Persistence may be defined as high sustained yield over multi-harvest. Genetic insights about persistence are essential to ensure the success of breeding programs and any biomass-based project. This paper focuses on assessing the biomass yield persistence for bioenergy purpose of 100 elephantgrass clones measured in six growth seasons in Brazil. To assess the clones' persistence, an index based on random regression models and genotype-ideotype distance was proposed. Results suggested the existence of wide genetic variability between elephantgrass clones, and that the yield trajectories along the harvests generate genetic insights into elephantgrass clones’ persistence and G x E interaction. A gene pool that acts over the biomass yield (regardless of the harvest) was detected, as well as other gene pools, which show differences on genes expression (these genes are the major responsible for clones’ persistence). The lower and higher clones’ persistence was discussed based on genome dosage effect and natural biological nitrogen fixation ability applied to bioenergy industry. The huge potential of energy crops necessarily is associated with genetic insights into persistence, so just this way, breeding programs could breed a new cultivar that fulfills the bioenergy industries.
Sorghum [Sorghum bicolor (L.) Moench] biomass hybrids with high productivity and enhanced levels of lignin are seen as a promising alternative of feedstock for direct burning in ovens designed for cogeneration of electricity. The objective of this study was to estimate the genetic combing capacity of biomass sorghum lines and conduct multivariable selection of photosensitive biomass sorghum hybrids for use in cogeneration. Thirty‐six photosensitive hybrids, the control BRS716, and 12 parental lines were evaluated in a seven‐by‐seven triple lattice design at two locations, and 12 characters were evaluated. There was superiority of additive effects on the genetic control of all the characteristics studied in both environments, less for female lines in the diallelic analysis of F1 hybrids. The inclusion of parents in the estimates of combining capacities indicated predominance of dominance effects involved in the genetic control of the traits analyzed. The results demonstrate the action of epistasis of the dwarf genes present in the female lines for the biomass parameter and the false interpretation when these lines are included in the diallelic analysis. With the use of the index based on factor analysis and genotype–ideotype distance (FAI‐BLUP index), four factors were established, which separated the characteristics of production and quality, as well as the two environments, resulting from the high hybrid × environment interaction. With the index, five hybrids with higher potential for burning (H5‐5, H2‐1, H1‐1, H1‐5, and H5‐1) were selected. However, no hybrids obtained gains for the characteristics of production and quality, simultaneously, which indicates the need for genetic improvement of the parents used in the program.
A crucial point in agricultural experimentation is to compare treatments with high accuracy. However, agricultural experimentation is prone to field heterogeneity, and a common source of error is the spatial variation between the plots used in an experiment. With plant breeding experiments, the high number of tested genotypes requires breeders to use large areas, which invariably increases the likelihood of spatial variation. The use of models that do not address this variation can lead to errors in selecting the best genotypes. Our goal was to evaluate the effects of two spatial models—first‐order autoregressive (AR1) and spatial analysis of field trials with splines (SpATS)—to control the spatial variation in 30 experiments from potato (Solanum tuberosum L.) breeding programs. Specifically, we sought to control for three traits: total tuber yield (TTY), marketable tuber yield (MTY), and tuber specific gravity (SG). The results obtained with the use of spatial models were compared with the base model (independent errors) based on precision, heritability, and the impact on the selection of the best clones. Spatial models were effective in controlling local and global errors and achieved greater accuracy and efficiency over the base model. The spatial approach also showed greater heritability for all analyzed traits. The spatial models led to differences in the clone ranking and consequently in the selection of the best clones. Thus, spatial analysis has the power to make more precise analyses, which leads to more accurate selections and should be used to analyze phenotype data of potato breeding programs.
