BackgroundThe selection of hybrids is an essential step in maize breeding. However, evaluating a large number of hybrids in field trials can be extremely costly. However, genomic models can be used to predict the expected performance of un-tested genotypes. Bayesian models offer a very flexible framework for hybrid prediction. The Bayesian methodology can be used with parametric and semi-parametric assumptions for additive and non-additive effects. Furthermore, samples from the posterior distribution of Bayesian models can be used to estimate the variance due to general and specific combining abilities even in cases where additive and non-additive effects are not mutually orthogonal. Also, the use of Bayesian models for analysis and prediction of hybrid performance has remained fairly limited.ResultsWe provided an overview of Bayesian parametric and semi-parametric genomic models for prediction of agronomic traits in maize hybrids and discussed how these models can be used to decompose the genotypic variance into components due to general and specific combining ability. We applied the methodology to data from 906 single cross tropical maize hybrids derived from a convergent population. Our results show that: (1) non-additive effects make a sizable contribution to the genetic variance of grain yield; however, the relative importance of non-additive effects was much smaller for ear and plant height; (2) genomic prediction can achieve relatively high accuracy in predicting phenotypes of un-tested hybrids and in pre-screening.ConclusionsGenomic prediction can be a useful tool in pre-screening of hybrids and could contribute to the improvement of the efficiency and efficacy of maize hybrids breeding programs. The Bayesian framework offers a great deal of flexibility in modeling hybrid performance. The methodology can be used to estimate important genetic parameters and render predictions of the expected hybrid performance as well measures of uncertainty about such predictions.Electronic supplementary materialThe online version of this article (10.1186/s13007-019-0388-x) contains supplementary material, which is available to authorized users.
Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (H 2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs.
A tropical forage breeding program contains several peculiarities, especially when it involves polyploid species and facultative apomixis. Urochloa spp. are excellent perennial forages, and the identification of superior genotypes depends on the selection of many characteristics under complex genetic control, with high cost and time‐consuming evaluation. Therefore, the use of tools such as multivariate analysis and diallel analyses could contribute to improving the efficiency of breeding programs. Thus, the objectives were to estimate (i) the contribution of additive and nonadditive effects on agronomical and nutritional traits in a population of interspecific hybrids of Urochloa spp., originated from a partial diallel between five apomictic and four sexual parents, and (ii) the accuracy of multivariate index selection efficiency. Genetic variability was detected between the parents, crosses, and hybrids for all the traits. There was no clear trend of the importance of the additive and nonadditive genetic effects on agronomical and nutritional traits. Furthermore, the predominant component of genetic variance changed depending on the characteristic. Moreover, there was no parent or cross that was outstanding for all traits simultaneously, showing the high variability generated from these crosses. The Mulamba and Mock index associated with principal components analysis allowed a more significant gain only for agronomic characteristics. However, the per se index, at the univariate level, promoted a more balanced response to selection for all traits.
Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an “enviromic assembly approach,” which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.
ResumoEste trabalho teve por objetivo a elaboração de um índice que permita a seleção acurada de cultivares de milho com aptidão tanto para a produção de minimilho quanto para de milho verde. Os experimentos foram realizados no ano agrícola de 2002/2003 com dez cultivares comerciais de milho em dois experimentos. O primeiro quanto ao rendimento de minimilho e o segundo quanto ao de milho verde, delineados em blocos ao acaso com três repetições. A importância relativa dos caracteres estudados foi estimada por meio do método dos componentes principais e o agrupamento destes foi realizado pela análise de fatores. O seguinte índice foi obtido: I = 0,031 NEM + 0,013 MEM + 0,207 NEV + 0,243 MEV -0,16 AP -0,058 MFP, em que, NEM, MEM, NEV, MEV, AP e MFP são o número e a massa de espigas empalhadas de minimilho e de milho verde, altura de planta e massa de pendão fresca respectivamente. Esse índice indicou que híbridos triplos DKB 350, AG 8080 e AG 6690 e o duplo DKB 747 revelaram os melhores desempenhos para as produções de minimilho e de milho verde.Palavras-chave: componentes principais, análise de fatores, Zea mays L. Selection index of maize cultivars with twice fitness: baby corn and green corn AbstractThis study aimed to propose an index for allowing accurate selection of corn hybrids for producing both baby corn and green corn. The experiments were carried out during the 2002/2003 growing season and ten commercial corn cultivars were evaluated in two experiments, the first for evaluating the yield of baby corn and the second for the yield of green corn. Both experiments were designed in randomized blocks, with three replications. The relative importance of traits was estimated by the principal components method and the cluster analysis was carried out. The following index was obtained: I = 0.031 NEM + 0.013 MEM + 0.207 NEV + 0.243 MEV -0.16 AP -0.058 MFP, being considered the variables: number and mass of husked ears for baby corn (NEM and MEM) and green corn (NEV and MEV), plant height (AP) and the fresh mass of tassel (MFP). This index indicated that the three-way cross hybrids DKB 350, AG 8080 and AG 6690 and the double-cross hybrids DKB 747 have the best performances for the production of baby corn and green corn.
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