Abstract
In plant breeding, the dialelic models univariate have aided the selection of parents for hybridization. Multivariate analyses allow combining and associating the multiple pieces of information of the genetic relationships between traits. Therefore, multivariate analyses might refine the discrimination and selection of the parents with greater potential to meet the goals of a plant breeding program. Here, we propose a method of multivariate analysis used for stablishing mega-traits (MTs) in diallel trials. The proposed model is applied in the evaluation of a multi-environment complete diallel trial with 90 F1's of simple maize hybrids. From a set of 14 traits, we demonstrated how establishing and interpreting MTs with agronomic implication. The diallel analyzes based on mega-traits present an important evolution in statistical procedures since the selection is based on several traits. We believe that the proposed method fills an important gap of plant breeding. In our example, three MTs were established. The first, formed by plant stature-related traits, the second by tassel size-related traits, and the third by grain yield-related traits. Individual and joint diallel analysis using the established MTs allowed identifying the best hybrid combinations for achieving F1's with lower plant stature, tassel size, and higher grain yield.
Highlights
Experimental mating designs, especially diallel crosses, are widely used in maize breeding for the selection of superior hybrid combinations
With the estimates of the genetic parameters the breeder can define which are the combinations of crosses that reveal greater heterosis in F1 for the desired trait
We carried out complete diallel crosses (F1’s and reciprocals) with ten maize inbred lines (S9 generation, KSP 1 to KSP 10) of the Maize Breeding Program of the KSP Seeds and research Ltda, Pato Branco, PR, Brazil
Summary
Experimental mating designs, especially diallel crosses, are widely used in maize breeding for the selection of superior hybrid combinations. These types of trials allow one to estimate the general combining ability (GCA), related to the additive gene effects and the specific combining ability (SCA), related to non-additive gene actions (Feher et al 2014, Oliveira et al 2016). The genotype × environment interaction is characterized by the phenotypic performance of the hybrids not being consistent in the multi-environments. The genetic causes of genotype × environment interaction can be attributed to physiological, biochemical, adaptive and related to scale representation of traits (Cruz et al 2012)
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