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Abstract The objective of this work was to compare the use of noninformative and informative priors in Bayesian models, as well as to evaluate the viability of including informative priors in the estimation of variance components and genetic values in soybean breeding programs. The used phenotypic data refer to the evaluation of 80 soybean genotypes in ten environments over three years. For each evaluated crop year, informative and noninformative priors were used, and the parameters were estimated using the Gibbs sampler algorithm. Parameter estimates from the previous crop year were used as prior information for the next evaluated crop year. The goodness-of-fit was calculated using the deviance information criterion (DIC). Selective accuracy showed the highest values for the models chosen through DIC for both crop years. However, the intervals of the highest posterior density are narrower for all models that adopted informative priors. Adding information into Bayesian inference does not always result in a better model fitting.

Resumo O presente trabalho objetivou estimar parâmetros genéticos e avaliar o desempenho das progênies (linhagens) de Leucena leucocephala [(Lam.) De Wit], com vistas à seleção do melhor material genético, para uso em diversos sistemas de produção. Trinta linhagens foram plantadas em forma de teste de progênie na Reserva Indígena Kaiowá e Guarani, em Caarapó/MS, sob delineamento de blocos ao acaso, com cinco repetições e dez plantas por parcela. Aos 13 meses de idade, foram avaliados os caracteres: altura total da planta e diâmetro do coleto. Os resultados demonstraram haver baixa variabilidade genética para os caracteres estudados, o que sugere que as 30 progênies podem ser oriundas de uma ou poucas linhagens ou cultivares. As estimativas de herdabilidades individuais foram de baixas magnitudes (3,38% a 5,13%) para os caracteres estudados. As herdabilidades da média de linhagens apresentaram moderadas magnitudes e podem proporcionar progressos genéticos consideráveis em resposta à seleção. Os resultados obtidos permitiram identificar as 4 melhores linhagens, porém para a continuidade do programa de melhoramento é necessário ampliar a base genética do material.

Abstract This paper intended to estimate genetic values and parameters for plant height, diameter at the base of the plant and leaf-storey number of rubber tree [Hevea brasiliensis (Willd ex Adr. de Juss.) Müell. Arg.] halfsib progenies using the mixed-model methodology (Reml/Blup procedure). The rubber tree progenies were obtained from a second generation population produced from the recombination of 30 original parents, pertaining to the Instituto Agronômico de Campinas (IAC) in São Paulo state. At the age of eight months the variables were evaluated. Results demonstrated the existence of significant genetic variability among the progenies for the studied traits. The estimates of the heritability coefficient showed moderate values for both height and leaf-storey number, and low values for plant diameter at the base of the plant. There are good possibilities of genetic gain for these main traits in the context of the improvement program that was started in Mato Grosso do Sul state.

Resumo A carência de programas de melhoramento genético da teca (Tectona grandis L.f.) no Brasil aponta para a necessidade premente de se estabelecer uma rede experimental, na qual parcerias seriam constituídas por instituições de ensino e de pesquisas associadas às empresas privadas envolvidas no cultivo da espécie. Com vistas à adequação dos procedimentos experimentais a serem utilizados nas pesquisas, alguns princípios devem ser observados. Neste contexto, os procedimentos experimentais apresentados ao longo do presente trabalho, poderão subsidiar as ações a serem desenvolvidas, considerando-se as peculiaridades das regiões de plantio no Brasil.

Abstract The aim of this study was to evaluate the performance of Quantile Regression (QR) in Genome-Wide Association Studies (GWAS) regarding the ability to detect QTLs (Quantitative Trait Locus) associated with phenotypic traits of interest, considering different population sizes. For this, simulated data was used, with traits of different levels of heritability (0.30 and 0.50), and controlled by 3 and 100 QTLs. Populations of 1,000 to 200 individuals were defined, with a random reduction of 100 individuals for each population. The power of detection of QTLs and the false positive rate were obtained by means of QR considering three different quantiles (0.10, 0.50 and 0.90) and also by means of the General Linear Model (GLM). In general, it was observed that the QR models showed greater power of detection of QTLs in all scenarios evaluated and a relatively low false positive rate in scenarios with a greater number of individuals. The models with the highest detection power of true QTLs at the extreme quantils (0.10 and 0.90) were the ones with the highest detection power of true QTLs. In contrast, the analysis based on the GLM detected few (scenarios with larger population size) or no QTLs in the evaluated scenarios. In the scenarios with low heritability, QR obtained a high detection power. Thus, it was verified that the use of QR in GWAS is effective, allowing the detection of QTLs associated with traits of interest even in scenarios with few genotyped and phenotyped individuals.

Multi-trait multi-environment (MTME) models were fitted to eucalyptus breeding trials data to assess residual variance structure, genetic stability and adaptability. To do so, 215 eucalyptus clones were evaluated in a randomized complete block design with 30 replicates and one plant per plot in four environments. At 36 months of age, tree diameter at breast height (DBH) and pilodyn penetration (PP) were measured. Two MTME models were fitted, for which residuals were considered homoscedastic and heteroscedastic, with the best MTME model selected using Bayesian information criterion. The harmonic mean of the relative performance of the genotypic values (HMRPGV) was used to determine stability and adaptability. Of the two models, the heteroscedastic MTME model had better fit and provided greater accuracy. In addition, genotype-by-environment interaction was complex, and there was low genetic correlation between DBH and PP. Rank correlation between the clones selected by the MTME models was high for DBH but low for PP. The HMRPGV facilitated clone selection through simultaneous evaluation of stability, adaptability, and productivity. Thus, our results suggest that heteroscedastic MTME model / HMRPGV can be efficiently applied in the genetic evaluation and selection of eucalyptus clones.

