Luiz Paulo Miranda Pires

Inclusion of Dominance Effects in the Multivariate GBLUP Model

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.

Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers

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.

Selection Index in the Study of Adaptability and Stability in Maize

This paper proposes an alternative method for evaluating the stability and adaptability of maize hybrids using a genotype-ideotype distance index (GIDI) for selection. Data from seven variables were used, obtained through evaluation of 25 maize hybrids at six sites in southern Brazil. The GIDI was estimated by means of the generalized Mahalanobis distance for each plot of the test. We then proceeded to GGE biplot analysis in order to compare the predictive accuracy of the GGE models and the grouping of environments and to select the best five hybrids. The G × E interaction was significant for both variables assessed. The GGE model with two principal components obtained a predictive accuracy (PRECORR) of 0.8913 for the GIDI and 0.8709 for yield (t ha−1). Two groups of environments were obtained upon analyzing the GIDI, whereas all the environments remained in the same group upon analyzing yield. Coincidence occurred in only two hybrids considering evaluation of the two features. The GIDI assessment provided for selection of hybrids that combine adaptability and stability in most of the variables assessed, making its use more highly recommended than analyzing each variable separately. Not all the higher-yielding hybrids were the best in the other variables assessed.This paper proposes an alternative method for evaluating the stability and adaptability of maize hybrids using a genotype-ideotype distance index (GIDI) for selection. Data from seven variables were used, obtained through evaluation of 25 maize hybrids at six sites in southern Brazil. The GIDI was estimated by means of the generalized Mahalanobis distance for each plot of the test. We then proceeded to GGE biplot analysis in order to compare the predictive accuracy of the GGE models and the grouping of environments and to select the best five hybrids. The G × E interaction was significant for both variables assessed. The GGE model with two principal components obtained a predictive accuracy (PRECORR) of 0.8913 for the GIDI and 0.8709 for yield (t ha−1). Two groups of environments were obtained upon analyzing the GIDI, whereas all the environments remained in the same group upon analyzing yield. Coincidence occurred in only two hybrids considering evaluation of the two features. The GIDI assessment provided for selection of hybrids that combine adaptability and stability in most of the variables assessed, making its use more highly recommended than analyzing each variable separately. Not all the higher-yielding hybrids were the best in the other variables assessed.

Maize hybrid stability in environments under water restriction using mixed models and factor analysis

In several crops, the water deficit is perhaps the main limiting factor in the search for high yields. The objective of this study was to evaluate the phenotypic stability of maize hybrids in environments with and without water restriction using the analytical factor (AF) approach. We evaluated 171 maize hybrids in 14 environments, divided into environments with (A1, A2, A3, A4, A5, A6, and A7) and without (A8, A9, A10, A11, A12, A13, and A14) water restriction, over a period of 7 years. Each year, 36 hybrids were evaluated. A square lattice design (6 x 6) was used, with common treatments between years. The characteristics of grain yield (GY), male flowering (MF) and female flowering (FF), plant height (PH), and ear height (EH) were evaluated. Phenotypic adaptability and stability of the hybrids were also verified. Hybrids G66, G99, G86, and G26 were the most stable and showed potential for use in environments with and without water restriction. The AF models showed to be useful for evaluating hybrids over many years, allowing selection of better hybrids with adaptability, specific and general stability, and correlation of hybrids with their production components, in addition to allowing identification of mega-environments that permit stability in the response of the adapted hybrids.

Plants population and harvesting times influence in saccharine sorghum BRS 506 production

Saccharine sorghum is an excellent option for ethanol production under industrial and agronomic perspectives, due to high green matter production and succulent stalks with fermentable sugars. The aim of this work was to evaluate the harvesting season and plants population effect over the ethanol and fodder production from sweet sorghum culture BRS 506. The experiment was installed in November 2012, at (CDTCA/UFLA), located in Lavras (MG). The experimental design was in randomized blocks, with 3 repetitions, factorial scheme 4 x 4 (4 populations: 70, 100, 130 and 160 thousand plants ha-1 ; 4 harvesting seasons: flowering (0 days after flowering (DAF), 10 DAF, 20 DAF and physiological maturity at 40 DAF). The variables evaluated at harvesting: green matter weight (GM), dry matter weight (DM), juice volume (JV), total soluble solids (obrix), total reducing sugars (TRS) and brix tonnes per hectare (TBH). The populations increase provided the highest JV and it has not affected other variables. The characteristics were influenced by the harvesting seasons. The harvesting season at 40 DAF provided the highest obrix. The obrix and the TRS showed linear growth with an increase after flowering for plants harvesting. The highest productivities (GM, DM, JV and TBH) were obtained close to 17, 22, 17 and 14 DAF, respectively, favoring higher fodder and ethanol production in these stages.

