A new genomic model that incorporates genotype × environment interaction gave increased prediction accuracy of untested hybrid response for traits such as percent starch content, percent dry matter content and silage yield of maize hybrids. The prediction of hybrid performance (HP) is very important in agricultural breeding programs. In plant breeding, multi-environment trials play an important role in the selection of important traits, such as stability across environments, grain yield and pest resistance. Environmental conditions modulate gene expression causing genotype × environment interaction (G × E), such that the estimated genetic correlations of the performance of individual lines across environments summarize the joint action of genes and environmental conditions. This article proposes a genomic statistical model that incorporates G × E for general and specific combining ability for predicting the performance of hybrids in environments. The proposed model can also be applied to any other hybrid species with distinct parental pools. In this study, we evaluated the predictive ability of two HP prediction models using a cross-validation approach applied in extensive maize hybrid data, comprising 2724 hybrids derived from 507 dent lines and 24 flint lines, which were evaluated for three traits in 58 environments over 12 years; analyses were performed for each year. On average, genomic models that include the interaction of general and specific combining ability with environments have greater predictive ability than genomic models without interaction with environments (ranging from 12 to 22%, depending on the trait). We concluded that including G × E in the prediction of untested maize hybrids increases the accuracy of genomic models.

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Genomic selection (GS) has become a tool for selecting candidates in plant and animal breeding programs. In the case of quantitative traits, it is common to assume that the distribution of the response variable can be approximated by a normal distribution. However, it is known that the selection process leads to skewed distributions. There is vast statistical literature on skewed distributions, but the skew normal distribution is of particular interest in this research. This distribution includes a third parameter that drives the skewness, so that it generalizes the normal distribution. We propose an extension of the Bayesian whole-genome regression to skew normal distribution data in the context of GS applications, where usually the number of predictors vastly exceeds the sample size. However, it can also be applied when the number of predictors is smaller than the sample size. We used a stochastic representation of a skew normal random variable, which allows the implementation of standard Markov Chain Monte Carlo (MCMC) techniques to efficiently fit the proposed model. The predictive ability and goodness of fit of the proposed model were evaluated using simulated and real data, and the results were compared to those obtained by the Bayesian Ridge Regression model. Results indicate that the proposed model has a better fit and is as good as the conventional Bayesian Ridge Regression model for prediction, based on the DIC criterion and cross-validation, respectively. A computing program coded in the R statistical package and C programming language to fit the proposed model is available as supplementary material.

Background: Capsicum spp. grow in environments with different incident radiation, that could modify the plant growth and the concentration of phytochemicals in fruits. Hypothesis: Shading positively affects phenology and fruit yield, decreases the total contents of phenols (TPC), flavonoids (FLV), proanthocyanidins (PAN) and carotenoids (CAT) in fruits of wild Capsicum species. Studied species: Capsicum annuum var. glabriusculum: amashito (AMA) and garbanzo (GAR), and C. frutescens (Pico Paloma, PIP) Study site and dates: Huimanguillo, Tabasco, Mexico; 2020 and 2021. Methods: Seeds were treated with gibberellic acid (GA3) (500 mg L-1) for 24 h prior to seeding. The seedlings were transplanted in an open field and under two levels of shade (35 and 70 %) under a subsplit plot design with four replicates. TPC, FLV, PAN and CAT were determinate in immature and ripe fruits by UV-vis spectrophotometry. Results: Shade accelerated the phenological processes from the first bifurcation of the stem, and decreased the fruits ripening time from anthesis of the genotypes studied. Shade only increased the yield of the AMA genotype and reduced the contents of TPC, FLV, and CAT; however, these metabolites increased under open field conditions. Conclusions: The shade reduced the duration of phenological stages including the ripening period of fruits, and increased the plant height of the Capsicum spp. The increase in yield by shading effect only was observed in AMA genotype. The content of phytochemicals in Capsicum fruits is reduced by shading levels.