Abstract Methods of multivariate analysis is a powerful approach to assist the initial stages of crops genetic improvement, particularly, because it allows many traits to be evaluated simultaneously. In this study, heat-tolerant genotypes have been selected by analyzing phenotypic diversity, direct and indirect relationships among traits were identified, and four selection indices compared. Diversity was estimated using K-means clustering with the number of clusters determined by the Elbow method, and the relationship among traits was quantified by path analysis. Parametric and non-parametric indices were applied to selected genotypes using the magnitude of genotypic variance, heritability, genotypic coefficient of variance, and assigned economic weight as selection criteria. The variability among materials led to the formation of two non-overlapping clusters containing 40 and 154 genotypes. Strong to moderate correlations were found between traits with direct effect of the number of commercial fruit on the mass of commercial fruit. The Smith and Hazel index showed the greatest total gains for all criteria; however, concerning the biochemical traits, the Mulamba and Mock index showed the highest magnitudes of predicted gains. Overall, the K-means clustering, correlation analysis, and path analysis complement the use of selection indices, allowing for selection of genotypes with better balance among the assessed traits.

Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cπ, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties.

The açai fruit yield is concentrated between the months of July to December (harvest), providing an important income to actors involved in the production chain, however in the off-season this brings serious socioeconomic problems, with the need for research that may offer selected genetic materials that can circumvent this problem. However, the age for making this selection in this species is unknown. Therefore, the objective of this work was to infer the best age for selection by estimating genetic and phenotypic parameters in the different stages of evaluating progenies of half-sibs of açai. The experiment followed a randomized block design with three replications and five plants per plot, with 30 progenies. The statistical analysis was performed using the REML/BLUP methodology. The results showed that the magnitudes of the estimates of heritability and genetic variation decrease with age; the correlations between the characters that make up fruit yield reveal that the number of bunches is the most important component of yield; for the fruit yield character, the age of progenies and harvest years indicate that early selections are more efficient; the early emission of tillers is an important characteristic to be sought in açai breeding programs guaranteeing greater fruit yield in adult plants; and higher fruit yield in the off-season is possible from the fourth year of harvest. = A produção de frutos de açaí se concentra entre os meses de julho e dezembro (safra), constituindo importante fonte de renda para os atores da cadeia produtiva, contudo, na entressafra, traz sérios problemas de ordem socioeconômica, por isso, são necessárias pesquisas que ofereçam materiais genéticos selecionados que possam contornar este problema, porém a idade de se efetuar a seleção nesta espécie é uma incógnita. Portanto, objetivou-se com este trabalho inferir sobre a melhor idade para seleção, empregando a estimação dos parâmetros genéticos e fenotípicos nas diferentes etapas de avaliação de progênies de meios-irmãos de açaizeiro. O experimento foi delineado em blocos aleatorizados com três repetições e cinco plantas por parcela, com 30 progênies. A análise estatística foi realizada com a metodologia REML/BLUP. Pelos resultados obtidos, as magnitudes das estimativas da herdabilidade e variação genética diminuem com a idade; as correlações entre caracteres componentes da produtividade de frutos revelam que o número de cachos é o mais importante componente da produção; para o caráter produtividade de frutos, a interação entre progênies e anos agrícolas indica que seleções precoces são mais eficientes; a emissão precoce de perfilhos é uma característica importante a ser buscada em programas de melhoramento do açaí, garantindo maior produtividade de frutos em plantas adultas; por fim, produtividade de frutos maior no período da entressafra é possível a partir do quarto ano agrícola.

Abstract Background Elephant 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. Results We 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

Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava’s products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm’s of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ∼14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs’ estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers’ effects that were trained with data from other research institutes/country’s germplasm to estimate their clones’ GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8–0.85) compared to the prediction accuracy of either germplasm’s sources individually (0.51–0.67). CC GP proved to have potential use under the present study’s scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa’s germplasm, whereas for IITA’s it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field.

ABSTRACT: The principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values.

ABSTRACT: The Fisher’s infinitesimal model is traditionally used in quantitative genetics and genomic selection, and it attributes most genetic variance to additive variance. Recently, the dominance maximization model was proposed and it prioritizes the dominance variance based on alternative parameterizations. In this model, the additive effects at the locus level are introduced into the model after the dominance variance is maximized. In this study, the new parameterizations of additive and dominance effects on quantitative genetics and genomic selection were evaluated and compared with the parameterizations traditionally applied using the genomic best linear unbiased prediction method. As the parametric relative magnitude of the additive and dominance effects vary with allelic frequencies of populations, we considered different minor allele frequencies to compare the relative magnitudes. We also proposed and evaluated two indices that combine the additive and dominance variances estimated by both models. The dominance maximization model, along with the two indices, offers alternatives to improve the estimates of additive and dominance variances and their respective proportions and can be successfully used in genetic evaluation.

Abstract: The objective of this work was to select superior sweet orange (Citrus sinensis) genotypes with higher yield potential based on data from eight harvests, using the residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology. The experiment was carried out from 2002 to 2008 and in 2010 in the municipality of Rio Branco, in the state of Acre, Brazil. Analyzes of deviance were performed to test the significance of the components of variance according to the random effects of the used model, and parameters were estimated from individual genotypic and phenotypic variances. A selection intensity of 20% was adopted regarding genotypic selection, i.e., only the best 11 of the 55 genotypes tested were selected. The estimates of the genetic parameters show the existence of genetic variability and the selection potential of the studied sweet orange genotypes. The genotypic correlation between harvests is of low magnitude, except for the variable average fruit mass, and, as a reflex, there is a change in the ordering of the genotypes. Genotypes 5, 48, 19, 14, and 47 stand out as being the most productive, and, therefore, are the most suitable for selection purposes. Genotypes 14 and 47 show superior performance for the character set evaluated.

