PSIII-8 Genomic prediction of reproductive performance of commercial sows in health challenged herds

The objective of this study was to perform genomic predictions for reproductive performance of sows under natural health challenge. Reproductive performance (1 to 4 parities) and genotype (~40K SNPs) were available for 2,604 crossbred sows, for a total of 7,635 farrowing records. Animals from 17 high-health multipliers from 7 breeding companies (PigGen Canada) were shipped to 23 commercial farms with recent history of common infectious diseases. Gilts entered farms with an average of 53 animals per contemporary group (CG). Traits included total number of piglets: born (TB), born alive (NBA), stillborn (SB), mummified (MUM), born dead (NBD), and weaned (NW). Genomic predictions were performed using Bayes-B (pi=0.995) with a seven-fold cross-validation using each company in turn for validation and the others for training. The model included the effects of CG (fixed) and SNP (random), and net number of fosters (covariate) for NW. Genomic predictions were done for animal lifetime performance (sum performance of parities) for each trait and using first parity performance as the training set to predict subsequent parity performance. Accuracy was calculated as the weighted average correlation between GEBV and adjusted phenotype across validation sets divided by the square root of heritability. Lifetime performance accuracies were low to moderate, ranging from 0.11 (TB) to 0.45 (NBD). Accuracies using parity 1 to predict subsequent performance were low, ranging from -0.07 (SB in parity 3) to 0.19 (NBD in parity 2), with average accuracies per trait ranging from 0.04 (SB) to 0.16 (NBD).Although most accuracies were low, the moderately high accuracies for some lifetime performance shows that genomic prediction can be used to improve performance under natural health challenge in sows. We appreciate the financial support of PigGen Canada, Canadian Swine Health Board, Genome Alberta and Swine Innovation Porc, and the late Dr. Stephen Bishop for his scientific contributions.