title: Crossbreed evaluations in single-step genomic best linear unbiased predictor using adjusted realized relationship matrices.
abstract: Combining purebreed and crossbreed information is beneficial for genetic evaluation of some livestock species. Genetic evaluations can use relationships based on genomic information, relying on allele frequencies that are breed specific. Single-step genomic BLUP (ssGBLUP) does not account for different allele frequencies, which could limit the genetic gain in crossbreed evaluations. In this study, we tested the performance of different breed-specific genomic relationship matrices () in ssGBLUP for crossbreed evaluations; we also tested the importance of genotyping crossbred animals. Genotypes were available for purebreeds (AA and BB) and crossbreeds (F) in simulated and real pig populations. The number of genotyped animals was, on average, 4,315 for the simulated population and 15,798 for the real population. Cross-validation was performed on 1,200 and 3,117 F animals in the simulated and real populations, respectively. Simulated scenarios were under no artificial selection, mass selection, or BLUP selection. Two genomic relationship matrices were constructed based on breed-specific allele frequencies: 1) , a genomic relationship matrix centered by breed-specific allele frequencies, and 2) , a genomic relationship matrix centered and scaled by breed-specific allele frequencies. All (the across-breed genomic relationship matrix), , and were also tuned to account for selective genotyping. Using breed-specific allele frequencies reduced the number of negative relationships between 2 purebreeds, pulling the average closer to 0, as in the pedigree-based relationship matrix. For simulated populations that included mass selection, genomic EBV (GEBV) in F, when using and , were, on average, 10% more accurate than ; however, after tuning to account for selective genotyping, provided the same accuracy as for breed-specific genomic relationship matrices. For the real population, accuracies for litter size in F were 0.62 for , , and , and tuning had no impact on accuracy, except for , which was 1 percentage point less accurate. Accuracy of GEBV for number of stillborns in F1 was 0.5 for all tested genomic relationship matrices with no changes after tuning. We observed that genotyping F increased accuracies of GEBV for the same animals by up to 39% compared with having genotypes for only AA and BB. In crossbreed evaluations, accounting for breed-specific allele frequencies promoted changes in G that were not influential enough to improve accuracy of GEBV. Therefore, the best performance of ssGBLUP for crossbreed evaluations requires genotypes for pure- and crossbreeds and no breed-specific adjustments in the realized relationship matrix.
authors.forename: D A L
authors.initials: DA
authors.lastname: Lourenco
authors.forename: S
authors.initials: S
authors.lastname: Tsuruta
authors.forename: B O
authors.initials: BO
authors.lastname: Fragomeni
authors.forename: C Y
authors.initials: CY
authors.lastname: Chen
authors.forename: W O
authors.initials: WO
authors.lastname: Herring
authors.forename: I
authors.initials: I
authors.lastname: Misztal
mesh_terms: D000818:Animals; D001947:Breeding; D003198:Computer Simulation; D005260:Female; D005787:Gene Frequency; D016678:Genome; D023281:Genomics / Q000379:methods*; D005838:Genotype; D006824:Hybridization, Genetic; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D013552:Swine / Q000235:genetics*