Accuracy of Genomic Selection Prediction in Barley Breeding Programs: A Simulation Study Based On the Real Single Nucleotide Polymorphism Data of Barley Breeding Lines

The aim of this study was to compare the accuracy of genomic selection (i.e., selection based on genome-wide markers) to phenotypic selection through simulations based on real barley (Hordeum vulgare L.) single nucleotide polymorphism (SNP) data (1325 SNPs by 863 breeding lines). We simulated 100 quantitative trait loci (QTL) at randomly picked SNPs, which were dropped from the marker data. The sum of heritability of all the QTL was set as 0.1, 0.2, 0.4, or 0.6. We generated 100 datasets for each simulation condition. A dataset was then separated into training (N = 200, 400, or 600) and validation sets. Bayesian methods, multivariate regression methods (partial least square and ridge regression), and machine learning methods (random forest and support vector machine) were used for building prediction models. The prediction accuracy was high for the Bayesian methods and ridge regression. Under medium and high heritability (h(2) = 0.4 and 0.6), the mean of predictions from all methods was more accurate than predictions based on any single method, suggesting that different methods captured different aspects of genotype-phenotype associations. The advantage of genomic over phenotypic selection was larger under lower heritability and a larger training dataset. The difference in prediction accuracy between polygenic and oligogenic traits was small. The models were also useful in increasing the accuracy of predictions on breeding lines with phenotypic records. The results indicate that genomic selection can be efficiently used in barley breeding programs.