Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations

Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (r(MG)) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability (h(2)), TPS and MD on r(MG) estimation. Our results showed that: (1) moderate r(MG) values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) r(MG) increased with an increase in h(2), TPS and MD, both correlation and variance analyses showed that h(2) is the most important factor and MD is the least important factor on r(MG) estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the r(MG) values for all the six trait-environment combinations were centered around zero, 49% predictions had r(MG) values above zero; (4) the trend observed in r(MG) differed with the trend observed in r(MG)/h, and h is the square root of heritability of the predicted trait, it indicated that both r(MG) and r(MG)/h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.