ABSTRACT: The development of efficient methods for genome–wide association studies (GWAS) between quantitative trait loci (QTL) and genetic values is extremely important to animal and plant breeding programs. Bayesian approaches that aim to select regions of single nucleotide polymorphisms (SNPs) proved to be efficient, indicating genes with important effects. Among the selection criteria for SNPs or regions, selection criterion by percentage of variance can be explained by genomic regions (%var), selection of tag SNPs, and selection based on the window posterior probability of association (WPPA). To also detect potentially associated regions, we proposed measuring posterior probability of the interval PPint), which aims to select regions based on the markers of greatest effects. Therefore, the objective of this work was to evaluate these approaches, in terms of efficiency in selecting and identifying markers or regions located within or close to genes associated with traits. This study also aimed to compare these methodologies with single–marker analyses. To accomplish this, simulated data were used in six scenarios, with SNPs allocated in non–overlapping genomic regions. Considering traits with oligogenic inheritance, WPPA criterion followed by %var and PPint criteria were shown to be superior, presenting higher values of detection power, capturing higher percentages of genetic variance and larger areas. For traits with polygenic inheritance, PPint and WPPA criteria were considered superior. Single–marker analyses identified SNPs associated only in oligogenic inheritance scenarios and was lower than the other criteria.

The genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive-dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits.

ABSTRACT: Genome-wide selection (GWS) is currently a technique of great importance in plant breeding, since it improves efficiency of genetic evaluations by increasing genetic gains. The process is based on genomic estimated breeding values (GEBVs) obtained through phenotypic and dense marker genomic information. In this context, GEBVs of N individuals are calculated through appropriate models, which estimate the effect of each marker on phenotypes, allowing the early identification of genetically superior individuals. However, GWS leads to statistical challenges, due to high dimensionality and multicollinearity problems. These challenges require the use of statistical methods to approach the regularization of the estimation process. Therefore, we aimed to propose a method denominated as triple categorical regression (TCR) and compare it with the genomic best linear unbiased predictor (G-BLUP) and Bayesian least absolute shrinkage and selection operator (BLASSO) methods that have been widely applied to GWS. The methods were evaluated in simulated populations considering four different scenarios. Additionally, a modification of the G-BLUP method was proposed based on the TCR-estimated (TCR/G-BLUP) results. All methods were applied to real data of cassava (Manihot esculenta) with to increase efficiency of a current breeding program. The methods were compared through independent validation and efficiency measures, such as prediction accuracy, bias, and recovered genomic heritability. The TCR method was suitable to estimate variance components and heritability, and the TCR/G-BLUP method provided efficient GEBV predictions. Thus, the proposed methods provide new insights for GWS.

Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρg ≤ 0.33), moderate (0.34 ≤ ρg ≤ 0.66), and high magnitude (ρg ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.

Breeding programs of the species Coffea canephora rely heavily on the significant genetic variability between and within its two varietal groups (conilon and robusta). The use of hybrid families and individuals has been less common. The objectives of this study were to evaluate parents and families from the populations of conilon, robusta, and its hybrids and to define the best breeding and selection strategies for productivity and disease resistance traits. As such, 71 conilon clones, 56 robusta clones, and 20 hybrid families were evaluated over several years for the following traits: vegetative vigor, incidence of rust and cercosporiosis, fruit ripening time, fruit size, plant height, canopy diameter, and yield per plant. Components of variance and genetic parameters were estimated via residual maximum likelihood (REML) and genotypic values were predicted via best linear unbiased prediction (BLUP). Genetic variability among parents (clones) and hybrid families was detected for most of the evaluated traits. The Mulamba-Rank index suggests potential gains up to 17% for the genotypic aggregate of traits in the hybrid population. An intrapopulation recurrent selection within the hybrid population would be the best breeding strategy because the genetic variability, narrow and broad senses heritabilities and selective accuracies for important traits were maximized in the crossed population. Besides, such strategy is simple, low cost and quicker than the concurrent reciprocal recurrent selection in the two parental populations, and this maximizes the genetic gain for unit of time.

ABSTRACT: The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genetic selection. Thus, the present study aimed to estimate genetic parameters and to compare different selection strategies in the context of mixed models for soybean breeding. For this, data referring to the evaluation of 30 genotypes in 10 environments, regarding the grain yield trait, were used. The variance components were estimated through restricted maximum likelihood (REML) and genotypic values were predicted through best linear unbiased prediction (BLUP). Significant effects of genotypes and G×E interaction were detected by the likelihood ratio test (LRT). Low genotypic correlation was obtained across environments, indicating complex G×E interaction. The selective accuracy was very high, indicating high reliability. Our results showed that the most productive soybean genotypes have high adaptability and stability.

