Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data

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.(AU)

Evaluation of a new additive-dominance genomic model and implications for quantitative genetics and genomic selection

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.

Determination of optimal number of independent components in yield traits in rice

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.

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

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.(AU)

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.(AU)

Estimates of genetic parameters, genetic variability, and selection in the S1 generation of peach / Estimativas de parâmetros genéticos, variabilidade genética e seleção em geração S1 de pessegueiros

The growing of peach in mild winter regions is made viable through the use of genotypes that have low need for cold conditions, and this is one of the main aims of breeding for these regions. Thus, the aims of this study were to estimate genetic parameters, evaluate genetic variability, and select families adapted to mild winter regions in the S1 generation of peach through mixed model methodology (REML/BLUP). For that purpose, 22 populations, 84 families, and 2090 individuals were evaluated for the following traits: bud burst rate (BR), node density (ND), plant height (PH), and trunk diameter (TD). Genetic variability was found for all the traits. Individual heritability in the broad sense was of low and medium magnitudes. The PH trait had positive genotypic correlation of high magnitude with TD. The ND trait had moderate negative genotypic correlation with PH and TD. Clustering by the Tocher method resulted in the formation of six mutually exclusive groups. Considering selection intensity of 25%, simultaneous selection for BR, ND, and TD led to predicted gains of 11.3% for BR, 9.7% for ND, -14.2% for PH, and -14.3% for TD, showing the great potential of the germplasm evaluated.(AU)

O cultivo do pessegueiro em regiões de inverno ameno é viabilizado pela utilização de genótipos que apresentam baixa necessidade de frio, sendo este um dos principais objetivos do melhoramento para essas regiões. Assim, os objetivos deste estudo foram estimar parâmetros genéticos, avaliar a variabilidade genética e selecionar famílias adaptadas a regiões de inverno ameno em geração S1 de pessegueiros via metodologia de modelos mistos (REML/BLUP). Para isso, 22 populações, 84 famílias e 2090 indivíduos foram avaliados quanto as características: taxa de brotação (TB), densidade de nós (DN), altura da planta (AP) e diâmetro do tronco (DT). Verificou-se variabilidade genética para todas as características. As herdabilidades individuais no sentido amplo foram de baixa e média magnitudes. A característica AP apresentou correlação genética positiva de magnitude elevada com DT. A característica DN apresentou correlação genética negativa moderada com AP e DT. O agrupamento pelo método de Tocher resultou na formação de seis grupos mutuamente excludentes. Considerando intensidade de seleção de 25%, a seleção simultânea para TB, DN e DT propiciou ganhos preditos de 11.3% para TB, 9.7% para DN, -14.2% para AP e -14.3% para DT, evidenciando o grande potencial do germoplasma avaliado.(AU)

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

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.(AU)

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.(AU)

New insights into genomic selection through population-based non-parametric prediction methods

Genome-wide selection (GWS) is based on a large number of markers widely distributed throughout the genome. Genome-wide selection provides for the estimation of the effect of each molecular marker on the phenotype, thereby allowing for the capture of all genes affecting the quantitative traits of interest. The main statistical tools applied to GWS are based on random regression or dimensionality reduction methods. In this study a new non-parametric method, called Delta-p was proposed, which was then compared to the Genomic Best Linear Unbiased Predictor (G-BLUP) method. Furthermore, a new selection index combining the genetic values obtained by the G-BLUP and Delta-p, named Delta-p/G-BLUP methods, was proposed. The efficiency of the proposed methods was evaluated through both simulation and real studies. The simulated data consisted of eight scenarios comprising a combination of two levels of heritability, two genetic architectures and two dominance status (absence and complete dominance). Each scenario was simulated ten times. All methods were applied to a real dataset of Asian rice (Oryza sativa) aiming to increase the efficiency of a current breeding program. The methods were compared as regards accuracy of prediction (simulation data) or predictive ability (real dataset), bias and recovery of the true genomic heritability. The results indicated that the proposed Delta-p/G-BLUP index outperformed the other methods in both prediction accuracy and predictive ability.

Triple categorical regression for genomic selection: application to cassava breeding

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.

Triple categorical regression for genomic selection: application to cassava breeding

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.(AU)

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

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 worlds 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.(AU)

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.(AU)

New insights into genomic selection through population-based non-parametric prediction methods

Genome-wide selection (GWS) is based on a large number of markers widely distributed throughout the genome. Genome-wide selection provides for the estimation of the effect of each molecular marker on the phenotype, thereby allowing for the capture of all genes affecting the quantitative traits of interest. The main statistical tools applied to GWS are based on random regression or dimensionality reduction methods. In this study a new non-parametric method, called Delta-p was proposed, which was then compared to the Genomic Best Linear Unbiased Predictor (G-BLUP) method. Furthermore, a new selection index combining the genetic values obtained by the G-BLUP and Delta-p, named Delta-p/G-BLUP methods, was proposed. The efficiency of the proposed methods was evaluated through both simulation and real studies. The simulated data consisted of eight scenarios comprising a combination of two levels of heritability, two genetic architectures and two dominance status (absence and complete dominance). Each scenario was simulated ten times. All methods were applied to a real dataset of Asian rice (Oryza sativa) aiming to increase the efficiency of a current breeding program. The methods were compared as regards accuracy of prediction (simulation data) or predictive ability (real dataset), bias and recovery of the true genomic heritability. The results indicated that the proposed Delta-p/G-BLUP index outperformed the other methods in both prediction accuracy and predictive ability.(AU)

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

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.(AU)

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.(AU)

Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves

Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied.

Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves

Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied.(AU)

Genome selection in fruit breeding: application to table grapes

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 Pierces 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.

