Tocochromanols (vitamin E) are an essential part of the human diet.
Plant products including maize grain are the major dietary source of
tocochromanols; therefore, breeding maize with higher vitamin content
(biofortification) could improve human nutrition. Incorporating exotic
germplasm in maize breeding for trait improvement including
biofortification is a promising approach and an important research
topic. However, information about genomic prediction of exotic-derived
lines using available training data from adapted germplasm is limited.
In this study, genomic prediction was systematically investigated for
nine tocochromanol traits within both an adapted (Ames Diversity Panel)
and an exotic-derived (BGEM) maize population. While prediction
accuracies up to 0.79 were achieved using gBLUP when predicting within
each population, genomic prediction of BGEM based on an Ames Diversity
Panel training set resulted in low prediction accuracies. Optimal
training population (OTP) design methods FURS, MaxCD, and PAM were
adapted for inbreds and, along with the methods CDmean and PEVmean,
often improved prediction accuracies compared to random training sets of
the same size. When applied to the combined population, OPT designs
enabled successful prediction of the rest of the exotic-derived
population. Our findings highlight the importance of leveraging genotype
data in training set design to efficiently incorporate new exotic
germplasm into a plant breeding program.