Molecular marker-based prediction of hybrid performance in maize using unbalanced data from multiple experiments with factorial crosses

In hybrid breeding, the prediction of hybrid performance (HP) is extremely important as it is difficult to evaluate inbred lines in numerous cross combinations. Recent developments such as doubled haploid production and molecular marker technologies have enhanced the prospects of marker-based HP prediction to accelerate the breeding process. Our objectives were to (1) predict HP using a combined analysis of hybrids and parental lines from a breeding program, (2) evaluate the use of molecular markers in addition to phenotypic and pedigree data, (3) evaluate the combination of line per se data with marker-based estimates, (4) study the effect of the number of tested parents, and (5) assess the advantage of haplotype blocks. An unbalanced dataset of 400 hybrids from 9 factorial crosses tested in different experiments and data of 79 inbred parents were subjected to combined analyses with a mixed linear model. Marker data of the inbreds were obtained with 20 AFLP primer–enzyme combinations. Cross-validation was used to assess the performance prediction of hybrids of which no or only one parental line was testcross evaluated. For HP prediction, the highest proportion of explained variance (R2), 46% for grain yield (GY) and 70% for grain dry matter content (GDMC), was obtained from line per se best linear unbiased prediction (BLUP) estimates plus marker effects associated with mid-parent heterosis (TEAM-LM). Our study demonstrated that HP was efficiently predicted using molecular markers even for GY when testcross data of both parents are not available. This can help in improving greatly the efficiency of commercial hybrid breeding programs.