Path analysis allows understanding the direct and indirect effects among traits. Multicollinearity in correlation matrices may cause a bias in path analysis estimates. This study aimed to: a) understand the correlation among soybean traits and estimate their direct and indirect effects on gain oil content; b) verify the efficiency of ridge path analysis and trait culling to overcome colinearity. Three different matrices with different levels of collinearity were obtained by trait culling. Ridge path analysis was performed on matrices with strong collinearity; otherwise, a traditional path analysis was performed. The same analyses were run on a simulated dataset. Trait culling was applied to matrix R originating the matrices R1 and R2. Path analysis for matrices R1 and R2 presented a high determination coefficient (0.856 and 0.832, respectively) and low effect of the residual variable (0.379 and 0.410 respectively). Ridge path analysis presented low determination coefficient (0.657) and no direct effects greater than the effects of the residual variable (0.585). Trait culling was more effective to overcome collinearity. Mass of grains, number of nodes, and number of pods are promising for indirect selection for oil content.

An efficient and informative statistical method to analyze genotype-by-environment interaction (GxE) is needed in maize breeding programs. Thus, the objective of this study was to compare the effectiveness of multiple-trait models (MTM), random regression models (RRM), and compound symmetry models (CSM) in the analysis of multi-environment trials (MET) in maize breeding. For this, a data set with 84 maize hybrids evaluated across four environments for the trait grain yield (GY) was used. Variance components were estimated by restricted maximum likelihood (REML), and genetic values were predicted by best linear unbiased prediction (BLUP). The best fit MTM, RRM, and CSM were identified by the Akaike information criterion (AIC), and the significance of the genetic effects were tested using the likelihood ratio test (LRT). Genetic gains were predicted considering four selection intensities (5, 10, 15, and 20 hybrids). The selected MTM, RRM, and CSM models fit heterogeneous residuals. Moreover, for RRM the genetic effects were modeled by Legendre polynomials of order two. Genetic variability between maize hybrids were assessed for GY. In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM. Thus, considering the criterion of parsimony and the possibility of predicting genetic values of hybrids for untested environments, RRM is a preferential approach for analyzing MET in maize breeding.

Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.

To increase yield, breeding programs must search for innovative and efficient methodologies for the development and selection of superior genotypes. Therefore, this study aimed to compare the single- and multiple-trait best linear unbiased prediction (BLUP) in genetic selection of parents and hybrids of grain sorghum. For this, an experiment conducted in alpha-lattice design with two replications was used. Flowering time (FT), plant height (PH), and grain yield (GY) were evaluated in 502 grain sorghum hybrids obtained by the cross of ten restorer lines and 54 male-sterile lines. Variance components were estimated via restricted maximum likelihood (REML). Significant effects of restorer lines, of male-sterile lines, and of specific combining ability were detected by the likelihood ratio test (LRT). The estimates of variance components, genetic parameters, and correlations were similar when obtained via single- and multiple trait- BLUP. Considering hybrids, the multiple-trait BLUP resulted in slightly higher predicted selection gain for the three evaluated traits and, therefore, can be efficiently applied in the genetic selection of grain sorghum.

Abstract: The objective of this work was to estimate selection gains for bunch production in hybrids obtained from “caiaué” (Elaeis oleifera) parents from the Manicoré population and oil palm (Elaeis guineensis) parents from the La Mé population, from the third to the eighth year after planting. Thirty-nine full-sibling progenies were evaluated in experiments conducted in a randomized complete block design, with four replicates and 12 plants per plot. The genetic gain estimates were 27.6% for the ten best selected F1 individuals, 13.7% for the four best selected progenies from parents of both sexes, 6.4% for the selected progenies of the five best male parents, 6.0% for the selected progenies of the two best families from male parents, and 4.0% for the selected progenies of the best descendants from male parents. Genetic gains for bunch production can be achieved, in the short term, through selection restricted to male parents, and, in the medium term, through selection of both male and female parents and cloning of F1 individuals.

Was used the REML/BLUP method to estimate the genetic parameters, select the best plants of Dura x Dura full-sib families, study the trait correlations, group families by multivariate similarity and to determine the number of repeated measurements required for the selection of the traits (bunch number, fresh fruit bunch yield and average bunch weight). Twenty-four families developed in three trials were tested, together with one control per test from the genebank of the experimental station of Santo Domingo - INIAP in Ecuador. The evaluation lasted five years and was arranged in a randomized block design with 12 plants per plot and four replications. The population variability for traits and genetic heritability between plots was close to that found within plots. The genetic gain of the 10 selected plants, was 43% higher than the overall average. The correlation was low and negative only between bunch number and average bunch weight. By Tocher cluster analysis, six groups were formed, and in group IV, families selected by average ranking (3A, 5C, and 7B). It was concluded that the BLUP estimates are encouraging with a view to a continuous breeding program of oil palm, with the possibility of maximizing genetic gains in future generations.

Abstract: The objective of this work was to evaluate the feasibility of early selection of open-pollinated yerba mate (Ilex paraguariensis) progenies, and to classify the best parents and candidates for clones. The germplasm is composed of 140 progenies collected from areas within the natural distribution of the species in Southern Brazil and a commercial genotype (control). The experiment was established in Ivaí, in the state of Paraná, Brazil, in March 1997, in randomized complete blocks, with ten replicates, and linear plots of six plants. The commercial mass (kg per plant) of leaves and branches smaller than 7 mm was harvested at 2.5, 4.5, 6.5, and 18.7 years of age. The statistical evaluation was performed using the mixed model procedure with restricted maximum likelihood/best linear unbiased prediction, using the Selegen software. High selective accuracy, significant progeny effects, and genetic variability for commercial mass production were observed for the four harvesting ages. The application of early selection for leaf mass production is feasible for yerba mate progenies harvested at 6.5 years. The selection of the best trees, either for use as parents or as candidates for cloning, provides significant gains for the genetic breeding of yerba mate.

