Abstract
New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components.
Highlights
Several factors can complicate the process of selection and recommendation of cultivars in a maize breeding program
The genetic group defined by the lines closely related to B73 was located in the upper left corner of the plot and that closely related to Mo17 was located in the central lower corner
Several genetic architecture scenarios with variations in heritability conditions were created to evaluate the usefulness of multivariate models in maize genetic breeding programs, primarily seeking to analyze background genetic architecture conditions previously reported in other scientific publications
Summary
Several factors can complicate the process of selection and recommendation of cultivars in a maize breeding program. One of the most severe factors is the potentially large number of hybrids that can be obtained from a relatively small set of lines. This number of crosses complicates the choice of the formation of combination pairs, even when well-established heterotic groups are available. Should the breeder perform all or most of these combinations, the high financial costs and operational difficulties under testing conditions with several replicates and installed at a high number of sites would render the evaluation of these hybrids financially. The availability of tailor-made methods of prediction of the best combinations between candidate lines may be a key point in maximizing the use of financial resources in breeding programs
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