Using visual scores for genomic prediction of complex traits in breeding programs.

KEY MESSAGE: An approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using visual scores traits to explore genetic information in breeding programs and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping.

Population size in QTL detection using quantile regression in genome-wide association studies.

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.

Multivariate analysis methods improve the selection of strawberry genotypes with low cold requirement.

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.

Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones.

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.

Can Cross-Country Genomic Predictions Be a Reasonable Strategy to Support Germplasm Exchange? - A Case Study With Hydrogen Cyanide in Cassava.

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.

Designing the best breeding strategy for Coffea canephora: Genetic evaluation of pure and hybrid individuals aiming to select for productivity and disease resistance traits.

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.

Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy.

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.

Soybean productivity, stability, and adaptability through mixed model methodology / Produtividade, estabilidade e adaptabilidade da soja por meio de metodologia de modelo misto

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.

RESUMO: A interação genótipo × ambiente (G × E) desempenha um papel essencial na expressão fenotípica e pode provocar dificuldades na seleção genética. Assim, o presente estudo teve como objetivo estimar parâmetros genéticos e comparar diferentes estratégias de seleção no contexto de modelos mistos para melhoramento da soja. Para isso, foram utilizados dados referentes à avaliação de 30 genótipos em dez ambientes, referentes à característica produtividade de grãos. Os componentes de variância foram estimados pela máxima verossimilhança restrita (REML) e os valores genotípicos foram preditos pela melhor previsão imparcial linear (BLUP). Efeitos significativos dos genótipos e interação G × E foram detectados pelo teste da razão de verossimilhança (LRT). Correlação genotípica baixa foi obtida entre os ambientes indicando interação G × E do tipo complexa. A acurácia seletiva foi muito alta, indicando alta confiabilidade. Os resultados mostraram que os genótipos de soja mais produtivos apresentam alta adaptabilidade e estabilidade.

Random regression for modeling yield genetic trajectories in Jatropha curcas 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.

Multiple-trait, random regression, and compound symmetry models for analyzing multi-environment trials in maize breeding.

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.

Unraveling candidate genes underlying biomass digestibility in elephant grass (Cenchrus purpureus).

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 < 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. CONCLUSIONS: The 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.

Cassava yield traits predicted by genomic selection methods.

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.

Combined index of genomic prediction methods applied to productivity / Índice combinado de métodos de predição genômica aplicado em características de produtividade em arroz

ABSTRACT: Rice cultivation has great national and global importance, being one of the most produced and consumed cereals in the world and the primary food for more than half of the world's population. Because of its importance as food, developing efficient methods to select and predict genetically superior individuals in reference to plant traits is of extreme importance for breeding programs. The objective of this research was to evaluate and compare the efficiency of the Delta-p, G-BLUP (Genomic Best Linear Unbiased Predictor), BayesCpi, BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator), Delta-p/G-BLUP index, Delta-p/BayesCpi index, and Delta-p/BLASSO index in the estimation of genomic values and the effects of single nucleotide polymorphisms on phenotypic data associated with rice traits. Use of molecular markers allowed high selective efficiency and increased genetic gain per unit time. The Delta-p method uses the concept of change in allelic frequency caused by selection and the theoretical concept of genetic gain. The Index is based on the principle of combined selection, using the information regarding the additive genomic values predicted via G-BLUP, BayesCpi, BLASSO, or Delta-p. These methods were applied and compared for genomic prediction using nine rice traits: flag leaf length, flag leaf width, panicles number per plant, primary panicle branch number, seed length, seed width, amylose content, protein content, and blast resistance. Delta-p/G-BLUP index had higher predictive abilities for the traits studied, except for amylose content trait in which the method with the highest predictive ability was BayesCpi, being approximately 3% greater than that of the Delta-p/G-BLUP index.