We combined linkage (LA) and linkage disequilibrium (LDA) analyses (emerging the term ‘LALDA’) for genomic selection (GS) purposes. The models were fitted to a simulated dataset and to a real data of feed conversion ratio in pigs. Firstly, the significant QTLs (quantitative trait locus) were identified through LA-based mixed models considering the QTL-genotypes as random effects by means of genotypic identity by descent matrix. This matrix was calculated at the positions of significant QTLs (based on LA) allowing to include the QTL-genotype effects additionally to SNP (single nucleotide polymorphism) markers (based on LDA) and additive polygenic effects in several GS models (Bayesian Ridge Regression – BRR; Bayes A – BA; Bayes B – BB; Bayes C – BC and Bayesian LASSO – BL). These models combing all mentioned effects were denominated LALDA. Goodness-of-fit and predictive ability analyses were performed to evaluate the efficiency of these models. For the real data, although slightly, the superiority of the LALDA models was verified in comparison to traditional LDA models for GS. For the simulated dataset, the models presented similar results. For both LDA and LALDA frameworks, BA showed the best fitting through Deviance Information Criterion and higher predictive ability in the simulated and real datasets.

The present study aimed to evaluate the selection indices efficiency for Pilodyn penetration combined with growth traits in Eucalyptus clones. It was carried out experiments in a randomized block design, with single tree plots and 30 replications. Diameter at breast height (DBH), total height (TH), and Pilodyn penetration as an indicator of basic density (BD) were measured. The volume was estimated. Based on predicted genotypic values, three indices presented the highest accuracies: I8 (based on partial correlation), I7 (based on the concept of multivariate BLUP) and I3 (based on two variables as ratio, which uses a third heritability estimate associated to the ratio DBH/BD, besides the two heritabilities of DBH and BD). Thus, it is possible to optimize the selection by combining properly the variables using their genetic control, precision and the relationships between them. The best options came from using only two no redundant traits DBH and BD.

Resumo: O objetivo deste trabalho foi avaliar a influência da distribuição dos efeitos de QTL, do tipo de população de validação e da correção dos fenótipos sobre a acurácia da seleção genômica ampla. Duas populações de irmãos completos, com 500 indivíduos, foram simuladas, tendo-se considerado, genotipicamente, 1.000 locos marcadores - 100 ligados a QTL. Os efeitos de QTL apresentaram distribuição uniforme ou exponencial. Na validação 1, uma amostra com 100 indivíduos constituiu a população de validação; na validação 2, aplicou-se a validação cruzada, com amostra de 100 indivíduos em cinco repetições; e na 3, uma segunda geração constituiu a população de validação. As metodologias de análise utilizadas foram RR-Blup e Blasso, com modelos mistos para correção dos fenótipos. Sem correção fenotípica, a distribuição exponencial proporcionou maiores acurácias, e o método Blasso foi mais acurado com essa distribuição; enquanto o RRBlup foi mais acurado com a distribuição uniforme. Nesse cenário sem correção, as validações 1 e 3 foram mais acuradas. Com correção, as distribuições exponencial e uniforme produziram acurácias similares, e o método Blasso mostrou-se mais acurado para ambas. Nesse cenário, as validações 1 e 2 foram mais acuradas. No geral, o método RR-Blup foi mais acurado, e o Blasso menos viciado.

Resumo O objetivo deste trabalho foi avaliar a eficiência da seleção genômica em diferentes cenários de estrutura populacional em milho-pipoca, com estimação dos efeitos e uso de marcadores na própria população de referência e em populações não relacionadas, e determinar a influência do tamanho efetivo e das relações de parentesco na população de estimação sobre a acurácia da predição. Foram simuladas populações com diferentes desequilíbrios de ligação (LD) e variâncias aditivas, tendo-se considerado diferentes caracteres, densidades de marcadores, herdabilidades e gerações, no total de 144 cenários. Também foram simuladas populações estruturadas em progênies. A acurácia da predição dos valores genéticos aditivos foi obtida por meio da correlação entre os valores paramétricos e os valores estimados por RR-BLUP (ridge regression-best linear unbiased prediction). Em populações com baixo LD e menor variância aditiva, o uso de maiores densidades de SNP (10 SNP 0,1 cM-1) é indicado, e, além disso, o candidato à seleção deve ser relacionado à população de estimação, para que a acurácia de predição seja satisfatória. O uso de população de seleção na mesma geração da população de estimação reduz em pelo menos 8% a acurácia. A estruturação da população em progênies de maior relacionamento e menor tamanho efetivo aumenta a eficiência da seleção genômica.

The software Selegen-REML/BLUP uses mixed models, and was developed to optimize the routine of plant breeding programs. It addresses the following plants categories: allogamous, automagous, of mixed mating system, and of clonal propagation. It considers several experimental designs, mating designs, genotype x environment interaction, experiments repeated over sites, repeated measures, progenies belonging to several populations, among other factors. The software adjusts effects, estimates variance components, genetic additive, dominance and genotypic values of individuals, genetic gain with selection, effective population size, and other parameters of interest to plant breeding. It allows testing the significance of the effects by means of likelihood ratio test (LRT) and analysis of deviance. It addresses continuous variables (linear models) and categorical variables (generalized linear models). Selegen-REML/ BLUP is friendly, easy to use and interpret, and allows dealing efficiently with most of the situations in plant breeding. It is free and available at http://www.det.ufv.br/ppestbio/corpo_docente.php under the author’s name.

