PSXIV-23 Prediction accuracies of genomic selection in American mink: a simulation study. - Karimi, K (J Anim Sci, 2018)
Selection based on genome-wide information has become a common approach to select desirable individuals in livestock species. The objective of this study was to compare the predictive accuracies of traditional BLUP, multi-step genomic BLUP (GBLUP) and single-step GBLUP (ssGBLUP) in a simulated mink population. Different levels of heritability (0.1, 0.2 and 0.5) and marker density (10k and 50k) were simulated using QMSim software for a genome including 5000 QTL across 14 autosomal chromosomes. The number of animals in the training set (TS) was set to be 1000, 2000, 3000, 4000 and 5000 in each scenario. Accuracy of genomic estimated breeding values was enhanced with increase in the levels of heritability and marker density. Higher number of animals in TS increased the accuracies of GBLUP and ssGBLUP methods. The prediction accuracies of GBLUP and ssGBLUP in 1000 TS scenarios were not higher than those obtained with BLUP. However, more accurate predictions were observed using genomic methods compared to BLUP in ≥2000 TS scenarios and for low heritability traits (h(2)=0.1 and 0.2). Furthermore, number of animals in TS should be at least 3000 for heritability of 0.5 to achieve more accuracy than using BLUP. For heritability of 0.1, predictions of ssGBLUP were more accurate than those obtained by GBLUP. However, no robust differences were observed for higher heritabilities. These results confirmed that genomic selection can effectively improve the reliability of genetic evaluations in mink farming.