RESUMO: A cultura do arroz tem grande importância nacional e mundial por ser um dos cereais mais produzidos e consumidos no mundo, caracterizando-se como o principal alimento de mais da metade da população mundial. Em função de sua importância alimentar, desenvolver métodos eficientes que visam a predição e a seleção de indivíduos geneticamente superiores, quanto a características da planta, é de extrema importância para os programas de melhoramento. Diante disso, o objetivo deste trabalho foi avaliar e comparar a eficiência do método Delta-p, G-BLUP, BayesCpi, BLASSO e o índice Delta-p/G-BLUP, índice Delta-p/BayesCpi e índice Delta-p/BLASSO, na estimação de valores genômicos e dos efeitos de marcadores SNPs (Single Nucleotide Polymorphisms) em dados fenotípicos associados a características de arroz. A utilização de marcadores moleculares permite alta eficiência seletiva e o aumento do ganho genético por unidade de tempo. O método Delta-p utiliza o conceito de mudança na frequência alélica devido à seleção e o conceito teórico de ganho genético. O Índice é baseado no princípio da seleção combinada, utiliza conjuntamente as informações dos valores genômicos aditivos preditos via G-BLUP, BayesCpi ou BLASSO e via Delta-p. Estes métodos foram aplicados e comparados quanto à predição genômica utilizando nove características de arroz (Oryza sativa), sendo elas: comprimento da folha bandeira, largura da folha bandeira; número de panículas por planta; número de ramos da panícula primária; comprimento de semente; largura de semente; teor de amilose; teor de proteína; resistência a bruzone. O índice Delta-p/G-BLUP obteve maiores capacidades preditivas para as características estudadas, exceto para a característica Conteúdo de amilose, em que o método que obteve maior capacidade preditiva foi o BayesCpi, sendo aproximadamente 3% superior ao índice Delta-p/G-BLUP.

Genetic parameters to production characters of palm heart in half-sibs progenies of palm peach / Parâmetros genéticos para caracteres de produção de palmito em progênies de meio-irmãos de pupunheira

ABSTRACT: The peach palm (Bactris gasipaes) is a palm tree that produces palm heart originated of Amazonia with economic, social and environmental sustainability. To obtain improved cultivars it is necessary the evaluation and selection of genotypes with characteristics that support producers, manufacturing and consumers. In this context, the objective of this study was to estimate genetic parameters for palm heart production traits of peach palm considering half sibs progenies. Twenty progenies of Putumayo macrocarpa race were evaluated in seven cultivation cycles. The experiment was designed in complete randomized blocks, with 40 repetitions and one plant per plot. The genetic parameters were estimated by REML/BLUP methodology. Low genetic variability was observed in the population, possibly due to the narrow genetic base from original population. However, considering the significant genetic effect and the progeny mean heritability, the selection performed between progenies is more efficient than individual selection. The high number of measurements required for most of the evaluated characters becomes impractical in peach palm breeding programs. The number of palm heart per plant can be used to perform indirect selection for total production of palm heart.

RESUMO: A pupunheira (Bactris gasipaes) é uma palmeira produtora de palmito oriunda da Amazônia, que apresenta sustentabilidade econômica, social e ambiental. Para a obtenção de cultivares melhoradas é necessário a avaliação e seleção de genótipos com características que atendam produtores, processadores e consumidores. Assim, o objetivo deste trabalho foi estimar parâmetros genéticos para os caracteres de produção de palmito de pupunha considerando progênies de meio-irmãos. Foram avaliadas 20 progênies da raça macrocarpa Putumayo em sete ciclos de cultivo. O experimento foi delineado em blocos completos ao acaso, com 40 repetições e uma planta por parcela. Os parâmetros genéticos foram estimados segundo metodologia REML/BLUP. Baixa variabilidade foi observada na população de estudo, possivelmente devido à estreita base genética da população original. Contudo, considerando o efeito genético significativo e as herdabilidades médias de progênie, a seleção praticada entre progênies é mais eficiente que a seleção individual. O grande número de medições necessárias para a maioria dos caracteres avaliados torna-se impraticável em programas de melhoramento de pupunheira. O número de palmitos por planta pode ser utilizado na seleção indireta para produção total de palmito.

Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.

Genomic Selection (GS) has allowed the maximization of genetic gains per unit time in several annual and perennial plant species. However, no GS studies have addressed Coffea arabica, the most economically important species of the genus Coffea. Therefore, this study aimed (i) to evaluate the applicability and accuracy of GS in the prediction of the genomic estimated breeding value (GEBV); (ii) to estimate the genetic parameters; and (iii) to evaluate the time reduction of the selection cycle by GS in Arabica coffee breeding. A total of 195 Arabica coffee individuals, belonging to 13 families in generation of F2, susceptible backcross and resistant backcross, were phenotyped for 18 agronomic traits, and genotyped with 21,211 SNP molecular markers. Phenotypic data, measured in 2014, 2015, and 2016, were analyzed by mixed models. GS analyses were performed by the G-BLUP method, using the RKHS (Reproducing Kernel Hilbert Spaces) procedure, with a Bayesian algorithm. Heritabilities and selective accuracies were estimated, revealing moderate to high magnitude for most of the traits evaluated. Results of GS analyses showed the possibility of reducing the cycle time by 50%, maximizing selection gains per unit time. The effect of marker density on GS analyses was evaluated. Genomic selection proved to be promising for C. arabica breeding. The agronomic traits presented high complexity for they are controlled by several QTL and showed low genomic heritabilities, evidencing the need to incorporate genomic selection methodologies to the breeding programs of this species.