Assessing non-additive effects in GBLUP model

Understanding non-additive effects in the expression of quantitative traits is very important in genotype selection, especially in species where the commercial products are clones or hybrids. The use of molecular markers has allowed the study of non-additive genetic effects on a genomic level, in addition to a better understanding of its importance in quantitative traits. Thus, the purpose of this study was to evaluate the behavior of the GBLUP model in different genetic models and relationship matrices and their influence on the estimates of genetic parameters. We used real data of the circumference at breast height in Eucalyptus spp and simulated data from a population of F2. Three commonly reported kinship structures in the literature were adopted. The simulation results showed that the inclusion of epistatic kinship improved prediction estimates of genomic breeding values. However, the non-additive effects were not accurately recovered. The Fisher information matrix for real dataset showed high collinearity in estimates of additive, dominant, and epistatic variance, causing no gain in the prediction of the unobserved data and convergence problems. Estimates presented differences of genetic parameters and correlations considering the different kinship structures. Our results show that the inclusion of non-additive effects can improve the predictive ability or even the prediction of additive effects. However, the high distortions observed in the variance estimates when the Hardy-Weinberg equilibrium assumption is violated due to the presence of selection or inbreeding can converge at zero gains in models that consider epistasis in genomic kinship.

FLUXO GÊNICO EM MILHO SOB DIFERENTES TAMANHOS DE AMOSTRAS E DISTÂNCIAS DE AMOSTRAGEM

RESUMO - A preocupacao a respeito do fluxo genico entre lavouras de milho transgenicas e convencionais tem recebi-do cada vez mais atencao no âmbito tecnico e politico no Brasil. Diante disso, objetivou-se, com este trabalho, estimar o fluxo genico em lavouras comerciais de milho, bem como estabelecer um tamanho de amostra adequado para estimar a taxa de contaminacao por transgenicos nos campos convencionais de milho com o uso de amostragem sistematica. As coletas das amostras foram realizadas em campos de producao de graos comerciais nos municipios de Itumirim e Madre de Deus, MG, na safra 2010/2011 e na cidade de Ingai, MG, na safra 2011/2012. Foram coletadas amostras em cinco distâncias do campo convencional em relacao ao campo de transgenicos: 5 m, 10 m, 20 m, 50 m e 100 m. Foram tambem coletados quatro diferentes tamanhos de amostra: 1 espiga, 5 espigas, 10 espigas e 15 espigas em quatro re-peticoes. Apos as coletas, retirou-se de cada amostra uma aliquota de 300 sementes para realizacao do PCR em tempo real para a estimacao do fluxo genico. Nao houve diferenca significativa entre os diferentes tamanhos analisados nos tres locais. Em Itumirim e em Madre de Deus, tambem nao houve diferenca entre as distâncias analisadas; ja no campo do municipio de Ingai, as amostras coletadas na distância de 10 m apresentaram maior taxa de contaminacao. Em todos os campos, a contaminacao media ficou abaixo de 1%. Palavras-chave: Zea may L, transgenico, contaminacao. ANALYSIS OF GENE FLOW IN MAIZE UNDER DIFFERENT SAMPLE SIZES AND SAMPLING DISTANCES ABSTRACT – The concern about gene flow between transgenic and conventional maize farming has been received increasing attention in technical and political scope in Brazil. Thus, the objective of this work was to estimate the gene flow in commercial maize farms, as well as the establishment of an adequate sample size to estimate the contamination rate by transgenic in conventional maize fields, using systematic sampling. The samples collections were carried out in fields of grain commercial production in the Itumirim and Madre de Deus municipalities, both in Minas Gerais state, in 2010/2011 crop season and in Ingai, also Minas Gerais state, in 2011/2012 crop season. Samples were collected at five distances between conventional and transgenic field: 5, 10, 20, 50 and 100 m. Four different sample sizes were also collected: 1, 5, 10 and 15 spikes, with four replications. After collection, a portion of 300 seeds of each sample was used in a real-time PCR for the gene flow estimation. There was no statistical difference among the different sample sizes analyzed at the three sites. In Itumirim and Madre de Deus also no statistical difference was observed among the distances analyzed; the samples collected in Ingai field in a distance of 10 m had a higher rate of contamination by transgenics when compared to the other samples. The average contamination was below of 1% in all fields. Palavras-chave: Zea mays, transgenic, contamination.