New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components.

BackgroundThe identification of lines resistant to ear diseases is of great importance in maize breeding because such diseases directly interfere with kernel quality and yield. Among these diseases, ear rot disease is widely relevant due to significant decrease in grain yield. Ear rot may be caused by the fungus Stenocarpella maydi; however, little information about genetic resistance to this pathogen is available in maize, mainly related to candidate genes in genome. In order to exploit this genome information we used 23.154 Dart-seq markers in 238 lines and apply genome-wide selection to select resistance genotypes. We divide the lines into clusters to identify groups related to resistance to Stenocarpella maydi and use Bayesian stochastic search variable approach and rr-BLUP methods to comparate their selection results.ResultsThrough a principal component analysis (PCA) and hierarchical clustering, it was observed that the three main genetic groups (Stiff Stalk Synthetic, Non-Stiff Stalk Synthetic and Tropical) were clustered in a consistent manner, and information on the resistance sources could be obtained according to the line of origin where populations derived from genetic subgroup Suwan presenting higher levels of resistance. The ridge regression best linear unbiased prediction (rr-BLUP) and Bayesian stochastic search variable (BSSV) models presented equivalent abilities regarding predictive processes.ConclusionOur work showed that is possible to select maize lines presenting a high resistance to Stenocarpella maydis. This claim is based on the acceptable level of predictive accuracy obtained by Genome-wide Selection (GWS) using different models. Furthermore, the lines related to background Suwan present a higher level of resistance than lines related to other groups.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0392-3) contains supplementary material, which is available to authorized users.

Plant with a more upright architecture offers many advantages to farmers. Recurrent mass selection (RS) programs for carioca type common bean have been implemented for the purpose of obtaining new lines that will generate the high yields that are associated with upright plant architecture. This study aimed to assess the efficiency of recurrent mass selection (RS) for upright plant architecture in common bean (Phaseolus vulgaris) and the effect of RS on grain yield and to verify whether or not there is still variability in the population that favors continuing selection programs, using information obtained from progenies evaluated in cycle five (CV) and cycle eight (CVIII) of the RS program. Mass selection for more upright plants was performed visually in the "S 0 " generation before flowering. Progenies S 0:3 and S 0:4 were evaluated in 2009 (CV) and 2011 (CVIII). Heritability (h²) and RS progress were estimated using adjusted means.After eight selection cycles, the population subjected to RS still had enough genetic variability to achieve continued success through recurrent selection. The RS progress was 1.62 % per cycle for the growth habit scores and 6.81 % for grain yield.

Core Ideas The Bayesian Additive Main Effects and Multiplicative Interaction can deal with unbalanced data. The Additive Main Effects and Multiplicative Interaction elipses can capture the uncertain about unbalanced data sets. The cross‐validation results showed the ability of Bayesian Additive Main Effects and Multiplicative Interaction in genotypes × environments interaction predictions. The identification of genotypes presenting wide adaptability and stability is pivotal in breeding programs. To identify such genotypes, it is necessary to use sophisticated analytical tools to establish the genotypes × environments interaction (GEI) pattern across multi‐environment trials and select for genotypic stability and adaptability. The aim of the present study was to estimate GEI using Bayesian analysis of Additive Main Effects and Multiplicative Interaction (AMMI) models for both balanced and unbalanced data sets and estimate the predictive ability of model. Two studies were assessed to showcase this approach; in the first, 10 commercial maize (Zea mays) single‐cross hybrids and 45 double‐cross hybrids were evaluated at 15 different locations. In the second study, 28 hybrids were evaluated in 35 different environments distributed over two different harvest seasons (first and second harvests) with unbalanced data sets within and between harvests. The Bayesian analysis of the AMMI models was robust in dealing with the unbalanced data. This approach is promising for the identification of interaction patterns and the estimation of GEI. The genotypes and environments could be grouped according to their interaction patterns even using the unbalanced data sets, showing that Bayesian analysis of AMMI models could be applied effectively for multi‐environment trials. The prediction for missing hybrids was satisfactory in a simulated unbalanced design and captured the GEI and patterns in the data. This allowed the direct comparison of genotypes from the first and second harvests and the estimation of selection gain.

No abstract