Optimization of unbalanced composites of base populations in breeding programs
DOI:
https://doi.org/10.33158/ASB.r277.v12.2026Keywords:
Maize breeding, varietal composites, performance prediction, heterosis, genetic optimization, cultivar composites, hybrid combinationsAbstract
The extremely large number of possible composites complicates the use of composites to form base populations for recurrent selection schemes, even when the number of parents is relatively small. Given the practical infeasibility of synthesizing and evaluating all possible composites, prediction formulas are used for different types of base populations. For composites, in 1970, Vencovsky) proposed the following formula: which allows the expected value of a quantitative trait to be predicted as a function of the parental contribution weights (here denoted as fi). This study presents a mathematical procedure for deriving formulas to calculate the parental contribution weights that maximize the predicted performance of an unbalanced composite. The derivations were developed for composites involving three parents, although the procedure can be extended to a larger number of parents. Mathematical optimization techniques were used to identify the point of maximum predicted performance. Two situations may arise: the optimum may involve contributions from all three parents, or it may exclude one of them, potentially reducing the genetic variability of the composite. In the latter case, a procedure is adopted to impose a minimum contribution from the excluded parent while minimizing the reduction in the predicted performance of the resulting composite. Another objective was to determine the factors that influence the increase in predicted performance of the maximized unbalanced composite relative to the balanced composite. Two practical examples using maize diallel-cross data illustrate the application of the maximization procedure under the two situations described above. Overall, the maximization procedure for unbalanced composites substantially enhances the utility of composite prediction methods.
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