Single-step methods for genomic evaluation in pigs

Abstract

Genetic evaluation based on information from phenotypes, pedigree and markers can be implemented using a recently developed single-step method. In this paper we compare accuracies of predicted breeding values for daily gain and feed conversion ratio (FCR) in Danish Duroc pigs obtained from different versions of single-step methods, the traditional pedigree-based method and the genomic BLUP (GBLUP) method. In particular, we present a single-step method with an adjustment of the genomic relationship matrix so that it is compatible to the pedigree-based relationship matrix. Comparisons are made for both genotyped and non-genotyped animals and univariate and bivariate models. The results show that the three methods with marker information (two single-step methods and GBLUP) produce more accurate predictions of genotyped animals than the pedigree-based method. In addition, single-step methods provide more accurate predictions for non-genotyped animals. The results also show that the single-step method with adjusted genomic relationship matrix produce more accurate predictions than the original single-step method. Finally, the results for the bivariate analyses show a somewhat improved accuracy and reduced inflation of predictions for FCR for the two single-step methods compared with the univariate analyses. The conclusions are: first, the methods with marker information improve prediction compared with the pedigree-based method; second, a single-step method, contrary to GBLUP, provides improved predictions for all animals compared to the pedigree-based method; and third, a single-step method should be used with an adjustment of the genomic relationship matrix.

Genomic selection in a pig population including information from slaughtered full sibs of boars within a sib-testing program

“…Nevertheless, in contrast to what it was expected from literature results (Aguilar et al, 2010;Christensen et al, 2012), in this study, the use of SSTEP models, in general, did not show any advantage, probably because of the considered data layout. We assumed that all tested boars were genotyped, whereas their litters were genotyped and phenotyped only when included in the sibtesting program.…”

“…Phenotypes were collected merely on carcasses of full sibs of candidate boars within the performance-testing programs; however, GEBV from SSTEP models accounted not only for the training population data (full sibs of candidate boars) but also for the pedigree data, and therefore for collateral related animal performances, as well as, although later in the boar's life, for the progeny performances. According to the literature, the SSTEP procedure is expected to increase the prediction ability of classical genetic evaluation by adding genomic information (Aguilar et al, 2010;Christensen et al, 2012). However, in the simulated population and for a trait with heritability of 0.40, results were slightly favorable in the whole population (0.76 on average v. 0.68 with BLUP_EBV), but prediction abilities of the group of prediction animals were comparable with those of the traditional genetic evaluation based on pedigree data (on average 0.72 with the SSTEP model and 0.73 with the BLUP_EBV, Table 4).…”

“…This is a common situation in dairy cattle (Aguilar et al, 2010). Christensen et al (2012), who evaluated pig populations selected for daily gain and feed conversion ratio, reported more accurate predictions with a SSTEP model than with the pedigree-based method for both genotyped and non-genotyped animals. In their study, the large number of phenotypic data recording, together with the use of traits measured directly on candidate breeding stock, exploits the advantages of the use of SSTEP models.…”

Ancestral Relationships Using Metafounders: Finite Ancestral Populations and Across Population Relationships

“…The prior distribution for G can be assigned Vitezica et al 2011;Christensen et al 2012). This forces the equivalence between expected changes of the mean and variance under genetic drift (Vitezica et al 2011;Christensen et al 2012) for the populations described by either the pedigree or the genomic relationship matrices. For a set of n random variables u with variance-covariance matrix K, the sample average u ¼ 19u=n has a variance VarðuÞ ¼ K, whereas the sample variance S 2 u ¼ u9u=n 2 u 2 has expectation EðS Searle, 1982, p. 355).…”

“…This joint matrix H can be understood as a linear imputation of genotypes over all nongenotyped individuals (Christensen and Lund 2010), considering also the uncertainty in the imputation. This covariance matrix is increasingly used in genomic predictions of genetic merit (Aguilar et al 2010;Christensen et al 2012) and also in QTL detection (Dikmen et al 2013). The algebraic development of matrix H assumes that base allelic frequencies are known or, equivalently, that mean and variance of the population do not change with time.…”