49 - Predicting Breeding Values of Purebred Pigs for Crossbred Performance Using Crossbred Phenotypes and Genotypes

Phenotyping a large number of crossbred progeny for the evaluation of purebred animals can be expensive. As genotyping with low-density panels is becoming cheaper, we aimed to evaluate the tradeoff between having different percentages of genotypes and phenotypes for crossbred progeny of candidate boars. We used the linear regression (LR) method to investigate changes in accuracy, bias, and inflation of breeding values for crossbred traits in purebred boars. A total of 304,582 purebred and 147,474 crossbred animals were phenotyped for average daily gain (ADG) and backfat thickness (BF), out of which 46,691 purebred and 13,117 crossbred animals were genotyped. Genomic information consisted of imputed genotypes for 40,247 SNP markers after quality control. A four-trait animal model under single-step GBLUP was used that included phenotypes recorded in purebred and crossbred animals as correlated traits. The LR statistics were calculated based on breeding values of young purebred sires from complete and partial data. The first complete data included genotypes for purebreds and phenotypes for purebreds and crossbreds, whereas the second included also genotypes for crossbreds. The partial data included phenotypes on 50% or none of the progeny of validation sires, with or without genotypes for crossbred animals. When 50% of the progeny has phenotypes, adding genotypes for crossbred progeny marginally increased accuracy of ADG (0.77 vs 0.78) for 47 boars with more than 150 progeny with phenotypes. No increase was observed for BF. A small increase in bias and inflation by adding crossbred genotypes was observed for ADG but not for BF. When no phenotypes were available for crossbred progeny, accuracy for both traits was lower but improved with crossbred genotypes for ADG (0.61 vs 0.64) for boars with more than 150 progeny. The tradeoff between phenotypes and genotypes should be further investigated in larger datasets with more validation boars.