The effect of different selection and evaluation approaches on the accuracy of genomic prediction in a simulated population

In this study, assessed genomic prediction accuracies based on different selection methods (phenotypic and estimated breeding value), evaluation procedures (GBLUP and ssGBLUP),training population (TP) sizes, heritability (h2) levels, marker densities and pedigree error (PE) rates in a simulated population. QMSim software was used to create a reference database of 1000 and number of animals was reduced to 200 (100 males and 100 females) during 95 generations to create LD and mutation-drift equilibrium. The heritability of the trait was 0.1, 0.3 and 0.5 and the marker density was simulated for three strategies of 1, 5 and 10 K. The proportions of errors substituted were 10%, 20% and 30%, respectively.The results of this study showed that, compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection.With increasing reference population size and trait heritability, genomic prediction accuracy increased in all strategies.When errors were introduced into the pedigree dataset from 0 to 30%, the prediction accuracies were only minimally influenced across all scenarios.Our study suggests that the use of ssGBLUP, EBV sThe results of this study showed that, compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection.With increasing reference population size and trait heritability, genomic prediction accuracy increased in all strategies.When errors were introduced into the pedigree dataset from 0 to 30%, the prediction accuracies were only minimally influenced across all scenarios.Our study suggests that the use of ssGBLUP, EBV selection, and high marker density could help improve genetic gainseven in the case of pedigree error in cattle.election, and high marker density could help improve genetic gainseven in the case of pedigree error in cattle.The results of this study showed that, compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection.With increasing reference population size and trait heritability, genomic prediction accuracy increased in all strategies.When errors were introduced into the pedigree dataset from 0 to 30%, the prediction accuracies were only minimally influenced across all scenarios.Our study suggests that the use of ssGBLUP, EBV selection, and high marker density could help improve genetic gainseven in the case of pedigree error in cattle.