SUMMARY It is becoming increasingly evident that a plant-pathogen interaction may be compared to an open warfare, whose major weapons are proteins synthesized by both organisms. These weapons were gradually developed in what must have been a multimillion-year evolutionary game of ping-pong. The outcome of each battle results in the establishment of resistance or pathogenesis. The plethora of resistance mechanisms exhibited by plants may be grouped into constitutive and inducible, and range from morphological to structural and chemical defences. Most of these mechanisms are defensive, exhibiting a passive role, but some are highly active against pathogens, using as major targets the fungal cell wall, the plasma membrane or intracellular targets. A considerable overlap exists between pathogenesis-related (PR) proteins and antifungal proteins. However, many of the now considered 17 families of PR proteins do not present any known role as antipathogen activity, whereas among the 13 classes of antifungal proteins, most are not PR proteins. Discovery of novel antifungal proteins and peptides continues at a rapid pace. In their long coevolution with plants, phytopathogens have evolved ways to avoid or circumvent the plant defence weaponry. These include protection of fungal structures from plant defence reactions, inhibition of elicitor-induced plant defence responses and suppression of plant defences. A detailed understanding of the molecular events that take place during a plant-pathogen interaction is an essential goal for disease control in the future.
O objetivo deste trabalho foi avaliar os métodos estatísticos de análise da interação de genótipos com ambientes (GxA), enfatizando a adaptabilidade e a estabilidade fenotípica. Utilizaram-se dados de produtividade de grãos de soja de sete experimentos em Goiás, testando 28 genótipos, dos quais quatro cultivares comerciais. Avaliaram-se os métodos Tradicional, Plaisted & Peterson, Wricke, Finlay & Wilkinson, Eberhart & Russell, Verma, Chahal & Murty, Toler, AMMI (additive main effect and multiplicative interaction), Hühn, Annicchiarico e Lin & Binns. Avaliou-se a associação entre os métodos pela correlação de Spearman. Observou-se forte associação entre os de Plaisted & Peterson e Wricke, cujo uso concomitante foi contra-indicado. A mesma conclusão é atribuída aos métodos Annicchiarico e Lin & Binns, também fortemente associados, o que implica em classificações fenotípicas muito semelhantes. O uso de um deles, entretanto, é recomendado. Métodos baseados, exclusivamente, em coeficientes de regressão, devem ser utilizados em associação com outro, fundamentado na variância da interação GxA, ou em medidas estatísticas como a variância dos desvios da regressão. O uso combinado do método de Eberhart & Russell e AMMI é outra indicação, em razão de suas correlações significativas com a maioria dos outros métodos e uma associação relativamente fraca entre eles.
Resumo -O objetivo deste trabalho foi avaliar a influência da interação de genótipos com ambientes (GxA) na produtividade de grãos de um conjunto de linhagens de soja (Glycine max L.). Foram utilizados dados de 11 experimentos (ambientes) realizados no Estado de Goiás. Em cada experimento foram avaliados 18 genótipos, sendo quatro cultivares comerciais como testemunhas. O método de análise da interação foi o procedimento AMMI (modelo de efeitos principais aditivos e interação multiplicativa). O padrão significativo das interações GxA foi captado apenas pelo primeiro eixo principal AMMI, o qual explicou 36% da soma de quadrados GxA original, sugerindo contaminação da matriz de interações clássica por ruídos que prejudicam a qualidade das predições de respostas fenotípicas obtidas pelos métodos tradicionais. Quanto à estabilidade de comportamento, a maioria das linhagens experimentais destacou-se (com menores interações com ambientes) em relação às cultivares testemunhas. Estas, no entanto, foram relativamente mais produtivas, sobretudo a cultivar Conquista. Entre as novas linhagens, os genótipos L-16, L-13 e L-14 mostraram ser os mais promissores para fins de recomendação como cultivares.Termos para indexação: Glycine max, progênie, interação genótipo-ambiente, adaptação, biplot. Application of AMMI analysis in the assessment of yield stability in soybeanAbstract -The objective of this work was to evaluate the influence of the genotype environment (GE) interaction on the grain yield of a soybean (Glycine max L.) lines group. Yield data from 11 trials (environments) conducted in the State of Goiás, Brazil were used. In each trial 18 genotypes were tested, from which four were commercial cultivars as checks. The statistical method was the AMMI analysis (additive main effect and multiplicative interaction analysis). A significant GE interaction pattern was captured only for the first principal AMMI axis, which explained 36% of the original square sum of the GE interaction, suggesting contamination of the classic GE interaction matrix by noise arising from unpredictable factors, assuring that AMMI analysis provides a better prediction of phenotypic responses than traditional methodologies. About yield stability, most experimental lines outstands (with low GE interaction) over checks. However, the checks, mainly the cultivar Conquista, showed higher yield averages. Among the experimental lines, the genotypes L-16, L-13 and L-14 appeared to be the most promising for cultivars recommendation.Index terms: Glycine max, progeny, genotype environment interaction, adaptation, biplot.(1) Aceito para publicação em 17 de dezembro de 2002. IntroduçãoAs interações de genótipos com ambientes (GxA) trazem aos melhoristas dificuldades na identificação de genótipos superiores, seja por ocasião da seleção, seja no momento da recomendação de cultivares. A presença dessas interações indica que o comportamento relativo dos genótipos nos testes depende, fundamentalmente, das condições ambientais a que estão submetidos. Desta forma, a res...
