Performance prediction of F1 hybrids between recombinant inbred lines derived from two elite maize inbred lines

Selection of recombinant inbred lines (RILs) from elite hybrids is a key method in maize breeding especially in developing countries. The RILs are normally derived by repeated self-pollination and selection. In this study, we first investigated the accuracy of different models in predicting the performance of F₁ hybrids between RILs derived from two elite maize inbred lines Zong3 and 87-1, and then compared these models through simulation using a wider range of genetic models. Results indicated that appropriate prediction models depended on genetic architecture, e.g., combined model using breeding value and genome-wide prediction (BV+GWP) has the highest prediction accuracy for high V _D/V _A ratio (>0.5) traits. Theoretical studies demonstrated that different components of genetic variance were captured by different prediction models, which in turn explained the accuracy of these models in predicting the F₁ hybrid performance. Based on genome-wide prediction model (GWP), 114 untested F₁ hybrids possibly having higher grain yield than the original F₁ hybrid Yuyu22 (the single cross between Zong3 and 87-1) have been identified and recommended for further field test.