Core Ideas Partitioning of the progenies effect within populations has several advantages. FAI‐BLUP index capitalizes the progenies × growth seasons interaction. The selected inbreed progenies showed favorable genotypes for the target traits. Genetic breeding towards the common bean ideotype can accelerate the cultivar release. ABSTRACTThe goal of breeding programs is selection toward the ideal plant type. In this study, field experiments were performed to select common bean inbred progenies that maximize the probability of extracting superior lines. A total of 124 inbred progenies of three consecutive generations (F2:3, F2:4, and F2:5) were conducted in field experiments over three different environments (one generation in each environment). Seven different traits, related to disease severity, commercial acceptance grain, and yield, were evaluated by best linear unbiased prediction. This work underscored the importance of incorporating population information into the statistical model as a means of comparing progenies from different populations with higher efficacy, even when kinship information between populations is not available. Toward the common bean ideotype, 20 inbred progenies of greater potential were selected using the factor analysis and genotype‐ideotype distance (FAI‐BLUP) index. This index is based on the structural equation models by joining the factor analysis technique (exploratory factor analysis) with the ideotype design (confirmatory factor analysis). The predicted genetic gain was increased for all the traits in all generations. Selection strategies that capture the multitrait information capitalize the progenies × growth season interactions and are based on the ideotype, such as as the FAI‐BLUP index, have the potential for use in genetic breeding toward the common bean ideotype and can accelerate the release of more adapted cultivars.
BackgroundElephant grass [Cenchrus purpureus (Schumach.) Morrone] is used for bioenergy and animal feed. In order to identify candidate genes that could be exploited for marker-assisted selection in elephant grass, this study aimed to investigate changes in predictive accuracy using genomic relationship information and simple sequence repeats for eight traits (height, green biomass, dry biomass, acid and neutral detergent fiber, lignin content, biomass digestibility, and dry matter concentration) linked to bioenergetics and animal feeding.ResultsWe used single-step, genome-based best linear unbiased prediction and genome association methods to investigate changes in predictive accuracy and find candidate genes using genomic relationship information. Genetic variability (p < 0.05) was detected for most of the traits evaluated. In general, the overall means for the traits varied widely over the cuttings, which was corroborated by a significant genotype by cutting interaction. Knowing the genomic relationships increased the predictive accuracy of the biomass quality traits. We found that one marker (M28_161) was significantly associated with high values of biomass digestibility. The marker had moderate linkage disequilibrium with another marker (M35_202) that, in general, was detected in genotypes with low values of biomass digestibility. In silico analysis revealed that both markers have orthologous regions in other C4 grasses such as Setaria viridis, Panicum hallii, and Panicum virgatum, and these regions are located close to candidate genes involved in the biosynthesis of cell wall molecules (xyloglucan and lignin), which support their association with biomass digestibility.ConclusionsThe markers and candidate genes identified here are useful for breeding programs aimed at changing biomass digestibility in elephant grass. These markers can be used in marker-assisted selection to grow elephant grass cultivars for different uses, e.g., bioenergy production, bio-based products, co-products, bioactive compounds, and animal feed.
RESUMO -A palmeira Euterpe edulis é uma espécie nativa da Mata Atlântica e atualmente se encontra na lista das espécies ameaçadas de extinção. Uma alternativa para retirá-la desta lista seria o estímulo para o plantio comercial, focando o manejo dos frutos, que recebem a classificação de "superfruta" pelas suas propriedades químicas e nutricionais. Entretanto, uma etapa de extrema importância que precede a seleção de genótipos superiores é o estudo das associações entre as variáveis, pois permite traçar estratégias de seleção alternativas para maximizar os ganhos. O presente trabalho teve por objetivos estimar as correlações genéticas pelo procedimento REML/BLUP e os efeitos diretos e indiretos sobre a massa dos frutos, por meio da análise de trilha, para seis caracteres de frutos de 198 acessos de E. edulis. Foram analisados frutos de 198 genótipos de juçara coletados em 20 fragmentos florestais na região Sul e Caparaó do Estado do Espírito Santo. De cada genótipo, avaliaram-se 25 frutos para as características: diâmetro longitudinal e equatorial do fruto e da semente; e massa do fruto