GenomicLand is free software intended for prediction and genomic association studies based on the R software. This computational tool has an intuitive interface and supports large genomic databases, without requiring the user to use the command line. GenomicLand is available in English, can be downloaded from the Internet (https://licaeufv.wordpress.com/), and requires the Windows or Linux operating system. The software includes statistical procedures based on mixed models, Bayesian inference, dimensionality reduction and artificial intelligence. Examples of data files that can be processed by GenomicLand are available. The examples are useful to learn about the operation of the modules and statistical procedures.

At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; [Formula: see text]) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of [Formula: see text]. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.

Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions' BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 -RR-BLUP to 0.4756-RKHS) and dry root yield (0.4689 -G-BLUP to 0.4818-RKHS) in comparison with dry matter content (0.5655 -BLASSO to 0.5670 -RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99-1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions.

Cassava improvement using traditional breeding strategies is slow due to the species' long breeding cycle. However, the use of genomic selection can lead to a shorter breeding cycle. This study aimed to estimate genetic parameters for productive traits based on pedigree (pedigree and phenotypic information) and genomic (markers and phenotypic information) analyses using biparental crosses at different stages of selection. A total of 290 clones were genotyped and phenotyped for fresh root yield (FRY), dry matter content (DMC), dry yield (DY), fresh shoot yield (FSY) and harvest index (HI). The clones were evaluated in clonal evaluation trials (CET), preliminary yield trials (PYT), advanced yield trials (AYT) and uniform yield trials (UYT), from 2013 to 2018 in ten locations. The breeding stages were analyzed as follows: one stage (CET), two stages (CET and PYT), three stages (CET, PYT and AYT) and four stages (CET, PYT, AYT and UYT). The genomic predictions were analyzed via k-fold cross-validation based on the genomic best linear unbiased prediction (GBLUP) considering a model with genetic additive effects and genotype × location interactions. Genomic and pedigree accuracies were moderate to high (0.56-0.72 and 0.62-0.78, respectively) for important starch-related traits such as DY and FRY; when considering one breeding stage (CET) with the aim of early selection, the genomic accuracies ranged from 0.60 (DMC) to 0.71 (HI). Moreover, the correlations between the genomic estimation breeding values of one-stage genomic analysis and the estimated breeding values of the four-stage (full data set) pedigree analysis were high for all traits as well as for a selection index including all traits. The results indicate great possibilities for genomic selection in cassava, especially for selection early in the breeding cycle (saving time and effort).

To identify superior genotypes, 42 progenies of interspecific hybrids between American oil palm and oil palm were evaluated in an area with incidence of bud rot. The following variables were assessed: total bunch yield (TBY), number of bunches (NB) and mean bunch weight (MBW) from the third to the sixth year of cultivation and estimates of genetic parameters obtained by REML/BLUP procedures. High heritability values of the evaluated traits were observed. The gain estimates for TBY were 14.49% for the selection of the five best progenies and 33.36% for the selection of the five best trees, considering multiplication by cloning. A medium correlation was observed between TBY and NB (r2 = 0.33±0.021), a high correlation between TBY and MBW (r2 = 0.53±0.019) and a negative high correlation between NB and MBW (r2 = –0.60±0.018). The results indicate a high expectation of gains with selection for TBY.

ABSTRACT This study aimed to estimate the genetic correlation among selection ages (juvenile - adult) and efficiency of early selection for the height, diameter, and volume traits of individuals from Pinus taeda families propagated via somatic embryogenesis. This study was carried out by genetic-statistical analysis, estimation procedure of variance (Reml), and prediction components of breeding values (Blup), using the Selegen-Reml/Blup software. Genetic correlations among juvenile ages and rotation age were performed by applying the linear model developed by Lambeth (1980). In accordance with results of the established model, the early selection can be performed in clones of Pinus taeda with high selection efficiency. Ages from 4 to 6 years old are enough to select Pinus taeda clones propagated via somatic embryogenesis for harvesting at 8 and 12 years old; and 6 to 10 years old are enough to select them for harvesting at 20 years old. On the basis of the genetic correlations estimates from the environments, the clones' selection of Pinus taeda propagated via somatic embryogenesis should be developed specifically for each environment. The clones' selection can be performed considering the diameter due to the high correlation between volume and diameter.

ABSTRACT Genomic selection (GS) has recently been proposed as a new selection strategy which represents an innovative paradigm in crop improvement, now widely adopted in animal breeding. Genomic selection relies on phenotyping and high-density genotyping of a sufficiently large and representative sample of the target breeding population, so that the majority of loci that regulate a quantitative trait are in linkage disequilibrium with one or more molecular markers and can thus be captured by selection. In this study we address genomic selection in a practical fruit breeding context applying it to a breeding population of table grape obtained from a cross between the hybrid genotype D8909-15 (Vitis rupestris × Vitis arizonica/girdiana), which is resistant to dagger nematode and Pierce’s disease (PD), and ‘B90-116’, a susceptible Vitis vinifera cultivar with desirable fruit characteristics. Our aim was to enhance the knowledge on the genomic variation of agronomical traits in table grape populations for future use in marker-assisted selection (MAS) and GS, by discovering a set of molecular markers associated with genomic regions involved in this variation. A number of Quantitative Trait Loci (QTL) were discovered but this method is inaccurate and the genetic architecture of the studied population was better captured by the BLasso method of genomic selection, which allowed for efficient inference about the genetic contribution of the various marker loci. The technology of genomic selection afforded greater efficiency than QTL analysis and can be very useful in speeding up the selection procedures for agronomic traits in table grapes.

