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Genomic selection (GS) is used to predict the merit of a genotype with respect to a quantitative trait from molecular or genomic data. Statistically, GS requires fitting a regression model with multiple predictors associated with the molecular markers (MM) states. The model is calibrated in a population with phenotypic and genomic data. The abundance and correlation of MM information make model estimation challenging. For that reason there are diverse strategies to adjust the model: based on best linear unbiased predictors (BLUP), Bayesian regressions and machine learning methods. The correlation between the observed phenotype and the predicted genetic merit by the fitted model provides a measure of the efficiency (predictive ability) of the GS. The objective of this work was to perform a metaanalysis on the efficiency of GS in cereals. A systematic review of related GS studies and a meta-analysis, in wheat and maize, was carried out to obtain a global measure of GS efficiency under different scenarios (MM quantity and statistical models used in GS). The meta-analysis indicated an average correlation coefficient of 0.61 between observed and predicted genetic merits. There were no significant differences in the efficiency of the GS based on BLUP (RR-BLUP and GBLUP), the most common statistical approach. The increase of MM data, make GS efficiency do not vary widely.

The genomic diversity, expressed in the differences between molecular haplotypes of a group of individuals, can be divided into components of variability between and within some factor of classification of the individuals. For such variance partitioning, molecular analysis of variance (AMOVA) is used, which is constructed from the multivariate distances between pairs of haplotypes. The classical AMOVA allows the evaluation of the statistical significance of two or more hierarchical factors and consequently there is no interaction test between factors. However, there are situations where the factors that classify individuals are crossed rather than nested, that is, all the levels of a factor are represented in each level of the other one. This paper proposes a statistical test to evaluate the interaction between crossed factors in a Non-Hierarchical AMOVA. The null hypothesis of interaction establishes that the molecular differences between individuals of different levels of a factor are the same for all the levels of the other factor that classifies them. The proposed analysis of interaction in a Non-Hierarchical AMOVA includes: calculation of the distance matrix and partition of it into blocks, subsequent calculation of residuals and analysis of non-parametric variance on the residuals. Its implementation is illustrated in simulated and real scenarios. The results suggest that the proposed interaction test for the Non-Hierarchical AMOVA presents high power.

Pinus pinea is characterized by phenotypic plasticity, tolerance to harsh soils and climates, but low differentiation in growth parameters and low genetic variability. Growth and cone production of six European stone pine provenances (two from Italy, three from Spain and one from Slovenia) were analyzed in a field trial experiment established in central Chile. The study evaluated height, diameter at breast height (DBH) and crown diameter growth of 147 nineteen-year-old trees per provenance, as well as fruiting variables (i.e., number of cones per tree and cone weight). Survival over the first 7 years was also evaluated. Provenances significantly differed in cone number per tree, cone weight, height and DBH growth, and crown diameter growth. Provenances were grouped according to growth and production variables: one group included the Italian and Slovenian provenances, the second group Andalucía and Sierra Morena (Spain), and the third included Meseta Castellana (Spain). Individual cone production was positively correlated with cone weight and other growth variables. Meseta Castellana provenance showed the highest growth and productivity. Our results provide useful information for the selection of P. pinea provenances to be used in new plantations in central Chile.

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Copyright of Agriscientia is the property of Revista AgriScientia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multienvironment Trials (MET) are used to make cultivar recommendations about genotypes in plant breeding programs. Because of the presence of genotype x environment interaction, METs are usually conducted in multiple environments using designs that involve several replications per environment. Blocking of plots within each trial enables one to account for between plot variation. To improve the comparison of genotype means, taking into account within-trial spatial correlation as well as between-trial residual variance heterogeneity, alternative mixed models can be used. The objective of this study was to compare several spatial models, including or excluding heterogeneity of residual variances for cultivar evaluation in a set of independent peanut (Arachis hypogaea L.) METs. The modeling impact was evaluated by comparing genotype means from each trial. A series of 18 METs from a peanut breeding program, as according to a randomized complete block design (RCBD) at each location, were simultaneously fitted by (i) a classic analysis of variance model for an RCBD with blocks random and (ii) mixed models incorporating spatial correlation through isotropic and anisotropic covariance structures for the error terms (power correlation function) and including homogenous and heterogeneous residual variances to take into account the different environments having different precision. Results suggest that the model with stationary anisotropic error structure AR1 x AR1 within each environment and heterogeneous residual variances constitutes a good alternative analysis for METs, but it was not always better than the RCBD models for peanut. Differences were found between long- and short-cycle peanut cultivars with respect to the best model., THE COMPARISON of genotype performance in METs requires the ability to make reliable mean yield comparisons. Commonly METs are conducted with multiple replications at each location. The stratification or blocking [...]

Multienvironment yield trials (MET) for advanced peanut lines are conducted each year at the EEA-Manfredi Peanut Breeding Program, the main INTA program for developing new peanut (Arachis hypogaea L.) cultivars for cultivation in the Argentinean crop area. The main objective of this work was the simultaneous analysis of several multienvironment yield tests first to identify superior cultivars for the peanut crop area in Argentina, and second to investigate if different megaenvironments exist. The simultaneous evaluation of several years of MET provides information that allows researchers to better guide breeding strategies. We analyze a 6-yr series of grain yield data from MET, involving 18 genotypes and five test locations using six by-year analyses of complete yield data sets and an Additive Main Effect and Multiplicative Interaction (AMMI) mixed model analysis combining all 6 yr of MET. AMMI models in a mixed model framework were used for exploring genotype-environment (GE) interaction since the lists of genotypes annually tested in multienvironment trials vary from year to year since new genotypes are introduced every year and others are withdrawn. The results allowed us to identify mf484 and mf505 as superior cultivars and confirm the existence of a unique megaenvironment for identifying high yield cultivars in the peanut crop area of Argentina. The mixed model approach of MET data was successfully implemented to analyze highly unbalanced GE data sets., THE GERMPLASM EVALUATION is a crucial activity in plant breeding (Stroup, 2000). MET are commonly conducted annually to obtain information that supports recommendations of superior cultivars for cultivation. MET are [...]

The sugarcane borer [Diatraea saceharalis (Frabricius)] causes significant damage to sugarcane (Saecharum spp.), rendering cultivar resistance important. Researchers assess borer-induced damage using up to five different measures: percentage bored internodes, percentage exited internodes, pupation success, moth production, and a damage rating. The inheritance of the different damage measures and the best approach to integrate the different variables into a simplified damage-resistance assessment has not been well studied. Furthermore, the relationships of the damage traits to sugar production have not been comparatively assessed. We planted a replicated, two-location test of 28 clones typical of the selection stage screened for borer resistance in the Louisiana sugarcane breeding programs. We recorded the five measures together with sucrose production and its components. Using appropriate variance components, the heritability, expected response to selection, and genetic correlations among the traits were used to construct selection indices of all combinations of the five damage traits studied. We used the regression coefficients of the damage traits on sucrose production as economic weights. The indices indicated that percentage bored internodes was the most effective single trait to reduce sugarcane borer damage. If data collection costs were considered, then the subjectively assessed damage rating was the most expeditious of the traits examined. High correlation values among several of the traits lead to the observation that inclusion of more than the bored internode, exited internodes, and the damage rating in an index were unnecessary., THE SUGARCANE BORER causes the majority of insect-related damage to Louisiana sugarcane (Long, 1969). Sugarcane borer larva damage cane by boring and pupating in the stalk internode. This directly reduces [...]

Copyright of Agriscientia is the property of Revista AgriScientia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Agriscientia is the property of Revista AgriScientia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)