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

Abstract

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 (rMG) 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 (h2), TPS and MD on rMG estimation. Our results showed that: (1) moderate rMG 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) rMG increased with an increase in h2, TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4) the trend observed in rMG differed with the trend observed in rMG/h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG/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.

Context‐Specific Genomic Selection Strategies Outperform Phenotypic Selection for Soybean Quantitative Traits in the Progeny Row Stage

“…Additionally, in a progeny row scenario, selections using GS can be made prior to harvest, improving the efficiency in comparison with PS. In contrast with this study, Zhang et al (2017) observed increased GS prediction accuracy for maize (Zea mays L.) with increased training population and marker densities. Although this study used the Infinium beadchip SoySNP50K (Song et al, 2013) for genotyping, many soybean studies have begun genotyping with the less dense BARCSoySNP6k array (Song et al, 2014).…”

“…For yield, the predictions were most accurate when the training population and the test population were identical. In contrast with this study, Zhang et al (2017) observed increased GS prediction accuracy for maize (Zea mays L.) with increased training population and marker densities. Continued refinement of training population and marker densities will be essential for maximizing the efficiency of GS in soybean breeding operations.…”

First Report of Recurrent Genomic Selection with Real Data in Popcorn and Genetic Gain Increases

“…The high heritabilities (> 0.8) computed for the traits 100GW, EH and PH indicate the confidence of the transmission of the traits to future generations, directly influencing the selection gains (1). The high correlation of heritability with accuracy found agrees with results of (41–43). According to (43), when breeders outline the phenotyping experiments for GS, they should consider that the heritability of the traits of interest in the training population should be high to ensure a satisfactory predictive accuracy.…”

“…The high correlation of heritability with accuracy found agrees with results of (41–43). According to (43), when breeders outline the phenotyping experiments for GS, they should consider that the heritability of the traits of interest in the training population should be high to ensure a satisfactory predictive accuracy. To this end, the number of locations and replications should be increased.…”

CVRMS: Cross-validated Rank-based Marker Selection for Genome-wide Prediction of Low Heritability

“…For those heritable traits, prediction of the phenotype or risk using DNA can usually reach moderately high prediction accuracy (de los Campos et al, 2018). Thus, GP tends to increase prediction accuracy with higher heritability (Zhang et al, 2017). In general, high-heritability traits, including flowering time in plants and human height, have already been studied (de Oliveira et al, Lello et al, 2018).…”