Comparison of different statistical methods in genomic selection based on selection effectiveness criteria

This study aimed to compare different methods of genomic evaluation using the criteria of correlation (ρ), regression (β), mean square error (MSE), and selection effectiveness (SE). For this purpose, nine different scenarios were designed based on different levels of heritability (0.1, 0.3, and 0.5) and different numbers of QTLs (20, 200, and 1000). To simulate different scenarios, five generations with a size of 1000 animals were simulated, of which the first two generations were considered as the reference population and the next three generations as the validation population. For each animal, a genome of 500 centimorgans with a marker density of 10000 SNP consisting of five chromosomes was simulated. Genomic breeding values were predicted using three statistical methods GBLUP, Bayes A, and Bayes B. The results showed that with increasing generation interval from the reference population, the accuracy of genomic breeding values decreased for three statistical methods, although Bayesian methods performed better in terms of continuity of accuracy. Based on the criteria of correlation, regression, and selection effectiveness, with increasing the levels of heritability, improved accuracy was observed, but the criterion of mean squares error showed the opposite trend. Bayesian methods performed better in low QTLs, but differences in different genomic evaluation methods were minimized in high QTLs. The results of selection effectiveness in comparison with the accuracy of genomic breeding value showed that accuracy can’t always be a suitable criterion for determining the superior method of genomic evaluation.