Application of hypergravity in Eucalyptus and Corymbia seeds / Aplicação da hipergravidade em sementes de Eucalyptus e Corymbia

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.

RESUMO: O presente trabalho objetivou avaliar o crescimento de espécies de Eucalyptus e Corymbia em diferentes intensidades de hipergravidade simulada em relação ao controle. Uma centrífuga foi usada para simular a hipergravidade. Este equipamento foi desenvolvido e construído no Centro de Microgravidade da Pontifícia Universidade Católica do Rio Grande do Sul, Brasil. Sementes de cinco espécies de Eucalyptus e uma de Corymbia (E. grandis, E. globulus, E. benthamii, E. saligna, E. dunnii, e C. maculata) foram colocadas em papeis de germinação e em recipientes plásticos, em que foram rotacionadas a velocidades simuladas de 5 Gz e 7 Gz, por diferentes períodos de tempo. A tecnologia da hipergravidade proporcionou aumento significativo na taxa de crescimento das plântulas (diâmetro, altura e sobrevivência aos 120 dias) no viveiro. Para Eucalyptus globulus, os efeitos da hipergravidade foram significativos na intensidade de 7 Gz em qualquer período de tempo (do primeiro até o nono dia). Os efeitos da hipergravidade foram significativos para as espécies E. benthamii e E. grandis na intensidade 7 Gz e 8 horas de exposição. Dessa maneira, a hipergravidade simulada apresenta potencial de uso em testes com plântulas de eucaliptos em estágios iniciais de desenvolvimento.

Regional heritability mapping and genome-wide association identify loci for complex growth, wood and disease resistance traits in Eucalyptus.

Although genome-wide association studies (GWAS) have provided valuable insights into the decoding of the relationships between sequence variation and complex phenotypes, they have explained little heritability. Regional heritability mapping (RHM) provides heritability estimates for genomic segments containing both common and rare allelic effects that individually contribute too little variance to be detected by GWAS. We carried out GWAS and RHM for seven growth, wood and disease resistance traits in a breeding population of 768 Eucalyptus hybrid trees using EuCHIP60K. Total genomic heritabilities accounted for large proportions (64-89%) of pedigree-based trait heritabilities, providing additional evidence that complex traits in eucalypts are controlled by many sequence variants across the frequency spectrum, each with small contributions to the phenotypic variance. RHM detected 26 quantitative trait loci (QTLs) encompassing 2191 single nucleotide polymorphisms (SNPs), whereas GWAS detected 13 single SNP-trait associations. RHM and GWAS QTLs individually explained 5-15% and 4-6% of the genomic heritability, respectively. RHM was superior to GWAS in capturing larger proportions of genomic heritability. Equated to previously mapped QTLs, our results highlighted genomic regions for further examination towards gene discovery. RHM-QTLs bearing a combination of common and rare variants could be useful enhancements to incorporate prior knowledge of the underlying genetic architecture in genomic prediction models.

Genotypic value in hybrid progenies of Panicum maximum Jacq / Valor genotípico de progênies híbridas de Panicum maximum Jacq

ABSTRACT: Genetic breeding of forage plants has increasingly contributed to the release of more productive plants. In this regard, evaluating the genotypic value is essential when aiming to rank genotypes based on the mean free of environmental factors. Therefore, this study aimed to predict the genotypic value of agronomic and nutritive value characters of three progenies of Panicum maximum. Hybrids were evaluated in a clonal test in an incomplete-randomized design with three treatments (progenies 1, 2, and 3) and two replications (clones). Six harvests were performed at 25cm from the ground level throughout one year. Progeny 2 provided better results for total and leaf dry mass yield, regrowth, and height, and lower incidence of leaf spot. Progenies 1 and 3 had a better response for qualitative characters such as higher crude protein and digestibility and lower lignin and fiber content. Hybrid progenies of P. maximum have forage characters of interest for breeding, and when using 'Mombaça' grass as parents, the progeny stands out for leaf production and resistance to leaf spot and for 'Tanzania' grass as parent has resulted in better forage quality.