Phenotypic yield stability is a trait of special interest for plant breeders. Many statistical procedures are available for stability analysis, each of them allowing for different interpretations. The objective of the present study was to determine the degree of correlation among the 13 statistical parameters that can be used for the analysis of phenotypic stability. Such correlations could be used to assess the extent to which these 13 parameters identify unique genetic effects. Yield data were obtained from 12 yield trials involving 76 common bean (Phaseolus vulgaris L.) genotypes and 12 location‐year production environments in Brazil. The stability statistics were divided in four groups according to the structure from which they were derived. On the basis of rank correlation, it was concluded that (i) there were highly significant correlations between many of the stability statistics (among and within groups) indicating that several of the statistics probably measure similar aspects of phenotypic stability; (ii) mean yields were positively correlated with many of the stability statistics; (iii) there was an association between the Group A statistics (variances and ranges) and the Group C statistics (regression and determination coefficients), and a similar association between the Group B (ecovalence) and Group D (variance of deviations from regression) statistics; (iv) the segmented linear regression coefficient (b1i) was overall the most independent parameter, indicating that the other stability statistics do not satisfactorily reflect genotypic responses in poor environments; (v) the strong correlation between the regression coefficients and the coefficients of determination indicates that the latter are not needed to measure the predictability of the estimated genotypic response; and (vi) the variance of the deviations from regression can provide assessment of the relative contribution of the genotype to the genotype × environment interaction as well as its biological stability.
Resumo -O objetivo deste trabalho foi quantificar os efeitos da interação genótipo x ambiente (GxE) sobre a produtividade de grãos em progênies de soja pré-selecionadas para resistência à ferrugem asiática (Phakopsora pachyrhizi). Doze ensaios de avaliação de progênies (linhagens F 6 e F 7 ) foram conduzidos em diferentes ambientes (combinação de locais, anos e tratamentos fungicidas para controle de doenças de final de ciclo, incluindo ou não a ferrugem). A análise "additive main effects and multiplicative interaction" (AMMI) capturou, como padrão da interação GxE, 57% da variação associada aos resíduos de não aditividade, dos quais 44% foram retidos no primeiro componente principal de interação e o restante, no segundo. O primeiro componente associou-se a diferenças entre os anos de avaliação, o que denota imprevisibilidade na predição. O segundo componente, no entanto, associou-se ao manejo diferenciado do cultivo, no que se refere ao controle ou não das doenças. Entre os genótipos de ampla adaptabilidade produtiva, as linhagens USP 02-16.045 e USP 10-10 apresentaram desempenho destacado.Termos para indexação: Glycine max, Phakopsora pachyrhizi, biplot, interação genótipo x ambiente. AMMI analysis of grain yield in soybean lines selected for resistance to Asian rustAbstract -The objective of this work was to quantify the effects of genotype x environment (GxE) interaction on grain yield in soybean progenies pre-selected for resistance to Asian soybean rust (Phakopsora pachyrhizi). Twelve trials evaluating progenies (lines F 6 and F 7 ) were carried out in different environments (combination of locations, years, and fungicide treatments to control late season diseases, including or not rust). Additive main effects and multiplicative interaction (AMMI) analysis captured, as GxE interaction pattern, 57% of the variance associated with the residues of non-additivity, of which 44% were retained in the first principal component of interaction, and the remainder in the second. The first component was associated with the differences between the years of evaluation, which denotes unpredictability. The second component, however, was associated with different crop managements, related to the control or not of the diseases. Among genotypes with wide yield adaptability, the USP 02-16.045 and USP 10-10 lineages stood out.
In recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single‐step, best linear unbiased prediction‐based reaction norm models (termed RN‐HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN‐HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic–environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single‐nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bias of phenotypic prediction. Within‐cycle data were sufficient for accurate prediction of untested progenies, even in untested environments. We concluded that the RN‐HBLUP model, with the comprehensive structure, could be useful in improving the prediction accuracy of quantitative traits in RGS programs.
-The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.Index terms: augmented design, mixed model, information recovery, autocorrelation, correlated data, geostatistics. Seleção de genótipos e análise estatística espacial no melhoramento de plantasResumo -O objetivo deste trabalho foi avaliar a eficiência da análise estatística espacial na seleção de genótipos de plantas num programa de melhoramento. Buscou-se demonstrar os benefícios potenciais dessa abordagem quando as observações experimentais não são espacialmente independentes. O material consistiu de um ensaio de competição de linhagens de soja, com cinco cultivares testemunhas (de efeitos fixos) e 110 novos genótipos (de efeitos aleatórios), delineado em blocos aumentados. O ajuste espacial foi feito pelo modelo linear de campo aleatório (RFLM), com função de autocovariância estimada a partir dos resíduos da análise sob erros independentes. Os resultados apontaram uma autocorrelação residual de magnitude e alcance significativos, o que garantiu à abordagem espacial uma melhoria considerável na discriminação dos tratamentos genéticos -aumento do poder dos testes estatísticos, redução nos erros padrão de estimativas e de preditores e alargamento na amplitude das predições genotípicas. A análise espacial levou a um diferente ordenamento das linhagens em relação à análise não espacial e, finalmente, a uma seleção menos influenciada por efeitos da variação local.Termos para indexação: delineamento aumentado, modelo misto, recuperação de informação, autocorrelação, dados correlacionados, geoestatística.
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