Summary• Genomic selection (GS) is expected to cause a paradigm shift in tree breeding by improving its speed and efficiency. By fitting all the genome-wide markers concurrently, GS can capture most of the 'missing heritability' of complex traits that quantitative trait locus (QTL) and association mapping classically fail to explain. Experimental support of GS is now required.• The effectiveness of GS was assessed in two unrelated Eucalyptus breeding populations with contrasting effective population sizes (N e = 11 and 51) genotyped with > 3000 DArT markers. Prediction models were developed for tree circumference and height growth, wood specific gravity and pulp yield using random regression best linear unbiased predictor (BLUP).• Accuracies of GS varied between 0.55 and 0.88, matching the accuracies achieved by conventional phenotypic selection. Substantial proportions (74-97%) of trait heritability were captured by fitting all genome-wide markers simultaneously. Genomic regions explaining trait variation largely coincided between populations, although GS models predicted poorly across populations, likely as a result of variable patterns of linkage disequilibrium, inconsistent allelic effects and genotype · environment interaction.• GS brings a new perspective to the understanding of quantitative trait variation in forest trees and provides a revolutionary tool for applied tree improvement. Nevertheless population-specific predictive models will likely drive the initial applications of GS in forest tree breeding.

We proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged from -0.58 to 0.03, -0.46 to 0.12, and 0.37 to 0.64, respectively. We concluded that combining different functions under a MTRRM approach can be a plausible alternative for joint genetic evaluation of milk yield and milk constituents in goats.

ABSTRACT. The aim of this study was to evaluate repeated measures over the years to estimate repeatability coefficient and the number of the optimum measure to select superior genotypes in Annona muricata L. The fruit production was evaluated over 16 years in 71 genotypes without an experimental design. The estimation of variance components and the prediction of the permanent phenotypic value were performed using REML/BLUP proceedings. The coefficient of determination, accuracy, and selective efficiency increased when measures increased. The coefficient of determination of 80% was reached beyond 8 crop seasons with high accuracy and selective efficiency. Thus, the evaluation of 8 crop seasons can be suitable to select superior genotypes in the A. muricata L. breeding program. Predicted selection gain had a high magnitude for fruit production indicating that it is possible to take a progressive genetic advance for this trait over cycle breeding.

ABSTRACT. The aim of this study was to estimate the genotypic gain with simultaneous selection of production, nutrition, and culinary traits in cowpea crosses and backcrosses and to compare different selection indexes. Eleven cowpea populations were evaluated in a randomized complete block design with four replications. Fourteen traits were evaluated, and the following parameters were estimated: genotypic variation coefficient, genotypic determination coefficient, experimental quality indicator and selection reliability, estimated genotypic values -BLUE, genotypic correlation coefficient among traits, and genotypic gain with simultaneous selection of all traits. The genotypic gain was estimated based on tree selection indexes: classical, multiplicative, and the sum of ranks. The genotypic variation coefficient was higher than 2 D.G. Oliveira et al. Genetics and Molecular Research 16 (3): gmr16039736the environmental variation coefficient for the number of days to start flowering, plant type, the weight of one hundred grains, grain index, and protein concentration. The majority of the traits presented genotypic determination coefficient from medium to high magnitude. The identification of increases in the production components is associated with decreases in protein concentration, and the increase in precocity leads to decreases in protein concentration and cooking time. The index based on the sum of ranks was the best alternative for simultaneous selection of traits in the cowpea segregating populations resulting from the crosses and backcrosses evaluated, with emphasis on the F 4 BC 12 , F 4 C 21 , and F 4 C 12 populations, which had the highest genotypic gains.

The present study was carried out to estimate genetic parameters and additive genetic values by the individual REML/BLUP procedure for rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell.-Arg.] selection in the presence of genotype x environment interaction. Twenty-two half-sib progenies were planted in Experimental Stations at Jaú, Pindorama and Votuporanga in São Paulo State, Brazil, in a randomized complete block design with five replicates and 10 plants per plot, and assessed for dry rubber yield at three years of age. The results for yield showed the presence of genetic variability among progenies with higher values for Pindorama, especially for additive genetic and phenotypic variance estimates, characterizing the location as a favorable environment for the expression of the population genetic variability. The high correlation among progenies observed between the Votuporanga and Jaú environments confirmed that a single breeding program can satisfactorily attend both regions. The genetic gains estimated by the multivariate and univariate BLUP methods showed superiority (5% to 21%) for the individual multivariate BLUP procedure, reflecting the great efficacy and flexibility of the method for predicting genetic values and gains in the presence of genotype x environment interaction. The use of information from the three locations combined leads to the maximization of the genetic gain with the selection.

O melhoramento animal é uma ciência preditiva que tem na variabilidade genética do rebanho a sua base e na seleção uma importante ferramenta de ação, cuja eficiência é ponderada pela herdabilidade das características de importância econômica, que é um parâmetro específico de cada rebanho. Por ser bom indicador das consequências do manejo genético nos sistemas de produção, a herdabilidade recebeu atenção da pesquisa e contribuição da evolução de recursos computacionais e de métodos estatísticos para a estimação de seus componentes de variância, resultando em estimativas com maior precisão, inicialmente pelo método de mínimos quadrados, em seguida por máxima verossimilhança e estatísticas bayesianas. O rebanho Nelore do Brasil passou por essas etapas no seu processo de melhoramento genético, que tem sido norteado pela seleção em características de crescimento (pesos em idades padrão) como fenótipos de interesse econômico, que apresentam herdabilidade de moderada a alta magnitude. A herdabilidade tem sido estimada por diversos procedimentos, mas a estimação de componentes de variância por máxima verossimilhança restrita (REML) tem sido preferida. No entanto, limitações como a demanda computacional e a grande quantidade de dados tem favorecido o uso da Inferência Bayesiana. Partindo dessas premissas, o objetivo com esta revisão é abordar a herdabilidade das características de crescimento em bovinos da raça Nelore estimada pelos métodos de Máxima verossimilhança restrita e Inferência Bayesiana.

No abstract

Multi-trait best linear unbiased prediction (BLUP) is, generally, the most appropriate method to genetic evaluation because it considers the genetic and residual correlations among traits and conduct to higher selection accuracy. Thus, the present study aimed to identify traits correlated to the fiber length via path analysis under multi-trait BLUP for the cotton breeding. To this end, thirty-six elite lines were evaluated in three environments and phenotyped for many traits related to fiber quality and agronomic traits. Variance components were estimated via residual maximum likelihood (REML). The genetic correlation coefficients among traits were obtained through mixed model output, and to graphically express these results a correlation network was built. Subsequently, we performed path analysis considering fiber length as a principal dependent variable. Genetic parameters obtained by multi-trait BLUP model indicate that the phenotypic variance for most traits is mostly composed of residual effects, which reinforces the need for using more accurate statistical methods such as multi-trait BLUP. The results found for genetic correlations and path analysis under multi-trait BLUP reveal the difficulty of selection based on important fiber quality traits, especially fiber length, since most traits show very low cause-and-effect relationship, and other important traits present undesirable cause-and-effect relationship. Highlights Multiple-trait BLUP is the most appropriate method to predict genetic values. This is the first study in cotton to perform path analysis under multiple-trait BLUP. The findings of this study indicate that there is no genotype presenting all desirable traits.