Accuracy of Genomic Prediction under Different Genetic Architectures and Estimation Methods

The accuracy of genomic breeding value prediction was investigated in various levels of reference popula-tion size, trait heritability and the number of quantitative trait locus (QTL). Five Bayesian methods, includ-ing Bayesian Ridge regression, BayesA, BayesB, BayesC and Bayesian LASSO, were used to estimate the marker effects for each of 27 scenarios resulted from combining three levels for heritability (0.1, 0.3 and 0.5), training population size (600, 1000 and 1600) and QTL numbers (50, 100 and 150). A finite locus model was used to simulate stochastically a historical population consisting 100 animals at first 100 genera-tions. Through next 100 generations, the population size gradually increased to 1000 individuals. Then the animals in generations 201 and 202 having both known genotypic and phenotypic records were assigned as reference population, and individuals at generations 203 and 204 were considered as validation population. The genome comprised five chromosomes of 100 cM length and 500 single nucleotide polymorphism markers for each chromosome that distributed through the genome randomly. The QTLs and markers were bi-allelic. In this study, the heritability had great significant positive effect on the accuracy (P<0.001). By increasing the size of the reference population, the average genomic accuracy increased from 0.64±0.03 to 0.70 ± 0.04 (P<0.001). The accuracy responded to increasing number of QTLs non-linearly. The highest and lowest accuracies of Bayesian methods were 0.40 ± 0.04 and 0.84 ± 0.05, respectively. The results showed having the greatest amount of information (i.e. highest heritability, highest contribution of gene action in phenotypic variation and large reference population size), the highest accuracy (0.84) was ob-tained, with all investigated methods of estimation.