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

Abstract

Introduction: In the present study, the accuracy of genomic prediction based on
different methods was investigated in a simulated population.
Material & methods: 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.
Results: 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.
Conclusion: Our study suggests that the use of ssGBLUP, EBV selection, and high
marker density could help improve genetic gain seven in the case of pedigree error in
cattle.