Combining ability of S3 progenies for key agronomic traits in popcorn: comparison of testers in top-crosses.

The successful development of hybrid cultivars depends on the reliability of estimated combining ability of the parent lines. The objectives of this study were to assess the combining ability of partially inbred S3 families of popcorn derived from the open-pollinated variety UENF 14, via top-crosses with four testers, and to compare the testers for their ability to discriminate the S3 progenies. The experiment was conducted in the 2015/2016 crop season, in an incomplete-block (Lattice) design with three replications. The following agronomic traits were evaluated: average plant height, grain yield (GY), popping expansion (PE), and expanded popcorn volume per hectare. The top-cross hybrid, originating from the BRS-Angela vs S3 progeny 10 combination, was indicated as promising, showing high values for specific combining ability for GY and PE. For the S3 progenies that showed high and positive GCA values for GY and PE, the continuity of the breeding program is recommended, with the advance of self-pollination generations. Fasoulas' differentiation index discriminated the BRS-Angela tester as the most suitable for identifying the superior progenies.

The contribution of dominance to phenotype prediction in a pine breeding and simulated population.

Pedigrees and dense marker panels have been used to predict the genetic merit of individuals in plant and animal breeding, accounting primarily for the contribution of additive effects. However, nonadditive effects may also affect trait variation in many breeding systems, particularly when specific combining ability is explored. Here we used models with different priors, and including additive-only and additive plus dominance effects, to predict polygenic (height) and oligogenic (fusiform rust resistance) traits in a structured breeding population of loblolly pine (Pinus taeda L.). Models were largely similar in predictive ability, and the inclusion of dominance only improved modestly the predictions for tree height. Next, we simulated a genetically similar population to assess the ability of predicting polygenic and oligogenic traits controlled by different levels of dominance. The simulation showed an overall decrease in the accuracy of total genomic predictions as dominance increases, regardless of the method used for prediction. Thus, dominance effects may not be accounted for as effectively in prediction models compared with traits controlled by additive alleles only. When the ratio of dominance to total phenotypic variance reached 0.2, the additive-dominance prediction models were significantly better than the additive-only models. However, in the prediction of the subsequent progeny population, this accuracy increase was only observed for the oligogenic trait.