Genomic breeding value prediction for simple maize hybrid yield using total effects of associated markers, under different imbalance levels and environments

• Published in: Genetics and molecular research Vol. 15; no. 1; p. 15017232
• Main Authors: Cantelmo, N F, Von Pinho, R G, Balestre, M
• Format: Journal Article
• Language: English
• Published: Brazil 11-03-2016
• Subjects: Brazil; Crosses, Genetic; Environment; Genetic Markers - genetics; Genome, Plant; Microsatellite Repeats; Models, Genetic; Phenotype; Plant Breeding - methods; Quantitative Trait Loci; Zea mays; Zea mays - genetics
• ISSN: 1676-5680; 1676-5680
• DOI: 10.4238/gmr.15017232

The main objective of a maize breeding program is to generate hybrid combinations that are more productive than those pre-existing in the market. However, the number of parents, and consequently the number of crosses, increases so rapidly that the phenotypic evaluation of all the possible combinations becomes economically and technically infeasible. In this context, predicting the performance of the most promising genotypes may increase the genetic gains with increased selection intensity and reduced breeding cycles. Thus, the present study aimed to use the total effects of associated markers method to predict genomic breeding values (GBVs) via cross-validation and by using different imbalance levels (10, 30, 50, and 70%). A set of 51 genotyped strains was used with 79 microsatellite markers and 273 hybrids that were generated by a partial diallel. A total of 186 and 272 hybrids were analyzed in the experiments within the southern and central regions of Brazil, respectively. The GBVs were, thus, predicted for each location in both the regions, and for training in one region and validation in another region. The correlation between the predicted and observed GBVs ranged from 0.48 to 0.91, depending on the imbalance level and the region analyzed. Overall, the results obtained in the present study were promising, particularly considering that a small number of markers were used and that the training and predictions occurred in the very distinct regions of southern and central Brazil.