ABSTRACT
This study investigated using imputed genotypes from non-genotyped animals which were not in the pedigree for the purpose of genetic selection and improving genetic gain for economically relevant traits. Simulations were used to mimic a 3-breed crossbreeding system that resembled a modern swine breeding scheme. The simulation consisted of three purebred (PB) breeds A, B, and C each with 25 and 425 mating males and females, respectively. Males from A and females from B were crossed to produce AB females (n = 1,000), which were crossed with males from C to produce crossbreds (CB; n = 10,000). The genome consisted of three chromosomes with 300 quantitative trait loci and ~9,000 markers. Lowly heritable reproductive traits were simulated for A, B, and AB (h2 = 0.2, 0.2, and 0.15, respectively), whereas a moderately heritable carcass trait was simulated for C (h2 = 0.4). Genetic correlations between reproductive traits in A, B, and AB were moderate (rg = 0.65). The goal trait of the breeding program was AB performance. Selection was practiced for four generations where AB and CB animals were first produced in generations 1 and 2, respectively. Non-genotyped AB dams were imputed using FImpute beginning in generation 2. Genotypes of PB and CB were used for imputation. Imputation strategies differed by three factors: 1) AB progeny genotyped per generation (2, 3, 4, or 6), 2) known or unknown mates of AB dams, and 3) genotyping rate of females from breeds A and B (0% or 100%). PB selection candidates from A and B were selected using estimated breeding values for AB performance, whereas candidates from C were selected by phenotype. Response to selection using imputed genotypes of non-genotyped animals was then compared to the scenarios where true AB genotypes (trueGeno) or no AB genotypes/phenotypes (noGeno) were used in genetic evaluations. The simulation was replicated 20 times. The average increase in genotype concordance between unknown and known sire imputation strategies was 0.22. Genotype concordance increased as the number of genotyped CB increased with little additional gain beyond 9 progeny. When mates of AB were known and more than 4 progeny were genotyped per generation, the phenotypic response in AB did not differ (P > 0.05) from trueGeno yet was greater (P < 0.05) than noGeno. Imputed genotypes of non-genotyped animals can be used to increase performance when 4 or more progeny are genotyped and sire pedigrees of CB animals are known.
In swine breeding, phenotypic information is often gathered from elite purebred (PB) breeding stock and occasionally terminal crossbred animals (CB). Using economically relevant traits expressed by dams of CB (F1) in genetic evaluations is not common due to the lack of pedigree and/or genomic relationships to relate phenotypes of F1 to PB selection candidates. Since swine often have large litters, this study aimed to develop strategies to incorporate phenotypes of F1 into genetic evaluations by imputing F1 genotypes. Using simulation, we investigated the impact of CB pedigree completeness, the number of CB genotyped progeny, the number of parities (and thus mates) a F1 had, and genomic diversity in PB breeds on imputation accuracy and the response to selection in F1 performance. When mates of F1 were in the pedigree and 4 or more CB progeny were genotyped per generation, imputation accuracy was high and the phenotypic response in F1 did not differ compared to when true F1 genotypes were used. Our results show that imputed genotypes can be used to increase performance in swine breeding programs, but the magnitude depends upon the number of CB progeny genotyped, the number of F1 mates, and the completeness of the pedigree.