Genome selection in fruit breeding: application to table grapes

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 Pierces 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.(AU)

Modelos multicaracterísticos e de repetibilidade para avaliação genética de características de leitegada em suínos considerando diferentes partos / Multi-trait and repeatability models for genetic evaluation of litter traits in pigs considering different farrowings

Objetivou-se comparar os modelos multicaracterístico e de repetibilidade na estimação de parâmetros genéticos para as características número de leitões nascidos vivos (NLN) e às três semanas de idade (NL3), peso da leitegada ao nascimento (PLN) e às três semanas de idade (PL3), e peso médio do leitão ao nascimento (PMLN) e às três semanas de idade (PML3), considerando os três primeiros partos de fêmeas da raça Landrace. As estimativas de herdabilidade (h2 ) aumentaram até a terceira ordem de parto para as características PLN e PML3. Para NLN, NL3, PL3 e PMLN as h2 aumentaram da primeira para a segunda parição e reduziram da segunda para terceira parição. Em geral, as herdabilidades estimadas via modelo de repetibilidade foram menores que a média das estimativas obtidas utilizando o modelo muticaracterístico. As características PLN, PMLN e PML3 apresentaram altas correlações genéticas entre as diferentes parições (0,961- 0,997), enquanto as características NLN, NL3 e PL3 (0,092-0,986) apresentaram valores irregulares de correlações genéticas entre as parições. Pelo critério de informação de Akaike corrigido o modelo de repetibilidade não foi indicado para a maioria das características estudadas. Esses resultados indicam que o modelo multicaracterístico é recomendado para avaliação genética das características número de leitões nascidos vivos e às três semanas de idade, peso da l(AU)

We aimed to compare multi-trait and repeatability models to estimate genetic parameters for the traits number of piglets born alive (NBA) and alive at 3 week of age (NP3), litter weight at birth (LW0) and at 3 week of age (LW3), and mean piglet weight at birth (MW0) and at 3 week of age (MW3), considering the first three farrowings of Landrace sows. Heritability (h2 ) estimates showed an increasing pattern up to the third farrowing for LW0 and MW3. For NBA, NP3, LW3, and MW0 h 2 increased from the first to the second and decreased from the second to the third farrowing. In general, heritability estimated in the repeatability model was lower than the mean of the estimates in the multi-trait model. The traits LWO, MW0, and MW3 presented high genetic correlation among different farrowings (0.961 0.997), while NBA, NP3, and LW3 (0.0920.986) presented irregular values among farrowings. The corrected Akaike information criterion shows that the repeatability model is not indicated for almost all of the studied traits. These results indicate that the multi-trait model is recommended for genetic evaluation of the traits number of piglets born alive and alive at 3 week of age, litter weight and mean piglet weight at birth and 3 week of age, in different farrowings, as different traits.(AU)

Modelos multicaracterísticos e de repetibilidade para avaliação genética de características de leitegada em suínos considerando diferentes partos / Multi-trait and repeatability models for genetic evaluation of litter traits in pigs considering different farrowings

Objetivou-se comparar os modelos multicaracterístico e de repetibilidade na estimação de parâmetros genéticos para as características número de leitões nascidos vivos (NLN) e às três semanas de idade (NL3), peso da leitegada ao nascimento (PLN) e às três semanas de idade (PL3), e peso médio do leitão ao nascimento (PMLN) e às três semanas de idade (PML3), considerando os três primeiros partos de fêmeas da raça Landrace. As estimativas de herdabilidade (h2 ) aumentaram até a terceira ordem de parto para as características PLN e PML3. Para NLN, NL3, PL3 e PMLN as h2 aumentaram da primeira para a segunda parição e reduziram da segunda para terceira parição. Em geral, as herdabilidades estimadas via modelo de repetibilidade foram menores que a média das estimativas obtidas utilizando o modelo muticaracterístico. As características PLN, PMLN e PML3 apresentaram altas correlações genéticas entre as diferentes parições (0,961- 0,997), enquanto as características NLN, NL3 e PL3 (0,092-0,986) apresentaram valores irregulares de correlações genéticas entre as parições. Pelo critério de informação de Akaike corrigido o modelo de repetibilidade não foi indicado para a maioria das características estudadas. Esses resultados indicam que o modelo multicaracterístico é recomendado para avaliação genética das características número de leitões nascidos vivos e às três semanas de idade, peso da l

We aimed to compare multi-trait and repeatability models to estimate genetic parameters for the traits number of piglets born alive (NBA) and alive at 3 week of age (NP3), litter weight at birth (LW0) and at 3 week of age (LW3), and mean piglet weight at birth (MW0) and at 3 week of age (MW3), considering the first three farrowings of Landrace sows. Heritability (h2 ) estimates showed an increasing pattern up to the third farrowing for LW0 and MW3. For NBA, NP3, LW3, and MW0 h 2 increased from the first to the second and decreased from the second to the third farrowing. In general, heritability estimated in the repeatability model was lower than the mean of the estimates in the multi-trait model. The traits LWO, MW0, and MW3 presented high genetic correlation among different farrowings (0.961 0.997), while NBA, NP3, and LW3 (0.0920.986) presented irregular values among farrowings. The corrected Akaike information criterion shows that the repeatability model is not indicated for almost all of the studied traits. These results indicate that the multi-trait model is recommended for genetic evaluation of the traits number of piglets born alive and alive at 3 week of age, litter weight and mean piglet weight at birth and 3 week of age, in different farrowings, as different traits.

Breeding new sugarcane clones by mixed models under genotype by environmental interaction

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.

Breeding new sugarcane clones by mixed models under genotype by environmental interaction

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.(AU)