RESUMO: O lançamento de forrageiras resultantes de programas de melhoramento genético tem sido importante fonte de liberação de novas forrageiras mais adaptadas e competitivas. Nessas situações, a avaliação do valor genético é essencial quando se objetiva ranquear os genótipos com base no valor genotípico, isento dos efeitos ambientais. O objetivo com este trabalho foi estimar e avaliar o valor genotípico de características agronômicas e de valor nutritivo de três progênies de P. maximum, resultantes do cruzamento entre duas progenitoras sexuais e as cultivares 'Mombaça' e 'Tanzânia'. O experimento foi implantado em teste clonal no delineamento em blocos incompletos com três tratamentos (progênies 1, 2 e 3) com duas repetições (clones). Os híbridos foram manejados por meio de cortes na altura de 25cm do nível do solo por um ano, realizando seis cortes. A progênie 2 proporcionou melhores resultados para produção de folhas, rebrota, altura de planta e baixa incidência de mancha foliar causada por Bipolaris maydis. As progênies 1 e 3 apresentaram, em média, melhores resultados para características qualitativas como proteína bruta e digestibilidade e menor teor de lignina. As progênies híbridas de P. maximum apresentam características forrageiras de interesse para o melhoramento, sendo que a utilização do capim-mombaça como parental proporciona maior produção de folhas e resistência à mancha foliar, ao passo que o capim-tanzânia como parental proporciona melhoria da qualidade da forragem.

Repeatability of biometric characteristics of Juçara palm fruit / Repetibilidade de características biométricas de frutos de palmito Juçara

Juçara is a plant that occurs in the Brazilian Atlantic Forest which presents high ecological importance to the biodiversity and is present on the list of endangered species. The fruits are known as super fruits because they present chemical characteristics of great importance. Due to its elevated economic importance and for the low number of researches with this species, the first step is to make the pre-breeding of the species. This consists in morphological characterization, which is very dependent on time and labour. Thus, the decrease of the number of evaluated measurements by individual is of great importance to the phases of pre-breeding and breeding. The objective of this work was to obtain estimates of the coefficients of repeatability and determination of six Euterpe edulis fruit characteristics to enable the prediction of the necessary number of measurements required to achieve given levels of certainty for the real values for each of the six fruit characteristics. The performances of various methods for repeatability estimation were compared. The characteristics longitudinal diameters of the fruits, longitudinal diameters of the seeds, equatorial diameters of the fruits, equatorial diameters of the seeds, fruit weights and seed weights of juçara palm fruits were measured, and the deviances, the coefficients of repeatability, the coefficients of determination, and the necessary numbers measurements for an accurate prediction of the real value of the population were estimated. The methods used do not differ as to estimate the repeatability coefficient to the longitudinal diameter characteristics of the fruit, seed equatorial diameter, fruit mass and seed mass. With 95% reliability is possible to use four (4) for mass measurements of fruit and five (5) measurements for longitudinal diameter of fruit, seed equatorial diameter, fruit mass and seed mass.

A juçara é uma planta de ocorrência da Mata Atlântica brasileira e apresenta grande importância ecologia para a biodiversidade e se encontra na lista de espécies ameaçadas de extinção. Os frutos são conhecidos como super frutos por apresentarem características químicas de grande importância. Devido a sua grande importância econômica e pelo baixo número de pesquisas com esta espécie, o primeiro passo é fazer o pré-melhoramento. Este consiste em caracterização morfológica, a qual é muito dispendiosa de tempo e mão de obra. Sendo assim, a diminuição do número de medições avaliadas por indivíduo é de suma importância durante as fases de pré-melhoramento e melhoramento. O objetivo deste trabalho foi obter estimativas dos coeficientes de repetibilidade e de determinação, predizer o número adequado de medições capaz de proporcionar níveis de certeza da predição do valor real dos indivíduos para cada uma das seis características analisadas de Euterpe edulis, e comparar diferentes métodos de estimação da repetibilidade. Foram mensuradas seis características nos frutos de juçara e posteriormente estimou-se a deviance, o coeficiente de repetibilidade, o coeficiente de determinação e o número de medições necessárias para uma predição adequada do valor real dos indivíduos. As metodologias utilizadas não diferem quanto à estimativa do coeficiente de repetibilidade para as características diâmetro longitudinal do fruto, diâmetro equatorial da semente, massa do fruto e massa da semente. Com 95% de confiabilidade é possível utilizar quatro (4) medições para massa do fruto e cinco (5) medições para diâmetro longitudinal do fruto, diâmetro equatorial do fruto, diâmetro longitudinal da semente, diâmetro equatorial da semente e massa da semente.

Ridge, Lasso and Bayesian additive-dominance genomic models.

BACKGROUND: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). RESULTS: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. CONCLUSIONS: Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (-2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.