Objetivou-se com este trabalho avaliar a capacidade de propagação de famílias dePinus taeda por embriogênese somática utilizando estimativas de parâmetros genéticos. Para a embriogênese somática, foram selecionados cones imaturos de 65 famílias-elite de Pinus taeda. O germoplasma utilizado para a implantação dos testes de campo foi composto por 238 clones de 31 famílias. O estudo foi realizado por meio de análise genetíoco-estatística pelo procedimento de estimação de componentes de variância via máxima verossimilhança residual (Reml) e de predição de valores genéticos via melhor predição linear não viesada (Blup), usando-se o software Selegen-Reml/Blup. De acordo com os resultados, a variabilidade genética possibilita ganhos genéticos altos pela seleção entre famílias, para os caracteres presença de embriões somáticos e número de clones por famílias dePinus taeda. Há baixa ou nenhuma correlação genética entre o número de clones propagados viaembriogênese somática e as características altura, diâmetro, sobrevivência e volume avaliados aos 4 anos de idade em testes clonais. Conclui-se que há maior ganho genético para a capacidade de propagação por embriogênese somática com a seleção de famílias de Pinus taeda.

ABSTRACT We aimed to evaluate the impact of using embryo transfer (ET) information on weaning weight estimated breeding values (EBV) and its accuracy. Data from Hereford and Braford cattle, raised under extensive conditions in Southern Brazil, were used. A model that included ET information in addition to maternal (genetic and permanent environmental) effects as a function of foster dams was compared to a model without ET information. Accuracy of both bulls and calves increased due to inclusion of ET records in 0.04 and 0.12 points, respectively. In general, the inclusion of ET records provided a greater amount of phenotypic variance and most accurate EBV for sires and progeny. The results obtained in this study encourage the use of ET phenotypic records in large-scale genetic evaluation programs, especially for bulls that have most of their progeny coming from ET. Most of the Brazilian genetic evaluation programs do not use phenotypic records of ET animals. Therefore, breeding values are predicted based only on parentage average, which implies in underestimated accuracies. Considering that ET has been widely used in Brazil and that such information improves genetic predictions, we suggest modifying the traditional adopted models by considering ET information in the Brazilian genetic evaluations.

ABSTRACT: The aim of this study was to evaluate the growth responses of various Eucalyptus and Corymbia species subjected to different intensities of simulated hypergravity relative to the control. A centrifuge was used to simulate hypergravity. It was developed and built at the Centro de Microgravidade of the Pontifícia Universidade Católica do Rio Grande do Sul, Brazil. Seeds of five Eucalyptus and one Corymbia species (E. grandis, Eucalyptus globulus, Eucalyptus benthamii, Eucalyptus saligna, Eucalyptus dunnii, and C. maculata) were placed on moist germination paper in plastic containers and rotated at speeds simulating 5 Gz and 7 Gz for different lengths of time. Hypergravity technology significantly increased seedling production (diameter, height, and survival at 120 days) in nurseries. In E. globulus, the effects of hypergravity were significant at 7 Gz at all lengths of time (from 1 d to 9 days). Effects of hypergravity were significant in both E. benthamii and E. grandis at 7 Gz and 8 h exposure. Therefore, simulated hypergravity could be used in performance tests of Eucalyptus seedlings in early stages of development.

The silvopastoral system is a viable technological alternative to extensive cattle grazing, however, for it to be successful, forage grass genotypes adapted to reduced light need to be identified. The objective of this study was to select progenies of Panicum maximum tolerant to low light conditions for use in breeding programs and to study the genetic control and performance of some traits associated with shade tolerance. Six full-sib progenies were evaluated in full sun, 50% and 70% of light reduction in pots and subjected to cuttings. Progeny genotypic values ​​(GV) increased with light reduction in relation to plant height (H) and specific leaf area (SLA). The traits total dry mass accumulation (DM) and leaf dry mass accumulation (LDM) had GV higher in 50% shade and intermediate in 70% shade. The GV of tiller number (TIL) and root dry mass accumulation (RDM) decreased with light reduction. The highest positive correlations were obtained for the traits H and RDM with SLA and DM; the highest negative correlations were between TIL and SLA and RDM, and H and LDM. The progenies showed higher tolerance to 50% light reduction and, among them, two stood out and will be used in breeding programs. It was also found that it is not necessary to evaluate some traits under all light conditions. All traits had high broad sense heritability and high genotypic correlation between progenies in all light intensities. There is genetic difference among the progenies regarding the response to different light intensities, which will allow selection for shade tolerance

The aim of this study was to assess the efficiency of the simulated individual BLUP (BLUPIS) method in selecting genotypes within full-sib families of sugarcane in ratoon stage, through comparison with selection using the individual BLUP method. The optimal number of genotypes to be selected in the best families were established for mean stems mass, total soluble solids assay (BRIX), ton of stalks per hectare, and BRIX tons per hectare traits. Seventeen full-sib families were assessed in the Centre for Experimentation in Sugarcane, located in Oratórios, MG, Brazil. Mixed model methodology was used to predict the genotypic effects of each family and the genotypic values of each individual within family. BLUPIS method is efficient for individual selection. The optimal number of genotypes to be selected in the best family for obtaining higher efficiency of the BLUPIS method is 100 in the majority of cases.

Eucalypts rust (Puccinia psidii) is currently one of the major diseases in commercial eucalypt plantations in Brazil. The primary method of disease control is the use of resistant genotypes, and, among the different species of Eucalyptus, E. pellita is indicated as a promising source of resistance. In this work, the genetic control of rust resistance in E. pellita through inoculations under controlled conditions of 441 plants from four full-sibling families was studied. Inoculations were performed using the monopostular isolate UFV-2, race 1. All families tested segregated for rust resistance, and the number of resistant plants was higher than susceptible in all crosses. Inheritance models based on few genes did not fully explain the observed segregation patterns, and the narrow-sense heritability of rust resistance was estimated between 32.7% and 37.3%. The results suggested that rust resistance in E. pellita is complex and is controlled by major- and minor-effect genes.

