Estimation and prediction of parameters and breeding values in soybean using REML/BLUP and Least Squares

The aim of this study was to compare REML/BLUP and Least Square procedures in the prediction andestimation of genetic parameters and breeding values in soybean progenies. F2:3 and F4:5 progenies were evaluated in the2005/06 growing season and the F2:4 and F4:6 generations derived thereof were evaluated in 2006/07. These progenies wereoriginated from two semi-early experimental lines that differ in grain yield. The experiments were conducted in a lattice designand plots consisted of a 2 m row, spaced 0.5 m apart. The trait grain yield per plot was evaluated. It was observed that earlyselection is more efficient for the discrimination of the best lines from the F4 generation onwards. No practical differences wereobserved between the least square and REML/BLUP procedures in the case of the models and simplifications for REML/BLUPused here

Optimizing QTL introgression via stochastic simulations: an example of the IRRI rice breeding program

A key limitation in the ability of breeding programs to leverage benefits of major-gene marker-assisted selection is the availability of those genes in appropriate elite germplasm. In this context, our study compared three strategies to develop new recipients for QTL introgression (Background recovery (BG), Selective sweep (SS), and Breeding values (BV)) in a short-term breeding program (over five breeding cycles). Furthermore, we evaluated two different numbers of recipients (10 and 20) in the introgression process and how they influence the population performance and the QTL fixation over cycles. Finally, we used rice as a model of a self- pollinated crop and implemented stochastic simulations. Each strategy was simulated and replicated 40 times. Regardless of the selection strategy used, the QTL introgression resulted in substantial penalties in yield performance. However, introducing fewer new parents to the augmentation process minimized this effect. Conversely, the time required to achieve fixation of target QTLs showed substantial differences, with selection for BV during augmentation out-performing other methods. Overall, the BV_10 strategy (10 parents selected based on genomic estimated breeding values) displayed the best trade-off between reduced penalty from introducing new QTLs with a reasonable speed at which those QTLs can achieve fixation over subsequent breeding cycles

Optimizing quantitative trait loci introgression in elite rice germplasms: Comparing methods and population sizes to develop new recipients via stochastic simulations

This study compared three strategies to develop new recipients for quantitative trait loci (QTL) introgression (background recovery [BG], selective sweep [SS] and breeding value [BV]) in a short-term rice breeding programme (over five breeding cycles). Furthermore, we evaluated two different numbers of recipients (10 and 20) in the introgression process and how they influence the population performance and the QTL fixation over cycles. Finally, we used the International Rice Research Institute (IRRI) rice breeding framework as the model to perform the stochastic simulations. Each strategy was simulated and replicated 100 times. Regardless of the selection strategy used, the QTL introgression resulted in substantial penalties in yield performance. However, introducing fewer new parents to the augmentation process minimized this effect. Conversely, the time required to achieve fixation of target QTLs showed substantial differences, with selection for BV during augmentation outperforming other methods. Overall, the BV_10 strategy (10 parents selected based on genomic estimated BV) displayed the best trade-off between reduced penalty from introducing new QTLs with a reasonable speed at which those QTLs can achieve fixation over subsequent breeding cycles

Accuracy and simultaneous selection gains for grain yield and earliness in tropical maize lines

Winter maize is sown between January and March in Brazil. Although this maize is sown in unfavorable weather conditions, many farmers are successful, and winter maize has become an important crop. The sowing of early hybrids is a strategy to reduce the effects of stress on yield; however, low yields may result from earliness. Thus, the objectives in this study were to investigate tropical maize lines for the possibility of simultaneous selection for yield and earliness and to compare the differences among the simultaneous selection methods. Therefore, 64 lines were evaluated in two locations for grain yield, days to female flowering and grain moisture at harvest. The genotypic values for these traits were predicted using Restricted Maximum Likelihood/Best Linear Unbiased Predictor (REML/BLUP) single-trait (univariate) and multi-trait (multivariate) methods. Using three simultaneous selection methods (i.e., Additive index, Mulamba-Mock index and Independent culling levels) with two methods of prediction for genotypic values (single-trait and multi-trait), six simultaneous selection scenarios were considered and then compared for selection gains and accuracy. Because of the low correlation between these traits, the pre- dictions of genotypic values were similar for single-trait and multi-trait methods. Thus, single-trait analysis should be prioritized because of its practicality. The Additive index obtained the highest selection gain for grain yield and simultaneously achieved good gains for days to female flowering and grain moisture at harvest. Therefore, the Additive index, using the single-trait prediction method, is the best simultaneous selection method for yield and earliness in tropical maize lines

Genomic selection in rubber tree breeding: A comparison of models and methods for managing G×E interactions

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 (H2) 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

The difference between breeding for nutrient use efficiency and for nutrient stress tolerance

This study aimed to verify the relationship between breeding for tolerance to low levels of soil nutrients and for nutrient use efficiency in tropical maize. Fifteen inbred lines were evaluated in two greenhouse experiments under contrasting levels of N and P. The relationship between nutritional efficiency and tolerance to nutritional stress was estimated by the Spearman ranking correlation between the genotypes for the traits related to N and P use efficiency and phenotypic plasticity indices. The lack of relationship between the traits, in magnitude as well as significance, indicates that these characters are controlled by different gene groups. Consequently, simultaneous selection for both nutrient use efficiency and tolerance to nutritional stress is possible, if the mechanisms that confer efficiency and tolerance are not competitive

Genetic effects of traits associated to nitrogen use efficiency in maize

The objectives of this work were to determine the genetic control of nitrogen use efficiency (NUE), to identify the importance of N acquisition (NAE) and utilization (NUtE) efficiencies on its composition, and to quantify the relation between production of shoot (SDM) and root dry matter mass with NUE and its components. Forty‑one hybrid combinations were evaluated in two N availabilities: low (LN) and high (HN). A randomized complete block design with two replicates, in a simple factorial arrangement (hybrid combination x N availability), was used. Statistical analyses were done using mixed model equations. Highmagnitude correlations were detected between NAE and NUE, and between these efficiencies and SDM in LN and HN. In both N availabilities, additive genetic effects were more important for the traits associated with NUE. Therefore, selection based on the individual performance of inbred lines as to SDM can allow for the obtainment of genotypes with high NUE. Independently of N availability, NAE is the most important component of NUE.Os objetivos deste trabalho foram determinar o controle genético da eficiência no uso do nitrogênio (EUN), identificar a importância das eficiências na absorção (EAN) e na utilização (EUtN) na sua composição, e quantificar relação entre produção de matéria seca da parte aérea (MPS) e do sistema radicular com a EUN e com seus componentes. Foram avaliadas 41 combinações híbridas em duas disponibilidades de N: baixa (BN) e alta (AN). Utilizou-se o delineamento de blocos ao acaso com duas repetições, em arranjo fatorial simples (combinação híbrida x disponibilidade de N). As análises estatísticas foram realizadas por meio das equações de modelos mistos. Correlações de elevada magnitude foram detectadas entre EAN e EUN, bem como entre essas eficiências e a MPS, tanto em BN como em AN. Em ambas as disponibilidades de N, efeitos genéticos aditivos apresentaram maior importância para os caracteres associados à EUN. Dessa forma, a seleção baseada no desempenho individual de linhagens quanto à MPS pode possibilitar a obtenção de genótipos com alta EUN. Independentemente da disponibilidade de N, a EAN é o componente mais importante da EUN

On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops

The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome, called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers, and genomic prediction of single crosses. For that, we used a method to assemble a reference genome to perform the single-nucleotide polymorphism (SNP) calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (array and genotyping-by-sequencing (GBS)). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population’s intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome

Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets

Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes