companies; BC) and introduced to 23 commercial farms with a history of common diseases, following standard acclimation procedures. Serum was used to quantify antibody response to swine influenza virus (SIV), Mycoplasma hyopneumoniae (MH), porcine circovirus type 2 (PCV2), and 8 serotypes of Actinobacilluspleuropneumoniae(APP1-3, 5, 7, 10, 12, and 13) at entry (S/PEntry), following acclimation (S/PAcclimation), and during parities 1 (S/PParity1) and 2 (S/PParity2). All animals were genotyped for 38,191 SNPs. Genomic prediction was performed using BayesB (pi=0.99), with the fixed effect of CG and random effects of SNPs included in the model. Training and validation were performed using 7-fold cross-validation, with data from each BC used as the validation dataset in one-fold. In general, prediction accuracies were low: SIV, from 0.13 (S/PAcclimation) to 0.26 (S/PParity1); MH, -0.07 (S/PAcclimation) to 0.13 (S/PParity2); PCV2, 0.04 (S/PParity1) to 0.32 (S/PAcclimation); APP, -0.08 (S/PEntry, APP10) to 0.26 (S/PAcclimation, APP7). At each point, average accuracies were 0.06 for S/PEntry, 0.09 for S/PAcclimationand S/PParity1, and 0.08 for S/PParity2, showing small increases in accuracy after the acclimation period. Among diseases, average accuracies ranged from 0.01 (APP1) to 0.22 (PCV2). Results show that, overall, the accuracy of genomic prediction of antibody response to common infectious diseases in commercial gilts is limited. The authors thank PigGen Canada, Genome Canada, and the Canadian Swine Health Board for financial support, and the late Dr. Stephen Bishop for his scientific contributions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".