e da semente. Os dados obtidos foram utilizados para a estimativa das correlações genéticas através do método de máxima verossimilhança restrita e melhor predição linear não viesada (REML/BLUP). Posteriormente, as correlações genéticas entre as variáveis de fruto foram submetidas à análise de trilha. Os seis caracteres de fruto estudados apresentam associação genética positiva com magnitude superior a 0,71 pelo procedimento REML/BLUP. O diâmetro longitudinal do fruto e a massa das sementes possuem maior efeito direto sobre a massa dos frutos, o que as torna mais indicadas para aumentar as chances de sucesso na seleção de genótipos de juçara com frutos maiores. As características diâmetro longitudinal do fruto e a massa das sementes são as principais determinantes das variações na massa dos frutos. Termos para indexação: Euterpe edulis, Pré-melhoramento, Seleção de Caracteres, Análise Biométrica. GENETIC CORRELATIONS AND PATH ANALYSIS FOR FRUIT CHARACTERS OF JUÇARA PALM TREEABSTRACT -The palm tree Euterpe edulis is a native species from Mata Atlântica and it is nowadays found in the list of species which are threatened by extinction. One option to withdraw it from this list would be the stimulation of the commercial planting aiming the management of fruits which receive the "super fruit" classification by their chemical and nutritional properties. However, one stage of extreme importance that precedes the superior genotypes selection is the study of associations among the variables, because it allows delineating alternate selection strategies to maximize the gains. The present study had as objective to estimate the genetic correlations by the REML/BLUP procedure and the direct and indirect effects upon fruit mass by path analysis for six fruit characters of 198 accessions of E. edulis. Fruits of 198 Juçara genotypes collected in 20 forestry fragments in the south and Caparaó region of the state of Espírito Santo were analyzed. Fro...
937 RESEARCHR andom variables can be classified as qualitative, discrete quantitative, or continuous quantitative, whose scales of measurement can be nominal, ordinal, interval, or ratio (Resende et al., 2014). The nominal scale classifies the data into distinct categories, assigning names and arbitrary numerical correspondence to the variable's categories (e.g., gender: 1 = female and 2 = male). The ordinal scale classifies data into distinct categories, and in this case, the order or position is assigned to the categories (e.g., water quality level: 1 = good, 2 = intermediate, and 3 = bad). Either nominal or ordinal scales show no relation of difference or ratio between the numerical values of the scale (Levine et al., 2014;Resende et al., 2014). The interval scale is an ordered scale in which the difference between any two numbers in the scale is known; however, 0 is included as an arbitrary point (e.g., temperature in °C). Finally, a ratio scale is an ordered scale in which the distances between any two numbers in the scale are known, and their measurements include the true zero as the point of origin (e.g., height in centimeters) (Levine et al., 2014;Resende et al., 2014).In plant evaluations, several multicategorical variables are visually evaluated by score scales, such as plant architecture and grain appearance in common bean (Phaseolus vulgaris L.; Batista et al., 2017); lodging in common bean (Soltani et al., 2016), soybean [Glycine max (L.) Merr.; Akpertey et al., 2018], and wheat (Triticum aestivum L.; Iqbal et al., 2016); vigor in chickpea (Cicer arietinum L.; Sivasakthi et al., 2018); leaf senescence and leaf rolling in maize (Zea mays L.; Soni et al., 2018); herbicide tolerance in lentils (Lens culinaris Medik.; Sharma et al., 2018); salt tolerance in soybean (Do et al., 2018); injury caused by pests in cotton (Gossypium hirsutum ABSTRACTIn plant breeding, several multicategorical variables are evaluated using score scales that are treated as interval scales. However, statistics that use ratio operations, such as the experimental CV and the selection gain, are only appropriate for ratio scales data. Thus, this work aimed to propose strategies to mitigate the inconveniences faced in the data analysis of score scales, especially those involving CV and selection gain obtained from different scales. This work proposes the following strategies: (i) the use of a standard score scale with the properties of a ratio scale (ascending scale with 0 as the point of origin); (ii) the conversion of the scores from different scales into a common ratio scale; (iii) the adjustment of the data from an interval scale to a ratio scale using the Delta Scale (Scale d ui ), and (iv) the use of unbiased estimator for CV and selection gain in score scales data. The proposed strategies resulted in unbiased estimates of CV and selection gain from data of different score scales. These strategies have the potential to be used in the meta-analysis of data within and between plant breeding programs.
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