Hybrid breeding programs are driven by the potential to explore the heterosis phenomenon in traits with nonadditive inheritance. Traditionally, progress has been achieved by crossing lines from different heterotic groups and measuring phenotypic performance of hybrids in multiple environment trials. With the reduction in genotyping prices, genomic selection has become a reality for phenotype prediction and is a promising tool to predict hybrid performances. However, its prediction ability is directly associated with models that represent the trait and breeding scheme under investigation. Herein we assessed modeling approaches where dominance effects and multienvironment were considered for genomic selection in maize (Zea mays L.) hybrids. To this end, we evaluated the predictive ability of grain yield and grain moisture collected over three cycles in different locations. Hybrid genotypes were inferred in silico based on their parental inbred lines using single nucleotide polymorphism (SNP) markers obtained via a 500k SNP chip. We considered the importance to decompose additive and dominance marker effects into components that are constant across environments and deviations that are group specific. Prediction within and across environments were tested. The incorporation of dominance effect increased the predictive ability for grain production by up to 30%. Contrastingly, additive models yielded better results for grain moisture. For multienvironment modeling, the inclusion of interaction effects increased the predictive ability overall. More generally, we demonstrate that including dominance and genotype × environment interactions resulted in gains in accuracy and hence could be considered for implementation in genomic selection in maize breeding programs.

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Abstract

The objective of this research was to analyze the use of cultivar index selection (GARCIA, 1998) ELSTON (1963), MULAMBA & MOCK (1978), SCHWARZBACH (1972) and GARCIA (1998) GARCIA (1998) proved to be more effi cient than the other indexes, since it showed more correlation with PRODV and selected more productive strains. The recommended genotypes were: UENF 7-5-1 and UENF 7-6-1, followed by the genotypes

ABSTRACT. In Brazil, the first genetically modified (GM) crop was released in 1998, and it is estimated that 84, 78, and 50% of crop areas containing soybean, corn, and cotton, respectively, were transgenic in 2012. This intense and rapid adoption rate confirms that the choice to use technology has been the main factor in developing national agriculture. Thus, this review focuses on understanding these dynamics in the context of farmers, trade relations, and legislation. To accomplish this goal, a survey was conducted using the database of the National Cultivar Registry and the National Service for Plant Variety Protection of the Ministry of Agriculture, Livestock and Supply [Ministério da Agricultura, Pecuária e Abastecimento (MAPA)] between 1998 and October 13, 2013. To date, 36 events have been released: five for soybeans, 18 for corn, 12 for cotton, and one for beans. From these events, 1395 cultivars have been developed and registered: 582 for soybean, 783 for corn and 30 for cotton. Monsanto owns 73.05% of the technologies used to develop these cultivars, while the Dow AgroScience -DuPont partnership and Syngenta have 16.34 and 4.37% ownership, respectively. Thus, the provision of transgenic seeds by these companies is an oligopoly supported by legislation. Moreover, there has been a rapid replacement of conventional crops by GM crops, whose technologies belong almost exclusively to four multinational companies, with the major ownership by Monsanto. These results reflect a warning to the government of the increased dependence on multinational corporations for key agricultural commodities.