Investigating the Effect of Genotyping Strategies on Genomic Prediction Accuracy in Sheep by Simulation

Genomic selection (GS) in sheep is challenging for several reasons, including greater genetic variability between and within sheep breeds, the cost of maintenance of reference populations, and the costs and limitations associated with genotyping. The Reduced cost of genotyping combined with a good comprehension of how to maximize the advantages of GS will dramatically increase the adoption of GS in sheep. Therefore, this study evaluated genotyping scenarios on sheep's prediction accuracy, considering limitations on phenotypic and genomic data availability. To this end, a multi-breed sheep population is simulated by imitating the sheep's genetic evaluation system. Four scenarios of genotyping were compared with pedigree-based evaluation. In each genotype scenario, to examine the impact of phenotypic record on prediction accuracy, 9 cases for phenotypic record were considered based on three assumptions (existence of phenotypic record for males and females, females and males). The results indicated improved prediction accuracy using genomic information, even when a finite amount of animals were genotyped. A comparison of different genotype scenarios showed that ewe genotyping has lower prediction accuracy and ewe and ram genotype determination had average prediction accuracy. This study showed that to achieve the most accurate prediction, it is not necessary to have phenotypic and genotypic records for all animals and that the highest prediction accuracy can be achieved by determining the genotype and phenotype of choice while reducing GS costs.