O objetivo deste trabalho foi estimar os parâmetros genéticos e predizer o valor genético de populações e indivíduos oriundos de populações segregantes de trigo, com o uso da metodologia de modelos mistos ("restricted maximum likelihood"/"best linear unbiased prediction", REML/BLUP). Trinta e seis populações segregantes de trigo e quatro controles foram avaliados na geração F3, em delineamento de blocos ao acaso, com informações de indivíduo retiradas de dentro das parcelas. Os caracteres avaliados foram: produção de grãos, índice de colheita, número de perfilhos e altura de planta. Observou-se a existência de variabilidade genética entre populações em todos os caracteres avaliados. A herdabilidade média variou de 39,15 a 92,78%, e a acurácia, de 62,57 a 96,32%, na seleção de populações. A herdabilidade individual no sentido restrito foi baixa dentro das populações, em todos os caracteres. A acurácia na seleção individual apresentou magnitude média, quanto ao caráter altura de plantas, e baixa quanto aos demais caracteres. A herdabilidade individual contribui para maior ganho nos caracteres altura de planta e índice de colheita com o uso do BLUP individual, em comparação ao BLUP de populações. As populações segregantes Embrapa22/BRS207, Embrapa22/VI98053, Embrapa22/IVI01041, BRS254/BRS207, BRS254/VI98053, BRS254/UFVT1Pioneiro e BRS264/BRS207 destacam-se por apresentar valor genético aditivo elevado em dois ou mais caracteres.

Heart of palm harvesting and exploitation has reduced the natural reserves of açaí and juçara; hence, the peach palm is an excellent option as source of heart of palm that can reduce the pressure on these two species. The peach palm has high tillering and no post-harvest oxidation. An experiment in a randomized block design with 72 progenies of peach palm, 36 replications and a single tree plot was performed for five years at the experimental field of Embrapa in the Western Amazon. Genetic parameters were estimated using two mixed models, one based on progeny means and the second on joint information from crops. A repeatability analysis, genotype selection, and progeny selection were performed; the Mulamba and Mock selection index was determined; and Tocher’s clustering method was applied. The repeatability analysis revealed the existence of genetic variability, low repeatability, which ranged from 0.3307 to 0.6698 for five years. The analysis performed through the progeny means showed a heritability that varied from 51% to 23% for in all variables; the clustering revealed two groups. The progeny selection should be the main selection strategy, and crosses of superior progenies of different groups should be emphasized.

Technological cotton fiber traits are essential for the quality and remuneration of its products. This study aimed to estimate the genetic divergence among cotton accessions and assort the best ones, based in a selection index combining all the important traits. A total of 248 cotton accessions were evaluated using multivariate analysis (Mahalanobis distance and Tocher grouping) and a selection index based in average rank via mixed models (REML/BLUP). The Tocher grouping analysis enabled the structuring of the accessions population by allocating them into 14 divergent groups. Selective accuracies were high for all traits, ranging from 0.89 to 0.94, indicating a favorable scenario for selection. Genetic correlations among the six traits were moderate to low, making impossible the breeding of a trait, via indirect selection, in another. The simultaneous selection for fiber traits, concerning the Mulamba and Mock selection index, showed to be promising. The best accessions for the six traits were simultaneously the 4S180, C96480, Giza75, 196Lasani11, Brown Egyptian, Early Fluff 316, C268-80 and 207MG-82607.

When it comes to recommending sorghum (Sorghum bicolor) cultivars, it is essential to carry out a genetic evaluation of the agronomic traits of promising genotypes from several common environments where the crop is cultivated. This study consisted of a genetic evaluation of 52 experimental grain sorghum hybrids and eight commercial cultivars. Hybrids were evaluated in 19 experiments representing the most varied cultivation conditions in Brazil. Traits of agronomic interest such as grain yield, flowering and plant height were analysed. Genotypic evaluation was performed following the REML/BLUP (Restricted Maximum Likelihood/Best Linear Unbiased Predictor) procedure; the MHPRVG (Harmonic Mean of Relative Performance of Genotypic Values) method was also employed to study stability and adaptability. Hybrids which stood out in terms of highest grain yield based on genotypic values, stability and adaptability were 0306037, 1G150, DKB 599, 0306039, 1G282 and 0307671. Of these, only 1G282 showed restrictions as to plant height. For flowering, experimental hybrids showed shorter cycles than commercial cultivars, confirming the efficiency of genetic improvement for this trait. With the analysis of grain yield considering days to flowering and plant height as covariates, it was observed that most of the hybrids of greater performance, showed grain yield to be positively influenced by plant height and days to flowering.

Sugarcane (Saccharum spp.) is one of the most important crops cultivated in the tropics and subtropics and plays a significant economic and environmental role in Brazil. Twentyfour new clones were evaluated in different locations as potential models for recommendation as new varieties. The mixed model methodology, using the harmonic mean of the relative performance of genetic values (MHPRVG), facilitated the analysis of genotypic stability and adaptability, culminating in the recommendation of clones for each location. MHPRVG ranked clones RB92579, RB867515, SP81-3250, RB947520 and RB931530 as the best five, and, additionally, clones with greater genotypic potential were identified for each test in the six localities.